Glacier changes using satellite data and effect of climate in Tirungkhad basin located in western Himalaya

Glaciers are widely recognized as key indicators of climate change, and melt water obtained from them is an important source of fresh water and for hydropower generation. Regular monitoring of a large number of Himalayan glaciers is important for improving our knowledge of glacier response to climate change. In the present study, Survey of India topographical maps (1966) and Landsat datasets as ETM+ (2000, 2006) and TM (2011) have been used to study glacier fluctuations in Tirungkhad basin. A deglaciation of 26.1% (29.1?km²) in terms of area from 1966 to 2011 was observed. Lower altitude small glaciers (area?<?1?km²) lost more ice (34%), while glaciers with an area <10?km² lost less (20%). The percentage of change in glacier length was 26% (31.9?km) from 1966 to 2011. The south-facing glaciers showed high percentages of loss. From 2000 to 2011, debris cover has increased by 1.34%. The analysis of the trend in meteorological data collected from Kalpa and Purbani stations was carried out by Mann Kendall non-parametric method. During the last two decades, the mean annual temperature (T_max and T_min) has increased significantly, accompanied with a fall in snow water equivalent (SWE) and rainfall. The increasing trend in temperature and decreasing trend in SWE were significant at 95% confidence level. This observation shows that the warming of the climate is probably one of the major reasons for the glacier change in the basin.     

Glacier surface characteristics derivation and monitoring using Hyperspectral datasets: a case study of Gepang Gath glacier,Western Himalaya

Mountain Glaciers are natural resources of fresh water and these affect the stream flow of the rivers, regional climate and further global climate. Observed trends and projected future evolutions of climate and Cryospheric variables clearly suggest a need to monitor these changes. Accordingly, the article presents the glacier features mapping using Hyperspectral remote sensing imagery. A freely available Hyperion satellite imagery acquired over Gepang Gath glacier in Himachal Pradesh, India is used for the study. Each class is identified based on their surface characteristics of spectral reflectance properties. Identification is simplified by demarcating the study glacier into accumulation and ablation areas through snowline. Accumulation area is characterized with high reflectance clean snow/ice and reduced moderate reflectance Snow/firn. The identification of classes in Hyperion imagery is validated using the spectral library from USGS and ASTER, and field spectra obtained from literature.     

Predicting grain protein content of winter wheat using remote sensing data based on nitrogen status and water stress

Advanced site-specific knowledge of grain protein content of winter wheat from remote sensing data would provide opportunities to manage grain harvest differently, and to maximize output by adjusting input in fields. In this study, remote sensing data were utilized to predict grain protein content. Firstly, the leaf nitrogen content at winter wheat anthesis stage was proved to be significantly correlated with grain protein content (R² = 0.36), and spectral indices significantly correlated to leaf nitrogen content at anthesis stage were potential indicators for grain protein content. The vegetation index, VI_green, derived from the canopy spectral reflectance at green and red bands, was significantly correlated to the leaf nitrogen content at anthesis stage, and also highly significantly correlated to the final grain protein content (R² = 0.46). Secondly, the external conditions, such as irrigation, fertilization and temperature, had important influence on grain quality. Water stress at grain filling stage can increase grain protein content, and leaf water content is closely related to irrigation levels, therefore, the spectral indices correlated to leaf water content can be potential indicators for grain protein content. The spectral reflectance of TM channel 5 derived from canopy spectra or image data at grain filling stage was all significantly correlated to grain protein content (R² = 0.31 and 0.37, respectively). Finally, not only this study proved the feasibility of using remote sensing data to predict grain protein content, but it also provided a tentative prediction of the grain protein content in Beijing area using the reflectance image of TM channel 5.     

