Search

Rational construction of a well-facilitated farmland become necessary for food security and modern agriculture in China. In farmland construction, the automatic management of spatial data can benefit to accurately distinguish well-facilitated farmland from the various land types. Various discriminating ways of well-facilitated farmland are adopted due to the different influence factors of agricultural production in in different regions, where the spatial distribution and structural features of well-facilitated farmland are quite different from place to place across the country. Most of the previous studies focus mainly on policies, benefit evaluation, preliminary planning and design in the well-facilitated farmland construction. There is still lacking on the studies of the discrimination and analysis for the regional structural features of the well-facilitated farmland. In this paper, a framework of an index system was constructed to identify the spatial distribution and structural features of the well-facilitated farmland using the law of spatial differentiation together with the related national standards and strategic planning, ranging from the scale and shape of farmlands, irrigation and drainage facilities, traffic accessibility, and farmland protection ratio. The identifying system was designed on the basis of the index framework with C#. NET and Arc Object. Subsequently, the proposed system was verified for its availability by taking a case study of a Loess Plateau and a southern mountainous region. The results were as follows: (1) In the Loess Plateau area, the standard rate of the farmland scale, farmland shape, irrigation and drainage facilities, traffic accessibility was 87.01%, 83.12%, 80.52%, 90.91% and 83.12%, respectively. The well-facilitated farmland area can be identified by the system was 38.33 hm² with the total discriminating precision was 94.38%. There were some farmlands that did not meet the requirements of the well-facilitated farmland, such as incomplete irrigation and drainage facilities, the irregular shapes of the fields that distributed in the branch furrow and furrow head around the farmland. (2) In the southern mountainous area, the standard rate of all spatial features was 100%. The well-facilitated farmland area that identified by the system was 30.07 hm², which was 10.34 hm² higher than that of the state-approved well-facilitated farmland. The high identified areas can be because some fields that meet the requirements of well-facilitated farmland have not been classed as the permanent basic farmland in some administrative regions. When regardless of this classification, the total discrimination precision was 100%. This index system can be used in different scenarios, such as macro monitoring and evaluation, project design and inspection of the well-facilitated farmland. This system was also applied for the Northeast Plain, North China Plain, Loess Plateau and southern mountainous regions, while the key elements of the spatial features of well-facilitated farmland construction can be detected with a reliable discriminant resolution. When combining with the remote sensing technology, the index system can help automatic management to know the real-time spatial data of well-facilitated farmland construction. Therefore, the developed system can be successfully applied to identify the extract well-facilitated farmland in the construction area, and then to analyze the obtained spatial data in real time, finally to implement automatic management for well-facilitated farmland construction. Furthermore, the proposed system in this paper can offer a promising reference to the current comprehensive supervision platform, and improve the management effectiveness of well-facilitated farmland construction in modern agriculture. [ABSTRACT FROM AUTHOR]

High-quality optical data is difficult to acquire in southwest China, due to the cloudy and rainy weather. The synthetic aperture radar (SAR) can work for all-time and all-weather, but the revisit period is usually long. Therefore, it is a high demand for the synergy of optical and radar data rather than a single remote sensing data source, particularly for the survey and assessment on the land resource, as well as the global change. This study performed an object-oriented guided segmentation on the difference images generated from 2016 and 2019 quad-pol Radarsat-2 data using the 2019 Sentinel-2 optical data of Meishan region. A Fractal Net Evolution Approach (FNEA) was first utilized to segment the 2016 optical image for an initial segmentation. Then, the second segmentation was implemented using the same approach, where the features of the SAR image were provided for the purpose of guided segmentation. As such, the regions of growth were merged from small to large, in order to eventually form a complete geographical object. A final segmentation was obtained to select the changed and unchanged samples. More importantly, an object contained both changed and unchanged pixels, particularly with a single type of pixel inside the samples. Then, the object-level multidimensional features were extracted using the samples. The initial radar parameters included the backward scattering coefficient, Pauli and Freeman-Durden polarization decomposition parameters. The segmentation was utilized to generate the geographic objects. Meanwhile, some parameters were calculated using the initial parameters, including the mean, and standard deviation, whereas, the texture feature parameters using the Gray-level co-occurrence matrix (GLCM) included the mean, standard deviation, homogeneity, contrast, entropy, and the correlation for a total of 72-dimensional features. The redundant feature dataset was filtered to select the extracted feature set. Consequently, an FSO-RF change detection framework was proposed to achieve the feature optimization and the acquisition of the final change detection. The inter-sample distance metric learning was used to optimize the separability of feature space. The optimal features were used to calculate the distance between the changed and unchanged samples in different features. Finally, the change detection task was implemented using an object-oriented random forest classifier. The existing change detection pixel-based methods were selected to verify the improved model, including the change vector analysis (CVA), principal component analysis (PCA), multivariate alteration detection (MAD), Iteration multivariate alteration detection (IR-MAD), support vector machines (SVM), and random forest (RF) change detection using radar difference image segmentation. The change detection framework showed a remarkable improvement, with an overall accuracy of 92.90% in the test area. Two representative areas were selected in the study area for sampling and validation. The experiments demonstrated that better performance was achieved than that of the traditional, although the accuracy indexes were degraded in some areas with the complex features. The overall accuracies of the two sample areas reached 95.08 % and 88.16%. The algorithm in this paper can well meet the needs of detecting changes in different feature categories such as towns and farmlands, and has certain application value in the monitoring of national land resources. [ABSTRACT FROM AUTHOR]

