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• The partial normalized sandy barren index (PNSBI) is proposed for differentiating sandy barren land from barren land. • PNSBI outperformed six conventional barren indexes in the identification of sandy barren areas in both TM and OMI images. • Spectral measurement and analysis enable rapid and effective discrimination of barren sandy pixels in multi-source images. • The spectral mixture analysis and bandwidth effectiveness should be considered in future work. Sandification is a serious global environmental problem. The spatiotemporal dynamics of sandy barren land is an important indicator in monitoring its ecological restoration. Such monitoring requires fast and accurate identification of sandy barren land. Although several bare indexes have been developed for the mapping of barren land, these have not proven effective in the mapping of sandy barren land. In this paper, the partial normalized sandy barren index (PNSBI), a new barren index based on spectrum reflectance measurement and analysis, is proposed. The efficiency and enhanced accuracy of PNSBI at retrieving sandy barren land was validated using Landsat Thematic Mapper (TM) and Operational Land Imager (OLI) images of the Poyang Lake and Ordos regions of China. These results suggest that the proposed PNSBI achieved higher accuracy than the other bare indexes in retrieving sandy barren land from the other types of bare land. And it is benefit for the study of the spatiotemporal dynamics of sandification, water distribution, soil erosion, etc. [ABSTRACT FROM AUTHOR]

• Integrate approach: remote sensing (Landsat 8 and ASTER thermal infrared data) and geospatial data overlay analysis was used. • Land surface temperature (LST) during the daytime and nighttime were retrieved by the SC algorithm and TES algorithm. • The addition of LST during daytime and nighttime can eliminate the relatively cold/thermal anomalies between land features. • The threshold determined by statistical analysis can adequately reflect geothermal anomalies and urban heat islands. • In addition to the 3 known hot springs and the 3 urban heat islands, 6 geothermal anomalies were also discovered in Dandong area, China. Traditional ground survey methods have limited the development and use of renewable energy from geothermal resources. In this paper, the geothermal potential area of Dandong, China is studied using the thermal infrared (TIR) remote sensing data from daytime Landsat 8 and nighttime Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER). Through pre-processing such as georeferencing, radiometric calibration, and atmospheric correction, the Landsat8 TIRS and ASTER data were used to invert the land surface temperature of the study area during the daytime and nighttime using the single-channel algorithm and temperature and emissivity separation algorithm. Furthermore, the land surface temperatures during daytime and nighttime of three natural land features—water, vegetation, and bare soil—were classified and analyzed. According to the results, vegetation and bare soil show relatively thermal anomalies during the day and relatively cold anomalies during the night. Conversely, water shows relatively cold anomalies during the day and relatively thermal anomalies during the night. Calculating the daytime and nighttime mean of land surface temperature (DNMLST) can eliminate the relatively cold/thermal anomalies exhibited by natural land features during the daytime and nighttime while highlighting the geothermal anomaly zone. Nine geothermal anomaly zones were identified using the threshold method. The auxiliary analysis of geological data excluded non-geothermal effects. Studies have shown that the distribution of the identified geothermal prospects is consistent with the development and distribution of faults. The fractures cut the land surface, causing groundwater to form structural fissure hot water through fracture structures and fissures after magma heating. The use of two higher-resolution TIR remote sensing data products to obtain the DNMLST for the detection of geothermal anomalies has proven cost-effective technical method. [ABSTRACT FROM AUTHOR]

• The community livability evaluation is quantitative, practical and fine-grained. • The evaluation criteria span dimensions of environment, traffic and population. • Remote sensing, surface observation and geospatial big data are used. • Most communities are distributed within the central and marginal urban areas. • Communities in urban area tend to have robustly high livability. Effective evaluation of community livability is in urgent need to avoid increased livability at the expense of sustainability. However, studies concerning community livability evaluation were still conceptual, qualitative or conducted at city or regional scales. The availability of abundant, fine-grained, and multi-source data in the big data era laid the foundation for comprehensive livability evaluation at much finer scales. This paper proposed a quantitative and practical method for up-to-date livability evaluation at individual community scale in China. Nine evaluation criteria were identified spanning dimensions of environment, traffic, convenience, and population. These criteria were calculated respectively from remote sensing, surface observation and geospatial big data. The Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) was applied for community livability evaluation, and the uncertainty and sensitivity of evaluation results were assessed. The livability evaluation in the case study area of Haidian District, Beijing, China demonstrated the practicality and effectiveness of the framework. A total number of 1242 communities in Haidian District were evaluated. Communities in urban area were generally associated with higher evaluation scores and lower uncertainties than those in rural area. The careful selection of criteria weights with high sensitivity, i.e., green space coverage within community and driving time to schools, can potentially significantly reduce the uncertainty of the livability evaluation. The community scale livability evaluation is expected to bridge the gap between theoretical concepts and practical implementations of livability evaluation, and enables the development of more effective and locally specific regulations and policies to improve community livability. [ABSTRACT FROM AUTHOR]

This paper takes Gaobeidian City of Hebei Province as the research area. Based on the Landsat TM/OLI data from 1999 to 2001, 2007-2009 and 2015-2017, the CART decision tree classification method is used to extract the distribution of abandoned arable land in the study area. Finally, we analyze its spatial pattern change characteristics and influencing factors. The study draws the following conclusions: 1) Using CART decision tree classification method to interpret remote sensing images in Gaobeidian City and verify the accuracy. The result shows that: 1）the classification accuracy of 18-stage images are between 87.5% and 96.4%, which can meet the accuracy requirements of this study; 2) The type of abandoned arable land in Gaobeidian City is mainly seasonal abandonment. The area of abandoned arable land reached 21 888.42 hm2 in the spring of 2001, and the area of seasonal abandoned arable land and perennial abandoned arable land are gradually decreasing; 3) The analysis of landscape indicators including plaque number (NP), average plaque area (MPS), median plaque area (PSMD), plaque area standard deviation (PSSD), and average shape index (MSI) of the abandoned arable land shows that the main form of the abandonment of arable land has changed from large-scale centralized abandonment to small-scale decentralized abandonment; 4) The development of rural industry is the main driving factor leading to the abandonment of arable land. The result of the buffer analysis shows that the closer the industrial center is, the higher the comprehensive abandonment rate; The traffic conditions and farming radius also affect the abandonment of arable land to a certain extent, but in the flat plains region, its impact gradually weakened; 5) The gap in crop yields leads to seasonal differences in the cultivated land reclamation in Gaobeidian City. The long-term low net income per unit area of wheat is the main factor leading to the large-scale spring abandonment of arable land in Gaobeidian City, and the arable land transfer can effectively inhibit the abandonment of arable land. The results of rural survey show that the arable land transfer rate in the six surveyed villages shows a significant negative correlation with the change of the arable land abandonment rate. Due to the low resolution of remote sensing images used in this study, the interpretation accuracy may be affected. In addition, the selection time of remote sensing images is mainly based on the growth of spring wheat and summer maize, which do not take into account the late planting of summer stubble carrot and other crops in different growth periods. Although the sowing area of such crops is small, it may still cause errors in the interpretation of abandoned arable land. In order to solve the above problems, it is necessary to use Google high-resolution image for manual visual interpretation and correction, but this method will cost a lot of manpower and time. In future research, other remote sensing data sources with higher spatial resolution and richer spectral information can be considered for interpretation in order to solve these problems. The research results can provide reference for the study of abandoned arable land in other similar areas in China, and provide basis for the formulation of national food security and regional sustainable development policies. [ABSTRACT FROM AUTHOR]

A catastrophic landslide occurred on 21 July 2020, 30 km from Enshi city, in Mazhe County of Hubei province, China. In this paper, we aimed to investigate the kinematic evolution and volumetric change related to this landslide using multi-source remote sensing measurements from synthetic aperture radar (SAR) and optical data. C-band Sentinel-1 and X-band TerraSAR-X SAR data are analyzed using several multi-temporal interferometry (MTI) time-series techniques including Persistent Scatterer Interferometry (PSI), Small Baseline subset (SBAS), and Combined eigenvalue maximum likelihood Phase Linking (CPL). The spatial pattern of surface deformation resulting from the interferometric analysis is then statistically analyzed to retrieve the pre-failure and post-failure displacements. Co-failure motions are analyzed using an image correlation technique applied to both the Planetscope and Sentinel-2 images. Moreover, 4 pairs of bistatic TerraSAR-X/TanDEM-X (TDX) data are utilized to generate high-precision digital elevation models (DEMs) and estimate the volumetric change related to the main slope failure. The pre-failure ground deformation analysis results suggest that the landslide was already active before the July 2020 failure, with the seasonality and hydraulic diffusivity being characteristics of a slow-moving landslide. Among the three different MTI methods applied, the CPL method results in a greater measurement points (MPs) density than the PSI and SBAS method when estimating the pre-failure movement of the Shaziba landslide. The July 2020 Shaziba disaster is divided into three main parts: (1) slightly horizontal deformation of 0.5–1.5 m within the northern part with ground and house cracks, (2) less collapse in the eastern part with horizontal motions reaching 30 m and (3) a highly eroded western part where vegetation was wholly lost in the main event, resulting in an collapse volume of approximately 4.98 million m3, out of which approximately around 3.4 million m3 was deposited and approximately 1.58 million m3 was washed away into the Qing River. After the failure, the marginal scrap of the main failure body, above crown of landslide and eastern part showed instability with rates of 20–30 mm/yr, suggesting that the failure zone may continue to expand. • Multi-sensor data integration to quantify kinematics and life cycle of the landslide. • Improvement in increasing density of scatterers using Combined eigenvalue maximum likelihood Phase Linking (CPL). • Analysis of the time lag between triggering factors and changes in background velocity. • Geodetic quantification of erosion and deposition during the main slope failure using time-series of Tandem-X data. [ABSTRACT FROM AUTHOR]

