Land Cover Conversion Research Articles

A fast hybrid approach for continuous land cover change monitoring and semantic segmentation using satellite time series

Land cover change detection and classification, including both inter-class changes (land cover conversion, LCC) and intra-class changes (land cover modification, LCM), is critical for understanding the Earth’s dynamic processes and promoting sustainability. However, previous studies have predominantly focused on LCC, with less emphasis on LCM. Land cover classification remains challenging, and its mapping results are often affected by salt and pepper noise. Here, we propose a hybrid approach for continuous change detection and classification of LCC and LCM using Jinchang City, China, as a case study. Firstly, we combined the Continuous Change Detection and Classification (CCDC) and the Bayesian Estimator of Abrupt change, Seasonal change, and Trend (BEAST) algorithms to identify LCC and LCM using all available Landsat time series (TS) data from 2000 to 2020. Then, the harmonic regression coefficients and RMSE values derived from CCDC (hereafter called CCDC features) were fed into the DCNN model for LCC classification. Our findings indicate: (1) For LCC and LCM accuracy assessment, the CCDC and BEAST ensemble achieved a spatial F1 score of 82.7% and an average temporal F1 score of 79.7%. (2) In LCC classification, the DCNN model with CCDC features, particularly DeepLabV3+, outperformed the pixel-based XGBoost and other multi-year land cover products, with frequency-weighted intersection over union (FWIoU), overall accuracy, and Kappa scores of 88.7%, 94%, and 0.87, respectively. (3) Seasonal LCM showed a more concentrated distribution than trend LCM. (4) In Jinchang City, LCM larger than LCC in area, and grassland and cultivated land are the most distributed. Our approach can be contributed to wall-to-wall land surface monitoring and enhance land management capabilities.

Long-term changes of mangrove distribution and its response to anthropogenic impacts in the Vietnamese Southern Coastal Region

Human activities related to land use and land cover (LULC) conversion have been the primary factor driving changes to mangrove distribution over recent decades. In order to quantify the anthropogenic influences associated with LULC changes on mangroves in the Vietnamese Southern Coast (VSC), we investigated the variations and trends in mangrove distribution between 1988 and 2023. We used a time-series of Landsat spectral indices from Google Earth Engine and applied hot spot analysis and machine learning algorithms to analyse mangrove variations and LULC classification, respectively. Our findings revealed that over the past 36 years, approximately half of the mangrove area has been lost due to LULC conversions. The most significant losses in mangrove cover occurred during the 1998–2011 period, with a decline of 46.79% in total area (an average of 3.6% per annum). The rate of mangrove deforestation more than halved to 17.49% (1.5% per annum) in the period between from 2011 to 2023. We attribute the reduction in mangrove loss to conservation efforts and natural regeneration processes. The emerging hot spot analysis indicated that the most significant restoration of new mangrove areas occurred between 1988 and 1998, totalling 1795 ha (1.4%), while the highest rate of mangrove deforestation was observed between 1998 and 2011, amounting to 2249 ha (2.0%). The primary causes of these variations in mangrove distribution were the conversion of mangrove areas to shrimp farming (38.91%), followed by other agricultural land use (5.82%) and the expansion of impervious surfaces (3.34%). In contrast, a result of enhanced conservation efforts and natural regeneration was associated with a 17.91% of mangrove area gain in the 2011–2023 period. Despite the regeneration potential of mangroves, our study highlighted the ongoing need to manage and protect mangrove forests to facilitate their expansion in the VSC. The analytical approach adopted in this study is applicable to other coastal areas when assessing changes in mangroves and land use practices.

Geospatial AI solution to monitor and mitigate increasing adverse ecological and hydrological impacts of climate change in Uttarakhand Himalaya (India).