Accurate and effective drought monitoring using remote sensing technology is essential for regional and global drought warning and forecasting. Especially, soil moisture (SM) is one of the key potential factors affecting agricultural drought. At present, most studies ignore the fact that soil moisture is a complex nonlinear coupling system，the research only using visible light, near infrared, short-wave infrared, thermal infrared and other remote sensing drought index has certain limitations in SM inversion. A new method of retrieving farmland SM of winter wheat at returning green stage based on MODIS and radial basis function neural network (RBFNN) is presented in this paper. Firstly, the adaptability of various MODIS-derived drought indices is analyzed in the period of seedling establishment of winter wheat, including soil water content, crop morphological change in water requirement, crop canopy water content and temperature and other parameters. Secondly, the correlation between the original remote sensing drought indices and the soil relative humidity 10 cm soil layer was analyzed. A comprehensive evaluation set of apparent thermal inertia (ATI), vegetation supply water index (VSWI), enhanced vegetation index (EVI), normalized difference infrared index band7 (NDIIB7), normalized multi-band drought index (NMDI) and temperature condition index (TCI) are selected to invert soil moisture of farmland. Finally, combining MODIS remote sensing drought index with RBFNN, the SM of farmland was retrieved synergistically, and the retrieved results were compared with those of BP-NN and linear regression (LR) models. The experimental study is conducted in Henan province of China, MODIS reflectance products (MOD09A1) and temperature products (MOD11A2) at returning green stage of winter wheat from 2001 to 2012 were used to extract remote sensing drought indices, and the global land cover product of MODIS (MCD12Q1) is selected to obtain cropland distributions. Soil moisture data is derived from the “China crop growth and development and farmland soil moisture ten-day dataset”, which contains the soil relative humidity of 10 and 20 cm soil layer observed every 10 days by 17 soil moisture stations. The results show that the average accuracy of SM inversion model using RBFNN and multiple drought indices is 93.27%, which is increased by 2.92 and 9.97 percentage points compared with BP-NN and LR model, respectively. Most of the data points of the three models are concentrated around the 1:1 line, which indicate that there is a good correlation between the predicted value and the measured value. Compared with BP-NN model and LR model, the deviation between predicted value of RBFNN model and the 1:1 line is the smallest, the higher the regression correlation coefficient is, the higher the determination coefficient is. SM is significantly correlated with ATI, NDIIB7 and VSWI in the early growth stage of winter wheat. Multi-band remote sensing drought monitoring indices can comprehensively reflect the changes of crop physiology and morphology under soil water stress, and also determine the retrieval accuracy of the humidity model simultaneously. In this paper, only winter wheat in returning green stage is selected as the research object, and the comprehensive evaluation index set selected is not suitable for SM inversion of other growth stages of winter wheat, the adaptability of different remote sensing drought indices in different growth stages needs further experimental study. The study provides a new case for regional SM inversion from remote sensing-based drought monitoring indices and neural network. [ABSTRACT FROM AUTHOR]