[Display omitted] • A novel framework is proposed to assess the risks of algal blooms in lakes. • Vine Copula was used to integrate the quantity-distribution of algal blooms. • The framework was used effectively in risk assessment of Chaohu Lake, China. • The framework can handle the problem of spatio-temporal dynamics in risk assessment. Algal blooms threaten water safety and quality, particularly in lakes with vulnerable environment and advanced urbanization. Due to low water exchange rate and multiple human activity influences, algal blooms in inland lakes display highly dynamic spatio-temporal distributions. This paper proposes a comprehensive framework for risk assessment of algal bloom coupling quantity and distribution based on remote sensing observations. The framework comprises algal bloom recognition, spatial zonation, quantity-distribution multivariate joint distribution construction, and risk assessment. The framework was applied to China's giant shallow and algae-prone lake, Chaohu Lake, to assess the annual algal bloom risk from 2000 to 2021. The results showed that this proposed method could achieve a more comprehensive and objective risk assessment by coupling the quantity and distribution of algal blooms. Furthermore, the internal dependence of algal bloom fluctuation in different zones was explicitly presented. This framework promotes systematic integration of algal bloom risk assessment, taking into account its highly spatio-temporal dynamic feature. It creates a quantitative approach to gain insights into the evolution of quantity and distribution in various environmental assessments. [ABSTRACT FROM AUTHOR]

Satellite remote sensing technology has been widely used in environmental protection in China. Based on China's environmental protection characteristics, this paper reviews the application advancements of optical, infrared, microwave and hyperspectral satellite remote sensing techniques in atmospheric environment, water environment and ecological environment, respectively, including the atmospheric environment monitoring of aerosols, particulate matter, pollution gases, and greenhouse gases; the water environment monitoring of water blooms water quality, oil spills, water pollution and drinking water safety; the ecological environment monitor of national nature reserves, environmental damages from mines development, soil contamination, urban/rural ecological environment and biodiversity richness. Furthmore, it summarizes the limitations and future directions of satellite remote sensing studies in China environmental protection. The paper specifically notes that the development of high-resolution satellite remote sensing technology, as well as the launch and use of China series satellites could significantly improve the level of government's environmental management. [ABSTRACT FROM AUTHOR]

In order to ensure China's food and ecological security, it's particularly necessary to perform desertification monitoring and early warning during the land consolidation projects execution. The study area was located in Kezuohouqi which is famous for the sandy land. Based on Landsat 8 OLI image data, desertification monitoring was achieved by the decision tree classification method. The desertification early warning system was constructed by PSR (pressure-state-response) model; the natural and human influencing factors of desertification were regarded as "pressure", the desertification monitoring results were regarded as "state", and the land consolidation projects were regarded as "response". The weight of each factor in the PSR model was set by expert scoring method, and DESI (desertification ecological security index) was adopted in this paper; the greater the index, the lower the risks of desertification. Based on the DESI, the results of desertification early warning were divided into 4 different levels through natural breakpoint method, namely, no desertification warning, slight desertification warning, medium desertification warning and severe desertification warning. In the end, the validity of land consolidation could be evaluated through calculating the area of different desertification early warning results in the land consolidation projects and conducting the comparison. The results showed that: 1) The area of desertification accounted for 88% of study area. To be exact, the area of medium and severe desertification accounted for 61% of the total area, and the extremely severe desertification was mainly concentrated in the western part of the study area whose area accounted for 6% of the total area. 2) The zonal differentiation of desertification early warning results was clear; the spatial distribution from east to west in Kezuohouqi in turn was no desertification warning, medium desertification warning, slight desertification warning, medium desertification warning, severe desertification warning, and then medium desertification warning. The area of medium desertification warning was the largest, and the distribution laws of severe warning desertification and extremely severe desertification were similar, but the severe warning desertification tended to gather in the northwest pastoral areas of Kezuohouqi, the slight warning and no warning areas were located in the eastern part of Kezuohouqi, and the land use pattern was mainly agricultural land. 3) In the land consolidation projects, high quality capital farmland construction and arable land consolidation were mainly distributed in the areas with no desertification warning. The potential land suitable for agriculture and the project of land reclamation were mainly distributed in slight desertification warning area and medium desertification warning area. There was the sever desertification warning land among the farmland consolidation, the potential land resources suitable for agriculture, and forage grass base construction projects, whose distribution style was sporadic. However, the severe desertification warning area in high quality capital farmland was not up to the mark and should be eliminated from the projects. Overall, in the context of land consolidation, the risk of further desertification is small. The severe desertification warning area would be a key point for land consolidation projects, and the government must put a high premium on the sever warning area. The results of desertification early warning in this paper can provide the reference for land consolidation and adjustment. [ABSTRACT FROM AUTHOR]

Ecological restoration has been risen to a national strategic project for China and become an significant way to realize ecological civilization construction. As an important way to measure the effectiveness of ecological restoration engineering, the corresponding evaluation provides a reliable basis for ecological restoration decision-making and ecosystem management. Based on remote sensing technology with the advantages of short revisit time, large monitoring range, high spatial resolution, this paper proposes a remote sensing evaluation method of watershed ecological restoration effect, so as to monitor the implementation effect of ecological restoration engineering in the watershed and technically check whether the expected goals set before the restoration are achieved. The comprehensive ecological restoration engineering of Yongding River has been implemented since 2016, and it was the first focus of the coordinated development of Beijing-Tianjin-Hebei region to make advances in ecology. In this paper, year 2015 and 2020 were selected as the before and the after the ecological restoration engineering of Yongding River. The research object was the riparian zone from Luopoling Reservoir to Daning Reservoir in Yongding River Watershed. After obtaining and processing remote sensing images, ZY-3 images were used to obtain land cover data by means of human-computer interactive interpretation, and Landsat images were used to retrieve NDVI data. Based on these data, the changes of the eight indicators of water resources, water ecology and ecological function in the study area were calculated to evaluate the ecological restoration engineering effect. The results showed that the study area has basically realized the maintenance of ecological water quantity, formed the ecological corridor of Yongding River, and effectively enhanced the ecological function and improved the water environment. By verifying the feasibility of remote sensing evaluation method, this paper could provide relevant decision makers and planners with an effective evaluation method for the ecological restoration engineering effect based on remote sensing data and technology. • Remote sensing images and land cover data were utilized to calculate the indicators before and after ecological restoration. • Ecological restoration engineering effect was evaluated from water resources, water environment and ecological function. • Based on the proposed method, the ecological restoration effect in the study area was proved to be satisfactory overall. [ABSTRACT FROM AUTHOR]

• A deep learning algorithm is used to build a comprehensive drought monitoring model. • Multi-source remote sensing data is processed as input data. • The comprehensive drought model has good applicability in the monitoring of meteorological drought and agricultural drought. • The model successfully monitored a drought process in Henan Province, China. • This study provides a new method for the comprehensive assessment of regional drought. Drought is a popular scientific issue in global climate change research. Accurate monitoring of drought has important implications for the sustainable development of regional agriculture in the context of increasingly complex global climate change. Deep learning is a widely used technique in the field of artificial intelligence. However, ongoing on drought monitoring using deep learning is relatively scarce. In this paper, the various hazard factors in drought development were comprehensively considered based on satellite data including Moderate Resolution Imaging Spectroradiometer (MODIS) and tropical rainfall measuring mission (TRMM) as multi-source remote sensing data. By using the deep learning technique, a comprehensive drought monitoring model was constructed and tested in Henan Province of China as an example. The results showed that the comprehensive drought model has good applicability in the monitoring of meteorological drought and agricultural drought. There was a significant positive correlation between the drought indicators of the model output and the comprehensive meteorological drought index (CI) measured at the site scale. The consistency rate of the drought grade of the two models was 85.6% and 79.8% for the training set and the test set, respectively. The correlation coefficient between the drought index of the model and the standard precipitation evapotranspiration index (SPEI) was between 0.772 and 0.910 (P < 0.01), which indicated a strong level of significance. The correlation coefficient between the drought index of the model and the soil relative moisture at a 10 cm depth was greater than 0.550 (P < 0.01), and there was a good correlation between them. This study provides a new method for the comprehensive assessment of regional drought. [ABSTRACT FROM AUTHOR]

Impervious surfaces are the most significant feature of human settlements. Timely, accurate, and frequent information on impervious surfaces is critical in both social-economic and natural environment applications. Over the past 40 years, impervious surface areas in China have grown rapidly. However, annual maps of impervious areas in China with high spatial details do not exist during this period. In this paper, we made use of reliable impervious surface mapping algorithms that we published before and the Google Earth Engine (GEE) platform to address this data gap. With available data in GEE, we were able to map impervious surfaces over the entire country circa 1978, and during 1985–2017 at an annual frequency. The 1978 data were at 60-m resolution, while the 1985–2017 data were in 30-m resolution. For the 30-m resolution data, we evaluated the accuracies for 1985, 1990, 1995, 2000, 2005, 2010, and 2015. Overall accuracies reached more than 90%. Our results indicate that the growth of impervious surface in China was not only fast but also considerably exceeding the per capita impervious surface area in developed countries like Japan. The 40-year continuous and consistent impervious surface distribution data in China would generate widespread interests in the research and policy-making community. The impervious surface data can be freely downloaded from http://data.ess.tsinghua.edu.cn. [ABSTRACT FROM AUTHOR]

