High-resolution Google Earth Images Research Articles

Evaluating socio-hydrological stress through potential stormwater harvesting sites using multi-criteria analysis in Mumbai, India

Estimating stormwater harvesting (SWH) is a significant characteristic of enhancing regional water obtainability and thereby preserving water resources. The foremost aim of this study was to approximate suitable SWH locations in Mumbai using several physical and geomorphic parameters by employing the Geographical Information System (GIS) based Analytical Hierarchy Process (AHP) method. Several observed and remotely sensed datasets were used to compute SWH after assigning the share of influence at GIS platform. Numerous physical attributes along with geomorphic features are taken into consideration and further reclassified and allocated ranks to produce the SWH map in Mumbai. The study indicates most suitable portion for SWH is associated with northwestern side of Mumbai along with barren land and vegetation cover of around 226 sq. km area. However, highly dense urban areas (182.5 sq. km) in central Mumbai experienced with low suitable SWH zone. Additionally, LULC-wise investigation exhibited that barren and wetlands are the most suitable part for SWH, averaging 62.7 sq.km, however, nearly coastal areas including central Mumbai, and some open urban spaces are also potentially suitable for SWH. Moreover, flood probability-wise SWH site demonstrates maximum suitability near the upper parts of coastal Mumbai (223.3 sq.km), while 82.9 sq.km area witnessed flood possibility. High-resolution Google Earth images were used to overlap the outcome of SWH in Mumbai. Moreover, the SWH sites were further cross-correlated with regional flood susceptibility, which exposed extreme north and northeastern parts of Mumbai and is eventually essential to yield SWH sites to diminish flood hazards and substantial water availability.

Assessing Industrial Past and Cultural Changes in Industrial Lands along the Hangzhou Section of the Grand Canal in China

The Grand Canal has been fundamental in supporting Chinese industrialization, as evidenced in the great changes witnessed across industrial lands, but few studies have approached this issue from a cultural perspective. After an extensive industrial recession in the early 21st century, canal cities like Hangzhou have seen changes in the cultural functions of industrial lands, relocating, transforming, and demolishing factories and other constructions to develop new cultural spaces and products. A deep analysis of these cultural changes is, therefore, quite helpful to appreciate the features of and reasons for Chinese national policy setting in the cultural sections of the Grand Canal, such as the UNESCO World Heritage-recognized purpose of the Grand Canal, etc. Therefore, by focusing on the Hangzhou section of the Canal, and via the integration of high-resolution Google Earth images with a 1.5 spatial resolution, we dynamically monitored the land use of the 10 specifically selected industrial lands and interpretated the various uses of the areas from 2000 to 2020. Then, with archival data from 1950 to 2024 and fieldwork conducted over 1 year, and in consultation with major experts, we designed a mark sheet with factors and scores to comprehensively assess cultural changes in those lands. Consequently, the history of industrialization along the Hangzhou section was thoroughly analyzed with these investigations and data, and the reasons for and influence of cultural changes were presented in detail; subsequently, the outstanding problems were addressed, and suggestions were offered. This study contributes to a better understanding of Chinese cultural policy regarding cities along the Canal after the industrial decline, especially the critical implication of cultural loss within these cities. In addition, it also provides novel perspectives for cultural research, heritage conservation, and urban planning of canals or waterfront spaces for world heritage.

Implications of Accuracy of Global Glacier Inventories in Hydrological Modeling: A Case Study of the Western Himalayan Mountain Range

