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Drylands are vast and face threats from climate change and human activities. Traditional reviews cannot capture interdisciplinary knowledge, but bibliometric analysis provides valuable insights. Our study conducted bibliometric research of scientific production on climate change and land degradation in drylands using remote sensing. We examined 1527 Scopus-indexed publications to identify geographic and thematic hotspots, extracting leading authors, journals, and institutions. China leads in publications, followed by the US, Germany, and Australia. The US has the highest citation count. Collaboration networks involve the US, China, and European countries. There has been an exponential increase in remote sensing of land degradation in drylands (RSLDD) publications since 2011. Key journals include "International Journal of Remote Sensing" and "Remote Sensing of Environment". The analysis highlights the growing interest in the field, driven by Australia, the US, and China. Key areas of study are vegetation dynamics and land use change. Future perspectives for this scientific field involve promoting collaboration and exploring emerging technologies for comprehensive land degradation and desertification research. [ABSTRACT FROM AUTHOR]

The net primary productivity (NPP) of vegetation is an important indicator reflecting the vegetation dynamics and carbon sequestration capacity in a region. In recent years, China has implemented policies to carry out ecological protection. To understand the changes in the distribution of vegetation NPP in China and the influence of climate factors, the Carnegie–Ames–Stanford approach (CASA) model was used to estimate the NPP from 2001 to 2020. In this paper, several sets of measurement datasets and products were collected to evaluate the effectiveness of the model and suggestions were provided for the modification of the CASA model based on the evaluation results. In addition to the correlation analysis, this paper presents a statistical method for analyzing the quantitative effects in individual climatic factors on NPP changes in large regions. The comparison found that the model has a better estimation effect on grassland and needleleaf forest. The estimation error for the evergreen needleleaf forest (ENF) and deciduous broadleaf forest (DBF) decreases with the warming of the climatic zone, while the evergreen broadleaf forest (EBF) and deciduous needleleaf forest (DNF) do the opposite. The changes in total CASA NPP were consistent with the trends of other products, showing a dynamic increasing trend. In terms of the degree of correlation between the NPP changes and climatic factors, the NPP changes were significantly correlated with temperature in about 10.39% of the vegetation cover area and with precipitation in about 26.92% of the vegetation cover area. It was found that the NPP variation had a negative response to the temperature variation in Inner Mongolia grasslands, while it had a positive but small effect (±10 g C) in the Qinghai–Tibet Plateau grasslands. Precipitation had a facilitative effect on the grassland NPP variation, while an increase in the annual precipitation of more than 200 mm had an inhibitory effect in arid and semi-arid regions. This study can provide data and methodological reference for the ecological assessment of large-scale regional and climate anomalous environments. [ABSTRACT FROM AUTHOR]

Corn is an important food crop worldwide, and its yield is directly related to Chinese food security. Accurate remote sensing extraction of corn can realize the rational application of land resources, which is of great significance to the sustainable development of modern agriculture. In the field of large-scale crop remote sensing classification, single-period optical remote sensing images often cannot achieve high-precision classification. To improve classification accuracy, multiple time series image combinations have gradually been adopted. However, due to the influence of cloudy and rainy weather, it is often difficult to obtain complete time series optical images. Synthetic aperture radar (SAR) data are imaged by microwaves, which have strong penetrating power and are not affected by clouds. A critical way to solve this problem is to use SAR images to compensate for the lack of optical images and obtain a complete time series image in the corn-growing season. However, SAR images have limited wavelengths and cannot provide important wavelengths, such as visible light bands and near-infrared information. To solve this problem, this study took Zhaodong City, a vital corn-planting base in China, as the research area; took GF-6/GF-3 and Sentinel-1/Sentinel-2 as remote sensing data sources; designed12 classification scenarios; analyzed the best classification period and the best time series combination of corn classification; studied the influence of SAR images on the classification results of time series images; and compared the classification differences between GF-6/GF-3 and Sentinel-1/Sentinel-2. The results show that the classification accuracy of time series combinations is much higher than that of single-period images. The polarization characteristics of SAR images can improve the classification accuracy with time series images. The classification accuracy of GF series images from China is obviously higher than that of Sentinel series images. The research performed in this paper can provide a reference for agricultural classification by using remote sensing data. [ABSTRACT FROM AUTHOR]

The rapid and precise acquisition of the agricultural plastic greenhouse (PG) spatial distribution is essential in understanding PG usage and degradation, ensuring agricultural production, and protecting the ecological environment and human health. It is of great practical significance to realize the effective utilization of remote sensing images in the agricultural field and improve the extraction accuracy of PG remote sensing data. In this study, Landsat operational land imager (OLI) remote sensing images were used as data sources, and Shandong Province, which has the largest PG distribution in China, was selected as the study area. PGs in the study area were identified by means of contour recognition, feature set construction of the spatial structure, and machine learning. The results were as follows. (1) Through an optimal segmentation parameter approach, it was determined that the optimal segmentation scale for size, shape, and compactness should be set at 20, 0.8, and 0.5, respectively, which significantly improved PG contour recognition. (2) Among the 72 feature variables for PG spatial recognition, the number of features and classification accuracy showed a trend of first gradually increasing and then decreasing. Among them, fifteen feature variables, including the mean of bands 2 and 5; six index features (NDWI, GNDVI, SWIR1_NIR, NDVI, and PMLI); two shape features, the density and shape index; and two texture features, the contrast and standard deviation, played an important role. (3) According to the recall rate, accuracy rate, and F-value of three machine learning methods, random forest (RDF), CART decision tree (CART), and support vector machine (SVM), SVM had the best classification effect. The classification method described in this paper can accurately extract continuous plastic greenhouses through remote sensing images and provide a reference for the application of facility agriculture and non-point-source pollution control. [ABSTRACT FROM AUTHOR]