Though climate change and its adverse ecological and geohydrological impacts are being experienced across the world in all types of ecosystems but as far as the Himalaya mountain ecosystem is concerned, the rate of climate change and subsequent impacts have reached an alarming stage due to anthropogenic and technogenic intervention on natural process and now need most effective and less time taking management strategy. Addressing this burning environmental problem, a geospatial artificial intelligence (GeoAI) technique-based case study is presented here from one of the most densely populated and urbanized regions of Himalaya mountain, viz Uttarakhand Himalaya, which is also called central Himalaya. The results of the study suggest that due to quite a high rate of climate change, the climatic zones shifting towards higher altitudes at the average rate of 5.6 2m/year, causing several adverse ecological impacts in terms of decreasing quality dense temperate forest cover (0.05%/year), snow cover (0.02%/year), water bodies (0.01%/year), agricultural land (0.31%/year), and horticultural land (0.01%/year). Conversion of these eco-friendly land use land cover into barren land, fallow land, and built-up land causes geohydrological consequences of climate change in terms of decreasing rainy days (1%/year), drying perennial springs (0.20%/year), perennial streams (0.11%/year), decreasing spring and stream discharge during non-monsoon season, increased extreme rainfall events (6-8%/year), and subsequent surface runoff during monsoon season. Further, the study advocates that the degraded geohydrological process has resulted in an increased frequency of disaster events (floods, cloudbursts, landslides. etc.) with a 3% (12 events) annual rate, causing great loss of environment, infrastructure, lives, and economy each year. Therefore, it has been very urgent to mitigate climate change and increase geohydrological disaster events through an integrated approach. Keep in view this, the present study proposed an integrated watershed management plan which is equally useful to be implemented across the Himalaya region and other similar ecosystems across the world.

Unraveling resilience amidst degradation: Recurring loss of freshwater marshes in the Paraná River Delta, Argentina

Wetland ecosystems have experienced several ecological and hydrological impacts in recent decades determined by human activities and natural disturbances. The Lower Delta of the Paraná River, one of the most important wetland ecosystems of South America, has seen significant losses in both the structural and functional components of wetland vegetation. These losses promoted not only a widespread conversion of freshwater marshes into grasslands between 1997 and 2013, but also a decline in ecosystem functional diversity between 2001 and 2015. These processes manifested as abrupt shifts in long-term vegetation dynamics, a distorted, transient, spatially heterogeneous relationship with the hydrologic regime, and altered plant communities. However, recent field observations (2015–2023) have partially challenged previous findings and assumptions. Thus, we ask whether previously observed wetland losses are part of a long-term periodic process, rather than a permanent change. To address this question, we studied land use and land cover conversions through an object-based supervised classification of yearly Landsat composites between 1985 and 2023, trained on 935 ground-truth points. To study the spatial and temporal patterns of wetland gain and loss, we implemented an Intensity Analysis (IA), as well as analyses that capture frequency-specific variations and identify significant shifts in linear trends. We produced a total of 39 land cover maps. The IA revealed non-stationarity at all levels of analysis: interval, category, and transition. The study area exhibited resilient patterns through significant and increasingly short-term, periodic dynamics guiding the gain and loss of freshwater marshes. On the opposite, long-term, negative trends depicted an absolute, sustained loss. These contrasting patterns suggest that despite experiencing absolute loss and degradation, wetland ecosystems thrive by exhibiting transient recovery or adaptation mechanisms. Our study unraveled the complexity of wetland ecosystem dynamics, emphasizing how resilience and degradation interplay in the context of land use intensification.

Spatial modelling of present and future groundwater potentials in Nigeria; towards a sustainable water demand and supply

