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Timely and accurate survey of construction and demolition waste (C&DW) distribution is of significance for managing C&DW and enhancing the quality of the urban environment, especially in rapidly urbanizing country of China. Automatic C&DW recognition using high-resolution remote sensing images is an extremely important surveying method. Existing C&DW recognition methods can only obtain a rough spatial distribution of C&DW. Mapping precise C&DW distribution on large-scale remote sensing images is still challenging. In this paper, to solve the problem of precise C&DW recognition, a novel deep learning algorithm based on high-resolution remote sensing images is proposed, which we refer to as the cross-channel multi-scale gated fusion network (CCMGNet). CCMGNet extracts depth features of C&DW from RGB and NIR bands by using two independent encoding streams. A gated fusion layer and a multi-scale attention module are designed to effectively fuse the features from two encoding streams. We manually labelled the sample set used for training and testing on 26 remote sensing images captured by the Gaofen-2 (GF-2) satellite. These images were taken in nine typical cities in China over the time period from 2019 to 2021. The results showed that the proposed method is effective in recognizing C&DW, realizing 89.42% and 84.52% in precision and IoU, and is superior to other state-of-the-art deep learning algorithms and existing C&DW recognition methods. The effectiveness of all significant components in CCMGNet was confirmed by ablation experiments. The proposed method was applied to extract C&DW regions in large-scale remote sensing images, with an example of a GF-2 image of Beijing. The high extraction efficiency and satisfactory visual effect demonstrate the potential of the proposed method for surveying the spatial distribution of C&DW. The novel method will play a crucial role in automatically surveying the spatial distribution and size of C&DW across the country. [ABSTRACT FROM AUTHOR]

The reflectance anisotropy of land surface serves as an important bridge between surface biophysical parameters and remote sensing observations. It can characterize by the linear kernel-driven bidirectional reflectance distribution function (BRDF), which is the combination of several kernel functions and kernel weights. These kernel weights can be estimated by remote sensing; however, the stability of current kernel weights products is still challenging, especially in urban areas with complex aerosol properties and heterogeneous surfaces. In this paper, we propose a method for robust estimation of kernel weights from the Moderate Resolution Imaging Spectroradiometer (MODIS) surface spectral reflectance products (MxD09GA) data based on the constrained least-squares method (CLSM) and a priori knowledge. The kernel weights data were obtained by the CLSM from 2014 to 2017 in Beijing region of China. Validations were carried out using the MxD09GA and BRDF/Albedo products (MCD43A1). The results show that the time series of kernel weights by the CLSM show small variability over different land cover types. The kernel weights estimated by the CLSM can clearly show the phenological signal and fitting ability of surface spectral reflectance is better than that of the MCD43A1 products in Beijing urban area. Experimental results demonstrate that the CLSM has the potential for the robust estimation of kernel weights in urban areas. [ABSTRACT FROM AUTHOR]

The XXIst International Congress of Photogrammetry and Remote Sensing was held at the Beijing International Convention Centre, China, from 3rd to 11th July 2008. Reports are given on the Congress as a whole, including the General Assembly, some Technical Commission activities and the Congress Exhibition. Papers from the Congress are published in Volume XXXVII of the International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. [ABSTRACT FROM AUTHOR]

This paper uses an urbanized high-resolution land data assimilation system (u-HRLDAS) to parameterize the urban land surface characteristics. The u-HRLDAS model is localized and developed in order to satisfy the need of the weather forecast in Beijing, China. The remote sensing data used to localize and drive u-HRLDAS include the soil type data and MODIS retrieved leaf area index (LAI) data. The evaporation and water depth for impervious surface in urban area are developed to improve the simulation of u-HRLDAS. The result of the urban weather forecast is used for the comparison based on the rapid update cycle system at Beijing Meteorological Bureau (BJ-RUC) without coupled with u-HRLDAS. The land surface temperature, land surface fluxes, and first layer soil moisture in several single sites and urban Beijing region by BJ-RUC are compared with u-HRLDAS after localization and development. The off-line simulation results indicate that compared with BJ-RUC, after the localization and development, u-HRLDAS can improve the simulation of land surface parameters and fluxes definitely. [ABSTRACT FROM AUTHOR]

