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An accurate assessment of the spatial–temporal variations in regional net ecosystem productivity (NEP), water use efficiency (WUE), and carbon use efficiency (CUE) are vital for understanding the water–carbon cycle. We analyzed the spatial–temporal patterns of the NEP, WUE, and CUE in the middle reaches of the Yellow River (MRYR) from 2001 to 2022, and the factors that influenced them using remote sensing data, NEP estimation models, and various statistical methods. The results indicate that the recovery of the ecosystem in the MRYR is a result of the combined effects of climate change and human activities. Climate change in the MRYR led to warming and humidification from 2001 to 2022. The NEP, WUE, and CUE were characterized by increasing trends, with average growth rates of 7.75 gC m−2a−1, 0.012 gC m−2 mm−1a−1, and 0.009a−1, respectively. For four vegetation types, the interannual rates of change were, in descending order, grassland, cropland, shrubs, and forest. Spatially, the NEP, WUE, and CUE showed significant regional heterogeneity, increasing from the northwest to the southeast. Based on an analysis of the interannual anomalies, precipitation accumulation contributed to carbon sink accumulation. The correlation of the NEP, WUE, and CUE with the drought severity index (DSI) was high, and their correlation with precipitation showed latitudinal zonality, which suggests that precipitation (PRE) is the main climatic factor influencing the water–carbon cycle in the MRYR rather than temperature (TEM). There were 67,671.27 km2 of land that changed use during 2001–2022, and 15.07 Tg of NEP was added to these areas. [ABSTRACT FROM AUTHOR]

Tropical peatlands in Southeast Asia have experienced widespread subsidence due to forest clearance and drainage for agriculture, oil palm and pulp wood production, causing concerns about their function as a long-term carbon store. Peatland drainage leads to subsidence (lowering of peatland surface), an indicator of degraded peatlands, while stability/uplift indicates peatland accumulation and ecosystem health. We used the Advanced Pixel System using the Intermittent SBAS (ASPIS-DInSAR) technique with biophysical and geographical data to investigate the impact of peatland drainage and agriculture on spatial patterns of subsidence in Selangor, Malaysia. Results showed pronounced subsidence in areas subjected to drainage for agricultural and oil palm plantations, while stable areas were associated with intact forests. The most powerful predictors of subsidence rates were the distance from the drainage canal or peat boundary; however, other drivers such as soil properties and water table levels were also important. The maximum subsidence rate detected was lower than that documented by ground-based methods. Therefore, whilst the APSIS-DInSAR technique may underestimate absolute subsidence rates, it gives valuable information on the direction of motion and spatial variability of subsidence. The study confirms widespread and severe peatland degradation in Selangor, highlighting the value of DInSAR for identifying priority zones for restoration and emphasising the need for conservation and restoration efforts to preserve Selangor peatlands and prevent further environmental impacts. [ABSTRACT FROM AUTHOR]

Recently, as global climate change and local disturbances such as wildfires continue, long- and short-term changes in the high-latitude vegetation systems have been observed in various studies. Although remote sensing technology using optical satellites has been widely used in understanding vegetation dynamics in high-latitude areas, there has been limited understanding of various landscape changes at different spatiotemporal scales, their mutual relationships, and overall long-term landscape changes. The objective of this study is to devise a change monitoring strategy that can effectively observe landscape changes at different spatiotemporal scales in the boreal ecosystems from temporally sparse time series remote sensing data. We presented a new post-classification-based change analysis scheme and applied it to time series Landsat data for the central Yakutian study area. Spectral variability between time series data has been a major problem in the analysis of changes that make it difficult to distinguish long- and short-term land cover changes from seasonal growth activities. To address this issue effectively, two ideas in the time series classification, such as the stepwise classification and the lateral stacking strategies were implemented in the classification process. The proposed classification results showed consistently higher overall accuracies of more than 90% obtained in all classes throughout the study period. The temporal classification results revealed the distinct spatial and temporal patterns of the land cover changes in central Yakutia. The spatiotemporal distribution of the short-term class illustrated that the ecosystem disturbance caused by fire could be affected by local thermal and hydrological conditions of the active layer as well as climatic conditions. On the other hand, the long-term class changes revealed land cover trajectories that could not be explained by monotonic increase or decrease. To characterize the long-term land cover change patterns, we applied a piecewise linear model with two line segments to areal class changes. During the former half of the study period, which corresponds to the 2000s, the areal expansion of lakes on the eastern Lena River terrace was the dominant feature of the land cover change. On the other hand, the land cover changes in the latter half of the study period, which corresponds to the 2010s, exhibited that lake area decreased, particularly in the thermokarst lowlands close to the Lena and Aldan rivers. In this area, significant forest decline can also be identified during the 2010s. [ABSTRACT FROM AUTHOR]

An accurate assessment of the spatial–temporal variations in regional net ecosystem productivity (NEP), water use efficiency (WUE), and carbon use efficiency (CUE) are vital for understanding the water–carbon cycle. We analyzed the spatial–temporal patterns of the NEP, WUE, and CUE in the middle reaches of the Yellow River (MRYR) from 2001 to 2022, and the factors that influenced them using remote sensing data, NEP estimation models, and various statistical methods. The results indicate that the recovery of the ecosystem in the MRYR is a result of the combined effects of climate change and human activities. Climate change in the MRYR led to warming and humidification from 2001 to 2022. The NEP, WUE, and CUE were characterized by increasing trends, with average growth rates of 7.75 gC m−2a−1, 0.012 gC m−2 mm−1a−1, and 0.009a−1, respectively. For four vegetation types, the interannual rates of change were, in descending order, grassland, cropland, shrubs, and forest. Spatially, the NEP, WUE, and CUE showed significant regional heterogeneity, increasing from the northwest to the southeast. Based on an analysis of the interannual anomalies, precipitation accumulation contributed to carbon sink accumulation. The correlation of the NEP, WUE, and CUE with the drought severity index (DSI) was high, and their correlation with precipitation showed latitudinal zonality, which suggests that precipitation (PRE) is the main climatic factor influencing the water–carbon cycle in the MRYR rather than temperature (TEM). There were 67,671.27 km2 of land that changed use during 2001–2022, and 15.07 Tg of NEP was added to these areas.

Tropical peatlands in Southeast Asia have experienced widespread subsidence due to forest clearance and drainage for agriculture, oil palm and pulp wood production, causing concerns about their function as a long-term carbon store. Peatland drainage leads to subsidence (lowering of peatland surface), an indicator of degraded peatlands, while stability/uplift indicates peatland accumulation and ecosystem health. We used the Advanced Pixel System using the Intermittent SBAS (ASPIS-DInSAR) technique with biophysical and geographical data to investigate the impact of peatland drainage and agriculture on spatial patterns of subsidence in Selangor, Malaysia. Results showed pronounced subsidence in areas subjected to drainage for agricultural and oil palm plantations, while stable areas were associated with intact forests. The most powerful predictors of subsidence rates were the distance from the drainage canal or peat boundary; however, other drivers such as soil properties and water table levels were also important. The maximum subsidence rate detected was lower than that documented by ground-based methods. Therefore, whilst the APSIS-DInSAR technique may underestimate absolute subsidence rates, it gives valuable information on the direction of motion and spatial variability of subsidence. The study confirms widespread and severe peatland degradation in Selangor, highlighting the value of DInSAR for identifying priority zones for restoration and emphasising the need for conservation and restoration efforts to preserve Selangor peatlands and prevent further environmental impacts.

Recently, as global climate change and local disturbances such as wildfires continue, long- and short-term changes in the high-latitude vegetation systems have been observed in various studies. Although remote sensing technology using optical satellites has been widely used in understanding vegetation dynamics in high-latitude areas, there has been limited understanding of various landscape changes at different spatiotemporal scales, their mutual relationships, and overall long-term landscape changes. The objective of this study is to devise a change monitoring strategy that can effectively observe landscape changes at different spatiotemporal scales in the boreal ecosystems from temporally sparse time series remote sensing data. We presented a new post-classification-based change analysis scheme and applied it to time series Landsat data for the central Yakutian study area. Spectral variability between time series data has been a major problem in the analysis of changes that make it difficult to distinguish long- and short-term land cover changes from seasonal growth activities. To address this issue effectively, two ideas in the time series classification, such as the stepwise classification and the lateral stacking strategies were implemented in the classification process. The proposed classification results showed consistently higher overall accuracies of more than 90% obtained in all classes throughout the study period. The temporal classification results revealed the distinct spatial and temporal patterns of the land cover changes in central Yakutia. The spatiotemporal distribution of the short-term class illustrated that the ecosystem disturbance caused by fire could be affected by local thermal and hydrological conditions of the active layer as well as climatic conditions. On the other hand, the long-term class changes revealed land cover trajectories that could not be explained by monotonic increase or decrease. To characterize the long-term land cover change patterns, we applied a piecewise linear model with two line segments to areal class changes. During the former half of the study period, which corresponds to the 2000s, the areal expansion of lakes on the eastern Lena River terrace was the dominant feature of the land cover change. On the other hand, the land cover changes in the latter half of the study period, which corresponds to the 2010s, exhibited that lake area decreased, particularly in the thermokarst lowlands close to the Lena and Aldan rivers. In this area, significant forest decline can also be identified during the 2010s.