The development of water resources in Nigeria has not risen alongside population increase. Although groundwater is the available source of water at all locations in Nigeria, groundwater aquifer is becoming deeper due to increased groundwater harvest, especially in urban areas. There is a need for continuous groundwater modelling using geological, climatic, environmental data, and spatial tools especially under climate change and intense landcover conversions. The Spatial Multi-Criteria Analysis (SMCA) was adopted in this study to model the current groundwater potential in Nigeria. The multi-criteria analysis tool in ArcGIS was explored to overlay the nine factor maps to model historic and futuristic groundwater potentials. Our groundwater factor maps show an interesting pattern across Nigeria, the southern parts show more potential considering suitability factors like; surface water density, rainfall, temperature, soil, land cover and elevation in the region. Whereas the upland regions even with higher rainfall and lower temperatures suitable for groundwater recharge, are disadvantaged by geomorphological factors. Northern part of Nigeria shows high potentials, considering the geology, soil, lineament density and slope, but disadvantaged by other factors like lower rainfall and higher temperatures. This informed the final groundwater potential maps; results for the historic potential revealed that, no location in Nigeria has optimal (9–10), very poor or no (1–2) groundwater potential. The results further revealed that areas with higher groundwater potentials are largely within the corridors of major rivers in Nigeria (Niger and Benue), covering about 17.6% of the Nigerian landmass, while 2.6%, 33.7, 44% and 2.2% are occupy areas with very low, low, moderate and very high groundwater potentials respectively. Further analysis highlighs locations of concern due to climatic and environmental changes. Interestingly, our groundwater projection results show a persistent increase in groundwater potential from 2021 to 2100 if current landuses and environmental factors are maintained, and if the projected increase in rainfall is true. Despite these groundwater potentials, the recent issues of shallow aquifers have been cautioned by scientists, especially due to groundwater uncertainties in Nigeria especially with intense landcover conversions, combined with accelerated water demands due to increase in population, and incessant groundwater extractions. Therefore, there is a need to seek caution in the pattern of unregulated and incessant groundwater harvest in Nigeria and we recommend frequent updates of the groundwater potential using geospatial tools to inform governing policies on a centralised consolidated sustainable water supply in Nigeria.

Quantifying land change dynamics, resilience and feedback: A comparative analysis of the lake Chad basin in Africa and Aral Sea basin in Central Asia

The intricate interaction of natural and anthropogenic factors drives changes in land and water in response to societal demands and climate change. However, there has been insufficient information on the feedback effects in dryland hotspots altered by land change dynamics. This research compared two transboundary inland lakes, the Lake Chad basin (LCB) in Africa and the Aral Sea basin (ASB) in Central Asia, using remote sensing and geographic information system techniques to analyze and quantify present and future land cover dynamics, resilience, and their feedback effects. The study integrated Cellular Automata, Markov Chain, and Multilayer Perceptron models to predict LULC changes up to 2030. Descriptive statistics, ordinary least squares regression, hotspot Gi-Bin, trend analysis, and advanced geostatistical methods were utilized to identify relationships, patterns, magnitudes, and directions of observed changes in the feedback effects. From 2000 to 2030, the analysis unveils intriguing trends, including an increase in cropland from 48% to 51% and a decrease in shrubland from 18% to 15% in the LCB. The grassland increased from 21% to 22%, and the settlement expanded from 0.10 to 0.60% in the ASB. Water bodies remained stable at 1.60 % in LCB, while in ASB, it declined from 3% to 2%. These changes were significantly influenced by population, elevation, and temperature in both basins, with irrigation also playing a significant role in the ASB and slope in LCB. The study further revealed discernible shifts in normalized difference vegetation index, temperature, and precipitation linked to specific land cover conversions, suggesting alterations in surface properties and vegetation health. This study underscores the complex interplay between land cover dynamics, resilience, climate variability, and feedback mechanisms in LCB and ASB.

Vegetation browning trend progressively leading to forest degradation in eastern himalaya in response to climatic and anthropogenic drivers