Alpine glaciers are a fundamental component of the cryosphere and are significantly sensitive to climate change. One such region is the Hindukush Karakoram Himalaya (HKH) and Tibetan Plateau (TP) region, which contains more than 40,000 glaciers. There are more than 12 glacier inventories available covering parts of (or the entire) HKH region, but these show significant uncertainties regarding the extent of glaciers. Researchers have used different glacier inventories without assessing their accuracy. This study, therefore, assessed the implications of the accuracy of global glacier inventories in hydrological modeling and future water resource planning. The accuracy assessment of most commonly used two global glacier inventories (Global Land Ice Monitoring from Space-GLIMS v 2.0 and Randolph Glacier Inventory-RGI v 6.0) has been carried out for three sub-basins of the Upper Indus Basin—the Swat, the Chitral, and the Kabul River basins (combined, this is referred to as the Great Kabul River Basin)—with a total basin area of 94,552.86 km2. Glacier outlines have been compared with various Landsat 7 ETM+, Landsat 8, high-resolution Google Earth images, and manually digitized debris-covered glacier outlines during different years. The total glacier area for the Great Kabul River Basin derived from RGI and GLIMS is estimated to be 2120.35 km2 and 1789.94 km2, respectively, which was a difference of 16.9%. Despite being sub-basins of the Great Kabul River Basin, the Swat, and the Chitral River basins were different by 54.74% and 19.71%, respectively, between the two inventories, with a greater glacierized area provided by RGI, whereas the Kabul River basin was different by 54.72%, with greater glacierized area provided by GLIMS. The results and analysis show that GLIMS underestimates glacier outlines in the Swat and the Chitral basins and overestimates glacier extents in the Kabul River basin. The underestimation is mainly due to the non-representation of debris-covered glaciers. The overestimation in GLIMS data is due to the digitization of seasonal snow as part of the glaciers. The use of underestimated GLIMS outlines may result in 5–10% underestimation of glacier-melt contribution to flows in the Swat River basin, while an underestimation of 7% to 15% is expected in the Chitral River Basin, all compared to RGI v 6.0 outlines. The overestimation of glacier-melt contribution to flows in the Kabul River basin is insignificant (1% to 2%) using GLIMS data. In summary, the use of the GLIMS inventory will lead to underestimated flows and show that the Great Kabul River Basin (particularly the Chitral River Basin) is less sensitive to climate change effects. Thus, the current study recommends the use of RGI v 6.0 (best glacier inventory) to revisit the existing biased hydro-climate studies and to improve future hydro-climate studies with the concomitant rectification of the MODIS snow coverage data. The use of the best glacier inventory will provide the best estimates of flow sensitivity to climate change and will result in well-informed decision-making, precise and accurate policies, and sustainable water resource management in the study area. The methodology adopted in the current study may also be used in nearby areas with similar hydro-climate conditions, as well as for the most recently released RGI v 7.0 data.

STF-Trans: A two-stream spatiotemporal fusion transformer for very high resolution satellites images

Spatiotemporal satellite image fusion is regarded as an effective approach to address the limitations of a single optical sensor and generate data with high spatial and temporal resolutions. Thanks to the recent advances of Deep Learning, spatiotemporal fusion has gained a significant performance improvement. However, most models require at least three inputs, including a high resolution one at a prior date, to make the prediction at the desired date. Such a requirement is not always guaranteed in practice, in particular due to the high cost of high resolution image and bad atmospheric conditions. In the last few years, Transformers have achieved tremendous performance on several computer vision tasks compared to convolutional neural networks. Inspired by the new trend, we proposed an end-to-end two-stream SpatioTemporal fusion technique based on encoder–decoder transformer architecture, termed STF-Trans. To deal with a complexity and high-cost of data preparation, the proposed approach aims to fuse a coarse-resolution image with high temporal resolution, and a very high resolution Google Earth image, to produce a very high resolution image at the desired date. The proposed technique employs a two-stream convolutional network to capture both temporal and spatial features from the inputs, and then the encoder–decoder transformer captures long-range dependency of the latter and generates a more efficient image representation from the shallow features. Finally, a shallow network maps the features into the desired fused image. The experimental results show that the proposed method outperforms the current state-of-the-art techniques at both quantitative and qualitative evaluations.

Estimation of Hyrcanian forests coverage using ALOS PALSAR and MODIS satellite data (2007–2017)