Coal is the most prevalent energy source in China and plays an important role in ensuring energy security. The continuous monitoring of coal mining activities is helpful to clarify the incremental space of coal production and establish a rational framework for future coal production capacity. In this study, a multi-source remote sensing approach utilizing SPOT 4, GF, and Landsat data is employed to monitor land cover and vegetation changes in the Juhugeng mining area of the Muli coalfield over a span of nearly 20 years. The analysis incorporates an object-oriented classification method and a vegetation parameter to derive insights. The findings reveal that the mining operations can be divided into two periods, since their initiation in 2003 until their cessation in 2021, with a dividing point around 2013/2014. The initial phase witnessed rapid and even accelerated expansion of the mine, while the subsequent phase was characterized by more stable development and the implementation of some restorative measures for the mine environment. Although the vegetation parameter, Fractional Vegetation Cover (FVC), indicates some reclamation efforts within the mining area, the extent of the reclaimed land remains limited. This study demonstrates the effective application of object-oriented classification in conjunction with the vegetation parameter FVC for monitoring coal mining areas. [ABSTRACT FROM AUTHOR]

The pathway, direction, and potential drivers of the evolution in global arid ecosystems are of importance for maintaining the stability and sustainability of the global ecosystem. Based on the Climate Change Initiative Land Cover dataset (CCILC), in this study, four indicators of land cover change (LCC) were calculated, i.e., regional change intensity (RCI), rate of change in land cover (CR), evolutionary direction index (EDI), and artificial change percentage (ACP), to progressively derive the intensity, rate, evolutionary direction, and anthropogenic interferences of global arid ecosystems. The LCC from 1992 to 2020 and from 28 consecutive pair-years was observed at the global, continental, and country scales to examine spatiotemporal evolution in the Earth's arid ecosystems. The following main results were obtained: (1) Global arid ecosystems experienced positive evolution despite complex LCCs and anthropogenic interferences. Cautious steps to avoid potential issues caused by rapid urbanization and farmland expansion are necessary. (2) The arid ecosystems in Australia, Central Asia, and southeastern Africa generally improved, as indicated by EDI values, but those in North America were degraded, with 41.1% of LCCs associated with urbanization or farming. The arid ecosystems in South America also deteriorated, but 83.4% of LCCs were in natural land covers. The arid ecosystems in Europe slightly improved with overall equivalent changes in natural and artificial land covers. (3) Global arid ecosystems experienced three phases of change based on RCI values: 'intense' (1992–1998), 'stable' (1998–2014), and 'intense' (2014–2020). In addition, two phases of evolution based on EDI values were observed: 'deterioration' (1992–2002) and 'improvement' (2002–2020). The ACP values indicated that urbanization and farming activities contributed increasingly less to global dryland change since 1992. These findings provide critical insights into the evolution of global arid ecosystems based on analyses of LCCs and will be beneficial for sustainable development of arid ecosystems worldwide within the context of ongoing climate change. [ABSTRACT FROM AUTHOR]

Hefei's gross domestic product (GDP) growth rate ranks first among all cities in China, and it was the fastest-growing city in China from 2000 to 2020. The high-speed economic development inevitably led to rapid changes in land cover types, e.g., an increment in artificial features (built-up) and decrement in natural features (vegetation). However, (1) many previous studies focus on the land cover change in metropolis or at the global scale, yet few focus on underdeveloped but fast-growing cities; (2) land cover studies mainly focus on global variations, yet seldom on local characteristics. Thus, it is of great significance to monitor the land cover change for the city with the fastest economic growth in China based on the long time-series satellite images from both global and local perspectives. In this study, with support from huge amounts of data (including 719 Landsat TM/ETM+/OLI satellite images, land surface temperature, nighttime satellite images, DEM, multiple land cover products, and various auxiliary data), processing and parallel computing abilities of the GEE platform, classification maps of land cover in Hefei from 2000 to 2020 are produced based on a random forest machine learning method, and the spatio-temporal variations and driving factors are analyzed from both global and local viewpoints. The results show that: (1) the classification accuracy is excellent; the average overall accuracy is 93% and the Kappa coefficient is 0.88; (2) the general spatio-temporal variations in land cover in Hefei from 2000 to 2020 are obvious; the built-up area expanded from 419.72 km2 to 1530.20 km2, with a total growth rate of 264.58%. With the expansion of the built-up area, the vegetation coverage decreased by 16.61% (1652.56 km2); (3) the land surface temperature shows an increment trend in the new town yet a decrement trend in the old town due to the change in vegetation coverage and the decentration of administration centers; further analysis shows that the population and the social economy are two driving factors for land cover changes. It is worth noting that both the area and coverage of vegetation in the old town and water body area in Hefei increased significantly, although the fast urbanization inevitably caused a decrement in vegetation and water area in the whole city, indicating both the high-speed economic development and improvement in green surfaces simultaneously experienced in Hefei from 2000 to 2020. [ABSTRACT FROM AUTHOR]

Since the 1990s, Morocco's agriculture has been characterized by the co-existence and transformation of both modern and traditional smallholder systems. In the Atlas Mountains, the effects of rural–urban transformation have led to intensified irrigated agriculture in some agricultural areas, while others were abandoned. To better understand these effects, this study aimed at (1) analyzing the land use and land cover (LULC) changes, (2) assessing the structure and dynamics of vegetation, and (3) comparing a Support Vector Machine (SVM) classification approach with a seasonal rules-based approach. We, therefore, employed a semi-automatic supervised classification of LULC using Landsat data from the 1990s to the 2020s to distinguish between Open Canopy Vegetation, Bareland, Forest, and Water. Overall accuracies achieved ranged from 88% to 90% in 1990, 2000, 2010, and 2020. SVM results indicated the share of Bareland as >80% of the landscape in all periods. With the seasonal rules-based approach, 10% less Bareland was detected than with the SVM approach. Our findings indicate the limitation of detecting vegetation reflectance in semi-arid mountainous regions such as that prevailing in Morocco using a single machine learning method. [ABSTRACT FROM AUTHOR]

Land cover change (LCC) is increasingly affecting global climate change, energy cycle, carbon cycle, and water cycle, with far-reaching consequences to human well-being. Web service-based online change detection applications have bloomed over the past decade for monitoring land cover change. Currently, massive processing services and data services have been published and used over the internet. However, few studies consider both service integration and resource sharing in land cover domain, making end-users rarely able to acquire the LCC information timely. The behavior interaction between services is also growing more complex due to the increasing use of web service composition technology, making it challenging for static web services to provide collaboration and matching between diverse web services. To address the above challenges, a Dynamic Service Computing Model (DSCM) was proposed for monitoring LCC. Three dynamic computation strategies were proposed according to different users' requirements of change detection. WMS-LCC was first developed by extending the existing WMS for ready-use LCC data access. Spatial relation-based LCC data integration was then proposed for extracting LCC information based on multi-temporal land cover data. Processing service encapsulation and service composition methods were also developed for chaining various land cover services to a complex service chain. Finally, a prototype system was implemented to evaluate the validity and feasibility of the proposed DSCM. Two walk-through examples were performed with GlobeLand30 datasets and muti-temporal Landsat imagery, respectively. The experimental results indicate that the proposed DSCM approach was more effective and applicable to a wider range of issues in land cover change detection. [ABSTRACT FROM AUTHOR]

Maintaining the integrity and stability of oasis ecosystems is an important topic in the field of ecological research. Assessment of ecosystem services and their changes can provide important support for the sustainable development of oases. This study took the Dunhuang oasis in the hyper-arid area as the research object and used 1990, 2010, and 2020 Landsat series satellite images to complete the land use interpretation by random forest classification. Then we estimated the ecosystem services value (ESV) by using benefit transfer method, and predicted the trend of ecosystem service value changes under three scenarios using the Analytic Hierarchy Process method and the patch generation land use simulation model (AHP-PLUS model). The results showed that the vegetation areas of the Dunhuang Oasis first decreased and then increased during 1990–2020. The decrease was largely due to the expansion of built-up land and farmland, and the increase was mainly contributed by the implementation of ecological protection policies. The path of changes in the ESV of the Dunhuang Oasis during 1990–2020 was well consistent with that of vegetation areas, with a maximum of 9068.15×106 yuan (in 1990) and a minimum of 6271.46×106 yuan (in 2010). Spatial autocorrelation analysis showed that urbanization reduced ESV, and the implementation of ecological policies enhanced ESV. The ESV of the Dunhuang Oasis for the year 2030 under the ecological conservation scenario could reach 7631.07×106 yuan, which is 381.1×106 yuan higher that under the economic development scenario. The ecological conservation scenario is the optimal option to achieve sustainable development of the Dunhuang Oasis. We suggested that the government should continuously enhance the protection of forests and waterbodies, reasonably restrict production and domestic water consumption, and efficiently increase the proportion of ecological water consumption. In addition, this study improved the evaluation method of oasis ESV based on the proportion of Normalized Difference Vegetation Index (NDVI) of grasslands with different coverage, which is important for improving the environment in arid areas. [ABSTRACT FROM AUTHOR]