Spatial sampling design is important for accurately assessing land use and land cover (LULC) classification results from remote sensing data. Spatial stratification can dramatically improve spatial sampling efficiency by dividing the study area into several strata when classification correctness is spatially stratified heterogeneous. By integrating the LULC classification results from different sources and spatial resolutions, a spatial stratification method for spatial sampling of accuracy assessment is presented in this paper. Its efficiency is demonstrated in the case study using LULC data of Beijing, China, in the following steps. First, we standardized and reclassified multiresolution remote sensing data, including China's land use/cover datasets (CLUDs) from 2017 (resolution: 30 m), 500 m MCD12Q1, and 10 m FROM-GLC10 data, into six classes. Second, we customized stratification rules, formulated a technical specification to realize 11 strata using CLUDs and MCD12Q1, and employed FROM-GLC10 as the reference data for accuracy assessment. Furthermore, six sample sets with sizes of 16,417; 1821; 652; 337; 198; and 142 were drawn using different methods, and their overall accuracy (OA), deviation accuracy (DA), root-mean-square error (RMSE), and standard deviation (STDEV) values were also evaluated to demonstrate the efficiency brought by spatial stratification. Compared with the spatial even sampling method, the OAs of the stratified even sampling method adopting the proposed stratification method was much closer to the true OA, and the corresponding RMSE and STDEV results decreased from 2.097% and 2.127% to 0.914% and 0.713%, respectively, due to the contribution of spatial stratification in the sampling scheme. The method can be used to distinguish the differences and improve the representativeness of samples, and it can be employed to select validation samples for LULC classification. [ABSTRACT FROM AUTHOR]

Farmland protection and delimitation in the urban fringe considers not only natural factors but also the spatial characters and site factors. Taking Daxing District, Beijing in China as a case study, this paper used landscape ecology and power-law methods to analyze and evaluate farmland loss during the period of 2004-2007 based on the interpretation results of SPOT5 remote sensing images in 2004 and 2007. At the patch level, we selected four landscape indices, namely patch size, shape index, the nearest neighbor distance between farmland and construction land (including residential land and other construction land), and cropping type, to evaluate the risk of farmland loss and establish a farmland site analysis indicator system. The results showed that patch size and shape index have a significant positive correlation with farmland loss, whereas the distance to construction land has a clear negative correlation with farmland loss. As regards cropping type, fallow farmland is much easier for non-agricultural use than cultivated farmland. The relative transition ratio among vegetable land, fallow farmland and cultivated farmland is 1:5.6:1. The patch size of lost farmland follows a power-law distribution, indicating that not only small parcels but also large parcels can be lost. Patch size less than 4 ha or more than 15 ha is in high loss risk, between 4 ha and 10 ha in medium loss risk, and larger than 10 ha and less than 15 ha in low risk. Farmland with a more regular shape has a higher likelihood of loss. Patch shape index less than 2.0 is in high loss risk, between 2.0 and 3.0 in medium loss risk, and larger than 3.0 in low risk. Construction land has a varying impact on farmland loss, the residential land effected distance is 1000 m, and that of the other construction land is 2000 m. This analysis showed the relationships between site factors and farmland loss, and the analysis framework can provide support and reference for farmland protection and delimitation of prime farmland in China. [ABSTRACT FROM AUTHOR]