Understanding the historical change trajectory of Land Use/Land Cover Change (LUCC) is helpful to analyze the land use trends under the interference of human activities and changes in the natural environment, thereby helping decision makers to eliminate the negative impact of unreasonable land use patterns to the greatest extent. Yellow River Basin is the fifth longest river in the world, its ecological environment continues to deteriorate due to the rapid population growth and urban expansion, which has become one of the regions with the most serious soil erosion in China. Aiming to promote the ecological quality and high-quality development of social economy under the human interference and natural environment change, we analyzed the spatiotemporal dynamic of the LUCC in the Yellow River Basin (Henan section) from the watershed scale and proposed policy recommendations. We introduced the chord diagram visualization model to intuitively show the flow, direction and diversity of land cover changes, which would enrich the visual research method system of the land cover quantity transfer trajectory. The land use change index, chord diagram model and gravity center transfer model were used in this paper to analyze the spatial-temporal evolution trajectory and policy implications of the land use pattern in the Yellow River Basin (Henan section) in the past 30 years based on the land use remote sensing data in 1990, 2000, 2010 and 2018. The results showed that: 1) Ecological conservation land (35%) was mainly concentrated in the mountainous areas with higher elevation in the middle reaches; food security land (55%) and production and living land (10%) were mainly distributed in the central and eastern plains. 2) The outflows and inflows of dry land from 1990 to 2018 showed significant dominance; paddy field, water and grassland were dominated by outflow; urban land, rural settlements and other construction land were dominated by inflow. 3) The land use change in central cities had significant regional driving effects throughout the period. 4) The gravity center of food security land and ecological conservation land moved to the West and the area showed a decreasing trend, which proved that the high-quality cultivated land in the plain area in the lower reaches decreased and the ecosystem service function gradually weakened; The shift of gravity center of urban land to the East indicated that the social and economic development in the lower reaches were gradually active. The shift of gravity center of rural residential and other construction land to the West indicated that the population scale in the middle reaches was gradually increasing. [ABSTRACT FROM AUTHOR]

Benggang as a fragmented landform type, can be characterized by a deep-cut slope with various shapes and depressions on the vast weathered crust slopes in southern China. Normally, the gully heads have been continuously collapsed to form a typical landform, such as chair-like erosion. Benggang usually develops rapidly due to large amount of erosion, and thereby to threaten land resources and ecological environment. The identification of Benggang development become necessary to control erosion, and then clarify the behind mechanism. However, conventional methods have low levels of automation for a large-scale work, particularly on local inquiry, in-situ search, and manual interpretation for high-resolution images from satellite remote sensing. This paper proposes a novel Bag of Visual-Topographic Words (BoV-TW) model combining high resolution Digital Orthophoto Map (DOM) and Digital Surface Model (DSM) local features to represent Benggang areas, according to the classification and recognition methods in remote sensing images. The local features of DOM can be set in Harris-Affine and Maximally Stable Extremal Regions (MSER), with Scale-Invariant Feature Transform (SIFT) descriptors. The local features of DSM were extracted by a 3D Douglas-Peucker algorithm, representing by gradient direction-invariant descriptors developed in this study. A Latent Dirichlet Allocation (LDA) was used to balance latent semantic analysis, thereby to construct low-dimensional high-level semantic representations. Support Vector Machine (SVM) was used as a supervised learning training classifier to achieve high-precision and fast automatic Benggang recognition. Three Benggang areas in Tongcheng County, Hubei Province were selected as the experimental objects. The original data were collected by DJI Phantom 3 Pro micro UAV in September 2016. Photoscan processing was used to obtain the DOM and DSM of three Benggang areas. Uniform grid division can be used to achieve DOM and DSM tiles in 0.15 m resolution of 256 × 256 pixels. A high-resolution 3D Benggang model was used to mark the areas with/without Benggang. Two Benggang areas were taken as the training set, and the rest were taken as the test set, in three comparative experiments. The results show that: 1) With changing numbers of LDA topics, the proposed method can maintain a total accuracy of 95%, while the recall rate and precision of Benggang 80%, indicating the maximums were 97.22% and 94.44%, respectively. The total accuracy, recall and precision have increased by 12%, 11% and 32%, respectively, compared with that of only DOM features. The recognition performance was significantly improved after combining with DSM features. 2) With changing sizes of the BoV-TW vocabulary, the total accuracy was 90%, and the maximum was 96.10%, while the recall rate reached 90%, the maximum recall rate of 100%. Precision gradually increased as the increase of vocabulary size, with the maximum of 85.00%. Compared with that of only BoV-TW, three performance indicators increased by 13%, 12% and 30%, respectively, indicating that LDA for latent semantic analysis can greatly improve the recognition detection performance. 3) The proposed method was better than that on the ResNet50 network using DOM and DOM + DSM as the data source. It infers that Benggang as a landform type cannot be well recognized using only DOM, or the simple combination of DOM and DSM. Meanwhile, it needs to combine feature extraction and fusion strategy for efficient application. This finding can deliver a useful tool for the quantitative analysis on the identification, control and erosion mechanism for Benggang. [ABSTRACT FROM AUTHOR]