Vegetation is a major natural resource that plays an important role in maintaining ecological balance. Vegetation in the eastern Himalaya has experienced severe consequences owing to deforestation, fragmentation, land use land cover (LULC) conversion and climate change. Disturbances in vegetation have a close connection to browning trend because they modify the terrestrial ecosystems. Remote sensing derived vegetation indices and the availability of long-term satellite data allowed us to infer greening and browning tendencies across the Northeast Region (NER) of India. The present study has employed almost four decades (1982–2020) of satellite data (i.e. NDVI and LAI) and climate data (precipitation, soil moisture, temperature, solar radiation) to deduce the spatial pattern of greening and browning trends over major vegetation ecosystems, namely forest, grassland and cropland. Statistical metrics such as the Mann–Kendall test (τ) and the Theil–Sen median trend were computed to understand the greening and browning including vegetation degradation. The key findings exhibited that a noticeable large-scale greening trend of forests persisted in the first period (1982–2000) and switched to browning in the latest period (2000–2020). The statistically significant areas of browning trends over forest regions in NER was 5.6% which increased to 10.7–15.6%. The browning trends over grassland were more pronounced in the southernmost part of NER. Such changes were ascribed mostly to persistent warming and declining soil moisture. This was further exacerbated by human-induced LULC change. Over croplands, greening was prominent in Assam in the latest period, whilst other regions switched to browning. Trend analysis gives critical information on temperature-induced moisture stress leading to browning that might have potential implications for the forest, grassland and agriculture ecosystems. Specifically, the areas at Ziro Valley of Arunachal Pradesh showed high risk of forest degradation (i.e. 19% of the total forest area of 1816.54 km2), whereas areas around the Loktak Lake in Manipur exhibited high risk of cropland degradation. The states in the southernmost part of NER showed high risk of grassland degradation. Nearly 20–27% of the areas were identified as high risk of degradation. The study underscores the potential areas of NER where land management strategies need attention in the context of complex climatic factors along the side of anthropogenic influences.

Modeling the impacts of oil palm plantations on water quantity and quality in the Kais River Watershed of Indonesia

Oil palm plantations can impact hydrological processes in many tropical watersheds. The rapid conversion of tropical rainforests for commercial operations in recent decades has been associated with water scarcity, flooding, and polluted rivers. However, this widespread and emerging issue is less studied and underreported due to limited data availability, modeling complexity, and the remote nature of these landscapes. Ecohydrologic modeling enables us to investigate changes in watershed conditions caused by large-scale land cover changes from plantations. This study examines the impact of oil plantations on water quantity and quality using the SWAT+ model in the Kais River Watershed, West Papua, Indonesia. The objective is to assess the hydrological changes concerning land cover conversion to oil palm plantations. Results show that establishing oil palm plantations increased surface runoff by 21 %, and sediment yields rose by 16.9 % compared to the baseline. There was also a significant increase of 78 % in mean annual total nitrogen and 144 % in total phosphorous after the plantations' establishment. The results show that forest conversion to oil palm plantations in the Kais River watershed is a primary driver of change in hydrological regimes, resulting in the deterioration of water quality. There is a need for conservation strategies to mitigate the impacts of significant landscape changes in watershed ecosystems.

Time series analysis of landsat data for urban heat island monitoring in Semarang City

The urban heat island (UHI) condition has received significant attention because it has the potential to negatively impact life quality and comfort, especially livability and level of comfort. Semarang City, the capital of Central Java, has been attracting many people to live and causing land use conversion to built-up areas. This study aims to observe the land surface temperature (LST) trend and to analyse the UHI spatiotemporal pattern in Semarang based on satellite time series data within 10 years. Land cover and LST were extracted from Landsat data of 2008, 2013, and 2019. Based on the results, it was observed that the vegetated area has reduced from 20.347 Ha to 7.241 Ha, while the average LST value has increased from 29.24°C to 31.31°C in 2008 and 2019 respectively. We also found that the UHI pattern, which was initially detected only in a few areas in 2008 and was focused in the city centre, has gradually spread to the east and south of Semarang in 2019. The result also shows the spatiotemporal trend of land cover conversion and UHI areas, which can be beneficial for mitigation planning in the future.