Shenzhen has experienced rapid urbanization since the establishment of the Special Economic Zone in 1978. However, it is rare to witness high-speed urbanization in Shenzhen. It is important to study the LUCC progress in Shenzhen (regarding refusing multisource data), which can provide a reference for governments to solve the problems of land resource shortages and urban expansion spaces. In this paper, nine Landsat images were used to retrieve land cover maps in Shenzhen, China, from 1979 to 2022. The classification method is based on support vector machines with assistance from visual interpretation. The results show that the urban area increased by 756.84 km2, the vegetation area decreased by 546.27 km2, the water area decreased by 132.95 km2, and the bare area decreased by 77.62 km2 in the last 43 years of our research region. Urban sprawl starts from the Luohu district, then propagates to Futian, Nanshan, and Yantian districts, and finally expands to other outlying districts (Baoan, Longgang, Guangming, Dapeng, and Pingshan). The spatial–temporal characteristics and the impact factors of urbanization were further analyzed. The visualization of land cover changes based on a complex network approach reveals that the velocity of urban expansion is growing. The coastline distributions were retrieved from nine observation times from 1979 to 2022; the results show that the west coastline changed more dramatically than the east and most of the east coastline remained stable, except for the parts near Yantian port and Mirs Bay, which experienced some changes. The impact factors of coastline changes are further discussed. Through a correlation analysis using urban data, such as transportation and socioeconomic factors, it was found that elevation and roads have strong constraints on the spatial patterns of a city's expansion. There is exponential decay in the urban land increase against the distance to the roads, implying that traffic factors greatly determine urban land expansion. The turning point of the exponential decay is a distance of around 150 m. Time and population are highly correlated with land use development, indicating that urban land grows linearly with time and the population, which are important driving forces of urban land development. Compared with secondary and tertiary industries, the primary industry is less related to urban land use in Shenzhen. [ABSTRACT FROM AUTHOR]

Coastal habitat dynamics and ecosystem function in response to human-induced disturbance, especially urbanization, are of increasing concern. However, how changes in landscape composition as well as habitat quantity and quality may affect the long-term sustainability of rapidly urbanizing coasts remains unclear. This study aimed to quantify the extent, change rate, patterns, change process and interrelationships of mangrove habitats, impervious surfaces, and other land cover types in Deep Bay in the Guangdong-Hong Kong-Macau Greater Bay Area (GBA), China, the world's largest megalopolis, from 1924 to 2020. We processed historical aerial photos (1924–2020) and multiple sources of satellite data (1973–2020) for different types of land cover mapping. Post-classification analysis, including correlation analysis and change detection analysis, was conducted based on the long time-series land cover classification results. Mangrove habitats increased in Deep Bay from 1924 to 2020, except for a large area decrease from 1954 to 1964 due to the construction of tidal aquaculture ponds. Mudflat areas contributed most to the expansion of mangrove habitats of about 275 ha from 1987 to 2020. During this period, reclamation and urbanization for the construction of the megacity of Shenzhen turned large areas of water and mudflat (about 4000 ha) on the northern shore into impervious surface and urban vegetation. Overall, the landscape pattern of mangrove habitats in Deep Bay showed increasing connectivity and decreasing degree of fragmentation from 1987 to 2020. These changes have significant implications for the ecosystem services, e.g., supporting migratory waterbirds, supported by these wetlands. [ABSTRACT FROM AUTHOR]

Land cover change (LCC) studies are increasingly using deep learning (DL) modeling techniques. Past studies have leveraged temporal or spatiotemporal sequences of historical LC data to forecast changes with DL models. However, these studies do not adequately assess the association between neighborhood size and DL model capability to forecast LCCs, where neighborhood size refers to the spatial extent captured by each data sample. The objectives of this research study were to: (1) evaluate the effect of neighborhood size on the capacity of DL models to forecast LCCs, specifically Temporal Convolutional Networks (TCN) and Convolutional Neural Networks (CNN-TCN), and (2) assess the effect of auxiliary spatial variables on model capacity to forecast LCCs. First, each model type and neighborhood setting configuration was assessed using data derived from multitemporal MODIS LC for the Regional District of Bulkley-Nechako, Canada, comparing subareas exhibiting different amounts of LCCs with trends obtained for the full region. Next, outcomes were compared with three other study regions. The modeling results were evaluated with three-map comparison measures, where the real-world LC for the next timestep, the real-world LC for the previous timestep, and the forecasted LC for the next year were used to calculate correctly transitioned areas. Across all regions explored, it was observed that increasing neighborhood sizes improved the DL model's capabilities to forecast short-term LCCs. CNN–TCN models forecasted the most correct LCCs for several regions while reducing error due to quantity when provided additional spatial variables. This study contributes to the systematic exploration of neighborhood sizes on selected spatiotemporal DL techniques for geographic applications. [ABSTRACT FROM AUTHOR]

Coal is the most prevalent energy source in China and plays an important role in ensuring energy security. The continuous monitoring of coal mining activities is helpful to clarify the incremental space of coal production and establish a rational framework for future coal production capacity. In this study, a multi-source remote sensing approach utilizing SPOT 4, GF, and Landsat data is employed to monitor land cover and vegetation changes in the Juhugeng mining area of the Muli coalfield over a span of nearly 20 years. The analysis incorporates an object-oriented classification method and a vegetation parameter to derive insights. The findings reveal that the mining operations can be divided into two periods, since their initiation in 2003 until their cessation in 2021, with a dividing point around 2013/2014. The initial phase witnessed rapid and even accelerated expansion of the mine, while the subsequent phase was characterized by more stable development and the implementation of some restorative measures for the mine environment. Although the vegetation parameter, Fractional Vegetation Cover (FVC), indicates some reclamation efforts within the mining area, the extent of the reclaimed land remains limited. This study demonstrates the effective application of object-oriented classification in conjunction with the vegetation parameter FVC for monitoring coal mining areas.

The pathway, direction, and potential drivers of the evolution in global arid ecosystems are of importance for maintaining the stability and sustainability of the global ecosystem. Based on the Climate Change Initiative Land Cover dataset (CCILC), in this study, four indicators of land cover change (LCC) were calculated, i.e., regional change intensity (RCI), rate of change in land cover (CR), evolutionary direction index (EDI), and artificial change percentage (ACP), to progressively derive the intensity, rate, evolutionary direction, and anthropogenic interferences of global arid ecosystems. The LCC from 1992 to 2020 and from 28 consecutive pair-years was observed at the global, continental, and country scales to examine spatiotemporal evolution in the Earth’s arid ecosystems. The following main results were obtained: (1) Global arid ecosystems experienced positive evolution despite complex LCCs and anthropogenic interferences. Cautious steps to avoid potential issues caused by rapid urbanization and farmland expansion are necessary. (2) The arid ecosystems in Australia, Central Asia, and southeastern Africa generally improved, as indicated by EDI values, but those in North America were degraded, with 41.1% of LCCs associated with urbanization or farming. The arid ecosystems in South America also deteriorated, but 83.4% of LCCs were in natural land covers. The arid ecosystems in Europe slightly improved with overall equivalent changes in natural and artificial land covers. (3) Global arid ecosystems experienced three phases of change based on RCI values: ‘intense’ (1992–1998), ‘stable’ (1998–2014), and ‘intense’ (2014–2020). In addition, two phases of evolution based on EDI values were observed: ‘deterioration’ (1992–2002) and ‘improvement’ (2002–2020). The ACP values indicated that urbanization and farming activities contributed increasingly less to global dryland change since 1992. These findings provide critical insights into the evolution of global arid ecosystems based on analyses of LCCs and will be beneficial for sustainable development of arid ecosystems worldwide within the context of ongoing climate change.