Evolution of cultivated land needs to be clarified in the driving mechanism for the sustainable land use in China. Most studies generally focused on the relationship between the evolutionary data and the driving mechanism in the evolution of cultivated land use. However, research on the drivers of marginal impact is rare, such as the spatial factor density that represented by the POI points. There can be a tipping point of marginal impact, when the quantity reaches a specific region, in the distance to the main road represented by the human factors. In this paper, the marginal effect analysis was used to make a preliminary evaluation, taking Southern Hubei as an example. Landsat remote sensing images were selected for the interpretation data of three periods from the year of 2000-2005, 2005-2010 and 2010-2015, including 15 types of POI spatial data. Firstly, the evaluation system of spatial factors was constructed from three aspects, including Euclidean distance indices, POI kernel density indices and locational indices. The Spatio-temporal evolution of cultivated land was systematically analyzed, together with dynamic change in the area from 2000 to 2015. Secondly, the principal component analysis was used to integrate the driving factors for the change of cultivated land. The extracted principal components were selected to construct the multiple Logistic regression models, including interaction terms. Lastly, an emphasis was put on the bidirectional marginal effect of spatial elements in the driving mechanism, and the change rule that illustrated by the graph of marginal effect. The results indicated that: (1) The evolution types of cultivated land showed the agglomeration of the same type, while the specific conversion type strongly depended on the spillover effect of neighboring land use. There was obvious evolution trend from cultivated land to construction, but others were not, possibly due to the influence from the macro-control policy of land use. From 2000 to 2015, the proportion of cultivated land converted to construction land increased from 28% to 44%, and then jumped to 96%, mainly concentrated in areas around towns and along major roads. (2) Marginal effects of spatial factors depend on types of cultivated land and periods. From 2000 to 2015, there was a gradual decrease in the variation range of marginal effect for different conversion types of cultivated land, while the homogeneous intersection point moved in the same direction for marginal effect curves of different transformation types. The dominated marginal effect of natural location factors was on the conversion of cultivated land to construction land, and cultivated land to water area, when the spatial elements of POI were different values. (3) The curve of spatial factors implied that there was a critical value of marginal effect. In the range of critical values, marginal effect changed significantly with the spatial factors, but outside the range, marginal effect tended to be stable. The qualitative and quantitative analysis on the marginal effect of spatial factors can offer a theoretical reference for the optimization of cultivated land layout, and the formulation of regional coordinated development strategy. [ABSTRACT FROM AUTHOR]