Mechanism and modeling of the land subsidence are complex because of the complicate geological background in Beijing, China. This paper analyzed the spatial relationship between land subsidence and three factors, including the change of groundwater level, the thickness of compressible sediments and the building area by using remote sensing and GIS tools in the upper-middle part of alluvial-proluvial plain fan of the Chaobai River in Beijing. Based on the spatial analysis of the land subsidence and three factors, there exist significant non-linear relationship between the vertical displacement and three factors. The Back Propagation Neural Network (BPN) model combined with Genetic Algorithm (GA) was used to simulate regional distribution of the land subsidence. Results showed that at field scale, the groundwater level and land subsidence showed a significant linear relationship. However, at regional scale, the spatial distribution of groundwater depletion funnel did not overlap with the land subsidence funnel. As to the factor of compressible strata, the places with the biggest compressible strata thickness did not have the largest vertical displacement. The distributions of building area and land subsidence have no obvious spatial relationships. The BPN-GA model simulation results illustrated that the accuracy of the trained model during fifty years is acceptable with an error of 51% of verification data less than 20 mm and the average of the absolute error about 32 mm. The BPN model could be utilized to simulate the general distribution of land subsidence in the study area. Overall, this work contributes to better understand the complex relationship between the land subsidence and three influencing factors. And the distribution of the land subsidence can be simulated by the trained BPN-GA model with the limited available dada and acceptable accuracy. [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]

Abstract: Remote sensing has become an important method for monitoring urban thermal environment, but, it is still difficult to analysis the spatio-temporal change of urban thermal environment quantitatively only by ground surface temperature (or brightness temperature). Three indexes on urban thermal environment monitoring are proposed in this paper: heat island intensity, heat field intensity index and heat island proportion index. Based on MODIS surface temperature products and FY3A/MERSI data of two years, urban thermal environment is monitored by remote sensing method in Beijing. The empirical results verify that three indexes proposed in this paper are meaningful in monitoring of urban heat island. It can not only monitor the intensity and changes of the urban heat island in Beijing effectively, but also, it is positive to carrying out meteorological operations in city thermal environment monitoring. [Copyright &y& Elsevier]

This paper addresses the estimation of adjacency effect in ground spectrum and Landsat-5 pixels. The adjacency effect influences the digital number value of a pixel by adding surface surrounding scatter signals and path scatter signals at the sensor. Along with the increasing use of satellite high-resolution imagery and quantitative remote sensing, much attention has been paid to the experimental measurement and estimation of the natural phenomena of adjacency effects. Based on the theory of radiation transfer, a procedure was designed to measure the reflectance from the surface target materials and the materials in a box which is 1.5 m above the surface to avoid upwelling reflectance. At every 3 × 3 sites, the measurement was carried out during 10:30 to 13:30 of local time at the Guanting Remote Sensing Test Site in north Beijing. The results show that the adjacency effect becomes stronger from visible, near infrared to shortwave infrared wavelength; the adjacency effect weakens with the increase of distance between testing site. At last, the adjacency effect of Landsat-5 image was corrected, and the quality of the resulting image was improved. [ABSTRACT FROM AUTHOR]

High-resolution hydrological modeling is crucial for detecting extreme hydrological events and understanding fundamental terrestrial processes. However, spatial resolutions in current hydrological modeling studies have been mostly constrained to relatively coarse resolution (~10–100 km), and they therefore have a difficult time addressing flooding or drought issues with fine resolutions. In this study, a continental-scale high-resolution hydrological modeling framework (0.0625°, ~6 km) driven by remote sensing products was used to detect extreme hydrological event occurrences in China and evaluated based on the Variable Infiltration Capacity (VIC) model. The results showed that the developed model provided more detailed information than the coarser resolution models (a 0.25° and 1°), thereby capturing the timing, duration, and spatial extent of extreme hydrologic events regarding the 2012 Beijing flood and 2009/10 drought in Hai River Basin. Here, the total water storage changes were calculated based on the VIC model (−0.017 mm/year) and Gravity Recovery and Climate Experiment (GRACE) satellite (−0.203 mm/year) to reflect the water availability caused by climate change and anthropogenic factors. This study found that the 0.0625° dataset could capture detailed changes, thereby providing reliable information during occurrences of extreme hydrological events. The high-resolution model integrated with remote sensing products could be used for accurate evaluations of continental-scale extreme hydrological events and can be valuable in understanding its long-term occurrence and water resource security. [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]