月球遥感制图回顾与展望

对月球探测任务、月球遥感制图技术与产品进行综述。从1958年开始,全世界已开展126次(其中70次成功)月球探测工程任务,其中月球遥感制图是其必需的基础性工作。由于月球环境的特殊性,其遥感制图技术与对地观测制图相比具有很大的挑战和更大的难度。目前,中国嫦娥二号轨道器获取的7 m分辨率立体影像是覆盖全月球分辨率最高的立体影像数据,美国月球侦察轨道器LRO任务的激光雷达高度计LOLA数据是精度和密度最高的激光测高数据,LRO NAC影像的分辨率最高(0.5—2 m)但未覆盖全球。在各个探测任务中,基于月球遥感数据和摄影测量技术,已经制作了大量的全球及区域的影像拼图、正射影像图和数字高程模型等制图产品。对月球遥感制图技术发展进行展望,探讨了利用国际多探测任务数据建立新一代控制网和进行精细制图的必要性及技术思路。     

Analysis of population dynamics using satellite remote sensing and US census data

The population dynamics from 1991 to 2006 for the seven-county Twin Cities Metropolitan Area (TCMA), Minnesota, USA, was analysed in this study. Per cent impervious surface areas (%ISA) for 1991, 1999 and 2006 were derived from Landsat Thematic Mapper (TM) images and were modified using two different masking methods. The modified %ISA images of 1991 and 1999 were correlated with 1990 and 2000 census block group data of the ‘two highly developed counties’, ‘five suburban counties’ and ‘all seven counties.’ Populations of both years were then modelled, assessed and compared. Next, the statistical models based on the 1999 %ISA and 2000 census data were applied to the 2006 residential %ISA image to estimate the 2006 population. These 2006 estimates were compared with census county-level population projections for 2006. In comparison to Method A, which uses ‘adjusted %ISA images’ by masking out highway centrelines and areas that have greater than 75% imperviousness, Method B based on ‘pure residential %ISA image’ has higher coefficient of determination (R ²) and much lower, consistent mean absolute relative errors (MARE). For both methods, the strongest R ² and lowest MARE values between modelled population density and true density were found in the five-county model, followed by the seven-county model. The two-county model ranks last in terms of model performance for both years. In general, populations for the two highly developed counties were underestimated whereas the opposite was true for the five suburban counties. Population was most accurately estimated based on data from counties with the same or similar characteristics. By comparing the 1990/1991 and 1999/2000 models, we also found that the rate of population density per unit of impervious surface declined from 1991 to 1999. High accuracy was achieved when applying the 1999/2000 model to predict the 2006 population, suggesting that the relationship between per cent imperviousness and population density were relatively stable between 1999 and 2006.     

Integrated remote sensing data utilization for investigating structural and tectonic history of the Ghadames Basin,Libya

This study was initiated to constrain the geological structure of the Ghadames Basin in northwest Libya. Detailed analysis was based on digital integration of surface data, including SRTM DEM, ETM+ and geologic maps with subsurface data, including well logs and potential field data. Integrated analysis of remotely sensed data of the SRTM and ETM+ were utilized to identify geologic lineaments in the area. Ground-based verification of the remote sensing data was achieved with field work. Interpretation and analysis of the lineaments indicate that the Ghadames Basin is controlled by four main fault systems that trend WNW, NNW, NW and ENE. Well logs and potential field data were used to delineate a detailed picture of the subsurface structure. The potential field data reveal two NE and NNE trending sedimentary basins. The depth of the basement inside the main basin ranges from 2 to 6 km. A two-dimensional (2-D) schematic model shows that the basin gradually deepens towards the southwest. The applied data integration gave new insight into the tectonic and structure patterns of the Ghadames Basin and the adjacent areas of northwest Libya.     

Glacier and glacial lake classification for change detection studies using satellite data: a case study from Baspa basin,western Himalaya

Using high-resolution Google Earth^TM images in conjunction with Landsat images, the glaciers and lakes in the Baspa basin are classified to explore the recent changes. A total number of 109 glaciers (187 ± 3.7 km²) are mapped and subsequently classified as compound valley glaciers, simple valley glaciers, cirques, niches, glacieretes and ice aprons. The compound and simple valley glaciers contribute 67.1 ± 1.3% and 19.8 ± 0.3% to the total glacier cover of the basin. Similarly, a total number of 129 glacial lakes (0.360 ± 0.007 km²) are identified. From 1976 to 2011, the compound valley glaciers have lost a small area of 10.3 ± 0.03% at a rate of 0.41 ± 0.002 km² a^-1, whereas the niche glaciers have lost higher area of 40.1 ± 0.001% at a rate of 0.04 ± 0.0001 km² a^-1. Change detection of two benchmark glacial lakes revealed a progressive expansion during recent decades. The Baspa Bamak proglacial lake has expanded from 0.020 ± 0.0004 km² (2000) to 0.069 ± 0.001 km² (2011). Due to the complete loss of source ice, another glacial lake has expanded from 0.09 ± 0.001 km² (1994) to 0.10 ± 0.002 km² (2011). During the study period, the mean annual temperature that is T_avg, T_min and T_max have increased significantly at the 95% confidence level by 1.5 ^oC (0.070 °C a^-1), 1.8 ^oC (0.076 °C a^-1) and 1.6 ^oC (0.0071 °C a^-1) from 1985 to 2008. However, the precipitation has decreased significantly from 1976 and 1985 to 2008.     