The black soil region of Northeast China is one of the most important grain production bases in China. It is also one of the four major water erosion areas in China, in which the erosion gully is the main form of water erosion. In order to analyze the causes of gully erosion and evaluate the basic situation of gully erosion in the northeast black soil area, it is necessary to analyze its morphology and distribution characteristic of gully erosion. In this paper, Nenjiang County was taken as the study area, which is located in the northwest of Heilong province, China. The aerial remote sensing images with ground resolution of 0.2 m and 1:50 000 Digital Elevation Model (DEM) were used to carry out census and statistics on the erosion gully, and the morphological characteristics such as the gully width, gully length and longitudinal slope of the erosion gully were analyzed, as well as the influence of terrain factors such as elevation, slope and aspects. The results of the second land use survey in China were used to obtain the land use situation around the erosion ditch. The results showed that: 1) The number of erosion gullies in Nenjiang County was 38 426, and the whole area of the erosion gullies were 16 277 095 m2. The erosion gully mainly occurred on hilly area, and was dominated by gully erosion, accounting for 74.50% and 51.05% of the total number and area, respectively. The next type was modern incised valley. Erosion gullies were mostly less than 500 m. 2) The width and length of the erosion gullies were positively correlated, and the slope was negatively correlated with the width and length. 3) Compared with slope, aspects generally had a little effect on the distribution of erosion gullies. Rills and ephemeral gullies mainly distributed on slopes of 2~6° and 6~15°, and gullies and modern incised valleys mainly distributed on slopes of 0~2° and 2~6°. More than 90% of rills and ephemeral gullies, and 80% of gullies and modern incised valleys were occurred on cultivated land. 4) Although the topographic threshold model of gulley in Nenjiang County was not fitted well, it could still be used to analyze the process of gully erosion. The model showed that the critical slope of the modern incised valley was small, the parameter which expressed the gully erosion process reflected the occurrence and development of the gullies might be influenced by the underground process. [ABSTRACT FROM AUTHOR]

Vegetation coverage in catchment has a direct impact on its ecological environment, and monitoring vegetation coverage change and its determinants is important to reconstruct effectively ecological engineering projects to sustain economic development without compromising ecological environment. Danjiangkou Reservoir is a waterhead at the middle route of the South-North Water Transfer Project in China, and its ecological environment has a direct impact on quality and quantity of the water in the transferring cannel. Available study on vegetation coverage change in this area is problematic, including short experiment duration and inadequate evaluation of the impact of the human activities. We address this knowledge gap in this paper using data extracted from GIMMS NDVI and MODIS NDVI. The empirical orthogonal teleconnection (EOT) algorithm was used to reconstruct the normalized difference vegetation index (NDVI) from 1982 to 2018, from which we analyzed the spatiotemporal change in the vegetation coverage and individual contribution of climate factors and human activities to the vegetation coverage changes. The results revealed that NDVI showed an oscillating increase from 1982 to 2018 at an average rate of 0.002 9 /a (P<0.05). Spatially, 89.93% of the studied areas saw a first-year increase in NDVI, and 10.06% witnessed a first-year decrease. Temperature impacted on NDVI most with a correlation coefficient of 0.792 (P<0.05), followed by precipitation with a correlation coefficient of - 0.43 (P<0.05). The influence of climate factors on vegetation coverage varied spatially. In mountainous areas, vegetation growth was affected by precipitation and temperature combined, while in basins and river valleys characterized by low and flat terrain, NDVI was positively correlated with temperature and negatively with precipitation, both at significant level. Human activities impacted on vegetation coverage in two opposite ways. On the one hand, human activity in 67.74% of the studied areas had a positive effect on vegetation coverage due to engineering measures being taken to restore and improve ecological functions of these area. On the other hand, human activity in 32.26% of the studies areas had a detrimental impact on vegetation coverage due to unsustainable activities used in social-economic development. The impacts of climate and human activities on regional vegetation coverage were separated by the residual method, and the results indicated that overall contribution of the climate factors to the vegetation coverage change was 92.14%, compared to the 7.86% from the human activities. Spatially, in areas with more human activities, such as areas that had taken ecological measures or in the proximity of central city, human activities have been becoming the determinants of the NDVI change. [ABSTRACT FROM AUTHOR]