Cities exposed their vulnerabilities during the COVID-19 pandemic. Unprecedented policies restricted human activities but left a unique opportunity to quantify anthropogenic effects on urban air pollution. This study aimed to explore the underlying urban development issues behind these restrictions and support a sustainable transition. The data from ground stations and Sentinel-5P satellite were used to assess the temporal and spatial anomalies of NO2. Beijing China was selected for a case study because this mega city maintained a "dynamic zero-COVID" policy with adjusted restrictions, which allowed for better tracking of the effects. The time-series decomposition and prediction regression model were employed to estimate the normal NO2 levels in 2020. The deviation between the observations and predictions was identified and attributed to the policy interventions, and spatial stratified heterogeneity statistics were used to quantify the effects of different policies. Workplace closures (54.8%), restricted public transport usage (52.3%), and school closures (46.4%) were the top three restrictions that had the most significant impacts on NO2 anomalies. These restrictions were directly linked to mismatched employment and housing, educational inequality, and long-term road congestion issues. Promoting the transformation of urban spatial structures can effectively alleviate air pollution. [ABSTRACT FROM AUTHOR]

The quantitative relationship between the groundwater level (GWL) and land subsidence is essential to understand, prevent and control subsidence. This study compares the subsidence due to GWL variation at different locations and aquifer layers during the operation of the South-to-North Water Diversion Project Central Route (SNWDP-CR) in China. Sixty-nine synthetic aperture radar (SAR) images in Beijing from 2012 to 2017 are obtained to calculate the displacement using the interferometry technique. The results indicate that the subsidence with uplift or mitigation is consistent with groundwater rise or drawdown deceleration. The dynamic time warping (DTW) method is applied to compare the similarity between subsidence and GWL time series waveforms. We find that the similarity between these two activities tends to increase after the SNWDP-CR. The similarity between the subsidence and confined GWL is controlled by the hydrogeological setting. In general, it tends to increase from the upper part to the lower part of the alluvial fan. However, the similarity between the subsidence and unconfined GWL shows irregular pattern due to rainfall interference. The average similarity between the subsidence and GWL at depths of 100–180 m and that at over 250 m is 75% and 64%, respectively, prior to the SNWDP-CR. In contrast, these two values increase to 83% and 81%, respectively, after the SNWDP-CR. This indicates that the impact of deeper aquifers to the subsidence increases after the SNWDP-CR, and the depth of the contributing aquifer is 100–180 m and locally exceeds 250 m. This study provides technical support for strategic decisions on water resource management and subsidence prevention within the context of the SNWDP-CR. [ABSTRACT FROM AUTHOR]

Abstract: Land utilization is mainly the one of the human conscious activity in natural environment, Remote sensing is a most common and most effective method of testing the changes in the economic environment and the type of land development. This paper conducts a preliminary analysis based on the land use in Beijing that land-use change in Beijing and the reasons according to data analysis, and pointed out the advantages and disadvantages and limitations of various methods. according to data analysis, land-use change in Beijingland the reasons For future land development and utilization of analytical basis. [Copyright &y& Elsevier]

Abstract: Based on the existing Urban Expansion Dynamic (UED) model, this paper develops a geographic information system (GIS)-based model that can be used to assess the current and potential spatial stress on a regional wetland ecosystem due to urban expansion. Synthetically utilizing the simulation capability of the UED model and the spatial analysis power of a GIS, this new model takes into account the present disturbance by ongoing urban expansion, the potential disturbance by future urban expansion, the accessibility via the transportation network, and the neighborhood stability. The model is used in particular to simulate the spatial stress on the major wetlands in Beijing from 1991 to 2004. The simulation results are found to be consistent with the actual remote sensing observations: a relatively high spatial stress normally leads to an eventual conversion into urban use. With the future urban lands predicted by the UED model, this model is used to further assess the potential spatial stress of urban expansion on the major wetlands in Beijing by 2015. Moreover, the spatially stressed wetland fringe areas in Beijing are identified based on the total spatial stress combining the current and the potential. These areas are found to be mainly distributed in downtown Beijing and the plains areas that have relatively easy accessibility via the transportation network. Effective measures should be taken to protect such areas from being further disturbed in order to achieve the goal of a more effective conservation of the wetland ecosystems in Beijing. [Copyright &y& Elsevier]