The mining area in the Muli region, Qinghai Province, China, is an important source of water and an ecological security barrier in the Qilian Mountains region and has a very important ecological status. A series of ecological problems such as vegetation degradation and loss of biodiversity caused by mining have attracted widespread attention. In this paper, we used Landsat secondary data from 2000 to 2021 from the Muli region to obtain the spatial and temporal distribution characteristics of the vegetation in the Muli region by inversion of the fractional vegetation cover, above-ground biomass and the land surface phenology to comprehensively analyze the ecological changes in the vegetation in the Muli region. The results showed the following: (1) the above-ground biomass and cover of grassland in the Muli region showed a decreasing trend between 2000 and 2021, with a particularly pronounced decrease in grassland cover between 2009 and 2016; (2) the start of the vegetation growth cycle, i.e., the beginning of the vegetation growing season (SOG) became more advanced, the end of the vegetation growing season (EOG) was delayed, and the length of the growing cycle (LOG) became longer for most of the vegetation in the Muli region; (3) the results of this comprehensive analysis showed that the grassland in the Muli region showed dynamic changes with complex characteristics from 2000 to 2021, and anthropogenic disturbances had some influence on ecological indicators such as fractional vegetation cover and biomass. The extension of the vegetation growing season might be related to climate change. Based on the results of this paper, it is recommended to utilize biomass and fractional vegetation cover as indicators to assess the grass growth status of mining sites. This study analyzed the spatial and temporal characteristics of grasslands in the Muli area with several indicators, which will help relevant departments continue to improve and optimize ecological restoration measures. In addition, this study provides a reference for achieving comprehensive restoration of the ecological environment and ecological functions in mining areas. [ABSTRACT FROM AUTHOR]

The significant heterogeneity in the spatial distribution of point of interest (POI) data, the absence of human socio-economic activity information in remote sensing images (RSI), and the high cost of land use (LU) data acquisition restrict their application in PLES spatial identification. Utilizing easily accessible data for detailed spatial identification of PLES remains an urgent challenge, especially when selecting a study area that encompasses both urban built-up areas (UBUA) and non-urban built-up areas (NUBUA). To address this issue, we proposed a PLES spatial identification method that combines POI data and land cover (LC) data in this paper. The proposed method first classified spatial analysis units (SAUs) into agricultural production space (APS), ecological space (ES), and ambiguous space (AS) based on the rich surface physical information from LC data. Subsequently, the AS was further classified into living space (LS) and non-agricultural production space (NAPS) based on the rich human socioeconomic information from POI data. For the AS that contains no POI, a simple rule was established to differentiate it into LS or NAPS. The effectiveness of the method was verified by accuracy evaluation and visual comparison. Applying the method to the Jiaodong Peninsula, we identified the PLES of the Jiaodong Peninsula for 2018 and 2022, further explored their spatial distribution characteristics, and analyzed their changes. Finally, we conducted a discussion on the real-world situations and driving mechanisms of the PLES changes and proposed several policy insights. The results indicated that both the spatial distribution characteristics of PLES and PLES change in the Jiaodong Peninsula were obvious and showed significant differentiation between UBUA and NUBUA. Climatic and natural resource conditions, geographic location, macro-policies, and governmental behaviors drove the PLES changes. [ABSTRACT FROM AUTHOR]

It is crucial for the protection and sustainable development of villages in China to effectively determine the changes that have occurred in traditional villages' architectural features and the causes of such changes in order to provide timely feedback, correct protection planning, and construction in these villages. This paper analyzed quantitative indexes at three different scales in Lingquan village, namely construction land, public infrastructure, and the form, material, and color of roofs, using remote sensing images at different time points, drone shooting, and a field survey. It was found that the rate of change of the architectural features at the village and street levels were low, with an overall trend of improvement observed. However, the residential roof features at the building level showed more changes, with a trend of deterioration observed. It was also found that the active participation of the government was the main reason for the substantial improvement observed in the village's street-level features. Further, for residential houses, the lack of standard guidance for their protection and renovation, as well as the lack of protection awareness and participation mechanisms, were the main reasons found for the deterioration of the village's building-level features. Finally, the study forwarded some suggestions in order to provide reference value for the timely revision of protection strategies for many villages in China similar to Lingquan village. [ABSTRACT FROM AUTHOR]

Rural settlement is an important part of studying the relationship between humans and land; it is highly significant in revealing the evolution, driving mechanism and reconstruction scheme of rural settlement pattern. In this paper, Jiangning District, a rapidly urbanized area, was selected as a typical case. Using remote sensing image data, the landscape pattern index, the rank-scale law, the local hot spot-detection model, and the geographical-detector were comprehensively used to analyze the rural settlements pattern evolution and driving mechanism in the rapidly urbanized areas. The results are as follows: (1) From 2010 to 2020, the number of rural settlements showed a trend of large-scale reduction, and the settlements scale system was relatively uniform in Jiangning. The settlements scale had the autocorrelation characteristics of spatial agglomeration, and the local hotspot agglomeration pattern was significant. (2) The spatial distribution of rural settlements in Jiangning showed an "agglomeration" pattern, and the settlements density showed a "multi-core" distribution characteristic. (3) The pattern of rural settlements in Jiangning was shaped by natural environmental factors such as topography, water system and cultivated land resources; economic social factors such as agricultural population, per capita GDP, distance from town, and policy and system were the leading factors that promoted the settlements' pattern evolution in Jiangning, and the interaction between the factors could enhance the interpretation of the settlements' pattern evolution. The research can provide a reference for optimizing the spatial layout of settlements in rapidly urbanized areas. [ABSTRACT FROM AUTHOR]

Coastal environments are highly threatened by invasive alien plants (IAP), and Remote Sensing (RS) may offer a sound support for IAP detection and mapping. There is still a need for an overview of the progress and extent of RS applications on invaded coasts that can help the development of better RS procedures to support IAP management. We conducted a systematic literature review of 68 research papers implementing, recommending, or discussing RS tools for IAP mapping in coastal environments, published from 2000 to 2021. According to this review, most research was done in China and USA, with Sporobolus (17.3%) being the better studied genus. The number of studies increased at an accelerated rate from 2015 onwards, coinciding with the transition from RS for IAP detection to RS for invasion modeling. The most used platforms in the 2000s were aircraft, with satellites that increased from 2005 and unmanned aerial vehicles after 2014. Frequentist inference was the most adopted classification approach in the 2000s, as machine learning increased after 2009. RS applications vary with coastal ecosystem types and across countries. RS has a huge potential to further improve IAP monitoring. The extension of RS to all coasts of the world requires advanced applications that bring together current and future Earth observation data. [ABSTRACT FROM AUTHOR]