Assessing the influence of human activities on flash flood susceptibility in mountainous regions of Vietnam

Flash floods pose a significant and escalating threat to life, infrastructure, and property in the mountainous regions of Vietnam. Over the past two decades, Vietnam has experienced rapid socio-economic development, making it one of the fastest-growing countries in the region. However, this progress has been accompanied by urbanization, land cover conversion, and reductions in natural forest coverage, exacerbating the risk of flash floods. The objective of our study was to evaluate the quantitative influence of human activities on flash flood susceptibility from 2001 to 2022. Employing various machine learning algorithms, we analyzed 15 independent factors and examined 452 historical data points to predict the probability of flash floods and build flash flood susceptibility maps for the period of 2013–2022. By assuming similar weather conditions that trigger flash floods, we also estimated the probability and spatial distribution of flash floods for 2001–2020. To assess the anthropogenic impact, we conducted a detailed analysis of changes in land use patterns and employed indices such as the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Built-up Index (NDBI). These factors played a significant role in shaping the differences in flash flood probability between the two time periods. The results demonstrated alarming trends, revealing a notable increase in the probability of flash floods by 7.69% and 4.01% in areas classified as high and very high susceptibility, respectively, over the past two decades. These findings provide tangible and robust evidence that policymakers can utilize to evaluate the implications of ongoing socio-economic growth. Furthermore, they serve as a critical foundation for formulating sustainable development plans that prioritize mitigating the future escalating risk of flash floods.

Biodiversity response to rapid successive land cover conversions in human-dominated landscapes

Much of the world’s formerly “intact” land cover has been modified by humans with major impacts on biodiversity. Extensive areas of already modified land are now being subjected to yet further land cover conversions. Indeed, in many places, land cover and land use are changing repeatedly and frequently. Here, we use ecological theory to develop a new conceptual framework with associated postulates about how biodiversity could respond to repeated and rapid changes in land cover (over timespans of years to decades). Our framework focuses on five drivers: (1) conversion completeness, (2) persistence of biological, chemical, and physical legacies from previous land cover type to a new kind of cover, (3) number of previous land cover conversions, (4) time elapsed since land cover change, and (5) levels of contrast between the previous and new land cover. We predicted that biodiversity responses to repeated conversions in human-generated land cover would be a function of the main effects of these five drivers, as well as complex interactions among them (e.g. each new land cover type may act as a filter for species assemblages and interact in complex spatio-temporal ways with the previous land cover). Biodiversity responses may therefore be species- and land cover type specific, and not be readily predictable from insights provided by studies of single landscape conversions from an intact to a human-dominated ecosystem. Ignoring the impacts of successive land cover conversions on biodiversity could lead to increased biodiversity loss with each land cover conversion. Therefore, impact assessments are needed each time a new land cover conversion is proposed. Policies and management actions need to focus on the maintenance and/or restoration of remnant vegetation and other kinds of natural features (like large trees and wetlands) with each land cover conversion.

Citizen and machine learning-aided high-resolution mapping of urban heat exposure and stress

Through conversion of land cover to more built-up, impervious surfaces, cities create hotter environments than their surroundings for urban residents, with large differences expected between different parts of the city. Existing measurements of ambient air temperature and heat stress, however, are often insufficient to capture the intra-urban variability in heat exposure. This study provides a replicable method for modeling air temperature, humidity, and moist heat stress over the urban area of Chapel Hill while engaging citizens to collect high-temporal and spatially-resolved air temperature and humidity measurements. We use low-cost, consumer-grade sensors combined with satellite remote sensing data and machine learning to map urban air temperature and relative humidity over various land-cover classes to understand intra-urban spatial variability of ambient heat exposure at a relatively high resolution (10 m). Our findings show that individuals may be exposed to higher levels of air temperature and moist heat stress than weather station data suggest, and that the ambient heat exposure varies according to land cover type, with tree-covered land the coolest and built-up areas the warmest, and time of day, with higher air temperatures observed during the early afternoon. Combining our resulting dataset with sociodemographic data, policymakers and urban planners in Chapel Hill can use data output from this method to identify areas exposed to high temperature and moist heat stress as a first step to design effective mitigation measures.