This study aims to monitor the extent changes of Hyrcanian forests using ALOS-/PALSAR-1 and ALOS/PALSAR-2 (PALSAR) mosaic data with HH and HV polarimetric bands from 2007 to 2017. To date, no comprehensive studies have been conducted on the total cover area of the Hyrcanian forests. This study attempts to provide specific and local threshold values for to extract the Hyrcanian forests cover through careful examination of satellite data over ten years. In this study, the acquired 57 PALSAR images were calibrated and converted to γ0 (dB) for the years 2007, 2010, and 2017. Then, the backscattering value ranges of the Hyrcanian forests were determined using the Decision Tree classification method. The backscattering values range from −14.5 to −7.2 dB and −17.5 to −8.5 dB for the years 2007 and 2010 and −18.5 to −7.5 dB for 2017 in the HV-band and from −7.5 to −14.5 dB for 2007 and 2010 and −6.2 to −12.5 dB for 2017 in the HH band correspond to forest. Finally, the results were compared with Japanese Aerospace Exploration Organization (JAXA) forest and non-forest (F/NF) maps from PALSAR data for validation purposes; the overall accuracies and Kappa coeffitients were 92.55%, 0.82% (2007), 91.14%, 0.79% (2010), and 88.19%, 0.74 (2017), respectively. This result shows that values between −7.2 and −13.5 dB are the best range for the Hyrcanian forests’ extent extraction from PALSAR image in the HV band. The output were further validated using three independent sources: (1) High-Resolution Google Earth (GE) image; (2) F/NF maps from the JAXA; and (3) MOD13Q1 Products (250 m spatial resolution). The results show good consistency with the mentioned datasets.

Genesis and spatio-temporal analysis of glacial lakes in the peri-glacial environment of Western Himalayas

Glaciers are retreating in the highest mountainous regions of the world, like Himalayan region as a result of climate change and global warming. This leads to the formation of different types of glacial lakes. These lakes are not only the source of fresh water but it also causes disaster in the form of Glacial Lake Outburst Flood (GLOF). Astore Drainage Basin is located in north eastern mountainous region of Himalayas. This area is prone to GLOFs because of the increasing number of glacial lakes and the growth of existing lakes as a result of global warming. To provide a detailed information about the spatial and temporal information of glacial lakes detailed inventories has been developed for the study area using Landsat images for the year 1989, 1999, 2009 and 2019. Glacial lakes were mapped and identified by using Normalized Different Water Index, Normalized Difference Snow Index and high-resolution Google Earth images. It was found from the analysis that the number of the glacial lakes increased from 120 to 128 in a period of thirty years (i.e., from 1989 to 2019). During the study period two lakes disappeared whereas ten new lakes were formed. There were 21 lakes which show area expansion more than 100% representing high susceptibility for GLOF. The results also showed that smaller lakes expanded more rapidly in area than the larger lakes.

A dataset of desertification distribution in Mongolia in 2015

Desertification is one of the most serious eco-environmental and socio-economic problems in the world. Mongolia is a hot area of global desertification because of its fragile ecological environment and serious land degradation. In this study, based on Google Earth Engine platform, we selected Landsat7 remote sensing images, normalized vegetation index (NDVI), surface albedo (Albedo), improved soil adjusted vegetation index (MSAVI) and topsoil grain size index (TGSI) as desertification discrimination indexes, and combined the geographical division with desertification inversion characteristic space models to complete the fine inversion of Mongolian desertification information. In this way, we obtained a dataset of desertification distribution in Mongolia in 2015. The quality and accuracy of this dataset are verified by referring to field survey data and high-resolution Google Earth images. The overall evaluation accuracy is 87.00% and the Kappa coefficient is 83.19%. This dataset directly reflects the spatial distribution of different degrees of desertification in Mongolia, and can provide detailed and reliable data support for the delineation of key areas for desertification control and the formulation of restoration strategies in Mongolia. It is of great significance for the ecological environment and green and sustainable development of the China-Mongolia-Russia Economic Corridor.

A dataset of land cover classifications with a spatial resolution of 30m in Mongolia in 2005 and 2015

The Mongolian Plateau is in the interior of Northeast Asia, and is extremely vulnerable to climate change and the deleterious effects of human activities. Mongolia is an important component unit of the Mongolian Plateau, and its resources, environment and ecological problems are closely related to the ecological barrier and resource security in northern China and the sustainable development of the China-Mongolia-Russia Corridor. However, there is still a lack of high-precision land cover data products suitable for the regional characteristics of Mongolia. In this study, according to the landscape pattern of Mongolia, we constructed a land cover classification system suitable for Mongolia; and based on the object-oriented remote sensing interpretation method, we adopted the split-scene interpretation to select a variety of indexes. According to certain rules and classification thresholds, we obtained a dataset of land cover classifications with a spatial resolution of 30m in Mongolia in 2005 and 2015. The land cover classifications of Mongolia includes 11 categories: forest, meadow steppe, real steppe, desert steppe, bare land, sand, desert, ice and snow, water, cropland and built areas. Based on multi-source validation point information and high-resolution Google Earth images, we completed an overall quality assessment and a single classification quality assessment of land cover classification results in Mongolia. In 2005, the overall classification accuracy is 78.85% and the Kappa coefficient is 0.77. In 2015, the overall classification accuracy is 80.49% and the Kappa coefficient is 0.78. The average annual classification accuracy is 79.67%, which meets the accuracy requirements. The dataset can directly reflect the changes of land cover pattern and trend in Mongolia and provide basic scientific data to support the sustainable development of Mongolia.