China's rapid urbanization has been accompanied by serious urban sprawl. Instead of measuring the physical urban boundaries (PUBs), most of existing studies in China rely on yearbook statistics to describe the growth of urbanized area; therefore, the understanding of the actual form and quantity of urban sprawl are restrained. As the statistical unit is generally at or above the county level, these studies tend to omit the lower-level "larger towns". This paper discusses the measurement of urban sprawl and compactness using multi-source data on the GIS platform through the case study of the Wen-Tai region in China. GlobeLand30 remote sensing image data, vector road network data, NPP/VIIRS nighttime light data, and points of interest (POIs) data are adopted. The new method enhances the identification of built-up areas in larger towns. Besides, the 2020s' PUBs of this region, data for 2010 and 2000 are retraced to assess the urban expansion rate, and two approaches are used to discuss the urban growth pattern. Additionally, a compactness model is constructed from four dimensions, i.e., the compactness of external contour, accessibility of road network, land-use intensity, and functional diversity, by which a high-resolution visual analysis tool is created for the provincial government to monitor urban sprawl. [ABSTRACT FROM AUTHOR]

Reasonable quantitative assessment on urban ecosystem health is conducive to the sustainable development of the economy and human society. This paper quantitatively evaluated the impact of land use change on ecosystem services and ecosystem health by building a comprehensive evaluation system (vigor–organization–resilience–ecosystem services), and then analyzed the spatial-temporal pattern, evolution characteristics, and driving factors in the Shanghai–Hangzhou Bay area (SHB) over the 2000–2015 period. The results show that: the area of cropland and forest accounted for more than 65% and was mainly converted into built-up land in the past 15 years. The overall ESV showed a trend of first increasing and then decreasing. Forest accounted for the largest proportion of the total ESV, more than 60% in each year. The ecosystem health value of SBH decreased from 2000 to 2015. At the city scale, the ecosystem health was significantly deteriorated. All cities reached the lowest value by 2015. At the districts/counties scale, the number with the relatively well or well level decreased from 32 in 2000 to 20 in 2015 by 24.64% of the total area. Overall, inland regions of SBH had better ecosystem health situation than coastal areas. The rapid urbanization of population and economy were driving factors for the decline of the ecosystem health. The indicator system of integrating the vigor, organization, resilience, and ecosystem service for ecosystem health assessment is a potential method which could provide a quantitative and comprehensive way for evaluating ecological and environmental effects in the future. [ABSTRACT FROM AUTHOR]

Cultivated land resources are important natural resource assets that are related to food security and sustainable development. Due to the many restrictive factors of the karst landform on agricultural production, the quantity and quality of cultivated land in the karst mountainous areas in Southwest China are poor. Reclaiming cultivated land to develop economy or to avoid transitional reclamation to protect ecology is an important proposition in this area. Analyzing changes in the physical and monetary value of cropland resources can help us to formulate more reasonable policies for the development and utilization of cultivated land resources, and to achieve a win-win scenario for economic development and ecological protection. Using multi-source remote sensing data and 20-year landcover data obtained by the GEE platform, this paper evaluated the cropland resources of the karst mountain areas of China at the pixel level. It was found that under the apparent outflow of the physical account of the cultivated land resources, the monetary value still maintained growth, proving that the current cultivated land-use policy in Guizhou Province has significantly improved the value of local cultivated land resources. [ABSTRACT FROM AUTHOR]

In this paper, we selected Xilingol League in Inner Mongolia, China, as the research area, based on the land-use data of five Landsat remote sensing images taken between 1980 and 2015. Then, we calculated the complex network eigenvalues, such as the average shortest path, betweenness centrality, and degree, to identify the key land-use types, stability, and ecological environment change regularity from the perspective of the land-use system. Finally, we used the Canonical Correspondence Analysis (CCA) method to explore the main driving forces behind changes in the land-use system, to provide scientific support for the study of changing trends in land-use, and regional grassland ecological management. The findings are shown below. First, in the last 35 years, grasslands have always been the major type of change in land-use transfer matrices. Grasslands play a controlling role in the whole land-use system. Second, grassland and cultivated land are the major "transfer out" type of land in the Xilingol area, while construction land and water area belong to the major "transfer in" type of land. Third, the average shortest path values of four transition matrix networks were all less than 1.5, indicating that the land-use system becomes less stable, but the average shortest path values of these four networks present an increasing trend, leading to a more stable development of the land-use system. However, on the whole, it shows an upward trend, and the land-use system is moving in a stable direction. Fourth, the average annual rainfall, population, topographic factors, GDP, and distance from settlements play a prominent role in determining the spatial distribution of change in land-use in Xilingol. [ABSTRACT FROM AUTHOR]

The main stream of the Tarim River in China is typical of ecologically sensitive areas that have been heavily disturbed by human activities; as such, the monitoring of the quality of its eco-environment constitutes an important task for researchers. By using GlobeLand30 data and applying the disturbance degree model and revised ecosystem service value (ESV) model, the study presented in this paper undertook a quantitative estimation of the effects of the disturbance impacts of human activities on the eco-environment of this area in the period of 2000 to 2020. The main conclusions are as follows: (1) disturbance index values, which reflect disturbance to the local ecosystem by human activities, increased over the study period. Further, cultivated land experienced the largest increase, which, in turn, brought about the most significant disturbance to the eco-environment. High disturbance index values presented a patchy distribution in the west of the main stream of the Tarim River and formed bands and dots in the east; the area of land characterized by high and moderate disturbance index values increased, with growth areas taking on a scattered distribution of patches, bands, and dots without significant spatial continuity. (2) The total ESV increased, indicating the quality of the eco-environment improved. The increase of cultivated land offset the increase in ESV, which counteracted the effects of ecological governance measures. Areas with high ESV values were mainly located in the western and central parts of the study area, while low values were found in the middle east and east. Areas with higher increases in ESV were mainly located in the western and the western part of the middle reaches and took on a zonal distribution, while areas of decrease followed a scattered distribution, presenting as dots or patches. Using the quantitative analysis methods and high-resolution remote sensing data to evaluate the changes in the eco-environment was considered as the innovation of this study, and the findings are useful in exploring the influence of human activities on ecosystems and evaluating the eco-environment in the minor watershed of an arid area. This piece of quantitative research contributes to the task of monitoring eco-environmental changes using remote sensing techniques in ecologically sensitive areas. [ABSTRACT FROM AUTHOR]