Patterns and consequences of invasion of tropical montane forests by Cestrum aurantiacum Lindl. in the Western Ghats

In the montane forest-grassland mosaics of the Western Ghats, land cover conversion to silviculture and agriculture over the last five decades has resulted in both loss of natural habitats and widespread invasion of remnant habitat patches. While invasion of the grassland habitats of the mosaic has been relatively well studied, there have been few attempts to understand the extent to which forest habitats (locally known as sholas) have been affected by the spread of exotic species. Here we examine the patterns and impacts of invasion of shola forest understoreys by Cestrum aurantiacum Lindl., an exotic shrub species. At the landscape scale, we demonstrate that the presence and abundance of this invasive in shola understories is negatively related to distance from tea plantations. Further, the intensity of invasion is higher in areas with greater seasonality of temperature and lower mean annual precipitation. At the patch scale, invasion is greatest at shola edges and away from stream courses. We find that C. aurantiacum abundance has negatively affected the regeneration of native shola tree species as well as the abundance of native shola understorey shrubs. Fifty three percent of invaded plots had no native shrubs present. In plots where both C. aurantiacum and native shrubs were present in large enough numbers, we found evidence of negative spatial dependence between stem locations of C. aurantiacum and native shrubs. Our findings have important implications for the management and conservation of these mosaics.

Carbon stock dynamics in a disturbed tropical forest ecosystem of Central India: Strategies for achieving carbon neutrality

Anthropogenic land use changes in the vicinity of forest catchments are explicitly responsible for the deforestation and degradation of tropical forest thereby altering the carbon (C) balance under rising atmospheric carbon dioxide (CO2). Thus, strategic mitigation measures are required to achieve climate neutrality by 2030. It becomes imperative to understand the impact of land cover conversion on the functioning of tropical forests and to determine the carbon dynamics. The present study examined the land cover changes (LUC) in dry tropical ecosystem of Central India during the last two decades and assessed its influence on the forest biomass, litter crop, and C stocks. Geospatial techniques coupled with ground measurements were employed in the study. The total biomass, vegetative C stocks and soil C were estimated as 64 to 338.3 Mg ha−1, 26.4 to 131.1 Mg C/ha and 24.6 to 50.2 Mg C/ha respectively. In the past decades (2000–2020), about 1851.8 ha of forest constituting 15.1% of the total area under study was lost (mainly to Agriculture) under open mixed forest, moderately mixed forest and mixed teak forest. The study demonstrated that forests of the studied region behave as a potential C source, which accounted for a net loss of 59 gigagram carbon (Gg C) equivalent to 216 Gg CO2eq. Besides, discussed three viable scenarios of restoration to offset C emissions and to achieve C neutral landscape by 2030. The conservation and restoration of 26–85% degraded forests (open mixed forest and moderately mixed dense forests), use of agroforestry on 1/5th of existing agricultural lands, or implementation of regenerative agricultural practices in 30% of the area will compensate for net losses, while attaining C neutrality. Further, the finding of this study helps to understand the climatic extremes as per the commitments of Paris Agreement, 2015 and Glasgow Pact, 2021.

Centers of Endemism and The Potential of Zoos and Botanical Gardens in Conservation of Endemics

Knowledge about ecological conditions and processes in centers of endemism (CoEs) is still limited with respect to various systematic groups of organisms, ecosystem types, ecological conditions, and ecosystem services. We review the characterization, identification, and meaning of CoEs. Endemics play an increasing and prominent role in nature conservation monitoring and management and in the organization of zoos, aquaria, and botanical gardens. We examine the importance of different groups of organisms and indicators for the characterization of endemic-rich regions, e.g., with regard to the richness of endemics per region and degree of endemism, the importance of heterogeneity in space, continuity in time, isolation, and ex situ management for the survival of endemic species. Currently, conversion of land cover and land use change are the most important causes of biodiversity decline and extinction risk of endemic and endangered species. These are followed by climate change, including severe weather, and then natural processes such as volcanism, landslides, or tsunamis. For conservation purposes, the management of regional land use, zoos, aquaria, botanical gardens, and social aspects of the diversity of endemics and CoEs have to be taken into account as well. We find that the ex situ representation of endemics in general is limited, and conservation networks in this regard can be improved. We need better answers to questions about the relationship between ecoregions, CoEs and regional awareness of endemism, which is linked with human culture including aesthetics, well-being, health, and trade.

Evaluating land restoration based on the land cover in the Horqin Sandy Land, China.