Abstract Air temperature is an important parameter affecting biosphere processes, air quality, regional climate, and human health, but is difficult to estimate, especially on a nationwide scale. Remote sensing provides high spatial resolution data that influence air temperature and can help overcome this problem in spatially heterogeneous environments. Generally, the simple linear relationships between air temperature, land surface temperature (LST), and auxiliary data, seldom consider the complex spatial variations in air temperature. The random forest (RF) regression model can characterize the complex temperature distribution by establishing the non-linear relationships among them. The main purpose of this paper is to present an RF regression method to estimate monthly average temperatures in China, using MODIS LST, normalized differential vegetation index (NDVI), night-time light, and a digital elevation model (DEM). Results based on cross-validation show that mean absolute error (MAE) and root mean square error (RMSE) at national level in different months ranged from 1.15 to 1.44 °C (mean ± standard deviation: 1.30 ± 0.10 °C) and from 1.57 to 1.99 °C (1.79 ± 0.13 °C), respectively. Potential sources of error and uncertainty were analyzed and investigated. Results show that regression model performs better in eastern (MAE: 0.68 ± 0.79 °C; RMSE: 0.75 ± 0.81 °C) and central China (MAE: 0.68 ± 0.41 °C; RMSE: 0.74 ± 0.41 °C). The model performance was strongly dependent on elevation, slope, land cover, and satellite data quality. In addition, the percentage increase in mean squared error from RF regression model was used to quantify the importance of variables. Positive importance scores indicated an improvement in accuracy with the assistance of NDVI, night-time lights, and the DEM. LST, and more auxiliary data sources, when integrated into an RF regression model, may be ways in which to improve the modeling of air temperature variations in the future. [ABSTRACT FROM AUTHOR]

GaoFen1 is the first high-resolution earth observation satellite built in China, and carries four wide field-of-view (WFV) cameras to achieve large-scale monitoring and mapping. However, the unstable attitude measurement accuracy of the satellite generally imparts low geopositioning accuracy and inconsistent geometric error in overlapping areas of WFV images. A feasible and effective large-scale planar block adjustment (PBA) method is presented that corrects the geometric errors of the vast WFV images integrally, further improving the geometric accuracy of these images. In addition, whether ground control points (GCPs) are needed, and the effect of different numbers of GCPs on PBA accuracy is also investigated. Two key technologies are used in this paper. First, a universal PBA error equation based on the virtual control points is presented to allow PBA with or without GCPs. Second, an adjustment method aided by a digital elevation model is adopted to overcome the weak convergence geometry among WFV images, further ensuring stable estimation of PBA. The effectiveness of the proposed method was verified by 664 WFV images covering most of mainland China. The satisfactory experimental results indicate that the method presented herein is reasonable and effective, but that a certain number of GCPs is needed to ensure the accuracy of large-scale PBA results for WFV images. [ABSTRACT FROM AUTHOR]

Highlights • We use remote sensing satellite data to fit the population distribution in China. • We combine the advantages of DMSP/OLS and NPP/VIIRS nighttime light data. • NPP and average slope index are added to improve the population fitting. • The fitting of population distributions in China (2012–2013) is clearly improved. • This method can improve population research in different regions and periods. Abstract Remote sensing satellite data from 2012 to 2013 are used to fit the Chinese cities' population distributions over the same period in order to verify the population distribution in China from a relatively objective perspective. Most scholars have used nighttime light data and vegetation indexes to fit the population distribution, but the fitting effect has not been satisfactory. In this paper, processed Visible Infrared Imaging Radiometer Suite (VIIRS) nighttime light data, net primary productivity of vegetation (NPP), and average slope data were used to fit the population distribution from the three dimensions of economic growth, ecological environment, and topographic factors, respectively. The fitting effect was significantly improved compared with other studies (R2 values of 0.9244 and 0.9253 in 2012 and 2013, respectively). Therefore, this method provides a practical and effective way to fit the population distribution for remote cities or areas lacking census data. Furthermore, there is important practical significance for the government to formulate its population policies rationally, optimize the spatial distribution of population, and improve the ecological quality of the city. [ABSTRACT FROM AUTHOR]

Oil seepage is one of the most important characteristics of hydrocarbon formation, and understanding oil seepage is crucial for oil-gas exploration and the assessment of petroleum resources. Remote sensing and geochemical methods have the same material and theoretical bases for extracting oil and gas information from underlying strata and the identification of media features. As an emerging exploration method, hyperspectral remote sensing is efficient compared with traditional geochemistry because it is a finer, and sometimes more directly quantitative method for determining the specific mineral anomaly content. Hence, the use of both methods together is important. This paper describes the analysis of hyperspectral remote sensing data and the extraction of abnormal index information, including the level of carbonate alteration and the content of acidolytical hydrocarbons, pyrolysis hydrocarbons, headspace gas, and ferric and ferrous ions. The two methods have mutual authentication, and they are complementary and are useful in oil-bearing areas. When these methods are integrated, the acidolytical hydrocarbon index is the most effective geochemical index and is better at characterizing the oil field distribution than other indices. Also, hydrocarbon geochemical anomalies occurring in oil fields generally show continuous distribution points and are consistent with oil reservoirs. Consequently, a 3D model was established to comprehensively utilize hyperspectral remote sensing and geochemical data to determine the distribution of petroleum reservoirs efficiently as well as to delineate oil- and gas-bearing prospects. There is great potential for determining oil- and gas-bearing fields through the integration of hyperspectral and geochemical data. [ABSTRACT FROM AUTHOR]

The Three- River Source Region is rich in grassland resources, and research of balance between forage and livestock can provide decision support for grassland environmental protection and sustainable development of regional economy. In this paper, Golog and Yushu Tibetan Autonomous Prefectures in Qinghai Province, China were selected as the study area. The grassland production was retrieved using model and the carrying capacity was calculated. Then, balance analysis between forage and livestock was carried out by combining balance index between grassland and livestock (BGLI) and index of grazing pressure (Ip). The result shows that the balance status between forage and livestock in the two autonomous prefectures has been constantly improved from 2006 (BGLI is 47.1%) to 2013 (BGLI is -13.8%), however, the status deteriorated partly during 2014- 2015. Compared to Golog Tibetan Autonomous Prefecture (average BGLI is 38.15% ), the Yushu Tibetan Autonomous Prefecture (average BGLI is 33.51%) is better in balance status. In the end, the research suggested that relevant departments in the two autonomous prefectures should strengthen planning and policy regulation, strengthen the fine management of livestock balance in accordance with the"grass livestock"principle, and build decision support information system of livestock balance driven by big data. [ABSTRACT FROM AUTHOR]

Extreme heat events have serious effects on human daily life. Accurately capturing the dynamic variance of extreme high-temperature distributions in a timely manner is the basis for analyzing the potential impacts of extreme heat, thereby informing risk prevention strategies. This paper demonstrates the potential application of multiple source remote sensing data in mapping and monitoring the extreme heat events that occurred on Aug. 8, 2013 in Jiangsu Province, China. In combination with MODIS products, the thermal sharpening (TsHARP) method and a binary linear model are compared to downscale the original daytime FengYun 2F (FY-2F) land surface temperature (LST) imagery, with a temporal resolution of 60 min, from 5 km to 1 km. Using the meteorological measurement data from Nanjing station as the reference, the research then estimates the instantaneous air temperature by using an iterative computation based on the Surface Energy Balance Algorithm for Land (SEBAL), which is used to analyze the spatio-temporal air temperature variance. The results show that the root mean square error (RMSE) of the LST downscaled from the binary linear model is 1.30°C compared to the synchronous MODIS LST, and on this basis the estimated air temperature has the RMSE of 1.78°C. The spatial and temporal distribution of air temperature variance at each geographical location from 06:30 to 18:30 can be accurately determined, and indicates that the high temperature gradually increases and expands from the city center. For the spatial distribution, the air temperature and the defined scorching temperature proportion index increase from northern to middle, to southern part of Jiangsu, and are slightly lower in the eastern area near the Yellow Sea. In terms of temporal characteristics, the percentage of area with air temperature above 37°C in each city increase with time after 10:30 and reach the peak value at 14:30 or 15:30. Then, they decrease gradually, and the rising and falling trends become smaller from the southern cities to the northern regions. Moreover, there is a distinct positive relationship between the percentage of area above 37°C and the population density. The above results show that the spatio-temporal distributions of heat waves and their influencing factors can be determined by combining multiple sources of remotely sensed image data. [ABSTRACT FROM AUTHOR]

• The landscape pattern of wetland is closely related to the population of cranes. • Landscape pattern has a time-lag effect on crane population. • Regression forecasting model based on Landscape pattern predicts crane population of each year. • Predicting crane populations can provide data support for wetland reserves. Reasonable prediction of the crane population that arrives at Poyang Lake Reserve for overwintering every year is of great significance to protect cranes and to provide data about reserve planning. In this paper, considering the time-lag effect of landscape index on crane population, the Landsat satellite images of Poyang Lake wetland in 19 winter periods from 2001 to 2019 were selected. The land-use types of the images were classified by support vector machine to acquire the landscape pattern index. In addition, the Pearson test was adopted to examine the correlation between crane population dynamics and landscape pattern index during overwintering. Finally, the regression model of the crane population was obtained by multivariate linear regression analysis. The results showed that the landscape index had a delayed impact on the crane population, which was significantly correlated with the crane population in the following five years. This effect on different crane population was disparate. The number of siberian cranes and white-naped crane populations showed a downward trend, while that of hooded cranes and common crane populations showed an upward trend. Furthermore, the landscape index affects the crane population to a certain extent, but the landscape pattern has diverse effects on different crane populations. It is suggested that corresponding protection measures be taken for different crane populations to provide a better habitat for cranes in Poyang Lake. [ABSTRACT FROM AUTHOR]