During the CAREBEIJING campaign in 2006, imaging differential optical absorption spectroscopy (I-DOAS) measurements were made from 08:00 to 16:00 on September 9 and 10 over Beijing, China. Detailed images of the near-surface NO2 differential slant column density (DSCD) distribution over Beijing were obtained. Images with less than a 30-min temporal resolution showed both horizontal and vertical variations in NO2 distributions. For DSCD to mixing ratio conversion, path length along the lines of I-DOAS lines of sight was estimated using the light-extinction coefficient and Ångstrom exponent data obtained by a transmissometer and a sunphotometer, respectively. Mixing ratios measured by an in-situ NO2 analyzer were compared with those estimated by the I-DOAS instrument. The obtained temporal and spatial variations in NO2 distributions measured by I-DOAS for the two days are interpreted with consideration of the locations of the major NO x sources and local wind conditions. I-DOAS measurements have been applied in this study for estimating NO2 distribution over an urban area with multiple and distributed emission sources. Results are obtained for estimated temporal and spatial NO2 distributions over the urban atmosphere; demonstrating the capability of the I-DOAS technique. We discuss in this paper the use of I-DOAS measurements to estimate the NO2 distribution over an urban area with multiple distributed emission sources. [Copyright &y& Elsevier]

In this paper, we report the results and analysis of a recent field campaign in August 2007 investigating the impacts of emissions from transportation on air quality and community concentrations in Beijing, China. We conducted measurements in three different environments, on-road, roadside and ambient. The carbon monoxide, black carbon and ultrafine particle number emission factors for on-road light-duty vehicles are derived to be 95gkg−1-fuel, 0.3gkg−1-fuel and 1.8×1015 particleskg−1-fuel, respectively. The emission factors for on-road heavy-duty vehicles are 50gkg−1-fuel, 1.3gkg−1-fuel and 1.1×1016 particleskg−1-fuel, respectively. The carbon monoxide emission factors from this study agree with those derived from remote sensing and on-board vehicle emission testing systems in China. The on-road black carbon and particle number emission factors for Chinese vehicles are reported for the first time in the literature. Strong traffic impacts can be observed from the concentrations measured in these different environments. Most clear is a reflection of diesel truck traffic activity in black carbon concentrations. The comparison of the particle size distributions measured at the three environments suggests that the traffic is a major source of ultrafine particles. A four-day traffic control experiment conducted by the Beijing Government as a pilot to test the effectiveness of proposed controls was found to be effective in reducing extreme concentrations that occurred at both on-road and ambient environments. [Copyright &y& Elsevier]

Change-vector analysis (CVA) is a valuable technique for landuse/land-cover change detection. However, how to reasonably determine thresholds of change magnitude and change direction is a bottleneck to its proper application. In this paper, a new method is proposed to improve CVA. The method (the improved CVA) consists of two stages, Double-Window Flexible Pace Search (DFPS), which aims at determining the threshold of change magnitude, and direction cosines of change vectors for determining change direction (category) that combines single-date image classification with a minimum-distance categorizing technique. When the improved CVA was applied to the detection of the land-use/land-cover changes in the Haidian District, Beijing, China, Kappa coefficients of "change/ no-change" detection and "from-to" types of change detection were 0.87 and greater than 0.7, respectively, for all kinds of land-use changes. The experimental results indicate that the improved CVA has good potential in land-use/land-cover change detection. [ABSTRACT FROM AUTHOR]