(1) Monitoring salinized soil in saline–alkali land is essential, requiring regional-scale soil salinity inversion. This study aims to identify sensitive variables for predicting electrical conductivity (EC) in soil, focusing on effective feature selection methods. (2) The study systematically selects a feature subset from Sentinel-1 C SAR, Sentinel-2 MSI, and SRTM DEM data. Various feature selection methods (correlation analysis, LASSO, RFE, and GRA) are employed on 79 variables. Regression models using random forest regression (RF) and partial least squares regression (PLSR) algorithms are constructed and compared. (3) The results highlight the effectiveness of the RFE algorithm in reducing model complexity. The model incorporates significant environmental factors like soil moisture, topography, and soil texture, which play an important role in modeling. Combining the method with RF improved soil salinity prediction (R2 = 0.71, RMSE = 1.47, RPD = 1.84). Overall, salinization in Minqin oasis soils was evident, especially in the unutilized land at the edge of the oasis. (4) Integrating data from different sources to construct characterization variables overcomes the limitations of a single data source. Variable selection is an effective means to address the redundancy of variable information, providing insights into feature engineering and variable selection for soil salinity estimation in arid and semi-arid regions. [ABSTRACT FROM AUTHOR]

Fishing ports are important fishery production platforms, and the transformation of these has had a profound impact on the fishing industry. A reasonable classification solution is crucial for scientific understanding, development, and management of fishing ports. Current research on the use of spatial distributional characterization and the construction of a classification system for fishing ports to improve their management is limited. Therefore, in this study, a fishing port classification system was constructed using remote sensing images, with fishing port boundaries accurately extracted for classification. Using graphical summarization, fishing ports were classified into five types, including inland, estuarine, shoreline, gulf, and islands. A port type identification system was also constructed based on distance from the shoreline, water area, and circulation. Finally, fishing port characteristics and differences were investigated based on spatial superposition. The results demonstrate that shoreline and gulf types are the most prevalent fishing port types along the Chinese coast, accounting for 43% and 26% of the total number of fishing ports, respectively. This provides a strong foundation for China's fishery production, processing, and trade. Through the establishment of a comprehensive scientific classification system, fishing port management can be modernized, supporting the sustainable development and utilization of coastal zones. [ABSTRACT FROM AUTHOR]

Green city areas are crucial in mitigating the Urban Heat Island Effect (UHI). However, the cooling effect of green city areas can be influenced by the surrounding complex urban spatial environment. This study focuses on Changsha, a subtropical city in China, where 40 green city areas were screened and analyzed. The study aims to quantify the specific impact of urban spatial morphology on the cooling effect of green city areas. Through statistical correlation and regression analysis, this study focused on six urban spatial morphology indicators: building density (BD), building floor area ratio (BFR), building volume density (BVD), building evenness index (BEI), building average height (BH), and building height standard deviation (BSD). The results indicate that the cooling effect of green city areas could be influenced by urban spatial morphology. Factors such as BD, BFR, BH, and BSD were found to be significantly correlated with the cooling effect of green city areas, with BH showing the strongest influence. BD and BFR were negatively correlated, while BH and BSD were positively correlated. The range values of BD, BFR, BH, and BSD were determined to achieve the optimal conditions for the cooling effect of green city areas. Additionally, the relative position of the green city areas in the neighboring urban areas affects the cooling effect of the green city areas. The cooling effect is most pronounced in the urban area situated to the south of the green city areas. These findings provide a solid foundation for urban planning around green city spaces and offer scientifically sound evidence for mitigating the UHI. [ABSTRACT FROM AUTHOR]

With the evolution of remote sensing, more data products concerning cropland distribution are becoming available. However, the accuracy and consistency across all datasets in crucial regions are inherently uncertain. We delved into the Middle Yangtze Plain, a complex and vital agricultural area with relatively high cultivation intensities in China. We used confusion matrices and consistency analysis to compare the accuracy and consistency of four multi-year cropland distribution data products. These include Global Land Analysis & Discovery Cropland Data (GLAD), Annual Global Land Cover (AGLC), the China Land Cover Dataset (CLCD), and China's Annual Cropland Dataset (CACD). Key findings include the following: GLAD has the highest precision at 96.09%, the CLCD has the highest recall at 98.41%, and AGLC and CACD perform well in achieving a balance between precision and recall, with F1 scores of 90.30% and 90.74%, respectively. In terms of consistency, GLAD and the CLCD show inconsistency at 69.58%. When all four products unanimously classify a pixel as cropland, the identified cropland area closely corresponds to the statistical data reported in the yearbook. The Jianghan Plain holds the majority of cropland in the Middle Yangtze Plain, constituting 50.88%. From 2003 to 2019, the cropland area experienced fluctuating and ascending trends. Shangrao City witnessed the most notable rise in cropland area, with an increase of 323.0 km2, whereas Wuhan City underwent the most substantial decline, amounting to 185.8 km². These findings contribute valuable insights into the precision and consistency of existing cropland distribution products, offering a foundation for further research. [ABSTRACT FROM AUTHOR]

To explore the influence of human activities on natural resources and the correlation between these two characteristics, taking the region around Poyang Lake in China as the research area. Utilizing data from 2010, 2013, 2015, 2017, and 2020, including remote sensing, gross domestic product (GDP), and population density data, the human activity intensity (HAI) and resource carrying capacity (RCC) were calculated using relevant models. Analyzing the dynamic change characteristics, the temporal and spatial correlations, and the interaction between the two aspects, bivariate spatial autocorrelation and other methods were used. The results show that: (1) from 2010 to 2020, there was a significant increasing trend in the HAI, while the RCC remained at a generally stable level. (2) In both the temporal and spatial dimensions, there was a certain positive correlation between the HAI and RCC. Their interaction, however, is primarily based on the intrinsic resources and human activity factors within each region, with limited observable factor flow between regions. (3) Regions with the medium level of HAI have greater development potential. The results are expected to enrich the research on human activities and natural resources in the region around Poyang Lake in China and provide a reference for relevant planning. [ABSTRACT FROM AUTHOR]

The unique natural geographical conditions and land use patterns in semiarid sandy areas have resulted in a distinct cultivated landscape. Identifying and classifying the morphological characteristics of cultivated land are key to improving land utilization efficiency. This study focuses on the semiarid sandy areas of China. We obtained information on cropland morphology by judging high-precision remote sensing images and landscape pattern analysis, and then determined the spatial clustering characteristics of different plot morphologies through local spatial autocorrelation analysis. The results are as follows: cultivated land can be classified into five main types: simple large plots, complex large plots, simple small plots, complex small plots, and scattered plots, and there are significant differences in morphology between eastern and western plots. In addition, different morphology types of plots formed a variety of aggregation patterns; clustered plots are surrounded by scattered plots, showing a staggered distribution pattern. Farmers and land managers can make more informed decisions regarding irrigation, fertilization, and crop selection, and this knowledge can provide a basis for further optimizing the layout of cultivated land, identifying fragmented cultivated land, and scientifically recovering farmland to ensure more scientific and refined classification management and zoning protection of cultivated land. [ABSTRACT FROM AUTHOR]