Cascade damming can shape land surfaces; however, little is known about the specific impacts of dam construction on watershed land cover changes. Therefore, we developed a framework in which remote sensing, transition patterns, and landscape metrics were coupled to measure the impact of dam construction on watershed land cover changes and landscape patterns in the Longmen–Su (L–S) Creek, a small headwater watershed in Southeast China. During the transition and post-impact periods of dam construction, the land cover in the L–S Creek watershed underwent dynamic changes within the affected area. Changes in land cover were dominated by a surge in water and buildup and a decrease in woodland and cropland areas; bareland also increased steadily during construction. Woodlands and croplands were mainly flooded into water areas, although some were converted to bareland and built-up areas owing to the combined impact of dam construction and urbanization. By linking land cover changes with landscape patterns, we found that land use changes in water were significantly associated with landscape fragmentation and heterogeneity in the impacted zone. Our research demonstrates how damming can change land cover locally and may provide a basis for sustainable land management within the context of the extensive development of cascade hydropower dams. [ABSTRACT FROM AUTHOR]

One of the main problems in developing countries is unplanned urban growth and land use change. Timely identification of new constructions can be a good solution to mitigate some environmental and social problems. This study examined the possibility of identifying new constructions in urban areas using images from unmanned aerial vehicles (UAV), Google Earth and Sentinel-2. The accuracy of the land cover map obtained using these images was investigated using pixel-based processing methods (maximum likelihood, minimum distance, Mahalanobis, spectral angle mapping (SAM)) and object-based methods (Bayes, support vector machine (SVM), K-nearest-neighbor (KNN), decision tree, random forest). The use of DSM to increase the accuracy of classification of UAV images and the use of NDVI to identify vegetation in Sentinel-2 images were also investigated. The object-based KNN method was found to have the greatest accuracy in classifying UAV images (kappa coefficient = 0.93), and the use of DSM increased the classification accuracy by 4%. Evaluations of the accuracy of Google Earth images showed that KNN was also the best method for preparing a land cover map using these images (kappa coefficient = 0.83). The KNN and SVM methods showed the highest accuracy in preparing land cover maps using Sentinel-2 images (kappa coefficient = 0.87 and 0.85, respectively). The accuracy of classification was not increased when using NDVI due to the small percentage of vegetation cover in the study area. On examining the advantages and disadvantages of the different methods, a novel method for identifying new rural constructions was devised. This method uses only one UAV imaging per year to determine the exact position of urban areas with no constructions and then examines spectral changes in related Sentinel-2 pixels that might indicate new constructions in these areas. On-site observations confirmed the accuracy of this method. [ABSTRACT FROM AUTHOR]

An increasing frequency of extreme atmospheric events is challenging our basic knowledge about the resilience mechanisms that mediate the response of small mountainous watersheds (SMW) to landslides, including production of water-derived ecosystem services (WES). We hypothesized that the demand for WES increases the connectivity between lowland and upland regions, and decreases the heterogeneity of SMW. Focusing on four watersheds in the Central Andes of Colombia and combining "site-specific knowledge", historic land cover maps (1970s and 1980s), and open, analysis-ready remotely sensed data (GLAD Landsat ARD; 1990–2000), we addressed three questions. Over roughly 120 years, the site-specific data revealed an increasing demand for diverse WES, as well as variation among the watersheds in the supply of WES. At watershed-scales, variation in the water balances—a surrogate for water-derived ES flows—exhibited complex relationships with forest cover. Fractional forest cover (pi) and forest aggregation (AIi) varied between the historic and current data sets, but in general showed non-linear relationships with elevation and slope. In the current data set (1990–2000), differences in the number of significant, linear models explaining variation in pi with time, suggest that slope may play a more important role than elevation in land cover change. We found ample evidence for a combined effect of slope and elevation on the two land cover metrics, which would be consistent with strategies directed to mitigate site-specific landslide-associated risks. Overall, our work shows strong feedbacks between lowland and upland areas, raising questions about the sustainable production of WES. [ABSTRACT FROM AUTHOR]

This paper discusses opportunities to use remote sensing (RS) technologies in addressing the persistent global challenges related to the artisanal and small-scale gold mining (ASGM) sector. The paper uses a systematic literature review to identify, analyze, and synthesize various uses of RS on the detection and monitoring of ASGM activities across the globe. The study covers the use of spaceborne sensors and available opportunities for data access and processing and emphasizes the important role that freely-available data has played in understanding ASGM activities. It discusses applications and opportunities offered in assessing the geospatial and temporal characteristics of ASGM and its impacts on the surrounding environment. Furthermore, it examines different indicators for the detection of ASGM in the landscape. Finally, technological capabilities described in the study are illustrated with case studies in the Democratic Republic of Congo and in Colombia using cloud computing with the Open Data Cube. The case studies demonstrate the identification and quantification of impacts of ASGM activities on land degradation and water turbidity in remote areas and results are dissiminated using the MapX platform. This facilitates policy development, implementation, and evaluation in the ASGM context. [ABSTRACT FROM AUTHOR]

Existing efforts to continuously monitor land cover change using satellite image time series have mostly focused on forested ecosystems in the tropics and the Northern Hemisphere. The notable difference in spectral reflectance that occurs following deforestation allows land cover change to be detected with relative accuracy. Less progress has been made in detecting change in low productivity or disturbance-prone vegetation such as grasslands and shrublands where natural dynamics can be difficult to distinguish from habitat loss. Renosterveld is a hyperdiverse, critically endangered shrubland ecosystem in South Africa with less than 5–10% of its original extent remaining in small, highly fragmented patches. I demonstrate that classification of satellite image time series using neural networks can accurately detect the transformation of Renosterveld within a few days of its occurrence and that trained models are suitable for operational continuous monitoring. A dataset of precisely dated vegetation change events between 2016 and 2021 was obtained from daily, high resolution Planet Labs satellite data. This dataset was then used to train 1D convolutional neural networks and Transformers to continuously detect land cover change events in time series of vegetation activity from Sentinel 2 satellite data. The best model correctly identified 89% of land cover change events at the pixel-level, achieving a f-score of 0.93, a 79% improvement over the f-score of 0.52 achieved using a method designed for forested ecosystems based on trend analysis. Models have been deployed to operational use and are producing updated detections of habitat loss every 10 days. There is great potential for continuous monitoring of habitat loss in non-forest ecosystems with complex natural dynamics. A key limiting step is the development of accurately dated datasets of land cover change events with which to train machine-learning classifiers. [ABSTRACT FROM AUTHOR]

Hefei’s gross domestic product (GDP) growth rate ranks first among all cities in China, and it was the fastest-growing city in China from 2000 to 2020. The high-speed economic development inevitably led to rapid changes in land cover types, e.g., an increment in artificial features (built-up) and decrement in natural features (vegetation). However, (1) many previous studies focus on the land cover change in metropolis or at the global scale, yet few focus on underdeveloped but fast-growing cities; (2) land cover studies mainly focus on global variations, yet seldom on local characteristics. Thus, it is of great significance to monitor the land cover change for the city with the fastest economic growth in China based on the long time-series satellite images from both global and local perspectives. In this study, with support from huge amounts of data (including 719 Landsat TM/ETM+/OLI satellite images, land surface temperature, nighttime satellite images, DEM, multiple land cover products, and various auxiliary data), processing and parallel computing abilities of the GEE platform, classification maps of land cover in Hefei from 2000 to 2020 are produced based on a random forest machine learning method, and the spatio-temporal variations and driving factors are analyzed from both global and local viewpoints. The results show that: (1) the classification accuracy is excellent; the average overall accuracy is 93% and the Kappa coefficient is 0.88; (2) the general spatio-temporal variations in land cover in Hefei from 2000 to 2020 are obvious; the built-up area expanded from 419.72 km2 to 1530.20 km2, with a total growth rate of 264.58%. With the expansion of the built-up area, the vegetation coverage decreased by 16.61% (1652.56 km2); (3) the land surface temperature shows an increment trend in the new town yet a decrement trend in the old town due to the change in vegetation coverage and the decentration of administration centers; further analysis shows that the population and the social economy are two driving factors for land cover changes. It is worth noting that both the area and coverage of vegetation in the old town and water body area in Hefei increased significantly, although the fast urbanization inevitably caused a decrement in vegetation and water area in the whole city, indicating both the high-speed economic development and improvement in green surfaces simultaneously experienced in Hefei from 2000 to 2020.

Since the 1990s, Morocco’s agriculture has been characterized by the co-existence and transformation of both modern and traditional smallholder systems. In the Atlas Mountains, the effects of rural–urban transformation have led to intensified irrigated agriculture in some agricultural areas, while others were abandoned. To better understand these effects, this study aimed at (1) analyzing the land use and land cover (LULC) changes, (2) assessing the structure and dynamics of vegetation, and (3) comparing a Support Vector Machine (SVM) classification approach with a seasonal rules-based approach. We, therefore, employed a semi-automatic supervised classification of LULC using Landsat data from the 1990s to the 2020s to distinguish between Open Canopy Vegetation, Bareland, Forest, and Water. Overall accuracies achieved ranged from 88% to 90% in 1990, 2000, 2010, and 2020. SVM results indicated the share of Bareland as >80% of the landscape in all periods. With the seasonal rules-based approach, 10% less Bareland was detected than with the SVM approach. Our findings indicate the limitation of detecting vegetation reflectance in semi-arid mountainous regions such as that prevailing in Morocco using a single machine learning method.