哈密遥感地质资源评价综合应用野外试验场建设进展

遥感地质试验场是实现遥感地质资源评价综合应用的一个重要的基础平台,其建立是为了更好地适应国土资源遥感地质应用需要,满足遥感地质应用技术自身的发展以及遥感技术与其他技术方法协同发展的需求,提高地质调查的遥感地学定量化水平,避免遥感数据应用与推广的盲目性。为此,在中国地质调查局的领导和支持下,开展了我国遥感地质试验场的建设和研究工作。经过几年的不懈努力,目前哈密遥感地质试验场的野外试验场(简称试验场)已基本建立,初步具备了对外提供应用服务的能力。这里主要介绍了试验场的构成、功能和性能,以及试验场所能提供的服务方式及服务能力,以便广大的遥感地质工作者了解试验场的建设进展情况,并利用试验场开展遥感技术方法及基础理论等研究工作,群策群力共同推进试验场的建设和发展。     

深度融合网结合条件随机场的遥感图像语义分割

为了充分利用遥感图像中丰富的细节信息和上下文信息,提高图像语义分割精度,提出一种深度融合网结合条件随机场模型的遥感图像语义分割方法。方法在全卷积神经网络框架中增加反卷积融合结构,搭建深度融合DFN (Deep Fusion Networks)网络,通过深层网络自动获取多尺度特征,避免人工设计和选择特征,提高模型的泛化能力;同时借助反卷积融合结构,利用多尺度信息,将浅层细节信息和深层语义信息相融合,提高模型的处理精度。由全连接条件随机场引入空间上下文信息,更好地定位边界,得到最终的语义分割结果。在遥感图像数据集上的实验结果显示:(1)随着不同尺度细节信息的融入,结果的边缘轮廓越精确、接近标签图像;(2)增加了空间上下文信息后,语义分割结果边缘更细化、准确,精度更高。实验表明,该方法可以有效提高遥感图像语义分割的精度,改善结果的过平滑现象。     

Combined use of socio economic analysis,remote sensing and GIS data for landslide hazard mapping using ANN

The term landslide includes a wide range of ground movements, such as slides, falls, flows etc. mainly based on gravity with the aid of several conditioning and triggering factors. Particularly in the last two decades, there has been an increasing international interest in the landslide susceptibility, hazard or risk assessments. In this paper we present a combined use of socioeconomic, remote sensing and GIS data for developing a technique for landslide susceptibility mapping using artificial neural networks and then to apply the technique to the selected study areas at Nilgiris district in Tamil Nadu and to analyze the socio economic impact in the landslide locations.     

Estimating fractional cover of crop,crop residue,and soil in cropland using broadband remote sensing data and machine learning

The fractional vegetation cover (FVC), crop residue cover (CRC), and bare soil (BS) are three important parameters in vegetation–soil ecosystems, and their correct and timely estimation can improve crop monitoring and environmental monitoring. The triangular space method uses one CRC index and one vegetation index to create a triangular space in which the three vertices represent pure vegetation, crop residue, and bare soil. Subsequently, the CRC, FVC, and BS of mixed remote sensing pixels can be distinguished by their spatial locations in the triangular space. However, soil moisture and crop-residue moisture (SM-CRM) significantly reduce the performance of broadband remote sensing CRC indices and can thus decrease the accuracy of the remote estimation and mapping of CRC, FVC, and BS. This study evaluated the use of broadband remote sensing, the triangular space method, and the random forest (RF) technique to estimate and map the FVC, CRC, and BS of cropland in which SM-CRM changes dramatically. A spectral dataset was obtained using: (1) from a field-based experiment with a field spectrometer; and (2) from a laboratory-based simulation that included four distinct soil types, three types of crop residue (winter-wheat, maize, and rice), one crop (winter wheat), and varying SM-CRM. We trained an RF model [designated the broadband crop-residue index from random forest (CRRF)] that can magnify spectral features of crop residue and soil by using the broadband remote sensing angle indices as input, and uses a moisture-resistant hyperspectral index as the target. The effects of moisture on crop residue and soil were minimized by using the broadband CRRF. Then, the CRRF-NDVI triangular space method was used to estimate and map CRC, FVC, and BS. Our method was validated by using both laboratory- and field-based experiments and Sentinel-2 broadband remote-sensing images. Our results indicate that the CRRF-NDVI triangular space method can reduce the effect of moisture on the broadband remote-sensing of CRC, and may also help to obtain laboratory and field CRC, FVC, and BS. Thus, the proposed method has great potential for application to croplands in which the SM-CRM content changes dramatically.     