Liu, C.; Qian, B.; Wang, L., and Miao, Q., 2019. Application of GIS and remote sensing in spatial distribution of nitrogen and phosphorus pollutant in urban rivers: A case study of Linyi Economic Development Zone, China. In: Guido-Aldana, P.A. and Mulahasan, S. (eds.), Advances in Water Resources and Exploration. Journal of Coastal Research, Special Issue No. 93, pp. 250–256. Coconut Creek (Florida), ISSN 0749-0208. The objectives of this paper were to develop, evaluate and apply the remote sensing based on models of ammonia nitrogen (NH3-N) and total phosphorus (TP) for the Yi and Shu River in Linyi Economic Development Zone, Shandong Province, China. The scenes of Landsat-8 OLI satellite data were used to establish the relationship between the planetary reflectance and the measured data for five years (i.e., 2014-2017). The five spectral bands (i.e., Coastal, Blue, Green, Red, NIR) were used to obtain the most suitable modeling factor from the different band combinations. The liner regression analysis showed that red band both had highest correlation with NH3-N and TP in Yi and Shu River. For the Yi River, the co-efficient of determination (R2) of red band was 0.541 for the NH3-N model and 0.498 for the TP model. For the Shu River, the co-efficient of determination (R2) of red band was 0.643 for the NH3-N model and 0.565 for the TP model. The best-fit Models were validated with measured data and found that the errors of the NH3-N and TP models of the Yi River were 34.93% and 29.33%, respectively, and while 33.66% and 32.48% for the Shu River. With the models, the distribution diagrams of NH3-N and TP were generated, which can be assisted analysis of spatial and temporal for Yi and Shu River. The results showed that the established models could indicate the trend of NH3-N and TP in rivers that was validated by field visit. The proposed approach in this paper has satisfactory validity and practicability, thus could be reference for study on NH3-N and TP in water based on remote sensing. [ABSTRACT FROM AUTHOR]

With rapid urban development in China in the last two decades, the three-dimensional (3D) characteristic has been the main feature of urban morphology. However, the vast majority of researches of urban growth have focused on the planar area (two-dimensional (2D)) expansion. Few studies have been conducted from a 3D perspective. In this paper, the 3D urban expansion of the Yangzhou City, Jiangsu Province, China from 2003 to 2012 was evaluated based on Geographical Information System (GIS) tools and high-resolution remote sensing images. Four indices, namely weighted average height of buildings, volume of buildings, 3D expansion intensity and 3D fractal dimension are used to quantify the 3D urban expansion. The weighted average height of buildings and the volume of buildings are used to illustrate the temporal change of the 3D urban morphology, while the other two indices are used to calculate the expansion intensity and the fractal dimension of the 3D urban morphology. The results show that the spatial distribution of the high-rise buildings in Yangzhou has significantly spread and the utilization of the 3D space of Yangzhou has become more efficient and intensive. The methods proposed in this paper laid a foundation for a wide range of study of 3D urban morphology changes. [ABSTRACT FROM AUTHOR]

Global change has a significant impact on the lives of humankind. Earth observation can help to better understand our earth and cope with global change. With the availability of more reliable environmental data sets, digital earth is becoming a popular way to monitor the Earth and provide information to researchers and decision makers on environment protection, disaster mitigation, and social benefits. Therefore, accessing data with lowering costs is essential for digital earth. Nevertheless, there are big challenges in ensuring the feasibility of access to Chinese remote sensing data. This paper outlines some of the main challenges in realizing data sharing, provides an analysis of the core reasons leading to these challenges, and proposes recommendations to overcome the challenges. Amongst the main challenges are differences in data policy to gain access to satellite data, diverse data formats, and delivery mechanisms. The major challenge for the decision makers is to define a more open policy and for the scientist the challenge is to implement these polices for the benefit of all. This paper proposes that governments should adopt policies encouraging more open distribution and access to their data, in order to generate an improved digital earth with increased benefits to human society. [ABSTRACT FROM PUBLISHER]

Marine aquaculture has developed very rapidly in China and at present, China has become the largest producer of marine aquaculture in the world. While meeting the growing demand for seafood consumption, the mariculture industry also poses serious ecological and environmental problems to the coastal zone. Remote sensing recognition of mariculture modes in coastal zone is of great significance to real-time monitoring, rational planning and orderly development of mariculture, which can help to manage the coastal aquaculture mode, aquaculture structure and aquaculture capacity in coastal zone. At present, there are four main methods for remote sensing identification in aquaculture waters: 1) Extraction by visual interpretation; 2) Extraction by spectral features; 3) Analysis by spatial morphology and structure; 4) Extraction based on Object-oriented techniques. There will have mixing problems caused by "different objects with the same spectrum", "same objects with the different spectrum" and salt-and-pepper noise in image processing, if aquaculture information is extracted by spectral information or texture information alone. In order to reduce the interference of human factors of object-oriented classification rules and improve the efficiency and automation of classification rules generation, in this paper, we combined the association rules method and Object-oriented method to build a mariculture modes classifier through automatic and intelligent acquisition for different modes classification. Taking Zhelin Bay in the east of Guangdong province as an example, the GF-1 image as data source, using the spectral, geometric and texture features and their correlations of the objects of different mariculture modes as transaction data, mariculture modes strong rules were mined by Apriori algorithm. Four kinds of mariculture modes information (pond culture, cage culture, beach aquaculture, floating raft) in bay aquaculture core area were extracted. The results showed that pond culture area in Zhelin Bay was 2 228.47 hm2, cage culture area was 111.95 hm2, beach aquaculture t area was 12.95 hm2, floating raft area was 48.34 hm2. Cages in Zhelin Bay were distributed in two regions, one was in the sea area between Suizhou Island and Xuxian Island and Xi’ao Island, the other was located between the northeast corner of Haishan Island and Xizhou Island. Ponds were mainly located in the northern part of the study area in Huanggang town. Beach aquaculture was in the innermost part of Zhelin Bay, near the south side of the pond. Floating rafts were distributed around the cages and tended to be at the side of the bay center. In order to compare the association rules mining with the object-oriented combination method and the traditional methods, K-neighboring object-oriented method and association rule mining method were used respectively for the classification and extraction of marine aquaculture modes. These two classifications were implemented in Envi5.5 and Weka3.7.12 software, respectively. The specific steps were as follows, 300 sample points in Google Earth high-resolution images and field survey samples were chosen, of them, 50% were used as training samples, and the other 50% for test samples. Then, the K-adjacent object-oriented method and association rule mining method were selected respectively for classification. Finally, the classification accuracy of the three methods was compared and evaluated. The classification accuracy of mariculture model extraction based on Association -Rules Object-Oriented Method was 88.65%, which was 14.38 percent point higher than that of K-Adjacent Object-Oriented classification, and 12.16 percent point higher than that of association rules mining classification. Association -Rules Object-Oriented Method broadened the access to information in the traditional logical reasoning classification method, made the classification more intelligent, and enhanced the classification speed and algorithm reliability. This method can improve the classification accuracy remarkably, which can be an effective support method for the complex mariculture modes recognition. [ABSTRACT FROM AUTHOR]

Knowledge of the spatial distribution of populations at finer spatial scales is of significant value and fundamental to many applications such as environmental change, urbanization, regional planning, public health, and disaster management. However, detailed assessment of the population distribution data of countries that have large populations (such as China) and significant variation in distribution requires improved data processing methods and spatialization models. This paper described the construction of a novel population spatialization method by combining land use/cover data and night-light data. Based on the analysis of data characteristics, the method used partial correlation analysis and geographically weighted regression to improve the distribution accuracy and reduce regional errors. China's census data for the years 1990, 2000, and 2010 were assessed. The results showed that the method was better at population spatialization than methods that use only night-light data or land use/cover data and global linear regression. Evaluation of overall accuracies revealed that the coefficient of correlation R-square was >0.90 and increased by >0.13 in the years 1990, 2000, and 2010. Moreover, the local R-square of over 90% of the samples (counties) was higher than the adjusted R-square of the general linear regression model. Furthermore, the gridded population density datasets obtained by this method can be used to analyse spatial-temporal patterns of population density and provide population distribution information with increased accuracy and precision compared to conventional models. [ABSTRACT FROM AUTHOR]

The most serious desertification in China occurs in the agro-pastoral ecotone of Northeast China and in the oases in Northwest China. It has resulted in the reduce of land productivity and serious ecological/environmental consequences. In this paper, we assessed the mechanisms of land desertification and the key strategies to monitor, control and mitigate land desertification in China, which will provide unique experiences and schemes to support academics and industrialists in other developing countries to reverse desertification. In aspect of monitoring desertification, satellite remote sensing can detect and characterize large-scale desertification, and in-situ field work can measure changes of soil physical & chemical properties induced by desertification. In aspect of desertification control and mitigation, the implementation of sustainable cultivation/grazing practices and wind-shelter forests are the key measures in Northeast China, while water use quotas and sustainable water management are the key measures in Northwest China. Finally, we suggested big-data-based water resource management, regional-climate-model-based agricultural planning, CO 2 storage with deep saline water recovery and desert geoengineering as possible solutions to future, large-scale reversal of deserts and desertification regions in China and other developing countries. [ABSTRACT FROM AUTHOR]