Few studies have attempted to investigate the impact of airports on the ecological environment. This paper compares the effects of airports in China and Germany on the surrounding ecological environment and discusses the underlying institutional mechanisms that lead to these impacts. We used remote-sensing ecological indicators to assess ecological environment quality. A buffer analysis was used to determine the influence scopes of airports on the ecological environment. The institutional analysis development framework was adopted to investigate the functions of different institutions on the influence scopes and intensities. The results showed that the Chinese airport had obvious negative effects and that its impact scope was wide, while the impact intensity of the German airport was weaker. These significant differences stem from the distinct institutional systems that structure the two airports. Our findings ultimately provide insight into how to improve the relationship between infrastructure construction and environmental protection in China. [ABSTRACT FROM AUTHOR]

Conventional and scientific cropping patterns are important in realizing the sustainable utilization of Black soil and promoting the high-quality development of agriculture. It also has far-reaching significance for protecting Black soil and constructing the crop rotation system to identify the cropping patterns in Northeast China and analyze their spatio-temporal dynamic change. Using the geo-information Tupu methods and transfer land matrix, this study identified the cropping patterns and their spatio-temporal change based on remote sensing data for three periods, namely 2002–2005, 2010–2013, and 2018–2021. The main results revealed that the maize continuous, mixed cropping, maize-soybean rotation, and soybean continuous cropping patterns were the main cropping patterns in Wangkui County, with the total area of the four patterns accounting for 95.28%, 94.66%, and 81.69%, respectively, in the three periods. Against the backdrop of global climate warming, the cropping patterns of continuous maize and soybean and the mixed cropping pattern in Wangkui County exhibited a trend towards evolving into a maize-soybean rotation in the northern region. Moreover, the maize-soybean rotation further evolved into a mixed cropping system of maize and soybean in the north. Furthermore, the spatio-temporal evolution of cropping patterns was significantly driven by natural and social factors. Specifically, natural factors influenced the spatio-temporal patterns of variation in cropping patterns, while social factors contributed to the transformation of farmers' cropping decision-making behavior. Accordingly, new insights, institutional policies, and solid solutions, such as exploring and understanding farmers' behavior regarding crop rotation practices and mitigating the natural and climatic factors for improving food security, are urgent in the black soil region of China. [ABSTRACT FROM AUTHOR]

The black soil region of Northeast China is one of the most fertile soil areas in the world and serves as a crucial grain-producing region in China. However, excessive development and improper utilization have led to severe land use issues. Conducting land cover extraction in this region can provide essential data support for monitoring and managing natural resources effectively. This article utilizes GF-6 remote sensing imagery as the data source and adopts the U-Net model as the backbone network. By incorporating residual modules and adjusting the convolution kernel size, a high-precision land cover extraction model called RAT-UNet is developed. Taking Qiqihar City as an example, the RAT-UNet model is applied to extract land cover information. The results are as follows: (1) The RAT-UNet model achieves high accuracy in land cover extraction, with the following accuracies for different land types: cropland (95.11%), forestland (93.61%), grassland (68.41%), water bodies (94.67%), residential land (89.40%), and unused land (87.25%). (2) The land cover extraction performance of the RAT-UNet model is superior to DeepLabV3, U-Net, SegNet, and LinkNet34 models. This research outcome provides methodological support for the intelligent and high-precision extraction of land cover information and also offers timely data for Qiqihar city's land use planning. [ABSTRACT FROM AUTHOR]

Ecological challenges resulting from soil salinization in the Tarim River Basin (TRB), exacerbated by climate change and human activities, have emphasized the need for a quick and accurate assessment of regional ecological environmental quality (EEQ) and driving mechanisms. To address this issue, this study has developed a remote-sensing ecological index with salinity adaptability (RSEISI) for EEQ assessment in the Tarim River Basin by integrating the comprehensive salinity index (CSI) into the remote-sensing ecological index (RSEI). The RSEISI enhances the sensitivity of soil salinity and characterizes the surface features of arid regions, thus expanding the applicability. Then, we used time-series analysis methods and a geodetector to quantify the spatial temporal trends and driving factors of EEQ in the TRB from 2000 to 2022. The results show that the RSEISI with salinity adaptation effectively monitors the EEQ of the TRB. The EEQ of the TRB displayed the situation of oasis expansion, desert deterioration, and glacier melting, and the multiyear average EEQ grades were dominated by medium and poor grades in desert and saline areas, while medium, good, and excellent grades were concentrated in oasis and mountainous areas. Looking at the trend of change in conjunction with land-use types, the EEQ of the TRB showed a mild degradation trend mainly in unused land, followed by a mild improvement trend in cropland and grassland. The Hurst index indicated that the EEQ of most areas of the TRB will improve in the future. Soil type, land use, precipitation, and temperature were considered to be key factors affecting the EEQ across the TRB, and changes in the EEQ were found to be the interaction of multiple factors. This study may provide innovative concepts and methodologies, scientific and technological support for ecological management, and green development models in the northwest arid zone. [ABSTRACT FROM AUTHOR]