Maintaining the integrity and stability of oasis ecosystems is an important topic in the field of ecological research. Assessment of ecosystem services and their changes can provide important support for the sustainable development of oases. This study took the Dunhuang oasis in the hyper-arid area as the research object and used 1990, 2010, and 2020 Landsat series satellite images to complete the land use interpretation by random forest classification. Then we estimated the ecosystem services value (ESV) by using benefit transfer method, and predicted the trend of ecosystem service value changes under three scenarios using the Analytic Hierarchy Process method and the patch generation land use simulation model (AHP-PLUS model). The results showed that the vegetation areas of the Dunhuang Oasis first decreased and then increased during 1990–2020. The decrease was largely due to the expansion of built-up land and farmland, and the increase was mainly contributed by the implementation of ecological protection policies. The path of changes in the ESV of the Dunhuang Oasis during 1990–2020 was well consistent with that of vegetation areas, with a maximum of 9068.15×106 yuan (in 1990) and a minimum of 6271.46×106 yuan (in 2010). Spatial autocorrelation analysis showed that urbanization reduced ESV, and the implementation of ecological policies enhanced ESV. The ESV of the Dunhuang Oasis for the year 2030 under the ecological conservation scenario could reach 7631.07×106 yuan, which is 381.1×106 yuan higher that under the economic development scenario. The ecological conservation scenario is the optimal option to achieve sustainable development of the Dunhuang Oasis. We suggested that the government should continuously enhance the protection of forests and waterbodies, reasonably restrict production and domestic water consumption, and efficiently increase the proportion of ecological water consumption. In addition, this study improved the evaluation method of oasis ESV based on the proportion of Normalized Difference Vegetation Index (NDVI) of grasslands with different coverage, which is important for improving the environment in arid areas.

Land cover change (LCC) is increasingly affecting global climate change, energy cycle, carbon cycle, and water cycle, with far-reaching consequences to human well-being. Web service-based online change detection applications have bloomed over the past decade for monitoring land cover change. Currently, massive processing services and data services have been published and used over the internet. However, few studies consider both service integration and resource sharing in land cover domain, making end-users rarely able to acquire the LCC information timely. The behavior interaction between services is also growing more complex due to the increasing use of web service composition technology, making it challenging for static web services to provide collaboration and matching between diverse web services. To address the above challenges, a Dynamic Service Computing Model (DSCM) was proposed for monitoring LCC. Three dynamic computation strategies were proposed according to different users’ requirements of change detection. WMS-LCC was first developed by extending the existing WMS for ready-use LCC data access. Spatial relation-based LCC data integration was then proposed for extracting LCC information based on multi-temporal land cover data. Processing service encapsulation and service composition methods were also developed for chaining various land cover services to a complex service chain. Finally, a prototype system was implemented to evaluate the validity and feasibility of the proposed DSCM. Two walk-through examples were performed with GlobeLand30 datasets and muti-temporal Landsat imagery, respectively. The experimental results indicate that the proposed DSCM approach was more effective and applicable to a wider range of issues in land cover change detection.

Shenzhen has experienced rapid urbanization since the establishment of the Special Economic Zone in 1978. However, it is rare to witness high-speed urbanization in Shenzhen. It is important to study the LUCC progress in Shenzhen (regarding refusing multisource data), which can provide a reference for governments to solve the problems of land resource shortages and urban expansion spaces. In this paper, nine Landsat images were used to retrieve land cover maps in Shenzhen, China, from 1979 to 2022. The classification method is based on support vector machines with assistance from visual interpretation. The results show that the urban area increased by 756.84 km2, the vegetation area decreased by 546.27 km2, the water area decreased by 132.95 km2, and the bare area decreased by 77.62 km2 in the last 43 years of our research region. Urban sprawl starts from the Luohu district, then propagates to Futian, Nanshan, and Yantian districts, and finally expands to other outlying districts (Baoan, Longgang, Guangming, Dapeng, and Pingshan). The spatial–temporal characteristics and the impact factors of urbanization were further analyzed. The visualization of land cover changes based on a complex network approach reveals that the velocity of urban expansion is growing. The coastline distributions were retrieved from nine observation times from 1979 to 2022; the results show that the west coastline changed more dramatically than the east and most of the east coastline remained stable, except for the parts near Yantian port and Mirs Bay, which experienced some changes. The impact factors of coastline changes are further discussed. Through a correlation analysis using urban data, such as transportation and socioeconomic factors, it was found that elevation and roads have strong constraints on the spatial patterns of a city’s expansion. There is exponential decay in the urban land increase against the distance to the roads, implying that traffic factors greatly determine urban land expansion. The turning point of the exponential decay is a distance of around 150 m. Time and population are highly correlated with land use development, indicating that urban land grows linearly with time and the population, which are important driving forces of urban land development. Compared with secondary and tertiary industries, the primary industry is less related to urban land use in Shenzhen.

Coastal habitat dynamics and ecosystem function in response to human-induced disturbance, especially urbanization, are of increasing concern. However, how changes in landscape composition as well as habitat quantity and quality may affect the long-term sustainability of rapidly urbanizing coasts remains unclear. This study aimed to quantify the extent, change rate, patterns, change process and interrelationships of mangrove habitats, impervious surfaces, and other land cover types in Deep Bay in the Guangdong-Hong Kong-Macau Greater Bay Area (GBA), China, the world’s largest megalopolis, from 1924 to 2020. We processed historical aerial photos (1924–2020) and multiple sources of satellite data (1973–2020) for different types of land cover mapping. Post-classification analysis, including correlation analysis and change detection analysis, was conducted based on the long time-series land cover classification results. Mangrove habitats increased in Deep Bay from 1924 to 2020, except for a large area decrease from 1954 to 1964 due to the construction of tidal aquaculture ponds. Mudflat areas contributed most to the expansion of mangrove habitats of about 275 ha from 1987 to 2020. During this period, reclamation and urbanization for the construction of the megacity of Shenzhen turned large areas of water and mudflat (about 4000 ha) on the northern shore into impervious surface and urban vegetation. Overall, the landscape pattern of mangrove habitats in Deep Bay showed increasing connectivity and decreasing degree of fragmentation from 1987 to 2020. These changes have significant implications for the ecosystem services, e.g., supporting migratory waterbirds, supported by these wetlands.

Cascade damming can shape land surfaces; however, little is known about the specific impacts of dam construction on watershed land cover changes. Therefore, we developed a framework in which remote sensing, transition patterns, and landscape metrics were coupled to measure the impact of dam construction on watershed land cover changes and landscape patterns in the Longmen–Su (L–S) Creek, a small headwater watershed in Southeast China. During the transition and post-impact periods of dam construction, the land cover in the L–S Creek watershed underwent dynamic changes within the affected area. Changes in land cover were dominated by a surge in water and buildup and a decrease in woodland and cropland areas; bareland also increased steadily during construction. Woodlands and croplands were mainly flooded into water areas, although some were converted to bareland and built-up areas owing to the combined impact of dam construction and urbanization. By linking land cover changes with landscape patterns, we found that land use changes in water were significantly associated with landscape fragmentation and heterogeneity in the impacted zone. Our research demonstrates how damming can change land cover locally and may provide a basis for sustainable land management within the context of the extensive development of cascade hydropower dams.

One of the main problems in developing countries is unplanned urban growth and land use change. Timely identification of new constructions can be a good solution to mitigate some environmental and social problems. This study examined the possibility of identifying new constructions in urban areas using images from unmanned aerial vehicles (UAV), Google Earth and Sentinel-2. The accuracy of the land cover map obtained using these images was investigated using pixel-based processing methods (maximum likelihood, minimum distance, Mahalanobis, spectral angle mapping (SAM)) and object-based methods (Bayes, support vector machine (SVM), K-nearest-neighbor (KNN), decision tree, random forest). The use of DSM to increase the accuracy of classification of UAV images and the use of NDVI to identify vegetation in Sentinel-2 images were also investigated. The object-based KNN method was found to have the greatest accuracy in classifying UAV images (kappa coefficient = 0.93), and the use of DSM increased the classification accuracy by 4%. Evaluations of the accuracy of Google Earth images showed that KNN was also the best method for preparing a land cover map using these images (kappa coefficient = 0.83). The KNN and SVM methods showed the highest accuracy in preparing land cover maps using Sentinel-2 images (kappa coefficient = 0.87 and 0.85, respectively). The accuracy of classification was not increased when using NDVI due to the small percentage of vegetation cover in the study area. On examining the advantages and disadvantages of the different methods, a novel method for identifying new rural constructions was devised. This method uses only one UAV imaging per year to determine the exact position of urban areas with no constructions and then examines spectral changes in related Sentinel-2 pixels that might indicate new constructions in these areas. On-site observations confirmed the accuracy of this method.