Research on the influence of site factors on the expansion of construction land in the Pearl River Delta,China: By using GIS and remote sensing

Landsat TM images of the Pearl River Delta taken in 1988, 1998 and 2006 are used to explore the site factors that influence the construction land expansion in this study. Several site factors, including landscape types and the distances to roads, coastlines, or city centers, had significant impacts on the expansion of construction land, influencing the direction, scale and intensity of the expansion. The site factors serve as important natural and spatial indicators of the preferable locales for construction land expansion, describing tendencies to expand to locations in suburbs, plains and areas near roads or coastlines.     

Land cover mapping at Alkali Flat and Lake Lucero,White Sands,New Mexico,USA using multi-temporal and multi-spectral remote sensing data

The goal of this research is to map land cover patterns and to detect changes that occurred at Alkali Flat and Lake Lucero, White Sands using multispectral Landsat 7 Enhanced Thematic Mapper Plus (ETM+), Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), Advanced Land Imager (ALI), and hyperspectral Hyperion and Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data. The other objectives of this study were: (1) to evaluate the information dimensionality limits of Landsat 7 ETM+, ASTER, ALI, Hyperion, and AVIRIS data with respect to signal-to-noise and spectral resolution, (2) to determine the spatial distribution and fractional abundances of land cover endmembers, and (3) to check ground correspondence with satellite data. A better understanding of the spatial and spectral resolution of these sensors, optimum spectral bands and their information contents, appropriate image processing methods, spectral signatures of land cover classes, and atmospheric effects are needed to our ability to detect and map minerals from space. Image spectra were validated using samples collected from various localities across Alkali Flat and Lake Lucero. These samples were measured in the laboratory using VNIR–SWIR (0.4–2.5 μm) spectra and X-ray Diffraction (XRD) method. Dry gypsum deposits, wet gypsum deposits, standing water, green vegetation, and clastic alluvial sediments dominated by mixtures of ferric iron (ferricrete) and calcite were identified in the study area using Minimum Noise Fraction (MNF), Pixel Purity Index (PPI), and n-D Visualization. The results of MNF confirm that AVIRIS and Hyperion data have higher information dimensionality thresholds exceeding the number of available bands of Landsat 7 ETM+, ASTER, and ALI data. ASTER and ALI data can be a reasonable alternative to AVIRIS and Hyperion data for the purpose of monitoring land cover, hydrology and sedimentation in the basin. The spectral unmixing analysis and dimensionality eigen analysis between the various datasets helped to uncover the most optimum spatial–spectral–temporal and radiometric-resolution sensor characteristics for remote sensing based on monitoring of seasonal land cover, surface water, groundwater, and alluvial sediment input changes within the basin. The results demonstrated good agreement between ground truth data and XRD analysis of samples, and the results of Matched Filtering (MF) mapping method.     