Modeling and Assessment of Land Degradation Vulnerability in Arid Ecosystem of Rajasthan Using Analytical Hierarchy Process and Geospatial Techniques

Wind erosion is a major natural disaster worldwide, and it is a key problem in western Rajasthan in India. The Analytical Hierarchy Process (AHP), the Geographic Information System (GIS), and remote sensing satellite images are effective tools for modeling and risk assessment of land degradation. The present study aimed to assess and model the land degradation vulnerable (LDV) zones based on the AHP and geospatial techniques in the Luni River basin in Rajasthan, India. This study was carried out by examining important thematic layers, such as vegetation parameters (normalized difference vegetation index and land use/land cover), a terrain parameter (slope), climatic parameters (mean annual rainfall and land surface temperature), and soil parameters (soil organic carbon, soil erosion, soil texture, and soil depth), using the Analytical Hierarchical Process (AHP) and geospatial techniques in the Luni River basin in Rajasthan, India. The weights derived for the thematic layers using AHP were as follows: NDVI (0.27) > MAR (0.22) > LST (0.15) > soil erosion (0.12) > slope (0.08) > LULC (0.06) > SOC (0.04) > soil texture (0.03) > soil depth (0.02). The result indicates that nearly 21.4 % of the total area is prone to very high degradation risks; 12.3% is prone to high risks; and 16%, 24.3%, and 26% are prone to moderate, low, and very low risks, respectively. The validation of LDV was carried out using high-resolution Google Earth images and field photographs. Additionally, the Receiver Operating Characteristic (ROC) curve found an area under the curve (AUC) value of 82%, approving the prediction accuracy of the AHP technique in the study area. This study contributes by providing a better understanding of land degradation neutrality and sustainable soil and water management practices in the river basin.

Individual Tree Species Classification Based on a Hierarchical Convolutional Neural Network and Multitemporal Google Earth Images

Accurate and efficient individual tree species (ITS) classification is the basis of fine forest resource management. It is a challenge to classify individual tree species in dense forests using remote sensing imagery. In order to solve this problem, a new ITS classification method was proposed in this study, in which a hierarchical convolutional neural network (H-CNN) model and multi-temporal high-resolution Google Earth images were employed. In an experiment conducted in a forest park in Beijing, China, GE images of several significant phenological phases of broad-leaved forests, namely, before and after the mushrooming period, the growth period, and the wilting period, were selected, and ITS classifications based on these images along with several typical CNN models and the H-CNN model were conducted. In the experiment, the classification accuracy of the multitemporal images was higher by 7.08–12.09% than those of the single-temporal images, and the H-CNN model offered an OA accuracy 2.66–3.72% higher than individual CNN models, demonstrating that multitemporal images rich in the phenological features of individual tree species, together with a hierarchical CNN model, can effectively improve ITS classification.

Use of High-Resolution Multi-Temporal DEM Data for Landslide Detection

Landslides in urban areas have been relatively well-documented in landslide inventories despite issues in accuracy and completeness, e.g., the absence of small landslides. By contrast, less attention has been paid to landslides in sparsely populated areas in terms of their occurrences and locations. This study utilizes high-resolution and LiDAR-derived digital elevation models (DEMs) at two different times for landslide detection to (1) improve the localization and detection accuracies in landslide inventories, (2) minimize human intervention in the landslide detection process, and (3) identify landslides that cannot be easily documented in the current state of the practice. To achieve this goal, multiple preprocessing steps were used to ensure the spatial alignment of the multi-temporal DEMs. Map algebra was then used to calculate the vertical displacement for each cell and create a DEM of Difference (DoD) to obtain a quantitative estimation of ground deformations. Next, the elevation changes were filtered via an appropriate Level of Detection (LoD) threshold to mark potential landslide candidates. The landslide candidates were further assessed with the aid of customized topographic maps as auxiliary data and pattern recognition to distinguish landslides (true positive changes) from construction, erosion, and deposition (false positives). The results from the proposed method were compared with existing landslide inventories and reports to evaluate its performance. The new method was also validated with temporal high-resolution Google Earth images. The results showed the successful application of the method in landslide detection and mapping. Compared with traditional methods, the proposed method provides a semi-automatic way to obtain landslide inventories with publicly available yet lowly utilized DEM data, which can be valuable in preliminary analysis for landslide detection.