Existing efforts to continuously monitor land cover change using satellite image time series have mostly focused on forested ecosystems in the tropics and the Northern Hemisphere. The notable difference in spectral reflectance that occurs following deforestation allows land cover change to be detected with relative accuracy. Less progress has been made in detecting change in low productivity or disturbance-prone vegetation such as grasslands and shrublands where natural dynamics can be difficult to distinguish from habitat loss. Renosterveld is a hyperdiverse, critically endangered shrubland ecosystem in South Africa with less than 5–10% of its original extent remaining in small, highly fragmented patches. I demonstrate that classification of satellite image time series using neural networks can accurately detect the transformation of Renosterveld within a few days of its occurrence and that trained models are suitable for operational continuous monitoring. A dataset of precisely dated vegetation change events between 2016 and 2021 was obtained from daily, high resolution Planet Labs satellite data. This dataset was then used to train 1D convolutional neural networks and Transformers to continuously detect land cover change events in time series of vegetation activity from Sentinel 2 satellite data. The best model correctly identified 89% of land cover change events at the pixel-level, achieving a f-score of 0.93, a 79% improvement over the f-score of 0.52 achieved using a method designed for forested ecosystems based on trend analysis. Models have been deployed to operational use and are producing updated detections of habitat loss every 10 days. There is great potential for continuous monitoring of habitat loss in non-forest ecosystems with complex natural dynamics. A key limiting step is the development of accurately dated datasets of land cover change events with which to train machine-learning classifiers.

An increasing frequency of extreme atmospheric events is challenging our basic knowledge about the resilience mechanisms that mediate the response of small mountainous watersheds (SMW) to landslides, including production of water-derived ecosystem services (WES). We hypothesized that the demand for WES increases the connectivity between lowland and upland regions, and decreases the heterogeneity of SMW. Focusing on four watersheds in the Central Andes of Colombia and combining “site-specific knowledge”, historic land cover maps (1970s and 1980s), and open, analysis-ready remotely sensed data (GLAD Landsat ARD; 1990–2000), we addressed three questions. Over roughly 120 years, the site-specific data revealed an increasing demand for diverse WES, as well as variation among the watersheds in the supply of WES. At watershed-scales, variation in the water balances—a surrogate for water-derived ES flows—exhibited complex relationships with forest cover. Fractional forest cover (pi) and forest aggregation (AIi) varied between the historic and current data sets, but in general showed non-linear relationships with elevation and slope. In the current data set (1990–2000), differences in the number of significant, linear models explaining variation in pi with time, suggest that slope may play a more important role than elevation in land cover change. We found ample evidence for a combined effect of slope and elevation on the two land cover metrics, which would be consistent with strategies directed to mitigate site-specific landslide-associated risks. Overall, our work shows strong feedbacks between lowland and upland areas, raising questions about the sustainable production of WES.

Monitoring forest disturbances has become essential towards the design and tracking of sustainable forest management. Multiple methodologies have been developed to detect these disturbances. However, few studies have focused on the automatic detection of disturbance drivers, an essential task as each disturbance has different implications for the functioning of the ecosystem and associated management actions. Wildfires and harvesting are two of the major drivers of forest disturbances across different ecosystems. In this study, an automated methodology is presented to automatically distinguish between the two once the disturbance is detected, using the properties of its geometry and shape. A cluster analysis was performed to automatically individualize each disturbance and afterwards calculate its geometric properties. Using these properties, a decision tree was built that allowed for the distinction between wildfires and harvesting with an overall accuracy of 91%. This methodology and further research relating to it could pose an essential aid to national and international agencies for incorporating forest-disturbance-driver-related information into forest-focused reports.

The Oriental migratory locust is a destructive agricultural pest in China. Large-scale locust area (the area possessing suitable breeding habitat for locusts and has locust infestation) extraction and its evolution analysis are essential for locust ecological control. Existing methods seldom consider the spatial differences in the locust development and habitat landscape structures in large areas. To analyze these effects, our study proposed a landscape-based habitat suitability model (LHS model) for large-scale locust area extraction based on remote sensing data, taking the middle and lower reaches of the Yellow River (MLYR) as an example. Firstly, the DD model was used to simulate locust development and obtain habitat factors of the corresponding dates; secondly, the patch distribution of different land cover classes and their adjacent landscape characteristics were analyzed to determine the landscape-based factors memberships; finally, the habitat suitability index was calculated by combining the factors memberships and weights to extract the locust area. Compared with the patch-based model using moving windows (patch based-analytic hierarchy process model, R2 = 0.77), the LHS model accuracy improved significantly (R2 = 0.83). Our results showed that the LHS model has a better application prospect in large-scale locust area extraction. By analyzing the locust areas evolution along the MLYR extracted using the LHS model, we found human activities were the main factors affecting the locust areas evolution from 2016 to 2020, including: (1) planting the plants that locusts do not like and urbanization caused the decrease of the locust area; (2) the wetland protection policies may cause the increase of the locust area. The model and research results help locust control and prevention to realize the sustainable development of agriculture.

Temperate grasslands are considered the most endangered terrestrial ecosystem worldwide; the existent areas play a key role in biodiversity conservation. The Aguapey Valuable Grassland Area (VGA), one of the most well-preserved temperate grassland areas within Argentina, is currently threatened by the anthropogenic expansion of exotic tree plantations. Little is known about the impacts of afforestation over temperate grassland landscape structures; therefore, the aim of this study is to characterize Aguapey VGA landscape structural changes between 1999 and 2020 based on remotely sensed data. This involves the generation of land cover maps for four annual periods based on unsupervised classification of Landsat 5 TM and 8 OLI images, the estimation of landscape metrics, and the transition analysis between land cover types and annual periods. The area covered by temperate grassland is shown to have decreased by almost 22% over the 20 year-period studied, due to the expansion of tree plantation cover. The afforestation process took place mainly between 1999 and 2007 in the northern region of the Aguapey VGA, which led first to grassland perforation and subsequently to grassland attrition; however, Aguapey’s cultural tradition of cattle ranching could have partially inhibited the expansion of exotic trees over the final years of the study. The evidence of grassland loss and fragmentation within the Aguapey VGA should be considered as an early warning to promote the development of sustainable land use policies, mainly focused towards the Aguapey VGA’s southern region where temperate grassland remains the predominant land cover type.

Land cover evolution is an environmental factor that can be used to characterize forest ecosystem services (FES). This study aims to analyze the change in forest cover and water bodies between 1990 and 2018 in the whole Czech Republic, and in the Central Bohemian and South Moravian regions, and its effects on freshwater provision. Additionally, we attempt to understand the societal implications of water quality, public perception, and environmental investment on natural ecosystems. Forest cover and water body data were obtained from the Corine land cover database, while water quality and investment were compiled from the Czech Statistical Office. Public perceptions on the Czech FES were collected from a national survey. Between 1990 and 2018, forest cover has increased by 3.94% and water bodies by 7.65%; however, from 2014 to 2018, severe droughts were reported that compromised the availability of surface water, presumably on artificial structures, causing an increase in the occupied area. Regarding public perception, respondents with less education, and the older population, obtained an assessment of the low performance of the FES, while the water quality and investment indicate that environmental funding has contributed to improving the quality of outflow water from the wastewater treatment plants, fulfilling all the allowed limits of the urban wastewater treatment directive. Hence, a multidisciplinary approach can help decision makers promote policies that integrate environmental management measures, investment protection, and contribute to sustainable development.

Understanding the relationship between land use and land cover and thermal environment has recently become an emerging issue for urban planners and policy makers. We chose Belgrade, as a case study, to present a cost- and time-effective framework for monitoring spatiotemporal changes of green spaces in relation to the land surface temperature (LST). Time series analysis was performed using Landsat 5 TM and Landsat 8 OLI/TIRS imagery from 1991 to 2019 with an approximate 5-year interval (18 images in total). Spectral vegetation indices and supervised land cover classifications were used to examine changes of green spaces. The results showed a fluctuating trend of the normalized difference vegetation index (NDVI) and the normalized difference water index (NDWI). The highest values were recorded in 2019, indicating vegetation recovery in the last decade. A significant positive correlation was determined between the spectral vegetation indices and the amount of precipitation during growing season. The land cover classification showed that the share of vegetated and bare land decreased by 11.74% during the study period. The most intensive conversion of green and bare land into built-up land cover occurred in the first decade (1991–2000). To assess spatiotemporal changes in the LST, Landsat Collection 2 Surface Temperature products were used. We found a negative correlation between change in the spectral vegetation indices and change in the LST. This indicates that the reduction in vegetation was associated with an increase in the LST. The municipalities that were the most affected in each decade were also identified with our framework. The findings of this study are of great relevance for actions targeting an improvement in urban thermal comfort and climate resilience.