GIS-based geomorphological mapping using remote sensing data and supplementary geoinformation: A case study of the Dhaka city area,Bangladesh

This study aims to prepare a detailed GIS-based geomorphological map accompanied with landfill sites of Dhaka city area which can be used for multipurpose functionality. Attainment of the geomorphological map is based upon interpretation of the oldest available aerial photographs (1:40,000) and contemporary topographic maps (1:8000) which reflect almost pre-urban ground of Dhaka. Randomly distributed 160 boreholes have been used to prepare representative soil profiles (RSP) to identify the near-surface lithology of the geomorphological units. The study reveals that 13 out of 18 low-lying geomorphic units, comprising 65% of the total area demand landfill practices for urban development. Landfill sites have been merged with urban growth on each low-lying geomorphic unit using a spatially enhanced fused image of IRS-1D PAN and ETM+ bands 5, 4 and 3, acquired February 2000 and 2002, respectively. We found that 43% area of the total low-lying geomorphic units experience fill practices so far. The fill sites have been differentiated into four classes based on their relative thickness. Integration of fill classes with geomorphological map shows the urban dynamics of Dhaka city area till 2002. Due to GIS integration, this map can be rapidly updated to demonstrate temporal modifications in urban ground. It can be used effectively in different geomorphological hazard mapping and urban land-use practices.     

Understanding the 2007-2008 eruption of Anak Krakatau Volcano by combining remote sensing technique and seismic data

One of the most violent volcanic eruptions in recorded history is the Krakatau eruption on August 27, 1883. This caldera-forming eruption destroyed two thirds of the Krakatau volcanic island in the Sunda Strait resulting in the remaining three small islands later known as the Krakatau complex. From 1927 to 1929, eruptions in the center of Krakatau complex have produced a new volcano named Anak Krakatau, which continuously builds its body through eruptions until now. One eruption event took place between 2007 and 2008 with several eruptions that lasted in total from the end of October 2007 to August 2008. Eruptions were characterized by Strombolian activity with ash columns 1 km high, as well as pyroclastic and lava flows. We monitored the ground deformation of Anak Krakatau Volcano by interfering PALSAR data from June 2007 to February 2009. The result of InSAR technique shows a complex pattern of ground deformation. Inflation up to 4 cm, together with subsidence around the crater, was measured for almost three months before the eruption with a volume increase of approximately 1 × 10⁶ m³. After the eruption, the southwest side of the volcanic cone subsided by 18 cm, whereas the northeast side of the cone uplifted 12 cm in almost two years. The observed ground deformation after the eruption can be explained by 4 m of tensile opening along a dipping rectangular tensile dislocation buried in an elastic half-space, approximately 400 m below sea level.     

Evaluation of geology and structure of eastern Harda-Barwah basin,Madhya Pradesh using remote sensing techniques with field checks in crucial sections

The geological studies of the eastern parts of Harda-Barwah basin covering about 3000 sq. km area on 1∶50,000 scale have been carried out using satellite imagery of IRS (FCC), supplemented by Landsat TM/MSS scences and air photos. The present studies with the aid of satellite imagery and remote sensing techniques have brought out the regional stratigraphy and structure of the area and helped in deciphering the interrelationship of various rock groups in the Narmada lineament zone. The area comprises Archaean/Proterozoic gneisses, the Mahakoshal volcano sedimentary suite, the Harda granitoids, the Bijawar Group and the rocks of Vindhyan Supergroup, Deccan Trap and Quaternary sediments. The Archaean/Proterozoic rocks comprising gneisses and schist form basement. The Mahakoshal Group of rocks occur along the ENE-WSW to E-W trending Narmada lineament zone and comprise foliated quartzite, actinolite-chlorite-sericite phyllite and metabasics. The Harda granitoids showing intrusive relationship with earlier rocks, comprise coarse-grained crudely foliated grey to pink granites, fine to medium grained homogeneous granites and porphyritic granites. The Bijawar Group comprises quarzite, chert breccia and dolomite in the order of younging and shows unconformable relationship with the Mahakoshal and the Harda granitoids. The Bijawar rocks show doubly plunging major synformal structure and numerous sympathetic minor folds along ENE-WSW to E-W axis and cross folds along NW-SW axis. The Bijawar rocks are unconformably overlain by a sequence of sandstone and shales belonging to the Rewa Group of the Vindhyan Supergroup. The Deccan Trap lava flows represent the last igneous activity in the area. The Quaternary deposits comprising a cyclic sequence of sand, silt, clay and rock gravels of the Narmada river represent the youngest formation of the area.     