Accurately identifying the boundary of urban clusters is a crucial aspect of studying the development of urban agglomerations. This process is essential for comprehending and optimizing smart and compact urban development. Existing studies often rely on a single category of data, which can result in coarse identification boundaries, insufficient detail accuracy, and slight discrepancies between the coverage and the actual conditions. To accurately identify the extent of urban clusters, this study proposes and compares the results of three methods for identifying dense urban areas of three major agglomerations in China: Beijing–Tianjin–Hebei, the Yangtze River Delta, and the Guangdong–Hong Kong–Macao Greater Bay Area. The study then integrates the results of these methods to obtain a more effective identification approach. The social economic method involved extracting a density threshold based on the fused nuclear density of socio-economic vitality data, including population, GDP, and POI, while the remote sensing method evaluated feature indices based on remote sensing images, including the density index, continuity index, gradient index, and development index. The traffic network method utilizes land transportation networks and travelling speeds to identify the minimum cost path and delineate the boundary by 20–30 min isochronous circles. The results obtained from the three methods were combined, and hotspots were identified using GIS overlay analysis and spatial autocorrelation analysis. This method integrates the multi-layered information from the previous three methods, which more comprehensively reflects the characteristics and morphology of urban clusters. Finally, the accuracy of each identification result is verified and compared. The results reveal that the average overall accuracy (OA) of the three areas delineated by the first three methods are 57.49%, 30.88%, and 33.74%, respectively. Furthermore, the average Kappa coefficients of these areas are 0.4795, 0.2609, and 0.2770, respectively. After performing data fusion, the resulting average overall accuracy (OA) was 85.34%, and the average Kappa coefficient was 0.7394. These findings suggest that the data fusion method can effectively delineate dense urban areas with greater accuracy than the previous three methods. Additionally, this method can accurately reflect the scope of urban clusters by depicting their overall boundary contour and the distribution of internal details in a more scientific manner. The study proposes a feasible method and path for the identification of urban clusters. It can serve as a starting point for formulating spatial planning policies for urban agglomerations, aiding in precise and scientific control of boundary growth. This can promote the rational allocation of resources and optimization of spatial structure by providing a reliable reference for the optimization of urban agglomeration space and the development of regional spatial policies. [ABSTRACT FROM AUTHOR]

Desertification is one of the most severe environmental and socioeconomic issues facing the world today. Gonghe Basin is located in the monsoon marginal zone of China, is a sensitive area of climate change in the northeastern of the Qinghai-Tibet Plateau in China, desertification issue has become very severe. Remote sensing monitoring provides an effective technical means for desertification control. In this study, we used Landsat images in 2010 and 2020 to extract desertification information to constructed the Albedo-NDVI feature space in the Gonghe Basin. And then analyzed temporal and spatial evolution of desertification and its driving factors using Geodetector in the Gonghe Basin from 2010 to 2020. The main conclusions are as follows: (1) Albedo-NDVI feature space method can accurately classify desertification information with accuracy of more than 90%, which was benefit to quantitative analysis of desertification. (2) The desertification situation in the Gonghe Basin had improved from 2010 to 2020, especially in the west of the basin, desertification land area decreased by 827.46 km2, and desertification intensity had been obviously reversed. (3) The changes of the desertification in the Gonghe Basin from 2010 to 2020 was affected by both natural and human factors, and the influence of human activities on desertification reversal had increased gradually. The results indicate that the desertification status in the Gonghe Basin had been effectively controlled, and can provide useful basis for the desertification combat in the Gonghe Basin. [ABSTRACT FROM AUTHOR]

This study examined the relationships between different types of urban green and blue space (UGS/UBS) and self-reported health (SRH), and the disparities in the health benefits associated with them. Using data from a social survey in Shandong Province and multi-source data including remote sensing land use and vector polygons of parks from map service providers, we measured the proximity and coverage ratio of various UGS/UBS types. The Euclidean distance measures the proximity of homes to parks and rivers. The coverage ratio measures the total green space, forests, grassland, and freshwater. The health benefits were gauged by SRH. We found that the proximity to parks and rivers had a positive influence on the SRH of all the respondents. For the elderly, the proximity to parks and the coverage ratio of total green space and grassland within a 0.5 km circular buffer were significantly associated with SRH. The coverage ratio of the total green space and grassland both had positive relationships with the SRH of the high-income groups. The closer they were to rivers, the healthier the youth and females were. Our results suggest that urban planners should take the types of UGS/UBS into account to create a better living environment that optimally benefits residents' SRH. [ABSTRACT FROM AUTHOR]

Ensuring compliance with China's "1.8 billion mu" (120 million hectares) cultivated land preservation policy is a fundamental goal of land policy. Northeast China has experienced significant cultivated land expansion due to rigorous compensation policies over the past two decades, resulting in sustainable increases in grain output. This research employs remote sensing data to examine the spatial–temporal pattern and vulnerability of newly increased cultivated land expansion in Northeast China and its potential impact on food security. Results indicate a 3.08% increase in newly increased cultivated land from 2000 to 2020, with the majority located in the Sanjiang Plain's humid area and Inner Mongolia's arid and semi-arid regions. The low quality of newly added cultivated land makes it highly vulnerable. Temperature instability significantly and negatively correlates with cultivated land expansion. The vulnerability of cultivated land is negatively and significantly related to grain yield, suggesting an adverse impact on national food security. This study focuses on the marginal impact of newly increased cultivated land and proposes policy recommendations. [ABSTRACT FROM AUTHOR]

In both developed and developing countries, port-city relationships have always attracted much attention. However, in the port–city interface, views differ as to whether the port drives the city or vice versa. The combination of remote sensing data and geospatial big data (point of interest) has provided a favorable solution. Taking the typical estuarine and coastal port cities in China's coastal zone as an example, this study examines the following contents based on the port–city interface: the formation age of urban built-up areas and port areas on both sides of the port–city boundary; interaction between port and urban built-up areas; and the distribution of urban functional areas outside the port. Results show that the degree of spatial integration in estuarine port cities is higher than that of coastal port cities and that in the past 30 years, the expansion of ports has led to the expansion of cities. This expansion is port- and sea-oriented, and the expansion direction of the port city is consistent. On the port–city interface, the estuarine and coastal port cities form different urban regional structure modes. Aside from enriching literature on the port–city relationship, this study provides a reference for the spatial planning and transformation of ports and cities in the future. [ABSTRACT FROM AUTHOR]

Quantitative assessment of the effectiveness of ecological restoration provides timely feedback on restoration efforts, and helps to accurately understand the extent of restoration, while providing scientific support for optimizing restoration programs. In recent decades, the Wuliangsuhai watershed in China's Inner Mongolia Autonomous Region has been affected by anthropogenic activities, resulting in an increasingly unbalanced ecological environment. In order to curb environmental degradation, the local government implemented the "mountain, water, forest, field, lake and grass ecological protection and restoration project of the Wuliangsuhai watershed" from 2018 to 2020. The project has been completed and there is an urgent need for remote sensing monitoring to aid in performance evaluation. We took the ecological protection and restoration area of the Wuliangsuhai watershed in China as the research object, applied multi-source remote sensing imagery and auxiliary data such as meteorology and geographic basic data, extracted information of each evaluation index before and after the implementation of this project, and used the entropy value method to determine the index weights to comprehensively evaluate the ecological restoration effect. The results showed that after the implementation of the ecological restoration project, the vegetation coverage was further improved, the effectiveness of desert management was obvious, soil and water conservation capacity was strengthened, the ecosystem became more stable, and the areas with good environment were mostly located in the central and eastern parts. A total of 37.86% of the areas had obvious ecological restoration effects, and all indicators were further improved. Among the main treatment areas, the restoration effect of the Wuliangsuhai water ecological restoration and biodiversity conservation area was the best. The restoration effect will be further accentuated over time. This study provides a scientific reference for the further management of the ecological environment in the watershed and can provide a reference for the evaluation of the ecological restoration effect in similar areas in the future. [ABSTRACT FROM AUTHOR]