Accurate detection of spatial patterns of urban growth is crucial to the analysis of urban growth processes. A common practice is to use post-classification change analysis, overlaying multiple independently derived land cover layers. This approach is problematic as propagation of classification errors can lead to overestimation of change by an order of magnitude. This paper contributes to the growing literature on change classification using pixel-based time series analysis. In particular, we have developed a method that identifies change in the urban fabric at the pixel level based on breaks in the seasonal and year-on-year trend of the normalised difference vegetation index (NDVI). The method is applied to a case study area in the south of England that is characterised by high levels of cloud cover. The study uses the Landsat data archive over the period 1984–2018. The performance of the method was assessed using 500 ground truth points. These points were randomly selected and manually assessed for change using high-resolution earth observation imagery. The method identifies pixels where a land cover change occurred with a user’s accuracy of change 45.3 ± 4.45% and outperforms a post-classification analysis of an otherwise more advanced land cover product, which achieved a user’s accuracy of 17.8 ± 3.42%. This method performs better where changes exhibit large differences in NDVI dynamics amongst land cover types, such as the transition from agricultural to suburban, and less so where small differences of NDVI are observed, such as changes in land cover within pixels that are densely built up already. The method proved relatively robust for outliers and missing data, for example, in the case of high levels of cloud cover, but does rely on a period of data availability before and after the change event. Future developments to improve the method are to incorporate spectral information other than NDVI and to consider multiple change events per pixel over the analysed period.

With uninterrupted space-based data collection since 1972, Landsat plays a key role in systematic monitoring of the Earth’s surface, enabled by an extensive and free, radiometrically consistent, global archive of imagery. Governments and international organizations rely on Landsat time series for monitoring and deriving a systematic understanding of the dynamics of the Earth’s surface at a spatial scale relevant to management, scientific inquiry, and policy development. In this study, we identify trends in Landsat-informed change detection studies by surveying 50 years of published applications, processing, and change detection methods. Specifically, a representative database was created resulting in 490 relevant journal articles derived from the Web of Science and Scopus. From these articles, we provide a review of recent developments, opportunities, and trends in Landsat change detection studies. The impact of the Landsat free and open data policy in 2008 is evident in the literature as a turning point in the number and nature of change detection studies. Based upon the search terms used and articles included, average number of Landsat images used in studies increased from 10 images before 2008 to 100,000 images in 2020. The 2008 opening of the Landsat archive resulted in a marked increase in the number of images used per study, typically providing the basis for the other trends in evidence. These key trends include an increase in automated processing, use of analysis-ready data (especially those with atmospheric correction), and use of cloud computing platforms, all over increasing large areas. The nature of change methods has evolved from representative bi-temporal pairs to time series of images capturing dynamics and trends, capable of revealing both gradual and abrupt changes. The result also revealed a greater use of nonparametric classifiers for Landsat change detection analysis. Landsat-9, to be launched in September 2021, in combination with the continued operation of Landsat-8 and integration with Sentinel-2, enhances opportunities for improved monitoring of change over increasingly larger areas with greater intra- and interannual frequency.

Dilijan National Park is one of the most important national parks of Armenia, established in 2002 to protect its rich biodiversity of flora and fauna and to prevent illegal logging. The aim of this study is to provide first, a mapping of forest degradation and deforestation, and second, of land cover/land use changes every 5 years over a 28-year monitoring cycle from 1991 to 2019, using Sentinel-2 and Landsat time series and Machine Learning methods. Very High Spatial Resolution imagery was used for calibration and validation purposes of forest density modelling and related changes. Correlation coefficient R2 between forest density map and reference values ranges from 0.70 for the earliest epoch to 0.90 for the latest one. Land cover/land use classification yield good results with most classes showing high users’ and producers’ accuracies above 80%. Although forest degradation and deforestation which initiated about 30 years ago was restrained thanks to protection measures, anthropogenic pressure remains a threat with the increase in settlements, tourism, or agriculture. This case study can be used as a decision-support tool for the Armenian Government for sustainable forest management and policies and serve as a model for a future nationwide forest monitoring system.

The territories between urban and rural areas, also called urban–rural fringe, commonly present inherent instability and notable heterogeneity. However, investigating the multifaceted urban–rural fringe phenomenon based on large-scale identification has yet to be undertaken. In this study, we adopted a handy clustering-based method by incorporating multidimensional urbanization indicators to understand how the urban–rural fringe development vary across space and shift over time in the Yangtze River Delta Urban Agglomeration, China. The results show that (1) the growth magnitude of urban–rural fringe areas was greater than urban areas, whereas their growth rate was remarkably lower. (2) The landscape dynamics of urban–rural fringe varied markedly between fast-developing and slow-developing cities. Peripheral sprawl, inter-urban bridge, and isolated growth were the representative development patterns of urban–rural fringe in this case. (3) Urban–rural fringe development has predominantly occurred where cultivated land is available, and significant inter-provincial differences and tendency shifts have been found in the land cover change processes therein. Our findings indicate that the urban–rural fringe development should be considered as a stage-dependent and place-specific process. This work extends previous knowledge by unraveling the diversity of urban–rural fringe in a fast urbanizing region, and can provide insights into reorienting the spatial planning and land use management within the urban–rural interface.

Accurate remote sensing of mountainous forest cover change is important for myriad social and ecological reasons, but is challenged by topographic and illumination conditions that can affect detection of forests. Several topographic illumination correction (TIC) approaches have been developed to mitigate these effects, but existing research has focused mostly on whether TIC improves forest cover classification accuracy and has usually found only marginal gains. However, the beneficial effects of TIC may go well beyond accuracy since TIC promises to improve detection of low illuminated forest cover and thereby normalize measurements of the amount, geographic distribution, and rate of forest cover change regardless of illumination. To assess the effects of TIC on the extent and geographic distribution of forest cover change, in addition to classification accuracy, we mapped forest cover across mountainous Nepal using a 25-year (1992–2016) gap-filled Landsat time series in two ways—with and without TIC (i.e., nonTIC)—and classified annual forest cover using a Random Forest classifier. We found that TIC modestly increased classifier accuracy and produced more conservative estimates of net forest cover change across Nepal (−5.2% from 1992–2016). TIC also resulted in a more even distribution of forest cover gain across Nepal with 3–5% more net gain and 4–6% more regenerated forest in the least illuminated regions. These results show that TIC helped to normalize forest cover change across varying illumination conditions with particular benefits for detecting mountainous forest cover gain. We encourage the use of TIC for satellite remote sensing detection of long-term mountainous forest cover change.

Forests with predominance of Norway spruce (Picea abies (L.) H. Karst.) and Scots pine (Pinus sylvestris L.) within the hemiboreal zone are considered as secondary communities formed under long-term human activity (logging, plowing, fires and silviculture). This study raises the question—how stable is current state of coniferous forests on the southern border of their natural distribution in the center of Eastern Europe using the example of the Moscow region (MR)? The object of the study are spruce and pine forests in different periods of Soviet and post-Soviet history within the Moscow Region (MR). The current proportion of spruce forests is 21.7%, and the proportion of pine forests is 18.5% from total forest area according to our estimates. The direction and rate of forest succession were analyzed based on current composition of populations of the main forest-forming species (spruce, pine, birch, aspen, oak, linden, and ash) based on ground-based research materials collected in 2006–2019. This allowed to develop the dynamic model (DM) of forest communities with the participation of Norway spruce and Scots pine for several decades. Assessment of the spatial distribution of coniferous communities is based on field data and spatial modeling using remote sensing data—Landsat 8 mosaic for 2020. In parallel, a retrospective model (RM) of the spatial-temporal organization of spruce and pine forests for a 30-year period was developed using two Landsat 5 mosaics. For this, nine different algorithms were tested and the best one for this task was found—random forest. Geobotanical relevés were used as a training sample combined with the 2006–2012 mosaic; the obtained spectral signatures were used for modeling based on the 1984–1990 mosaic. Thus, two multi-temporal spatial models of coniferous formations have been developed. Detailed analysis of the structure of spruce and pine forests based on field data made it possible to track trends of successional dynamics for the first time, considering the origin of communities and the ecological conditions of habitats. As a result, ideas about the viability of spruce and pine cenopopulations in different types of communities were formulated, which made possible to develop a dynamic model (DM) of changes in forest communities for future. Comparison of the areas and nature of changes in the spatial structure of coniferous formations made possible to develop the RM. Comparison of two different-time models of succession dynamics (DM and RM) makes possible to correct the main trends in the transformation of coniferous forests of natural and artificial origin under the existing regime of forestry. A set of features was identified that indicates risk factors for coniferous forests in the region. A further decrease of the spruce and pine plantations and increase of the spruce-small-leaved and deciduous formations are expected in the study area. The proportion of pine-spruce forests does not exceed 3% of the area and can be considered as the most vulnerable type of forest.