Delineation and evaluation of the captive limestone mining area change and its influence on the environment using multispectral satellite images for industrial long-term sustainability

Dynamics in Limestone mining sites are usually subjected to spatiotemporal land cover change, which has a long-term impact on the land and environment. The objectives of this article are mapping demarcation, and transformation assessment of active captive limestone mines and their impact assessment on the environment at the Yerraguntla cement industrial region in YSR Kadapa district, Andhra Pradesh, India from the year 2006–2019 using Remote Sensing & Geographical Information System (RS&GIS). In this research work, first, the Normalized Differential Vegetation Index (NDVI) is computed, second NDVI image is resampled using NDVI threshold values, and the Nearest-Neighbor sampling method uses the QGIS raster calculator, which produces the mines area of 469.92 ha with Sentinel-2A MSI and 555.39 ha with Landsat-8(OLI) which is evaluated with delineated high resolution (∼1m/pixels) Google Earth image (486.47 ha) from Google Earth Pro (freeware) and industry reports (487.10 ha) in 2019. Third active mines are demarcated using NDVI breakpoints. Results found that the limestone Mining area increased by 206.10% from 2006 to 2019, with Producer's Accuracy (PA), User's Accuracy (UA), and Kappa coefficients of 98.82%, 92.79%, and 0.92 in 2019, and 96.74%, 85.17 %t, and 0.82 in 2006. The compatibility between Landsat-8 and Sentinel-2A NDVI images is determined with Pearson's correlation coefficient (PCC) of 0.84 in 2019. In 2019, a linear negative Regression slope was revealed between NDVI & LST with a Correlation coefficient of 0.248, indicating negative environmental influences and vegetation control on LST. This research will aid industry and environmental engineers in comprehending the effects of limestone mine changes on LST and proposing an environmentally acceptable land-environmental management plan for long-term sustainability.

Performance study of imageries from MODIS, Landsat 8 and Sentinel-2 on measuring shoreline change at a regional scale

In monitoring shoreline change, remote sensing data plays a pivotal role. Although different freely accessible satellite data are used in studies of shoreline change at various scales, it is imperative to identify their effectiveness at a regional scale. This study aims to determine how effectively MODIS, Landsat 8, and Sentinel-2 satellite images measure regional shoreline change. Digital Shoreline Analysis System (DSAS), an ArcGIS extension, was utilized to analyze the changes in the shoreline of Ganges deltaic coast in Bangladesh. The Shoreline Change Envelope (SCE) technique was applied to compare the shorelines extracted from MODIS, Landsat 8, and Sentinel-2 for the year 2016 with high-resolution Google Earth image. Next, using the End Point Rate (EPR) method, shoreline change rates between 2016 and 2019 were calculated separately from each satellite data. The study points out which satellite data was more accurate in delineating shoreline. Due to a higher spatial resolution, Sentinel-2 data were more effective than Landsat and MODIS data at a regional scale. In addition, MODIS, Landsat 8, and Sentinel-2 data indicate that the Ganges deltaic coast experiences landward movements of 54.65 m/yr, 12.56 m/yr, and 1.79 m/yr, respectively. Hence, it can be concluded that if the spectral resolution is coarse, shoreline change is overestimated. Therefore, in order to analyze or predict shoreline change (even at a regional scale), priority should be given to using images with fine spatial resolution. The spectral analysis of MODIS, Landsat and Sentinel showed a similar pattern of separability among the land cover classes observed along the Ganges deltaic coast. The separability is higher in NIR (Near Infrared) and SWIR (Short Wave Infrared) bands. This study will direct researchers in the selection of remote sensing data sources for shoreline change detection within a regional scale.