As the main carbon sink in the carbon cycle process, vegetation is an important support for achieving "carbon peaking" and "carbon neutrality. How does the vegetation coverage of urban built-up areas change in the process of urbanization in China? Taking advantage of Landsat remote sensing data, we extract urban built-up areas, calculate the Normalized Difference Vegetation Index (NDVI), estimate fractional vegetation cover (FVC), and analyze the temporal and spatial dynamics of vegetation coverage in built-up areas of provincial capitals from the national and individual city levels in China. Major conclusions are as follows: (1) The FVC in the urban built-up areas has increased by 7.97%, and the urban green space has gradually changed from the "green core" distribution model to the "green vein" distribution mode. (2) The disparities in FVC of built-up areas of provincial capitals are notable, presumably due to a variety of factors that include the natural geographic environment, economic development level, built-up area expansion, land type conversion, afforestation of greening policy, etc. (3) Not just simply raise or lower, the FVC curves in the built-up areas of provincial capital cities present oscillating patterns with gradually weakening amplitude. Our study is expected to provide scientific references of an important theoretical basis for urban ecological construction and practical support for promoting the harmonious development of urban residents and urban environments in China. [ABSTRACT FROM AUTHOR]

An efficient, convenient, and accurate method for monitoring the distribution characteristics of soil salinity is required to effectively control the damage of saline soil to the land environment and maintain a virtuous cycle of the ecological environment. There are still problems with single-monitoring data that cannot meet the requirements of different regional scales and accuracy, including inconsistent band reflectance between multi-source sensor data. This article proposes a monitoring method based on the multi-source data fusion of unmanned aerial vehicle (UAV) multispectral remote sensing, Sentinel-2A satellite remote sensing, and ground-measured salinity data. The research area and two experimental fields were located in the Yellow River Delta (YRD). The results show that the back-propagation neural network model (BPNN) in the comprehensive estimation model is the best prediction model for soil salinity (modeling accuracy R2 reaches 0.769, verification accuracy R2 reaches 0.774). There is a strong correlation between the satellite and UAV imagery, while the Sentinel-2A imagery after reflectivity correction has a superior estimation effect. In addition, the results of dynamic analysis show that the area of non-saline soil and mild-saline soil decreased, while the area of moderately and heavily saline soils and solonchak increased. Additionally, the average area share of different classes of saline soils distributed over the land use types varied in order, from unused land > grassland > forest land > arable land, where the area share of severe-saline soil distributed on unused land changed the most (89.142%). In this study, the results of estimation are close to the true values, which supports the feasibility of the multi-source data fusion method of UAV remote sensing satellite ground measurements. It not only achieves the estimation of soil salinity and monitoring of change patterns at different scales, but also achieve high accuracy of soil salinity prediction in ascending scale regions. It provides a theoretical scientific basis for the remediation of soil salinization, land use, and environmental protection policies in coastal areas. [ABSTRACT FROM AUTHOR]

Land consolidation (LC) in China is an important means by which we can increase the quantity and improve the quality of cultivated land. At present, large areas of cultivated land are abandoned in mountainous areas. It is unclear whether the increased cultivated land from LC in mountainous areas is sustainably used. Data from 64 land consolidation zones completed in 2016 in the Qinba Mountain Area were collected. The land-use status was obtained from high-resolution remote sensing images by the method of visual interpretation, and land-use changes were analyzed. According to our results, the increased cultivated land by LC is mainly terrace, accounting for 92.22% of the total area of increased cultivated land. The increased cultivated land is mainly distributed in the Qinba Mountainous Area, and terrace is the main type of increased cultivated land in both the Hanzhong Basin Area and Qinba Mountainous Area. The transformation rate of cultivated land from LC, especially terrace, is small. The transformation rates of terrace in the Hanzhong Basin Area and Qinba Mountainous Area are 0.36% and 0.09%, respectively. The socioeconomic development in mountainous areas is relatively lagging, and the per capita cultivated land area is small. Many farmers are still engaged in agricultural production and earn a basic income. Thus, high-quality cultivated land with convenient transportation is sustainably used. LC remains a key avenue for increasing cultivated land area, improving agricultural productivity, increasing farmers' incomes, and promoting rural development in the mountainous areas. [ABSTRACT FROM AUTHOR]

The Mu Us Sandy Land is a key region of man-made desert control and farmland to forest (grass) return in China. Despite global change and the strong influence of human activities, the vegetation in this region has been significantly improved and restored. In this study, multisource remote sensing data and multiple indicators were used to quantitatively monitor and evaluate the vegetation restoration status in this area. The driving factors were also analysed. The results show that in the past 20 years, nearly the entire Mu Us Sandy Land significantly and substantively recovered. The regional fractional vegetation cover increased, with an average annual growth rate of 0.59% and obvious spatial heterogeneity. The nine most important driving factors could comprehensively account for 58.38% of the spatial distribution of the vegetation coverage. Factors such as land use and land cover, the aridity index, and gross domestic product had the most significant impact, followed by precipitation and temperature. The results confirmed that the vegetation was restored and improved in the Mu Us Sandy Land and determined the main driving factors, which is helpful for vegetation restoration and ecological improvement on sandy land similar to the Mu Us Sandy Land. [ABSTRACT FROM AUTHOR]