Mangrove is classified as an important ecosystem along the shorelines of tropical and subtropical landmasses, which are being degraded at an alarming rate despite numerous international treaties having been agreed. Iskandar Malaysia (IM) is a fast-growing economic region in southern Peninsular Malaysia, where three Ramsar Sites are located. Since the beginning of the 21st century (2000–2019), a total loss of 2907.29 ha of mangrove area has been estimated based on medium-high resolution remote sensing data. This corresponds to an annual loss rate of 1.12%, which is higher than the world mangrove depletion rate. The causes of mangrove loss were identified as land conversion to urban, plantations, and aquaculture activities, where large mangrove areas were shattered into many smaller patches. Fragmentation analysis over the mangrove area shows a reduction in the mean patch size (from 105 ha to 27 ha) and an increase in the number of mangrove patches (130 to 402), edge, and shape complexity, where smaller and isolated mangrove patches were found to be related to the rapid development of IM region. The Moderate Resolution Imaging Spectro-radiometer (MODIS) Leaf Area Index (LAI) and Gross Primary Productivity (GPP) products were used to inspect the impact of fragmentation on the mangrove ecosystem process. The mean LAI and GPP of mangrove areas that had not undergone any land cover changes over the years showed an increase from 3.03 to 3.55 (LAI) and 5.81 g C m−2 to 6.73 g C m−2 (GPP), highlighting the ability of the mangrove forest to assimilate CO2 when it is not disturbed. Similarly, GPP also increased over the gained areas (from 1.88 g C m−2 to 2.78 g C m−2). Meanwhile, areas that lost mangroves, but replaced them with oil palm, had decreased mean LAI from 2.99 to 2.62. In fragmented mangrove patches an increase in GPP was recorded, and this could be due to the smaller patches (

Woody species encroachment on grassland ecosystems is occurring worldwide with both negative and positive consequences for biodiversity conservation and ecosystem services. Remote sensing and image analysis represent useful tools for the monitoring of this process. In this paper, we aimed at evaluating quantitatively the potential of using high-resolution UAV imagery to monitor the encroachment process during its early development and at comparing the performance of manual and semi-automatic classification methods. The RGB images of an abandoned subalpine grassland on the Western Italian Alps were acquired by drone and then classified through manual photo-interpretation, with both pixel- and object-based semi-automatic models, using machine-learning algorithms. The classification techniques were applied at different resolution levels and tested for their accuracy against reference data including measurements of tree dimensions collected in the field. Results showed that the most accurate method was the photo-interpretation (≈99%), followed by the pixel-based approach (≈86%) that was faster than the manual technique and more accurate than the object-based one (≈78%). The dimensional threshold for juvenile tree detection was lower for the photo-interpretation but comparable to the pixel-based one. Therefore, for the encroachment mapping at its early stages, the pixel-based approach proved to be a promising and pragmatic choice.

Flood risk is a significant challenge for sustainable spatial planning, particularly concerning climate change and urbanization. Phrasing suitable land planning strategies requires assessing future flood risk and predicting the impact of urban sprawl. This study aims to develop an innovative approach combining land use change and hydraulic models to explore future urban flood risk, aiming to reduce it under different vulnerability and exposure scenarios. SPOT-3 and Sentinel-2 images were processed and classified to create land cover maps for 1995 and 2019, and these were used to predict the 2040 land cover using the Land Change Modeler Module of Terrset. Flood risk was computed by combining hazard, exposure, and vulnerability using hydrodynamic modeling and the Analytic Hierarchy Process method. We have compared flood risk in 1995, 2019, and 2040. Although flood risk increases with urbanization, population density, and the number of hospitals in the flood plain, especially in the coastal region, the area exposed to high and very high risks decreases due to a reduction in poverty rate. This study can provide a theoretical framework supporting climate change related to risk assessment in other metropolitan regions. Methodologically, it underlines the importance of using satellite imagery and the continuity of data in the planning-related decision-making process.

It is confirmed that China has been greening over the last two decades. Such greening and its driving factors are therefore significant for understanding the relationship between vegetation and environments. However, studies on vegetation changes and attribution analyses at the national scale are limited in China after 2000. In this study, fractional vegetation cover (FVC) data from Global Land Surface Satellite (GLASS) was used to detect vegetation change trends from 2001 to 2018, and the effects of CO2, temperature, shortwave radiation, precipitation, and land cover change (LCC) on FVC changes were quantified using generalized linear models (GLM). The results showed that (1) FVC in China increased by 14% from 2001 to 2018 with a greening rate of approximately 0.0019/year (p < 0.01), which showed an apparent greening trend. (2) On the whole, CO2, climate-related factors, and LCC accounted for 88% of FVC changes in China, and the drivers explained 82%, 89%, 90%, and 89% of the FVC changes in the Qinghai–Tibet region, northwest region, northern region, and southern region, respectively. CO2 was the major driving factor for FVC changes, accounting for 31% of FVC changes in China, indicating that CO2 was an essential factor in vegetation growth research. (3) The statistical results of pixels with land cover changes showed that LCC explained 12% of FVC changes, LCC has played a relatively important role and this phenomenon may be related to the ecological restoration projects. This study enriches the study of vegetation changes and its driving factors, and quantitatively describes the response relationship between vegetation and its driving factors. The results have an important significance for adjusting terrestrial ecosystem services.

The monitoring of land cover and land use change is critical for assessing the provision of ecosystem services. One of the sources for long-term land cover change quantification is through the classification of historical and/or current maps. Little research has been done on historical maps using Object-Based Image Analysis (OBIA). This study applied an object-based classification using eCognition tool for analyzing the land cover based on historical maps in the Main river catchment, Upper Franconia, Germany. This allowed land use change analysis between the 1850s and 2015, a time span which covers the phase of industrialization of landscapes in central Europe. The results show a strong increase in urban area by 2600%, a severe loss of cropland (−24%), a moderate reduction in meadows (−4%), and a small gain in forests (+4%). The method proved useful for the application on historical maps due to the ability of the software to create semantic objects. The confusion matrix shows an overall accuracy of 82% for the automatic classification compared to manual reclassification considering all 17 sample tiles. The minimum overall accuracy was 65% for historical maps of poor quality and the maximum was 91% for very high-quality ones. Although accuracy is between high and moderate, coarse land cover patterns in the past and trends in land cover change can be analyzed. We conclude that such long-term analysis of land cover is a prerequisite for quantifying long-term changes in ecosystem services.

The natural shift in land cover from non-vegetated to vegetated land is termed as vegetation expansion, which has substantial impacts on regional climate conditions and land surface energy balance. Barrens dominate the northwestern Tibetan Plateau, where vegetation is predicted to expand northwestward with the ongoing climate warming. However, rare studies have confirmed such a forecast with large-scale vegetation monitoring. In this study, we used a landcover dataset, classified according to the International Geosphere–Biosphere Program criteria, to examine previous model-based predictions and the role of climate on the expansion rate across the plateau. Our results showed that shrublands, open forests, grasslands, and water bodies expanded while evergreen and deciduous broadleaf forests, croplands and barrens shrank during the period 2001–2018. Vegetation expanded by 33,566 km2 accounting for about 1.3% of the total area of this plateau and the land cover shifting from barrens to grasslands was the primary way of vegetation expansion. Spatially, the vegetation expanded northwestward to lands with colder, drier, and more radiation in the climate. Increasing precipitation positively correlated with the vegetation expansion rate for the arid and semi-arid northwest Tibetan Plateau and warming contributed to the vegetation expanding in the semi-humid southeast Tibetan Plateau. Our results verified the predictions of models and highlighted the “greening” on barrens in recent years.

Urban growth and natural hazard events are continuous trends and reliable monitoring is demanded by organisations such as the Intergovernmental Panel on Climate Change, the United Nations Office for Disaster Risk Reduction, or the United Nations Human Settlements Programme. CORONA is the program name of photoreconnaissance satellite imagery available from 1960 to 1984 provides an extension of monitoring ranges in comparison to later satellite data such as Landsat that are more widely used. Providing visual comparisons with aerial or high-resolution OrbView satellite imagery, this article demonstrates applications of CORONA images for change detection of urban growth and sprawl and natural hazard exposure. Cases from El Alto/ La Paz in Bolivia, Santiago de Chile, Yungay in Peru, Qazvin in Iran, and Mount St. Helens in the USA are analysed. After a preassessment of over 20 disaster events, the 1970 Yungay earthquake-triggered debris avalanche and the natural hazard processes of the 1980 Mt St. Helens volcanic eruption are further analysed. Usability and limitations of CORONA data are analysed, including the availability of data depending on flight missions, cloud cover, spatial and temporal resolution, but also rather scarce documentation of natural hazards in the 1960s and 70s. Results include the identification of urban borders expanding into hazard-prone areas such as mountains, riverbeds or erosion channels. These are important areas for future research, making more usage of this valuable but little-used data source. The article addresses geographers, spatial planners, political decision makers and other scientific areas dealing with remote sensing.