Effect of Brick Field on Riverine Morpho-hydrological Environment: Study in Kopai River of South Bengal Plateau Fringe

Brick field is one of the important seasonal industrial activities in India but it has an antithetic impact on river. The majority of brick fields are of recent origin and situated along the bank of the lower catchment area. Nowadays, the rivers are adversely affected by increasing pollution and sedimentation, dumping of ashes, extraction of sand from the river bed and hinder the natural flow of the river. The present study is undertaken to assess the impact of the riparian brick field on geomorphic responses of hydraulic variables, planform change in different segments of Kopai River during 1990 to 2018 and spatial variation of river water quality. Location of riparian brick fields have been identified with the help of satellite imageries of different time periods (1990 to 2018), high-resolution Google Earth image and verified by GPS during field survey. The channel was divided into three segments to quantifying the geomorphic responses and changing planform indices. The study denotes that brick field activities lead to reduce the channel meander as well as decrease the sinuosity index and indicates a decreasing tendency of the braiding index as well as deteriorating the river health.

Phenology-based cropland retirement remote sensing model: a case study in Yan’an, Loess Plateau, China

ABSTRACT Cropland retirement is a widespread phenomenon across the world. The conversion of inefficient cropland to forest or grassland is a policy aimed at restoring ecology, improving the environment, and promoting economic development. However, in most developing countries, the results of cropland retirement and land restoration are characterized by spatial fragmentation, and there are significant temporal differences as a result of poor agricultural intensification, human interference, and regional environmental differences. This substantially increases the difficulty of information extraction and reduces the extraction accuracy of remote sensing methods. In this paper, we developed a new phenology-based cropland retirement remote sensing (PCRRS) model to detect the extent and timing of cropland retirement. Considering the characteristic growth of crops, the normalized difference vegetation index (NDVI), at the start, middle, and end of the growth cycle, is the phenological metric to distinguish cropland from other vegetation types. In addition, the interannual variation of phenological metrics are significant after cropland retirement, which is the key to effectively identify retired cropland. High-resolution Google Earth images were used to verify the accuracy of the algorithm. The results suggested that the overall accuracy of our algorithm exceeded 85%, and was more suitable for sloping cropland. In comparison with other cropland retirement extraction methods, the PCRRS model had high sensitivity and stability. We found it was common for sloping cropland to be retired earlier, and we also identified the existing inter-planting phenomena between crops and shrubs in areas with gentle slopes. Overall, this study provided a basis for understanding the drivers of cropland retirement and evaluating their environmental effects.

Long-term detection and spatiotemporal variation analysis of open-surface water bodies in the Yellow River Basin from 1986 to 2020

Accurately investigating long-term information about open-surface water bodies can contribute to water resource protection and management. However, due to the limits of big-data calculations for remote sensing, there has been no specific study on the long-term changes in the water bodies in the Yellow River Basin. Thus, in this study, we developed a new combined extraction rule to build an entire annual-scale open-surface water body dataset for 1986–2020 with excellent effectiveness in eliminating the interference of shadows in the Yellow River Basin using all of the available Landsat images. For the first time, the spatial distribution, change trends, conversion processes, and the heterogeneity of the surface water bodies in the Yellow River Basin were analyzed comprehensively to the best of our knowledge. The extraction results had an overall accuracy of 99.70 % and a kappa coefficient of 0.90, which were validated using 34,073 verification points selected on high-resolution Google Earth images and random Landsat images. The total area of water bodies initially decreased (1986–2000) and then increased (2001−2020); however, only the size of the permanent water bodies increased in most areas, while the size of most of the seasonal water bodies decreased. In regions with human-made water bodies, the non-water areas were substantially converted to seasonal and permanent water bodies; however, in areas with natural water bodies, many permanent and seasonal water bodies were gradually converted to non-water areas. Thus, most of the increases in the water bodies occurred in the form of artificial lakes and reservoirs, while most of the decreases in the water body area occurred in natural wetlands and lakes. The areas of both the permanent and seasonal water bodies were positively correlated with precipitation, but only the area of the seasonal water bodies was negatively correlated with temperature.