Emergency flood relief needs fast and precise spatial information of flood-inundated area. Because of spectral similarity to some extent, inundated area was often viewed as water body and then derived from remote sensing classification result of the water areas before flood disaster minus that after flood disaster, which often caused difficulties in separating inundated area from true water body (e.g. river, lake or reservoir) and waterlogged cropland. This paper proposed an optimized method for identifying inundated area based on human perceptual visual attention and spatio-temporal variation characteristics of flood. Flood-inundated area is a temporary compound of water and other flooded objects (e.g., crops). It has specific spatio-temporal dynamic property, which differs from that of water body and could be used as a stable basis for remote sense identification of the inundated area. After the specific property was digitalized into numerical and visual data by generating a composite RGB color imagery of NDVI, MNDWI before the flood event and NDWI after the inundation, apparent coloring difference between water body and the inundated area in the imagery was reached. To gain reliable machine-vision detection based results of the flood-inundated area, an imagery of Munsell HLC color was transformed from the RGB imagery and then adopted in unsupervised classification based on coefficient of NBS color distance and K-means clustering algorithm. A case was studied on applying the proposed method to derive information of the inundated area in 2016 flood disaster in middle reaches of Yangtze River Basin and using Landsat OLI images. And an error confusion matrix method was adopted in the accuracy assessment on recognition results of the inundated area. The results showed that the proposed method gained excellent detection of the area with coefficients of Kappa and composite classification accuracy (CCA) equal to 93.4% and 88.5%, respectively. A controlled experiment on the credibility of different remote sensing classification methods for the same identification object (i.e. flooded-inundated area) was also made. It proved the proposed method for detection of flood-inundated area based on the selective visual attention mechanism could effectively improve the accuracy of remote sensing identification on the area with its CCA coefficient of 5% larger than that of the traditional method. The proposed method also had good performance on easily separating the flooded-inundated areas from water and waterlogged cropland, especially helped solving the misclassification between the flooded-inundated area and the water body. These benefited from three keys: 1) The inundated area was viewed as that not equal to water coverage in remote sensing classification, which could be proven by the former’s higher heterogeneity and its different spatio-temporal dynamic property. As a result, the identification uncertainty of the area was reduced. 2) Digital visualization of spatio-temporal variation features of the inundated area was completed through data fusion of NDWI after the inundation and NDVI and MNDWI before the flood event, which enhanced the area’s visual saliency on the remote sensing image and made it possible to identify them using machine-vision color clustering. 3) Difference of the flooded-inundated areas from the water and waterlogged cropland was precisely captured in the NBS image in HLC space that was transformed from the fusion image at an optimized RGB color matching scheme (i.e. red given to NDWI after the inundation, green and blue to NDVI and MNDWI before the inundation, respectively). During summer flooding of the study area in 2016, the waterlogged farmland with crops uncovered by water was about 19 143.35 hm² while the inundated acreage was up to 142 157.5 hm², among which 16.6% (i.e. 23 579 hm²) was being planted with rice paddy. Additionally, most of the inundated areas located in low-lying land along mainstream of the middle Yangtze River and its two branches, namely Hanjiang River and Fuhe River. The most severe flood disaster occurred in Wuhan City with 34 492 hm² of the inundated acreage. [ABSTRACT FROM AUTHOR]

Crop yield monitoring and forecast has great impact on food security, ecological environment, and farmers’ incomes. Crop growth and yield monitoring and forecast by using crop growth model has the advantages of clear mechanism, high precision, and high monitoring frequency, but its monitoring scale is usually limited to land block level. Along with the fast development of remote sensing satellite technology, using remote sensing data combined with crop growth model to accurately monitor regional crop growth and yield in a large regional scale has gradually become an important means of regional level and even national level crop growth monitoring. However, due to differences in crop types, climatic conditions, soil conditions and monitoring areas, the regionalization and localization of crop growth model is the major bottleneck of crop growth monitoring by using crop growth model combined with remote sensing data, and it is urgent to conduct targeted studies on the identification of assimilation parameters of crop growth model, pre-processing of meteorological data, and the setting of crop parameters. Based on soil-water-atmosphere-plant model (SWAP), and by taking the major commodity grain production base of China, Northeast China Region as a study region, in this the paper, we conducted a study by taking the major crop of spring maize of the region as its target crop. Firstly, we used Landsat to obtain maize (Zea mays) planting area in the study area, and used it as the basic data for estimating the total maize yield in the study area. The overall accuracy of maize area classification was 93.2%, with R² of 0.951 2. By considering the influence of latitude and altitude on temperature, in the study, we used the coKriging method in crop growth model meteorological data interpolation acquisition, so as to improve the precision of input parameters of the model. The result showed that the average standard error of minimum temperature of coKriging method was 0.31 ℃, while that of the Kriging method was 1.51 ℃. The average standard error of maximum temperature of coKriging method was 0.30 ℃, while that of the Kriging method was 1.14 ℃. In the study, leaf area index (LAI) and evapotranspiration (ET) were used as assimilative remote sensing data sources, and we proposed a novel method to adjust the LAI product of MODIS to make it closer to actual value. By optimizing maize irrigation and crop emergence date, we obtained spatial distribution result of maize yield of the study area of 2013. The monitoring result was compared with the statistical data. The R² reached 0.939 4, with RMSE of 148 065 t, and MAE of 114 335 t. Moreover, the correlation coefficient of predicted yield and statistical yield reached 0.724 5, with RMSE of 598.5 kg/hm², and MAE of 531.5 kg/ hm²。The study result showed that, using SWAP model, taking meteorological data spatial interpolation results obtained by using coKriging method as input data and assimilation of LAI and ET remote sensing, can effectively conduct corn yield remote sensing monitoring of the study region, which provided reference for the remote sensing monitoring and forecast of crop growth and productivity of the region. [ABSTRACT FROM AUTHOR]