Land degradation is a severe ecological environmental issue in arid and semiarid areas. Evaluating land degradation exactly is essential for maintaining the security of the ecological environment. This study evaluated land restoration in terms of land cover changes in the Horqin Sandy Land from 2000 to 2018 and analyzed the related influencing factors. The results showed that land restoration and land degradation accounted for 37.55% and 4.47% of the total area, respectively. Land restoration has improved substantially, and desertification control has achieved remarkable effects. NPP trends should be analyzed in combination with land cover types. When the land cover has changed, the type of land cover change and the period need to be clarified before analyzing the corresponding NPP trend to depict the land productivity accurately. The vegetation NPP changes following land cover conversions were more essential than land cover change types. Changes in precipitation and temperature had positive effects on the growth of vegetation and land restoration. In general, human activities promoted land restoration, including grazing policies, cropland utilization activities, and ecological restoration projects. Current land restoration, however, essentially sacrifices the groundwater resources, which challenges the sustainability of land restoration. Appropriate land use strategies were proposed to promote sustainable land use, including preventing vegetation degradation following land cover conversions, improving grassland construction, and coordinating the usage of cropland and water resources. Such results are meaningful for promoting land restoration and realizing sustainable land development.

Characterization and mapping of photovoltaic solar power plants by Landsat imagery and random forest: A case study in Gansu Province, China

Numbers and sizes of photovoltaic solar power plants have grown unprecedentedly over the last few years in China, which aims to achieve a carbon emission peak by 2030 and carbon neutrality by 2060. Thus, timely and accurate monitoring of photovoltaic solar power plants is crucial to the design and management of renewable electricity systems in China. Random forest algorithm has been used to map photovoltaic solar power plants at multiple scales, however, it always causes several salt-and-pepper noises, limiting its application at larger spatial scales. Here we first develop a photovoltaic solar power plant mapping method through integrating time series Landsat imagery, random forest, and morphological characteristics. Then we apply this method in Gansu Province, which has abundant solar and wind energy resources and provide large amounts of potential lands for photovoltaic development, and generate the annual photovoltaic maps from 2015 to 2020. We further analyze the spatial-temporal dynamics of sizes and areas of photovoltaic solar power plants and major land cover conversion of expansive photovoltaic regions. Finally, we discuss the reliability, uncertainties, implications, and future development of our improved methods. We find our photovoltaic mapping method can remove most of salt-and-pepper noises effectively, and the resultant maps in Gansu for 2020 have very high accuracies with user's and producer's accuracies of 97.57% and 99.22%, respectively. There are 165.29 km2 photovoltaic solar power plants in Gansu for 2020, and most of which are located in the northwestern Gansu. In addition, the photovoltaic with patch size > 1 km2 and ≤ 2 km2 (53.4 km2, 32.3%) has largest patch number (39, 15.7%). The improved photovoltaic mapping methods and further analysis in this study provide critical information for accurate and automatic classification of photovoltaic solar power plants in the future, as well as the environmental and sustainable development of solar energy in China.

The counteracting effects of large-scale vegetation restoration and increased precipitation on drought in the Huang-Huai-Hai-Yangtze River basin