The livability evaluation of living space was an important topic, which had important guiding significance for improving the livability of living space.This study aimed to explore the theoretical logical relationship between the multi-function of land use and livability of living space, and make use of the research about multi-function of land use for the optimization practice of livability of living space. So, this paper discussed the theoretical logical relationships between multiple functions of land use and livability of living space from the perspective of human needed products and services supplied by land use, and the evaluation index system was built based on the relationship and previous research. And then, the scheme of index spatialization was designed based on the technology of geographical information system(GIS) and remote sensing(RS). At the same time, multi-source data like remote sensing, survey, statistical and other spatialdataset were integrated by analytic hierarchy process (AHP) to quantify the spatial distribution of living space in ZhangbeiCounty, Hebei Province. The results showed that the livability of living space in ZhangbeiCounty presented the trend that the high value center was concentrated in the ZhangbeiTown and the subcenterwas distributed in Ertai-ShagouTown. The living space area above relatively strong levelwas only 2.42% of all county area.The spatial distribution characteristics of 3 types of land use were significantly difference;livingbearing function presented a trend like network distribution takingmain traffic trunk line as high value axis and ZhangbeiCounty as high value center. Living support function presented 2 groupsof high value centerscontaining the ZhangbeiTownand the north of YoulougouTown, and Ertai-shagouTown. The high value center of mental purification function was distributed in the ZhangbeiTown and the subcenterwas distributed in the area along the southwest of ZhangbeiTown to DaheTown and Ertai-shagouTown. There was a large difference in the distribution of livability between towns and villages. Most of the areas of livability space in town land were above the moderate level, especially, the area the livability level of which belonged to the higher and highest accounted for 78.37% of the total area of the town land, and meanwhile, accounted for 74.94% of the total area with higher and highest level of living space. This area was mainly distributed in the town of Zhangbei, Ertaiand Shagou, and YoulougouVillage. Most of the livability areas in rural land were under the moderate level, and the area oflower and lowest level accounted for 78.39% of the total area of the rural land, whatwas more, accounted for 95.32% of the total area with lower and lowest level of living space. This area was mainly concentered in the town ofHailiutu, Erquanjing, Dahe, Danjinghe, and Liangmianjing. The spatial difference of the functional elements of land use was the main reason to the difference in livability of living space. So, according to the above results, some conclusions can be drawn in this research. The villages, which containlarge lower and lowest livability space area, are the key areas of primary concern inthe implementation process of the“Revitalizing the countryside” strategy; what’s more, the theory and method of land use multi-function can be applied to the study of livability evaluation of living space. [ABSTRACT FROM AUTHOR]

Sri Lanka is experiencing speedy urbanization by converting the agriculture land and other natural land cover into built-up land. The urban population of Sri Lanka is expected to reach to 60% by 2030 from 14% in 2010. The rapid growth in urban population and urban areas in Sri Lanka may cause serious socioeconomic disparities, if they are not handled properly. Thus, planners in Sri Lanka are in need of information about past and future urban growth patterns to plan a better and sustainable urban future for Sri Lanka. In this paper, we analyzed the characteristics of past land use and land cover trends in Matara City of Sri Lanka from 1980 to 2010 to assess the historic urban dynamics. The land use change detection analysis based on remote sensing datasets reveal that the conversion of homestead/garden and paddy into urban land is evident in Matara City. The historic urban trends are projected into the near future by using SLEUTH urban growth model to identify the hot spots of future urbanization and as well as the urban growth patterns in Matara City up to the basic administrative level, i.e., Grama Niladari Divisions (GND). The urban growth simulations for the year 2030 reveal that 29 GNDs out of 66 GNDs in Matara City will be totally converted into urban land. Whereas, 28 GNDs will have urban land cover from 75% to 99% by 2030. The urban growth simulations are further analyzed with respect to the proposed Matara city development plan by the Urban Development Authority (UDA) of Sri Lanka. The results show that the UDA's city development plan of Matara will soon be outpaced by rapid urbanization. Based on the calibration and validation results, the SLEUTH model proved to be a useful planning tool to understand the near future urbanization of Sri Lankan cities. [ABSTRACT FROM AUTHOR]

The present paper has used a comprehensive approach to study atmosphere pollution sources including the study of vertical distribution characteristics, the epicenters of occurrence and transport of atmospheric aerosol in North-West China under intensive dust storm registered in all cities of the region in April 2014. To achieve this goal, the remote sensing data using Moderate Resolution Imaging Spectroradiometer satellite (MODIS) as well as model-simulated data, were used, which facilitate tracking the sources, routes, and spatial extent of dust storms. The results of the study have shown strong territory pollution with aerosol during sandstorm. According to ground-based air quality monitoring stations data, concentrations of PM 10 and PM 2.5 exceeded 400 μg/m 3 and 150 μg/m 3 , respectively, the ratio PM 2.5 /PM 10 being within the range of 0.123–0.661. According to MODIS/Terra Collection 6 Level-2 aerosol products data and the Deep Blue algorithm data, the aerosol optical depth (AOD) at 550 nm in the pollution epicenter was within 0.75–1. The vertical distribution of aerosols indicates that the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) 532 nm total attenuates backscatter coefficient ranges from 0.01 to 0.0001 km −1  × sr −1 with the distribution of the main types of aerosols in the troposphere of the region within 0–12.5 km, where the most severe aerosol contamination is observed in the lower troposphere (at 3–6 km). According to satellite sounding and model-simulated data, the sources of pollution are the deserted regions of Northern and Northwestern China. [ABSTRACT FROM AUTHOR]

Investigating the characteristics of urban expansion is helpful in managing the relationship between urbanization and the ecological and environmental issues related to sustainable development. The Defense Meteorological Satellite Program/Operational Line-scan System (DMSP/OLS) collects visible and near-infrared light fromthe Earth's surface at nightwithoutmoonlight. It generates effective time series data for mapping the dynamics of urban expansion. As a major urban agglomeration in the world, the Yangtze River Delta Urban Agglomeration (YRDUA) is an important intersection zone of both the "Belt and Road Initiative" and the "Yangtze River Economic Belt" in China. Therefore, this paper analyses urban expansion characteristics of the YRDUA for 1993-2012 from urban extents extracted from the DMSP/OLS for 1993, 1997, 2002, 2007, and 2012. First, calibration procedures are applied to DMSP/OLS data, including intercalibration, intra-annual composition, and inter-annual series correction procedures. Spatial extents are then extracted from the corrected DMSP/OLS data, and a threshold is determined via the spatial comparison method. Finally, three models are used to explore urban expansion characteristics of the YRDUA from expansion rates, expansion spatial patterns, and expansion evaluations. The results show that the urban expansion of the YRDUA occurred at an increasing rate from 1993-2007 and then declined after 2007 with the onset of the global financial crisis. The Suxichang and Ningbo metropolitan circles were seriously affected by the financial crisis, while the Hefei metropolitan circle was not. The urban expansion of the YRDUA moved from the northeast to the southwest over the 20-year period. Urban expansion involved internal infilling over the first 15 years and then evolved into external sprawl and suburbanization after 2007. [ABSTRACT FROM AUTHOR]

In recent decades, various kinds of remote sensing-based drought indexes have been proposed and widely used in the field of drought monitoring. However, the drought-related software and platform development lag behind the theoretical research. The current drought monitoring systems focus mainly on information management and publishing, and cannot implement professional drought monitoring or parameter inversion modelling, especially the models based on multi-dimensional feature space. In view of the above problems, this paper aims at fixing this gap with a component-based system named RSDMS to facilitate the application of drought monitoring by remote sensing. The system is designed and developed based on Component Object Model (COM) to ensure the flexibility and extendibility of modules. RSDMS realizes general image-related functions such as data management, image display, spatial reference management, image processing and analysis, and further provides drought monitoring and evaluation functions based on internal and external models. Finally, China’s Ningxia region is selected as the study area to validate the performance of RSDMS. The experimental results show that RSDMS provide an efficient and scalable support to agricultural drought monitoring. [ABSTRACT FROM AUTHOR]

Land use and land cover changes (LUCC) are thought to be amongst the most important impacts exerted by humans on climate. However, relatively little research has been carried out so far on the effects of LUCC on extreme climate change other than on regional temperatures and precipitation. In this paper, we apply a regional weather research and forecasting (WRF) climate model using LUCC data from Heilongjiang Province, that was collected between the 1900s and 2010s, to explore how changes in forest cover influence extreme temperature indexes. Our selection of extreme high, low, and daily temperature indexes for analysis in this study enables the calculation of a five-year numerical integration trail with changing forest space. Results indicate that the total forested area of Heilongjiang Province decreased by 28% between the 1900s and 2010s. This decrease is most marked in the western, southwestern, and northeastern parts of the province. Our results also reveal a remarkable correlation between change in forested area and extreme high and low temperature indexes. Further analysis enabled us to determine that the key factor explaining increases in extreme high temperature indexes (i.e., calculated using the number of warm days, warm nights, as well as tropical nights, and summer days) is decreasing forest area; data also showed that this factor caused a decrease in extreme low temperature indexes (i.e., calculated using the number of cold days and cold nights, as well as frost days, and ice days) and an increase in the maximum value of daily minimum temperature. Spatial data demonstrated that there is a significant correlation between forest-to-farmland conversion and extreme temperature indexes throughout most of our study period. Spatial data demonstrated that there is a significant correlation between forest-to-farmland conversion and extreme temperature indexes throughout most of our study period. Positive correlations are also present between decreasing forest area, the more frequent occurrence of extreme high temperature events, and a rise in the maximum value of daily minimum temperature. At the same time, we found clear negative correlations between decreasing forest area and less frequent occurrence of extreme low temperature events. [ABSTRACT FROM AUTHOR]