In the acceleration of industrialization and urbanization, brownfield is an important resource for land regeneration and restoration targeting to recover its ecosystem function. Landfills, as a type of brownfield distributed in the highly populated areas, occupy massive land resources. The ecological restoration of landfills becomes an engineering approach to achieve multiple effects of society, environment, and economy. This paper aims to monitor and assess the restoration microclimatic effects of landfills restoration in China with remote sensing data and techniques. We selected representative landfill restoration projects, including Nanhaizi Park in Beijing, Nanhu Park in Tangshan, and Tianziling Park in Hangzhou, as the study cases. Using the Landsat imagery, the scenes of the pre-restoration, mid-restoration, and post-restoration were retrieved, and the surface energy balance equation was applied to calculate normalized difference vegetation index (NDVI), land surface temperature (T s), and evapotranspiration (ET), representing vegetation conditions and hydro-heat microclimate conditions. The study areas are divided into four subzones and extracted the calculated values for statistics and comparison. We defined an indicator of restoration intensity (RI) using the different subzones to the outer contour distance radius and restoration phases to evaluate the spatial-temporal restoration degrees. The spatial pattern dynamics was displayed in the urbanization context in the cases of Beijing and Tangshan. We examined and quantified the relations between RI and the key indicators, NDVI, T s , and ET. The overall results revealed that with higher restoration intensity, NDVI and ET became higher, and T s became lower. It suggested that the ecological restoration on the landfills brought better vegetation, cooling, and humidification effects for the microclimatic environment. Our research has the implications for monitoring and assessing the climatic effects of ecological restoration in the brownfield regeneration in a macroscopic and ecosystem perspective. Unlabelled Image • With satellite dataset, we developed a way to monitor vegetation and microclimatic changes in the restored landfills. • The indicator to quantify the ecological restoration intensity was invented with spatial distance and restoration phase. • Overall, with higher restoration intensity, vegetation and evapotranspiration improved, and surface temperature declined. [ABSTRACT FROM AUTHOR]

Soil erosion by water is a major cause of land degradation. Agricultural practices and many other ecological environmental problems contribute to land degradation worldwide, especially in arid and semi-arid areas. Miyun County, which is located in a mountainous region of North China, is an important natural ecological zone and surface source of drinking water for Beijing and is very vulnerable to soil erosion due to its thin soil layer and human activities. Landsat images from 2003 and 2013 were used to analyze the land-use and land-cover change (LULCC) over this period. The revised universal soil loss equation (RUSLE) model integrated with Geographic Information System (GIS) was used to quantify soil loss and to map erosion risk. In addition, the response of soil erosion to LULCC was evaluated. The results showed that the areas under cropland, forest, and water bodies increased over the study period by 66.03, 243.44, and 9.01 km2, respectively. The increase in forested land indicated that the improved ground vegetation cover was due to the implementation of active ecological measures. Between 2003 and 2013, light soil erosion increased by 587.46 km2, and extremely severe soil erosion increased by 9.57 km2. The extents of slight, moderate, severe, and very severe soil erosion, however, decreased by 8.02, 445.21, 142.69, and 1.11 km2, respectively. A total of 57.5% of land with moderate soil erosion has been converted to light soil erosion, which could be highly beneficial for the improvement of vegetation control of soil and water losses. In terms of area, forestland exhibited the greatest increase, while moderate soil erosion exhibited the greatest decrease over the study period. Land-use change led to an alteration in the intensity of soil erosion due to changes or loss of vegetation. The conversion from high intensity soil erosion to low intensity was attributed to the implementation of ecological environmental protection. The results generated from this study may be useful for planners and land-use managers to make appropriate decisions for soil conservation. [ABSTRACT FROM AUTHOR]

With the implementation of human activities, such as logging, reclamation, and construction, the increasing fragmentation of ecological space and the increasing blockage of biological migration corridors cause many threats to biodiversity conservation. In this study, we used the Northwest Beijing Ecological Containment Area as the research area. Based on an integrated circuit theoretical model, we identified functional connectivity networks and analyzed the spatial and temporal changes of ecological blockage patterns in the region from 1998–2018 in terms of the landscape connectivity, ecological breakpoints, pinch points, and barriers, respectively. The results show that the average remote sensing ecological index had a trend of decreasing and then increasing, and a total of 33, 34, and 63 habitat core areas and 70, 74, and 152 ecological corridors were identified in 1998, 2010, and 2018, respectively. The regions with high ecological blockage were mainly in the central part of Yanqing District, the southwest corner of the study area, and the eastern urban area. Although the number of potential ecological corridors gradually increases with the probability of migration in the study area, the blockage status and vulnerability of the ecological corridors continue to increase due to the conflict between land uses. The ecological status of the study area reflects the comprehensive effectiveness of the capital's high-quality development under the strategic deployment of ecological civilization. In the context of habitat fragmentation, the effective protection and restoration of the ecological conditions in the ecological function areas is of great importance in guaranteeing the ecological quality and sustainable development of the country. [ABSTRACT FROM AUTHOR]