Drought has become a problem that is universally faced by global terrestrial ecosystems. Northeast China is dominated by a temperate monsoon climate and located in an area sensitive to global climate changes, and one of the main impacts of climate changes in Northeast China is manifested as drought in growing seasons. The drought area has a gradual increase trend, and drought has become the main agro-meteorological disaster in this region which is also the main maize planting area in China. Drought normalization seriously restricts the stable development of maize production in Northeast China. So in this paper, we took spring maize in Liaoning Province as an example and made clear the drought variation characteristics in different growth stages by using the data of MODIS, FY-3A, relative soil moisture and growth stages and the methods of RS, GIS and statistical analysis. Then we analyzed the correlation between multi-time scale remote sensing indexes and relative soil moisture in different soil depth, and established the remote sensing monitoring index set and models of relative soil moisture for each growth stage and soil depth. The accuracy of the monitoring models was verified and the application research of monitoring the drought of spring maize in Liaoning Province was studied. The results showed that: From 1993 to 2012, drought occurred in each growth stage of spring maize in Liaoning Province and the highly frequent drought occurred in the period from 1999 to 2002. The drought grade in each year was mainly light drought, and the drought was the most serious at milk stage, followed by heading stage. There was no significant correlation between the monitoring indexes and relative soil moisture at sowing and emergence stage, but there was a significant or extremely significant correlation between them at three-leaf and maturity stage. The monitoring index LSWI (land surface water index) had a strong ability to monitor relative soil moisture, followed by WI (water index). Multi-index coordination could improve the monitoring ability of relative soil moisture by remote sensing means, and the monitoring ability at 10-day scale was generally higher than that at 5-day scale and 20-day scale. Based on the monitoring models of soil relative humidity, the drought condition of partial growth stages and soil depth from 2001 to 2004 was monitored. The overall monitoring accuracy was 73.32%, in which the monitoring accuracy of jointing and three-leaf stage was more than 85%. The dynamic monitoring of drought grades in different growth stages in 2000 was realized. The monitoring results were consistent with the agro-meteorological observation records in terms of the growth stages and space. This study would be very significant to improve the capacity of disaster prevention and mitigation by monitoring spring maize accurately and synchronously on a large range. [ABSTRACT FROM AUTHOR]