Given the rapid development within China, the inequality of available water resources has been increasingly of interest. Current methods for assessing water stress are inadequate for province-scale rapid monitoring. A more responsive indicator at a finer scale is needed to understand the distribution of water stress in China. This paper selected Defense Meteorological Satellite Program Operational Line-scan System night-time stable lights as a proxy for water stress at the province level in China from 2004 to 2012, as night-time lights are closely linked with population density, electricity consumption and other social, economic and environmental indicators associated with water stress. The linear regression results showed the intensity of night-time lights can serve as a predictive tool to assess water stress across provinces with an R2 from 0.797 to 0.854. The model worked especially well in some regions, such as East China, North China and South West China. Nonetheless, confounding factors interfered with the predictive relationship, including population density, level of economic development, natural resource endowment and industrial structures, etc. The model was not greatly improved by building a multi-variable linear regression including agricultural and industrial indicators. A straightforward predictor of water stress using remotely sensed data was developed. [ABSTRACT FROM AUTHOR]

Urbanization has proceeded at an unprecedented speed in China during the last 20 years, resulting in extensive natural landscapes being transformed into impervious surface. The “ghost city” phenomenon has emerged due to the unreasonable urban expansion which far exceeds the actual demand of human habitat. Previously, few research studies have provided objective and sufficient knowledge with regard to identify “ghost cities” and their spatial distribution. In this paper, we proposed an effective and feasible framework to monitor and evaluate “ghost cities” utilizing nighttime light imagery obtained from day-night band (DNB) of Visible Infrared Imaging Radiometer Suite (VIIRS). We established a “ghost city” index (GCI) to quantify the intensity of the phenomenon in the northeast of China, and analyzed the spatial pattern of “ghost cities” for different GCI classes. Our results indicate that the intensity of “ghost city” phenomenon decrease from regions adjacent to the border to interior areas, whilst regions with extremely high GCI are mostly districts and county cities. Tests of typical regions show that non-lit built-up area for high GCI regions is spatially clustered and low population regions have a high tendency to suffer from the “ghost city” phenomenon. Therefore, our findings provide a spatial-explicit insight into the “ghost city” phenomenon, and consequently can be beneficial to assist sustainable urban planning. [ABSTRACT FROM AUTHOR]

Detailed studies on the spatiotemporal patterns of urban agglomeration in the Middle Yangtze River Basin (MYRB) are rare. This paper analyzed the spatiotemporal patterns of urbanization in the MYRB using multi-temporal remote sensing data circa 2000, 2008 and 2016 integrated with geographic information system (GIS) techniques and landscape analysis approaches. A multi-level analysis of the rate and intensity, type as well as the landscape changes of urban expansion at regional, prefectural and inner-city levels was performed. Results show that the MYRB experienced rapid urban expansion with an annual expansion rate of 3.199%, especially in the Chang-Zhu-Tan and Poyang Lake metropolitan areas. The small and medium cities presented faster urban expansion than the larger cities with annual growth rates three times the average level. Urban expansion within the three capital cities was further analyzed in detail. It is found that outlying expansion and edge-expansion were the dominant growth patterns at all the three levels. Although urbanization in the MYRB has a remarkable increase in the past sixteen years, its annual growth rate of urban land expansion has fallen behind the three other large urban agglomerations in China as a result. Finally, the spatial evolution of the socioeconomic structure of the MYRB was further explored. It indicated that urban land was distributed mainly along the "northwest-southeast" direction and that the economic spatial interactions among cities showed a pattern of "multi-polarization and fragmentation", which illustrates the weak radiative driving forces of the central cities. The MYRB urban agglomeration faces a great challenge to manage trades-offs between narrowing the intra-regional disparity and maintaining synergetic development among cities. [ABSTRACT FROM AUTHOR]

In this paper, Huailai County in China's Hebei Provice is considered an example for analyzing the characteristics of landscape patterns with three remote sensing datasets in 1995, 2005, and 2015. The land use change and landscape pattern of the study area were analyzed and it was found that from 1995 to 2015, the area with the largest change of land use type was cultivated land and construction land. Results show an increasing landscape fragmentation in this area. Meanwhile, the connectivity presents a downward trend. In combination with the distribution of different landscape type patterns on the terrain niche and the functions of ecosystem services, a minimum cumulative resistance model was used to construct the landscape components of ecological sources, ecological corridors, and ecological nodes. The obtained dIIC and dPC show that river confluences and the surrounding area of the reservoirs are most important for landscape connectivity. Based on the above research, maintenance areas, restoration areas, enhancement areas, and protection areas of the landscape are divided, which will be useful for optimizing the ecological landscape. [ABSTRACT FROM AUTHOR]

A large number of studies have dealt with the driven forces of land expansion, in which the remote sensing data and statistical data are most commonly used. The recent progress based on the statistical data have not been fully tested and discussed by the remote sensing data, and the remote sensing data used in the previous studies are usually interpreted within certain areas which is not convenient for global comparison. In this paper, the 30-m GlobalLand Cover Dataset (GlobeLand30) and socioeconomic data from 2000 to 2010 are adopted to investigate the factors driving impervious surface expansion in China based on a multilevel regression model. The GlobeLand30 provides a world-wide data framework which has a sound basis for regional comparison research. The variables are selected according to the existing research. Most, but not all, results are consistent with the previous studies when using impervious surface data of GlobeLand30. The main findings are: (1) the market demand caused by economic development, such as the increase in GDP from 2000 to 2010, plays a positive role in the expansion of developed land; (2) the land supply, as reflected by the ratio of the total of land transfer fees to fiscal revenue, also has a positive effect on the increase in impervious surfaces; (3) the percentage of the increase by private workers to the increase in total workers and certain other frequently-used variables are not relevant after controlling for land demand- and supply-related variables; and (4) the growth in impervious surfaces is related to the amount of the cultivated land, which implies the necessity for a more stringent farmland protection policy. Considering the need to compare across regions, we suggest that GlobeLand30 should be used for more studies to better understand the driving forces of land expansion. [ABSTRACT FROM AUTHOR]

Since the launch of its first satellite in 1972, the Landsat program has operated continuously for more than forty years. A large data archive collected by the Landsat program significantly benefits both the academic community and society. Thermal imagery from Landsat sensors, provided with relatively high spatial resolution, is suitable for monitoring urban thermal environment. Growing use of Landsat data in monitoring urban thermal environment is demonstrated by increasing publications on this subject, especially over the last decade. Urban thermal environment is usually delineated by land surface temperature (LST). However, the quantitative and accurate estimation of LST from Landsat data is still a challenge, especially for urban areas. This paper will discuss the main challenges for urban LST retrieval, including urban surface emissivity, atmospheric correction, radiometric calibration, and validation. In addition, we will discuss general challenges confronting the continuity of quantitative applications of Landsat observations. These challenges arise mainly from the scan line corrector failure of the Landsat 7 ETM + and channel differences among sensors. Based on these investigations, the concerns are to: (1) show general users the limitation and possible uncertainty of the retrieved urban LST from the single thermal channel of Landsat sensors; (2) emphasize efforts which should be done for the quantitative applications of Landsat data; and (3) understand the potential challenges for the continuity of Landsat observation ( i.e. , thermal infrared) for global change monitoring, while several climate data record programs being in progress. [ABSTRACT FROM AUTHOR]

Social media texts spontaneously produced and uploaded by the public contain a wealth of disaster information. As a supplementary data source for remote sensing, they have played an important role in disaster reduction and emergency response in recent years. However, social media also has certain flaws, such as insufficient location information, etc. This affects the efficiency of combining these data with remote sensing data. For flood disasters in particular, extensively flooded areas limit the distribution of social media data, which makes it difficult for these data to function as they should. In this paper, we propose a disaster reduction framework to solve these problems. We first used an approach that was based on search engine and lexical rules to automatically extract disaster-related location information from social media texts. Then, we combined the extracted information with the upload location of social media itself to construct location-pointing relationships. These relationships were used to build a new social network, which can be used in combination with remote sensing images for disaster analysis. The analysis integrated the advantages of social media and remote sensing. It can not only provide macro disaster information in the study area but can also assist in evaluating the disaster situation in different flooded areas from the perspective of public observation. In addition, the timeliness of social media data also improved the continuity and situational awareness of flood monitoring. A case study of the flood disaster in the Yangtze River Basin in China in 2020 was used to verify the effectiveness of the method described in this paper. [ABSTRACT FROM AUTHOR]

Due to the country's rapid economic growth, the problem of air pollution in China is becoming increasingly serious. In order to achieve a win-win situation for the environment and urban development, the government has issued many policies to strengthen environmental protection. PM2.5 is the primary particulate matter in air pollution, so an accurate estimation of PM2.5 distribution is of great significance. Although previous studies have attempted to retrieve PM2.5 using geostatistical or aerosol remote sensing retrieval methods, the current rough resolution and accuracy remain as limitations of such methods. This paper proposes a fine-grained spatiotemporal PM2.5 retrieval method that comprehensively considers various datasets, such as Landsat 8 satellite images, ground monitoring station data, and socio-economic data, to explore the applicability of different machine learning algorithms in PM2.5 retrieval. Six typical algorithms were used to train the multi-dimensional elements in a series of experiments. The characteristics of retrieval accuracy in different scenarios were clarified mainly according to the validation index, R2. The random forest algorithm was shown to have the best numerical and PM2.5-based air-quality-category accuracy, with a cross-validated R2 of 0.86 and a category retrieval accuracy of 0.83, while both maintained excellent retrieval accuracy and achieved a high spatiotemporal resolution. Based on this retrieval model, we evaluated the PM2.5 distribution characteristics and hourly variation in the sample area, as well as the functions of different input variables in the model. The PM2.5 retrieval method proposed in this paper provides a new model for fine-grained PM2.5 concentration estimation to determine the distribution laws of air pollutants and thereby specify more effective measures to realize the high-quality development of the city. [ABSTRACT FROM AUTHOR]