To investigate geological mining hazards using digital techniques such as high-resolution remote sensing, a semi-automatically geological mining hazards extraction method is proposed based on the case of the Shijiaying coal mine, located in Fangshan District, Beijing, China. In the method, the vegetation is first removed using the normalized difference vegetation index (NDVI) on the GeoEye-1 data. Then, geological mining hazards interpretation features are determined after color enhancement using principal component analysis (PCA) transformation. Bitmaps mainly covered by geological mining hazards are isolated by masking operation in the environment for visualizing images software. Next, each bitmap is classified into a two-valued imagery using support vector machine algorithm. In the two-valued imagery, 1 denotes the geological mining hazards, while 0 denotes none. Afterwards, the two-valued imagery is converted into a vector graph by corresponding functions in the ArcGIS software and no geological mining hazards regions in the vector graph are deleted manually. Finally, the correlation between factors (such as mining activity, lithology, geological structure, and slope) and geological mining hazards is analyzed using a logistic regression and a hazardous-area forecasting model is built. The results of field verification show that the accuracy of the geological mining hazards extraction method is 98.1% and the results of the hazardous-area forecasting indicate that the logistic regression is an effective model in assessing geological hazard risks and that mining activity is the main contributing factor to the hazards, while geological structure, slope, lithology, roughness of the surface, and aspect are the secondary. [ABSTRACT FROM AUTHOR]

China has undergone rapid urbanization in the past few decades, and it has been accompanied by overdevelopment. Residential vacancies caused by overdevelopment result in a waste of resources and generate greenhouse gases associated with land surface changes. Due to the poor spatial resolution and limited availability of data, previous studies performed analyses at low resolutions at the county scale, thus lacking spatial detail. In addition, they used complicated subjective indicators difficult to apply to cities of various sizes across China. To understand the detailed spatial pattern of residential vacancies in megacities, we designed a more generally applicable approach with multisource high-resolution spatiotemporal data and tested it in Beijing, the capital of China. At first, a statistical regression with features derived from multisource data was used. Then, the predicted values of the regression function were used as standard heat values, and the observed heat value in each unit was divided by the corresponding standard heat value. Next, residential vacancies were estimated by calculating the quantiles of these division results in all analysis units. This approach requires no prior knowledge or complicated indicators and can be easily applied across cities in China, which is beneficial for development planning at the provincial and national levels. [ABSTRACT FROM AUTHOR]

Urbanization has induced profound landscape changes. While the spatiotemporal patterns of urban landscapes have been extensively studied, the manner by which the internal structures of already urbanized areas change remains little understood. Characteristic scales are an important measure of landscape structure, and they represent the typical spatial extents of landscape elements in hierarchies. In this study, we quantified temporal variations of the characteristic scales in the central urban landscapes of Beijing and Shanghai over an 18 year period. Using transect data from Landsat images, characteristic scales were identified through wavelet analysis and then classified into several discrete domains using the k-means clustering method. These characteristic scale domains appeared to correspond with the typical extents of the blocks and block clusters in the study areas. Results showed that the number of the characteristic scale domains changed within a small range of 3-5 while the mean values of the characteristic scales within the domains showed substantial temporal variation. Larger characteristic scales were more variable than smaller ones in both cities. Distinguishing relative change rates of building forms, land use and street layout of urban landscapes allowed us to interpret these differences. The street layout of urban landscapes usually reacts slowly to the force of change, acting as the skeleton of the urban landscape. As a result, block sizes can remain relatively stable and corresponding characteristic scales present inheritance features. Land use and building forms are more susceptible to changes. Block clusters with flexible extents could result in significant variation of characteristic scales. [ABSTRACT FROM AUTHOR]