A global forest reference set with time series annual change information from 2000 to 2020

ABSTRACT Mapping and monitoring forest with time-series remote sensing methodologies requires reference data constructed in a sampling framework. Few worldwide reference data sets with time-series information are currently available for large areas. We produced a global forest reference set at 30 m with time-series information from 2000 to 2020 based on a stratified random sampling scheme. All available Landsat, high-resolution Google Earth images and other relative land cover/land change products were used in a visual interpretation approach. This reference dataset contains 10339 sample units (6252 persisting forest sample units, 2049 change sample units and 2038 persisting non-forest sample units) attributed with annual forest/non-forest information from 2000 to 2020. The results of our analysis highlight many cases of undetected forest change due to definition and other factors, and suggest that change of forest canopy cover could be monitored instead of land cover types. The reference set can be potentially used for many large-area applications such as validation of forest mapping projects or as auxiliary data for forest monitoring analysis. And this reference set will be available online (https://doi.org/10.5281/zenodo.5524258).

Alpine Treeline Dynamics and the Special Exposure Effect in the Hengduan Mountains.

Alpine treeline is highly sensitive to climate change, but there remains a lack of research on the spatiotemporal heterogeneity of treeline and their relationships with climate change at the landscape scale. We extracted positions of alpine treeline from high-resolution Google Earth images from three periods (2000, 2010, and 2020) and analyzed the elevation patterns and dynamics of treeline positions in the Hengduan Mountains. Based on the treeline positions in 2020, a buffer zone of 300 m is established as the treeline transition zone, and the changing trend of the fraction vegetation cover (FVC) from 2000 to 2020 and its relationship with climate are also analyzed. Due to the special geographical and climatic environment, the treeline in the Hengduan Mountains area is high in the middle but lower in the surrounding areas. We found that over the past 20 years, the treeline position did not change significantly but that the FVC increased in 80.3% of the treeline areas. The increase in FVC was related to the decrease in precipitation in the growing season. The results also revealed a special exposure effect on the alpine treeline in the Hengduan Mountains. Because of the lower treeline, isotherm position caused by the monsoon climate, the treeline position on south-facing slopes is lower than that on slopes with other exposures. Our results confirmed that the pattern and dynamics of the alpine treeline are driven by the regional monsoon climate regime.

Influence of geological conditions on gully distribution in the Dry–hot Valley, SW China

Geological conditions are important in the initiation and expansion of a gully. In order to explore the relationship between spatial distribution of gullies and the geological conditions, the methods of geospatial statistics and correlation analysis were used based on interpretations of high-resolution Google Earth images in the Dry–hot Valley (SW China). The results showed that the gullies had a clustering pattern in the study area (Moran’s I = 0.89, P < 0.01). The correlation between geological conditions (lithology and faults) and clustering characteristics of gullies was strong in lithology (F(6) = 139.75, p < 0.01, η2 = 0.184) and irrelevant in faults (F(2) = 0.64, p > 0.05, η2 = 0.001). The resistance to erosion of a particular lithology had a negative correlation with the degree of gully clustering. The lithologies of sandstones, mudstones and siltstones, loose quaternary sediments, of phyllite and schist were conducive to gully development; however, the conglomerate, quartzite and marble, and granite may led to low gully clustering. The results will help to understand the law of spatial distribution of gullies at the regional scale and also provide a scientific reference for their controls.

Continuous Change Detection and Classification—Spectral Trajectory Breakpoint Recognition for Forest Monitoring

Forest is one of the most important surface coverage types. Monitoring its dynamics is of great significance in global ecological environment monitoring and global carbon circulation research. Forest monitoring based on Landsat time-series stacks is a research hotspot, and continuous change detection is a novel approach to real-time change detection. Here, we present an approach, continuous change detection and classification-spectral trajectory breakpoint recognition, running on Google Earth Engine (GEE) for monitoring forest disturbance and forest long-term trends. We used this approach to monitor forest disturbance and the change in forest cover rate from 1987 to 2020 in Nanning City, China. The high-resolution Google Earth images are collected for the validation of forest disturbance. The classification accuracy of forest, non-forest, and water maps by using the optima classification features was 95.16%. For disturbance detection, the accuracy of our map was 86.4%, significantly higher than 60% of the global forest change product. Our approach can successfully generate high-accuracy classification maps at any time and detect the forest disturbance time on a monthly scale, accurately capturing the thinning cycle of plantations, which earlier studies failed to estimate. All the research work is integrated into GEE to promote the use of the approach on a global scale.