• The monitoring and estimation method of Winter Wheat Soil Moisture Based on multi-source sensing data and CNNR model is studied in this paper. • The proposed CNNR model has the best prediction accuracy. With the decrease of the SNR of radar echo, CNNR always has higher R2 and RPD, which indicates that the CNNR model can better predict the change trend close to the real value. Therefore, CNNR model has the best noise robustness among the three models. Rapid and accurate estimation of soil moisture content (SMC) is an important part of precision agriculture, and it is also one of the key problems to be solved in field real-time monitoring and precision irrigation. Most of the existing studies are limited to SMC monitoring of bare soil, which can be obtained by optical remote sensing (e.g., multispectral, hyperspectral) or Synthetic Aperture Radar (SAR). However, for the soil covered by vegetation, such as farmland, there are some theoretical defects with only one of the measuring methods. Meanwhile, in order to break through the limitations of low spatial and temporal resolutions of satellite remote sensing, it is of great significance to study SMC retrieval based on multi-source remote sensing data for the near earth UAV remote sensing systems. Based on this, this paper, taking the winter wheat planting area in Guanzhong plain of China as the research area, combines the advantages of ultra-wideband (UWB) radar, and multispectral remote sensing data, to reduce the influences of vegetation coverage on the estimation accuracy. A one-dimensional regression convolution neural network model is constructed to realize the quantitative prediction and estimation of SMC in farmland. The carried out experiments show that the proposed CNNR model has a better performance than traditional SVR and GRNN models and the R2, RMSE and RPD are 0.7453, 0.0140 cm3/cm3 and 1.9246, respectively. After introducing NDVI, MSAVI and DVI vegetation indices generated from multispectral images, the accuracy of the three models increased significantly. Among the three models, the constructed CNNR model has the best performance, and its R2, RMSE and RPD reach 0.9168, 0.0089 cm3/cm3, and 3.0201. Furthermore, after adding different levels of Gaussian noise to the original radar echoes, the CNNR model constructed in this paper still has the highest prediction accuracy and the strongest noise robustness. [ABSTRACT FROM AUTHOR]

[Display omitted] • Soil salinity can be well reflected by vegetation and salinity indicators. • The conversion of spectral indicators can effectively reduce the upscaling error. • Precise land regionalization will improve the accuracy of scale conversion. • The risk of soil salinization gradually decreases from coastal to inland. • Soil salinization experienced a change of increasing first and then decreasing. As a global problem of soil degradation, salinization has become a major obstacle to the sustainable development of the ecological environment and agriculture in coastal plains. However, the traditional process of salinity survey is too cumbersome, expensive and time-consuming to meet the mapping needs in a large scale. Remote sensing technology has become an important tool for digital soil mapping because of its rich sources, real-time and low cost. In order to meet the objective demand for rapid, accurate, and efficient acquisition and monitoring of soil salinization. This paper collected 61 soil samples from the Kenli District (experimental area) and extracted vegetation and salinity indicators from the Landsat image to construct the salinity inversion model by random forest algorithm. Then, taking the Yellow River Delta as the study area, the conversion coefficient of spectral indicators between Landsat and MODIS images was constructed in the form of the ratio of the mean value. Through optimization, the upscaling conversion method based on land use regionalization was proposed to realize the upscaling inversion and dynamic monitoring of soil salinization. The results showed that: (1) The random forest model based on NDVI , RVI , EVI , SI3 , and SI5 can better predict the soil salinity in the experimental area, with R2 = 0.821 and RMSE = 2.811 (validation accuracy). (2) The upscaling conversion method based on land use regionalization can effectively reduce the statistical error and collinearity of spectral indicators constructed by MODIS images and improve their correlation with OLI data and soil salinity. (3) From coastal to inland, soil salinization gradually decreases in the Yellow River Delta. From 2000 to 2020, soil salinization increased first and then decreased, and the salinized soil accounted for 20.35%∼35.10%. This study used multi-source remote sensing data to realize the collaborative inversion at different scales, which was significant for the quantitative estimation of soil salinity, salinization control, and sustainable agricultural development in coastal plains. [ABSTRACT FROM AUTHOR]

Evapotranspiration (ET) plays a vital role in surface energy balance and water management. Paddy rice is one of the most water-intensive crops and a staple food worldwide; thus, paddy field ET estimation is an impactful task. In this paper, an improved Surface Energy Balance Algorithm for Land (SEBALR) model is proposed to estimate the paddy field ET and is validated in different climate zones; it considers the advection effect and its applicable condition at the pixel scale and requires no ground-measured data. SEBALR contains two core modules: rice growth period detection and ET retrieval. SEBALR accurately monitored the planting and harvest dates, with an average error of less than 9 days, except for a paddy field located in the US, which had adopted dry seeding. The comparison between the estimation results and eddy covariance data shows that SEBALR can provide a more precise ET estimation than the original Surface Energy Balance Algorithm for Land, yielding a root mean squared error (RMSE) of 1.02 mm·d-1, mean relative error (MRE) of 22.97% and Pearson's correlation (R2) of 0.790. SEBALR successfully monitored paddy field ET from 2001 to 2019 in Nanchang City (NC), China. Yearly ET did not show an apparent change in trend from 2001 to 2019 in NC. SEBALR can be used to monitor the paddy rice growth period and estimate paddy field ET at a regional scale with limited data, providing helpful information for agricultural practices and water management. ● An improved SEBAL was proposed to quantify the ET advection effect of paddy fields. ● SEBALR can provide rice growth period information and more accurate ET. ● SEBALR can be applied on a large scale for its regional applicability and data independence. ● Paddy fields ET and its driving factors were analyzed from 2001 to 2019 in Nanchang, China. ● The structure and uncertainty of SEBALR were evaluated. [ABSTRACT FROM AUTHOR]

With the increasing problem of eutrophication, lacustrine ecosystem can undergo complex changes, often resulting in a shift from a clear macrophyte-dominated state to a turbid phytoplankton-dominated state. However, it's not clear how lake transitions occur at regional and global scales. This is due to a lack of long-term monitoring and an effective algorithm that can distinguish floating/emergent aquatic vegetation (FEAV), submerged aquatic vegetation (SAV) and algal bloom (AB). This paper presents a novel three-step classification algorithm based on Landsat imagery, (ie., vegetation and bloom indices (VBI) algorithm): (1) to distinguish between aquatic vegetation (AV) and non-aquatic vegetation (non-AV) extents by an aquatic vegetation index (AVI) derived from tasseled cap transformation; (2) to identify FEAV and SAV within AV extent by using the normalized difference vegetation index (NDVI), and (3) to extract AB from non-AV extent with floating algae index (FAI). The performance of the VBI algorithm was validated using 1307 field samples collected across 22 lakes in the middle and lower reaches of the Yangtze River (MLY) in China, with a pooled overall accuracy of 84.49%. Finally, the VBI algorithm was applied to Landsat data from 1985 to 2021 to quantify the temporal changes of FEAV, SAV and AB in large lakes (i.e., > 50 km2) along the MLY. Within these lakes, AV showed a significant decrease over the past 37 years, mainly due to the decrease of SAV; while AB occurred with higher frequency and in more lakes. The transition from a macrophyte-dominated state to a phytoplankton-dominated aquatic systems is still ongoing in the MLY area. This study shows outstanding innovation in the construction of a new classification index (i.e., AVI), and the method of calculating the adaptive threshold, which makes VBI algorithm have great potential in mapping long-term changes and capturing transformation between lake communities on a global scale. • A novel Landsat-based algorithm was developed for quantifying aquatic communities. • An index for aquatic vegetation (AV) was proposed to classify AV and non-AV. • Long-term algal blooms and AV (floating/emergent/submerged) in MLY lakes were mapped. • AV decreased while algal blooms increased in most MLY lakes from 1985 to 2021. • Shift from macrophyte- to phytoplankton-dominated state is ongoing in MLY lakes. [ABSTRACT FROM AUTHOR]

• Created a refined water resource allocation model. • Combining grid idea and remote sensing technology. • It reflects the change of water supply and demand in space and numerical value. • PSO algorithm is introduced to reduce the difficulty in solving the model. • It changes the extensive consideration in water supply nodes and water use nodes. The traditional optimal allocation of water resources mostly focuses on the allocation of different water supply sources to various types of water users in life, production, and ecology, and cannot reflect the differences in time and space of a certain type of water users. Therefore, this paper constructs a refined model for the optimal allocation of water resources, and further explores the optimal allocation of water resources with multiple water sources, multiple users, and multiple objectives. The main steps of the model construction are: (1) Regional gridding. The planting structure map and land use type map of the same resolution in the study area are selected and superimposed on them, the application attributes of each cell can be directly obtained. (2) Water demand gridding. On the basis of gridding in the study area, according to the application attributes of each cell, the quota method is used to predict the water demand of each cell. Then the water demand forecast result for the entire research area is obtained. (3) Water supply gridding. It is possible to reflect the types of water supply sources that a certain or a certain type of cell can accept, as well as the types of water supply sources that can be supplied by a certain type of water supply sources, by determining the types of water supply sources in the research area. The supply ranges that various water supply sources can reach, followed by the completion of the water supply grid. (4) According to the results of water demand gridding and water supply gridding, the water demand of each cell and the corresponding water supply source and water supply topological relationship are established, and the objective function is to minimize water shortage and maximize economic benefits to build a refined model for the optimal allocation of water resources.(5) The particle swarm algorithm is introduced to solve it, and the Pareto optimal solutions is obtained to determine the optimal water distribution for each cell. (6) Taking Guantao County, China as an example, the model was applied. In terms of minimum water shortage, the total water demand in Guantao County is predicted to be 118.8719 million m3 in the planning year (P = 50 %), while the total water supply is 125.2818 million m3. The water supply can meet the demand for water. In terms of financial advantages, the planned level of water Annual economic benefits will amount to 4.0865 million yuan. [ABSTRACT FROM AUTHOR]