After Beijing wins the bit to host the 29th Olympic Games, in order to manifest the technical support advantages and capabilities of the autonomously-developed RS and GIS based change detection techniques in 2008 Beijing Olympic Games, and from the standpoints of executing new city planning, relieving the traffic congestion as well as maintaining the historic features, an automatic satellite monitoring system has been studied and established to accomplish the mission of unauthorized construction inspection within the Sixth Ring Road of Beijing city quarterly, by adopting the CBERS-2 satellite images and combining technologies of GIS, GPS, etc. This article discusses the applicable procedures and key issues when utilizing such Chinese satellite images and relevant techniques to discover the illegal constructions, and introduces the monitoring system from both the design and implementation aspects; additionally, some typical application cases in the practice of the system are also illustrated. The monitoring system can timely supply abundant information to facilitate the policy-making of relevant planning departments, thus providing consolidate technical support to eliminate the illegal constructing behaviors in the blossom. During the five years' excellent performance, it has helped China save large amounts of expenditures for processing of unauthorized constructed buildings. [ABSTRACT FROM AUTHOR]

China's Sponge City initiative will involve widespread installation of new stormwater infrastructure including green roofs, permeable pavements and rain gardens in at least 30 cities. Hydrologic modelling can support the planning of Sponge Cities at the catchment scale, however, highly detailed spatial data for model input can be challenging to compile from the various authorities, or, if available, may not be sufficiently detailed or updated. Remote sensing methods show great promise for mitigating this challenge due to their ability to efficiently classify satellite images into categories relevant to a specific application. In this study Geographic Object Based Image Analysis (GEOBIA) was applied to WorldView-3 satellite imagery (2017) to create a detailed land cover map of an urban catchment area in Beijing. While land cover classification results based on a Bayesian machine learning classifier alone provided an overall land cover classification accuracy of 63%, the subsequent inclusion of a series of refining rules in combination with supplementary data (including elevation and parcel delineations), yielded the significantly improved overall accuracy of 76%. Results of the land cover classification highlight the limitations of automated classification based on satellite imagery alone and the value of supplementary data and additional rules to refine classification results. Catchment scale hydrologic modelling based on the generated land cover results indicated that 61 to 82% of rainfall volume could be captured for a range of 24 h design storms under varying degrees of Sponge City implementation. [ABSTRACT FROM AUTHOR]

The study investigates land use/cover classification and change detection of urban areas from very high resolution (VHR) remote sensing images using deep learning-based methods. Firstly, we introduce a fully Atrous convolutional neural network (FACNN) to learn the land cover classification. In the FACNN an encoder, consisting of full Atrous convolution layers, is proposed for extracting scale robust features from VHR images. Then, a pixel-based change map is produced based on the classification map of current images and an outdated land cover geographical information system (GIS) map. Both polygon-based and object-based change detection accuracy is investigated, where a polygon is the unit of the GIS map and an object consists of those adjacent changed pixels on the pixel-based change map. The test data covers a rapidly developing city of Wuhan (8000 km2), China, consisting of 0.5 m ground resolution aerial images acquired in 2014, and 1 m ground resolution Beijing-2 satellite images in 2017, and their land cover GIS maps. Testing results showed that our FACNN greatly exceeded several recent convolutional neural networks in land cover classification. Second, the object-based change detection could achieve much better results than a pixel-based method, and provide accurate change maps to facilitate manual urban land cover updating. [ABSTRACT FROM AUTHOR]

Exploiting several key characteristics of quantum cascade (QC) lasers, including wide tunability and room-temperature operation, the Quantum Cascade Laser Open-Path System (QCLOPS) was designed for the detection of a range of trace gases and for field deployment in urban environments. Tunability over a wavelength range from 9.3 to 9.8 m potentially provides the capability for monitoring ozone, ammonia, and carbon dioxide, a suite of trace gases important for air quality and regional climate applications in urban environments. The 2008 Olympic Games in Beijing, China drew attention to air quality problems in urban environments. Prior to and during the Olympic games, regional air quality modifications through factory shutdowns, car restrictions, and construction halts in Beijing and its surrounding areas created a unique test bed for new sensor technologies such as the QCLOPS sensor. We report the design of this novel, open-path air quality sensor and the results of both laboratory tests and field trials during the 2008 Olympic Games in Beijing, China. [ABSTRACT FROM AUTHOR]