Quantifying the causes of drought trends is critical for disaster prevention and water resource management. In recent years, not only climatic variables, such as temperature and precipitation (PRE), have changed, but also environmental factors, including vegetation restoration, increased CO2 concentration, and land cover conversion. However, the role of diverse factors, particularly large-scale vegetation restoration, in influencing drought trends is still indeterminate. In this study, a Ball-Berry canopy conductance model was incorporated into the Shuttleworth–Wallace (S-W) model to estimate the potential evapotranspiration (PET) in the Huang-Huai-Hai-Yangtze River basin from 2001 to 2019. The standardized precipitation evapotranspiration index (SPEI), which considers both water supply (from PRE) and demand (from PET), was adopted to identify drought conditions. Given the complex non-linear relationships in the model, an interpretable machine learning model was applied to the attribution of PET and SPEI trends. The results were as follows: (1) The PRE and PET showed significant upward trends, whereas the change rate of PET (60 mm/10a, p < 0.01) was higher than that of PET (44 mm/10a, p < 0.1) in the region; (2) The SPEI at an annual scale non-significantly decreased at a rate of −0.12/10a (p > 0.1). Increasing PET dominated SPEI trends with a contribution of −0.28/10a (p < 0.01), whereas increasing PRE contributed 0.16/10a (p > 0.1); (3) The interpretable machine learning model performed better than multiple linear regression in attributing PET trends; (4) Large-scale vegetation restoration (the increased leaf area index, LAI) and wind speed, were the two dominant factors influencing PET trends with contributions of 32.4 mm/10a (32%) and 19.6 mm/10a (20%), respectively; (5) In terms of the trends in drought across the entire region, the increase in LAI almost offset the positive contributions of PRE to SPEI variations, hence, combined with the influence of the other factors, drought was non-significantly accelerated.

Assessment of public and private land cover change in the United States from 1985–2018

An assessment of annual land cover on publicly and privately managed lands across the conterminous United States (CONUS) from 1985–2018 was performed, including land cover conversions within their management category, to inform future policy and land-use decision-making in natural resource management. Synthesizing land cover data with land management delineations aids our ability to address effects of land management decisions by public or private entities. The U.S. Geological Survey (USGS) Protected Areas Database of the United States (PAD-US) version 2.1 data delineate land management categories and enable examination of land cover composition and change using the USGS Land Change Monitoring, Assessment, and Projection (LCMAP) reference data. Average composition of our delineated CONUS results using LCMAP land cover classes is 40% Grass/Shrub (GS), 29% Tree Cover (TC), 18% Cropland (CP), 5% Developed (DV), 5% Wetland (WL), 1.8% Water (WR), and 0.9% Barren (BN). Private (public) land is composed of 35% (52%) GS, 27% (36%) TC, 25% (1%) CP, 7% (1%) DV, 5% (5%) WL, 2% (2%) WR, and less than 1% (3%) BN. Land cover change averaged less than 1% per year. The largest net percentage gains across CONUS were in DV land and GS, and the greatest net losses were in CP and TC. Approximately 73% of CONUS is private land and, thus, land cover change across CONUS is largely a reflection of private land change dynamics. Private compositional changes show net gains from 1985–2018 in DV (2.3%), WR (0.2%), and GS (0.1%) classes, while net losses occurred in CP (−1.9%), TC (−0.6%), WL (−0.1%), and BN (−0.01%). Public land cover changes show net gains in GS (1%), DV (0.2%), WR (0.01%), WL (0.05%), and BN (0.1%) classes, and net losses in CP (−0.3%) and TC (−1%). Our study reveals connections between land cover conversion and various policy and socioeconomic decisions through time.

City densification and temporal dynamics of traditional inner core of Ibadan, Nigeria

The conversion of vegetation land cover contributes to the retention of solar radiation in the environment, resulting in the formation of the urban heat island. This study analyses the distribution pattern of urban heat island in the traditional core of Ibadan amidst the rapid urbanization experienced. The remote sensing tool was used to analyse the trend of land surface temperature, normalized difference built-up index, and normalized difference vegetation index for the traditional Ibadan's core between the year 2000 and the year 2020. This reveals that increasing built-up areas will continue to strengthen the effects of urban heat island in the traditional core of Ibadan, while vegetated land covers will weaken the effects of urban heat island. This is because anthropogenic activities resulting from rapid urbanisation has adversely altered the natural landscape in the traditional core of Ibadan. This alteration manifests in converting vegetation land covers into physical developments and other impervious surfaces by the increasing urban population. Thus, increasing the land surface temperature. The maximum average land surface temperature of 35.34℃, 36.62℃ and 31.86℃ were record for the years 2000, 2013 and 2020 respectively. This study further recommends that urban greening and proper urban planning should be encouraged in the traditional core of Ibadan. Keywords: Land Surface Temperature, Normalized Difference Built-up Index, Normalized Difference Vegetation Index, traditional core, urban heat island