This paper, firstly, investigates the relationship between clean energy and economic growth using satellite remote sensing data to proxy economic activities. Then, this paper constructs a spatial autoregressive model, spatial error model, and spatial Durbin model to study the spatial spillover effects of financial development and clean energy. Further, this paper adopts the difference-in-differences (DID) model and the propensity score matching-DID (PSM-DID) method to conduct quasi-natural experiments on clean energy and examine whether China's energy policy can promote clean energy, thereby promoting economic growth. The analysis results reveal a positive correlation between clean energy and economic growth; local financial development promotes clean energy in the region but negatively impacts the clean energy in surrounding areas. Moreover, the findings confirm a significant positive relationship between the "policy treatment effect" and clean energy. Finally, we put forward relevant policy recommendations based on the empirical results. • Satellite remote sensing data acquired by DMSP/OLS is used to proxy GDP. • Clean energy has a significant role in promoting economic growth. • Spatial econometric models are constructed to study the spatial spillover effects. • The DID model and PSM-DID method are used to conduct quasi-natural experiments. • China's energy policy has a significant promoting effect on clean energy and GDP. [ABSTRACT FROM AUTHOR]

Digitizing the land surface temperature (Ts) and surface soil moisture (mv) is essential for developing the intelligent Digital Earth. Here, we developed a two parameter physical-based passive microwave remote sensing model for jointly retrievingTsandmvusing the dual-polarizedTbof Aqua satellite advanced microwave scanning radiometer (AMSR-E) C-band (6.9 GHz) based on the simplified radiative transfer equation. Validation using in situTsandmvin southern China showed the average root mean square errors (RMSE) ofTsandmvretrievals reach 2.42 K (R2= 0.61,n= 351) and 0.025 g cm−3(R2= 0.68,n= 663), respectively. The results were also validated using global in situTs(n= 2362) andmv(n= 1657) of International Soil Moisture Network. The corresponding RMSE are 3.44 k (R2= 0.86) and 0.039 g cm−3(R2= 0.83), respectively. The monthly variations of model-derived Ts and mv are highly consistent with those of the Moderate Resolution Imaging SpectroradiometerTs(R2= 0.57; RMSE = 2.91 k) and ECV_SMmv(R2= 0.51; RMSE = 0.045 g cm−3), respectively. Overall, this paper indicates an effective way to jointly modelingTsandmvusing passive microwave remote sensing. [ABSTRACT FROM AUTHOR]

Stipa purpurea is the representative type of alpine grassland in Tibet and the surviving and development material for herdsmen. This paper takes Shenzha County as the research area. Based on the analysis of typical hyperspectral variables sensitive to chlorophyll content of Stipa purpurea, 10 spectral variables with significant correlation with chlorophyll were extracted. The estimation model of chlorophyll was established. The photosynthetic pigment contents in the Shenzha area were calculated by using HJ-1A remote sensing images. The results show that (1) there are significant correlations between chlorophyll content and spectral variables; in particular, the coefficient of Chlb in Stipa purpurea with RVI is the largest (0.728); (2) 10 variables are correlated with chlorophyll, and the order of correlation is Chlb > Chla > Chls; (3) for the estimation of Chla, the EVI is the best variable. RVI, NDVI, and VI2 are suitable for Chlb; RVI and NDVI are also suitable for the estimation of Chls; (4) the mean estimated content of Chla in Stipa bungeana is about 4.88 times that of Chlb, while Cars is slightly more than Chlb; (5) the distribution of Chla is opposite to Chlb and Chls content in water area. [ABSTRACT FROM AUTHOR]

In this paper, we focus on the characteristics of the landslides developed in the epicentral area of AD 1556 M~8.5 Huaxian Earthquake, and discuss their relations to the active normal faults in the SE Weihe Graben, Central China. The results from analyzing high-resolution remote-sensing imagery and digital elevation models (DEMs), in combination with field survey, demonstrate that: (i) the landslides observed in the study area range from small-scale debris/rock falls to large-scale rock avalanches; (ii) the landslides are mostly developed upon steep slopes of ≥30°; and (iii) the step-like normalfault scarps along the range-fronts of the Huashan Mountains as well as the thick loess sediments in the Weinan area may facilitate the occurrence of large landslides. The results presented in this study would be helpful to assess the potential landslide hazards in densely-populated areas affected by active normal faulting. [ABSTRACT FROM AUTHOR]

Wetland databases can provide the basic data that necessary for the protection and management of wetlands. A large number of wetland databases have been established in the world as well as in China. In this paper, we review China's wetland databases based on remote sensing (RS) technology after introducing the background theory to the application of RS technology in wetland surveys. A key conclusion is that China's wetland databases are far from sufficient in fulfilling protection and management needs. Our recommendations focus on the use of the hyper-spectral imagery, microwave data, multi-temporal images, and automatic classifications in order to improve the accuracy and efficiency of wetland inventory. Further, attention should also be paid to detect major biophysical features of wetlands and build wetland databases in years after the 1980s in China. Considering that great gap exists between RS experts and wetland experts, further cooperation between wetland scientists and RS scientists are needed to promote the application of RS in the foundation of wetland databases. [ABSTRACT FROM AUTHOR]

Wheat is one of the most important crops for many countries. It is important to obtain accurate wheat planting area. Optical imagery has been widely used in crop classification and made great progress, but the optical imagery in the critical period of wheat growth is often affected by weather and other external conditions. In contrast, synthetic aperture radar (SAR) is potentially effective data source for wheat mapping because of its all-weather, all-day imaging capabilities. The study site was located in a typical urban agricultural region in Gaochun District of Nanjing, the provincial capital of Jiangsu Province, China, with the central coordinates of 118°52'E and 31°19'N. Six SAR (Sentinel-1A) and 4 optical (Landsat-8) images were obtained from the ESA during the growing period of winter wheat. The objective of this study was to explore the abilities of multi-temporal satellite SAR data, optical images and the combination of both types of images to identify winter wheat in an urban agriculture region with complex planting structures. The investigations were through the following steps: 1) The texture features were extracted from the SAR images using the gray level co-occurrence matrix (GLCM), and the backscatter intensity data were also obtained; 2) A support vector machine (SVM) was used as the classifier to map winter wheat, using the different combinations of the Sentinel-1A derived information and optical images. 3) Then, the classification accuracies for different combinations of SAR variables and optical images were evaluated. The results showed that: 1) In the process of winter wheat growth, the backscatter characteristics changed obviously. In the middle of April, the backscatter characteristics were different between winter wheat and other vegetation in the study area, which could provide the theoretical basis for winter wheat classification. 2) Using single polarization, single-temporal SAR data were difficult to meet the accuracy requirements of winter wheat with complex planting structures, and the classification accuracy of VV polarization was higher than that of VH polarization; 3) Compared with VV polarization, the producer's and user's accuracy of VV+VH combination were increased by 27.3% and 13.6%, respectively. When VV polarization was added to VH backscatter intensity images, the producer's and user's accuracy were increased from 50.3% to 85.7% and from 54.5% to 87.9%, respectively; 4) When texture images were added to backscatter intensity images, the winter wheat fields could be mapped with a satisfactory classification accuracy, and the user's and producer's accuracy could reach up to 94.6% and 93.3%, respectively; 5) The combination of multi-temporal SAR and optical images had the best classification accuracy, and the producer's and user's accuracy were 97% and 98.4% (overall accuracy 95.3%, Kappa coefficient 0.91), respectively. The addition of multi-temporal SAR to optical images allowed us to increase producer's and user's accuracy of winter wheat by 3.7% and 3.8%, respectively. The results in this paper show that the satisfactory classification of winter wheat can be achieved using Sentinel-1A data alone in urban agricultural regions with complex planting structures. In addition, texture features of SAR can improve SAR data classification accuracy. The SAR and optical images can make use of the advantages of information complementation of each other to make up for their deficiencies in crop classification. It is indicated that the SAR data can not only be used as a data source instead of optical images to distinguish winter wheat, but also as a supplementary data source to improve the classification accuracy of optical data. [ABSTRACT FROM AUTHOR]

Rapid urbanization has resulted in great changes in rural landscapes globally. Using remote sensing data to quantify the distribution of rural settlements and their changes has received increasing attention in the past three decades, but remains a challenge. Previous studies mostly focused on the residential changes within a grid or administrative boundary, but not at the individual village level. This paper presents a new change detection approach for rural residential settlements, which can identify different types of rural settlement changes at the individual village level by integrating remote sensing and Geographic Information System (GIS) analyses. Using multi-temporal Landsat TM image data, this approach classifies villages into five types: “no change”, “totally lost”, “shrinking”, “expanding”, and “merged”, in contrast to the commonly used “increase” and “decrease”. This approach was tested in the Beijing metropolitan area from 1984 to 2010. Additionally, the drivers of such changes were investigated using multinomial logistic regression models. The results revealed that: (1) 36% of the villages were lost, but the total area of developed lands in existing villages increased by 34%; (2) Changes were dominated by the type of ‘expansion’ in 1984–1990 (accounted for 43.42%) and 1990–2000 (56.21%). However, from 2000 to 2010, 49.73% of the villages remained unchanged; (3) Both topographical factors and distance factors had significant effects on whether the villages changed or not, but their impacts changed through time. The topographical driving factors showed decreasing effects on the loss of rural settlements, while distance factors had increasing impacts on settlement expansion and merging. This approach provides a useful tool for better understanding the changes in rural residential settlements and their associations with urbanization. [ABSTRACT FROM AUTHOR]