Global urbanization has brought about a significant transition to rural areas. With the development of remote sensing technologies, land use/land cover (LULC) datasets allow users to analyze the changes in global rural settlements. However, few studies have examined the performances of the LULC datasets in mapping rural settlements. Taking China as the study area, this research selected eight of the latest LULC datasets (ESRI Land Cover, WSF, ESA WorldCover, GHS-BUILT-S2, GISD30, GISA2.0, GLC30, and GAIA) to compare their accuracy for rural settlement detection. Spatial stratified sampling was used for collecting and sampling rural settlements. We conducted omission tests, area comparison, and pixel-based accuracy tests for comparison. The results show that: (1) the performances of the 10 m resolution datasets are better than those of the 30 m resolution datasets in almost all scenarios. (2) the mapping of villages in Western China is a challenge for all datasets. (3) GHS-BUILT-S2 performs the best in almost every scenario, and can allow users to adjust the threshold value for determining a proper range of rural settlement size; ESRI outperforms any other dataset in detecting the existence of rural settlements, but it dramatically overestimates the area of rural settlements. (4) GISD30 is the best among the 30 m resolution datasets, notably in the Pearl River Delta. Finally, we provide useful suggestions on ideal map selection in various regions and scenarios. [ABSTRACT FROM AUTHOR]

As China is currently in a critical period of transition to green development, a quantitative analysis of the coupled synergy between economic growth, population migration, and environmental protection performance can provide a rational reference for the formulation of macro-policies in relevant regions. Based on these objectives, this study built a matching analysis framework between gross domestic production (GDP) density (GD), population density (PD), and gross ecosystem production (GEP) density (ED) to analyze the spatial heterogeneity of these three indicators among 362 municipal units in different regions of the Chinese Mainland from 2000 to 2020 based on satellite remote sensing images and statistical data. The spatial adaptability between them was explored by employing a center-of-gravity model. The findings of this study show that: (1) the GD, PD, and ED on the Chinese Mainland exhibited varying degrees of spatial heterogeneity on both sides of the Hu Line during the investigation period, with the general feature of being higher in the southeastern region and lower in the northwestern region; (2) the centers of gravity of GD, PD, and ED were all located in Hubei Province during the investigation period. The centers of gravity of PD and GD shifted 79.39 km and 109.72 km to the southwest, respectively, whereas the center of gravity of ED, remained relatively stable during the investigation period; and (3) the center of gravity distances between PD–GD, ED–PD, and ED–GD in 2020 were 99.31 km, 247.52 km, and 346.27 km, respectively, and the percentages of highly matched units ranked among the 362 samples were 72.93%, 23.48%, and 25.69% for GD–PD, GD–ED, and ED–PD, respectively. This study concluded that a synergistic spatial pattern of the population, economic layout, and land use on the Chinese Mainland has not yet been formed. Therefore, this study suggests that future policies should be committed to promoting the northwest Chinese Mainland movement of the center of gravity of GD and PD, as well as the southeast movement of the ED center. [ABSTRACT FROM AUTHOR]

Under the short-term economic development goal, the excessive exploitation of natural resources and the destruction of the ecological environment make the ecological environment of Huaibei cities increasingly fragile. This study constructed the Remote Sensing Ecological Index (RSEI) to evaluate the ecological environment change trend and its driving factors in Huaibei City from 2000 to 2020. The barycenter migration model was used to determine the RSEI spatial change trend, and the geographic detector was used to analyze the influencing factors of the RSEI value change. The results showed that: (1) the average RSEI value of Huaibei City generally fluctuates within the range of good and excellent grades. (2) The migration direction of the barycenter of RSEI is similar when the level of RSEI improves or decreases from 2000 to 2020, and the barycenter migration is most severe from 2005 to 2015. (3) The driving factors of RSEI change were population density (0.47) > land use (0.24) > slope (0.14) > precipitation (0.08) > temperature (0.04) > altitude (0.03). All the factors had interaction effects on the RSEI, mainly with nonlinear enhancement. (4) From 2000 to 2010, urban construction encroached on all kinds of land, which was the direct reason for the decline in ecological environment quality. From 2010 to 2020, the surge of water and meadow areas improved the ecological environment quality of Huaibei city. Therefore, reducing the expansion of artificial land, returning farmland to forests and meadows, wetland park construction, and other ecological protection measures are the keys to ensuring the sustainable development of regional social and economic development. This study can provide a reference and scientific basis for sustainable development strategy and ecological protection planning to improve the ecological environment quality of Huaibei City. [ABSTRACT FROM AUTHOR]

Sustainable development is a common challenge for all global economies, and the assessment of the spatial distribution and development process of ecological resources is the basis of sustainable development. Considering the heterogeneity of regional ecological resource endowment, it is necessary to conduct a targeted assessment for different regions. In this study, we selected Guizhou Province, which is rich in ecological resources, but has a fragile environment, as our study area, and cultivated land, woodland, grassland, and water resources were selected as critical evaluation indicators. Notably, we applied Kernel density analysis methods, based on the remote sensing data of 2000, 2005, 2010, 2015, and 2020, to explore the spatiotemporal distribution characteristics and evolution pattern of regional ecological resources. The results indicated that the evolution of ecological resource development in Guizhou could be divided into three phases: degenerative (2000–2005), recovery (2005–2015), and development (2015–2020). The spatial distribution of Guizhou's ecological resource was shown to be heterogeneous in the north and south and more homogeneous in the middle regions. Guizhou has diverse land-use types, with obvious regional differences in land-use structure. Notably, even though the development of ecological resources in Guizhou has improved, the development and utilization degree of ecological resources is still low, and the ecological damage is serious. This study can be used as a scientific reference by policymakers and decisionmakers to develop new regulations for ecological resources protection and sustainable development in China. We suggest that different regions should adopt more detailed measures; particularly, it is important to establish a spatial governance system to promote ecological resources development in the context of local conditions. [ABSTRACT FROM AUTHOR]

Field surveys and questionnaires are a cornerstone of rural socioeconomic research, providing invaluable firsthand data regarding on-the-ground situations. However, cost-effective and efficient methods for validating the accuracy of self-reported data in such questionnaires are lacking. Biased data are likely to lead to incorrect conclusions. In this study, we propose a new index, the survey bias index (SBI), for evaluating the degree of survey bias in field surveys. This index was obtained by comparing the data recorded in questionnaires with those from portable unmanned aerial vehicles (UAVs). In a case study, we employed SBI to reveal the degree of survey bias of questionnaires in field surveys on rural homesteads. The SBI of self-reported areas of rural homesteads reached 0.439, implying that 43.9% of data were significantly different from those collected using UAVs. A greater SBI was obtained in the pre-urban zone (0.515) than in the pure rural zone (0.258). These results indicate that homestead areas in the pre-urban zone have more incentive to expand than those in the pure rural zone. UAV remote sensing can strongly support research in the field of social economy, which reveals key information hidden in field surveys and questionnaires. [ABSTRACT FROM AUTHOR]