Drought is a natural hazard caused by long-term precipitation deficiency and high evaporation resulted from high temperature, which is among the most common disasters around the world. In recent decades, with the intensification of climate change and human activities, drought occurrence is becoming more frequent in China, causing negative impacts on agricultural production, ecological environment and social economy. Therefore, it is significant to explore the impact of drought on vegetation for agricultural production and healthy development of ecological systems. In this paper, the response and sensitivity of vegetation to drought in different climatic zones in China were analyzed, and the effects of different climatic factors, including water balance (precipitation-potential evapotranspiration), annual precipitation, mean temperature and sunshine hours, on the relationship between different vegetation types including forestland, grassland and cultivated land and drought were explored, so as to reveal the response mechanism of ecological vegetation to drought from a macro perspective. Using the self-calibrating Palmer drought severity index (scPDSI) and normalized difference vegetation index (NDVI) data for the period of 1982-2015, the response rule and sensitivity of different types of vegetation to drought were analyzed by applying time-delay correlation method, and the influence of various climate factors on the relationship between vegetation and drought was analyzed by applying linear regression method. The results of the research showed that in northwest China, Inner Mongolia, north China plain and southern Qinghai-Tibet plateau, the correlation of shorter lag time scPDSI (1-3 months) and NDVI was close (Rmax>0.4), which indicated that vegetation growth was more sensitive to drought in these regions and that vegetation biomass were influenced mainly by the spatiotemporal characteristics of the water availability. In the humid areas of south China, including the middle and lower reaches of the Yangtze River and the Pearl River basin, the precipitation is abundant so that the correlation between NDVI and scPDSI was negative, and the response of vegetation was not sensitive to drought. The growth of vegetation is mainly controlled by energy factors such as temperature and sunshine hours. The relationship between vegetation and drought was significantly affected by the average annual water balance, precipitation, sunshine hours and temperature, among which the annual water balance was the main water controlling factor (R²=0.45). It can be concluded that the change of water availability was the key factor behind the change of vegetation vigor. Sunshine hours were the main energy controlling factor. When average annual temperature was less than 5, the effect of ℃ temperature on the correlation between scPDSI and NDVI was also significant. The sensitive areas of drought impact on vegetation were mainly in the northern arid regions, while the southern humid areas had strong resilience and resilience to drought. The sensitivity of different types of vegetation to drought varied greatly, and the order of sensitivity is grassland >forestland>cultivated land. The results can provide a scientific basis for the evaluation of the impact of drought on ecological vegetation in different regions of China, and thus help to guide agricultural and environmental production, protect vulnerable ecosystems and provide implications for sustainable socioeconomic development. [ABSTRACT FROM AUTHOR]

Estimating soil salinity is imperative for scheduling irrigation and remediating saline soil but difficult at large scales. Remote sensing can bridge this gap because of its advantages in low cost and large-area coverage; it has become an efficient method for assessing soil salinization in field. One issue in use of remote sensing to assess saline soil is the presence of plastic film mulch and bare soil because of their difference in reflecting waves in the spectral bands. In order to investigate the effect of plastic film mulch on soil salinity inversion using UAV multispectral remote sensing, we studied four plots with plastic film mulch at the Shahaoqu Irrigation area in the Hetao Irrigation District, Inner Mongolia of China. From each plot, we took soil samples and measured their salt contents from May to July. We also flew a drone to simultaneously take multispectral images of the sampling sites and extracted the spectral reflectance to calculate the spectral indices. Correlation analysis found that the S4, S6, SI1, SI2, SI3 and BI indices can be used to calculate soil salinity. Six-band spectral reflectances and six spectral indices obtained from different datasets were used as independent variables to calculate the salt content with the support vector machine (SVM), the back propagation neural network (BPNN) and the extreme learning machine (ELM), respectively, before and after the mulch film was removed. We compared the three models based on their determination coefficient (R²), root mean squared error (RMSE) and relative error (RE). The results showed that plastic film mulch did impact on soil salinity inversion. Although all three models could adequately estimate the soil salt contents before and after the film removal, they worked better after the film removal than before the film removal. Models based on the spectral indices were more accurate than those based on the spectral reflectances, and the accuracy of the inversely calculated salt content varied with sampling time and treatment. The inversion results based on monthly data differed from those based on by pooling all data. After the film was removed in June, the salt content estimated using the model was most accurate, with its associated R² and RMSE being 0.695 and 0.182 respectively for the spectral reflectance-based method, and 0.663 and 0.191respectively for the spectral indices-based method. The salt content estimated by BPNN was least accurate in May, with its associated R² and RMSE being 0.766 and 0.161 respectively for the spectral reflectance-based method, and 0.769, 0.162 respectively for the spectral indices-based method. Comparison of the three models revealed that ELM was most accurate, followed by SVM and BPNN, although their errors were within the tolerable range. In summary, this paper provides an effective method to inversely calculate soil salinization at large mulched farmland using UAV multispectral remote sensing. [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]

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]

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]

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]

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]