Mapping alteration mineral zones and lithological units in Antarctic regions using spectral bands of ASTER remote sensing data

Abstract

Geological mapping is one of the primary tasks of remote sensing. Remote sensing applications are especially useful when extreme environmental conditions inhibit direct survey such as in Antarctica. In this investigation, a satellite-based remote sensing approach was used for mapping alteration mineral zones and lithological units using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data in the Oscar II coast area, north-eastern Graham Land, Antarctic Peninsula. Specialized band ratios and band combinations were developed using visible and near infrared, shortwave infrared (SWIR) and thermal infrared spectral bands of ASTER for detecting alteration mineral assemblages and lithological units in Antarctic environments. Constrained Energy Minimization, Orthogonal Subspace Projection and Adaptive Coherence Estimator algorithms were tested to ASTER SWIR bands for detecting sub-pixels’ abundance of spectral features related to muscovite, kaolinite, illite, montmorillonite, epidote, chlorite and biotite. Results indicate valuable applicability of ASTER data for Antarctic geological mapping.     

应用GPS速度场和地震数据反演中国大陆构造应变场

为获得更准确的中国大陆地壳应力应变场,文章基于ITRF2005全球地心坐标参考框架,采用高精度数据处理方法计算了中国地壳运动观测网络1999—2009年间的观测数据,获得了中国大陆GPS速度场以及相对欧亚板块的区域形变场,计算结果显示中国大陆形变运动仍是西强东弱的格局;并利用中国大陆同期近30年的地震矩张量及第四纪活动断层研究成果资料,联合反演了中国大陆区域的构造应变场,结果表明:从总体来看,应变相对集中的区域为喜马拉雅构造带、青藏高原、帕米尔高原、阿尔金断裂带、新疆天山及川滇地区,中国大陆区域过去10年的构造变化以挤压为主,拉张为辅。     

Estimation of aboveground biomass and carbon in a tropical rain forest in Gabon using remote sensing and GPS data

The knowledge of biomass stocks in tropical forests is critical for climate change and ecosystem services studies. This research was conducted in a tropical rain forest located near the city of Libreville (the capital of Gabon), in the Akanda Peninsula. The forest cover was stratified in terms of mature, secondary and mangrove forests using Landsat-ETM data. A field inventory was conducted to measure the required basic forest parameters and estimate the aboveground biomass (AGB) and carbon over the different forest classes. The Shuttle Radar Topography Mission (SRTM) data were used in combination with ground-based GPS measurements to derive forest heights. Finally, the relationships between the estimated heights and AGB were established and validated. Highest biomass stocks were found in the mature stands (223 ± 37 MgC/ha), followed by the secondary forests (116 ± 17 MgC/ha) and finally the mangrove forests (36 ± 19 MgC/ha). Strong relationships were found between AGB and forest heights (R² > 0.85).     

Integration of remote sensing,county-level census,and machine learning for century-long regional cropland distribution data reconstruction

The Lower Mississippi Alluvial Valley (LMAV) was home to about ten million hectare bottomland hardwood (BLH) forests in the Southern U.S. It experienced over 80 % area loss of the BLH forests in the past centuries and large-scale afforestation in recent decades. Due to the lack of a high-resolution cropland dataset, impacts of land use change (LUC) on the LMAV ecosystem services have not been fully understood. In this study, we developed a novel framework by integrating the machine learning algorithm, county-level agricultural census, and satellite-based cropland products to reconstruct the LMAV cropland distribution during 1850–2018 at a 30-m resolution. Results showed that the LMAV cropland area increased from 0.78 × 10⁴ km² in 1850 to 6.64 × 10⁴ km² in 1980 and then decreased to 6.16 × 10⁴ km² in 2018. Cropland expansion rate was the largest in the 1960s (749 km² yr⁻¹) but decreased rapidly thereafter, whereas cropland abandonment rate increased substantially in recent decades with the largest rate of 514 km² yr⁻¹ in the 2010s. Our dataset has three notable features: (1) the depiction of fine spatial details, (2) the integration of the county-level census, and (3) the inclusion of a machine-learning algorithm trained by satellite-based land cover product. Most importantly, our dataset well captured the continuous increasing trend in cropland area from 1930–1960, which was misrepresented by other cropland datasets reconstructed from the state-level census. Our dataset would be important to accurately evaluate the impacts of historical deforestation and recent afforestation efforts on regional ecosystem services, attribute the observed hydrological changes to anthropogenic and natural driving factors, and investigate how the socioeconomic factors control regional LUC pattern. Our framework and dataset are crucial to developing managerial and policy strategies for conserving natural resources and enhancing ecosystem services in the LMAV.