Time Series Of Landsat Images Research Articles

Morphological changes of river Bank Erosion and channel shifting assessment on Arial Khan River of Bangladesh using Landsat satellite time series images

The continuous processes of erosion and deposition in rivers are intricately linked to changes in land use and land cover (LULC), reflecting dynamic landscape transformations. This study investigates the rate of riverbank erosion, channel shifting, and the impact of erosion on LULC dynamics along the Arial Khan River. Utilizing GIS and remote sensing techniques, we analyzed time-series satellite images from 1993 to 2024 within a 2 km buffer zone of the floodplain. Supervised classification methods were applied for LULC analysis, with results validated using confusion matrices to assess classification accuracy. ArcGIS 10.8 was employed to analyze channel centerline migration, while NDVI analysis assessed vegetation health. The study reveals that between 1993 and 2024, 5142.60 ha were lost to erosion, while deposition affected 4756.52 ha, with erosion surpassing deposition overall. The river shifted an average of 0.66 km during this period, with higher migration rates in Muladi Upazila compared to Babuganj Upazila. Settlement areas expanded significantly from 137.82 ha in 1993 to 2768.68 ha in 2024, while agricultural land declined substantially. These findings provide valuable insights for policymakers to address erosion severity in confluence zones and lay the groundwork for sustainable land management practices.

Transformation trajectory of wetland and suitability of migratory water bird habitat in the moribund Ganges delta.

Wetland is a suitable habitat for water birds, and it enhances cultural ecosystem services. But the rapid transformation of such habitat, especially in floodplain environments, is an emerging crisis. Wetland reclamation and fragmentation are two major issues leading to poor habitat and landscape. The present paper aimed to explore the spatio-temporal changes in the suitability of wetland bird habitat, wetland landscape pattern, and the connection between them. Two wetlands, including a wetland of national importance, were taken as cases for this study. Time series Landsat and Sentinel images were taken for developing modeling parameters and Land Use Land Cover (LULC) for the years 2016 and 2020. The first transformation of wetland was accounted from the LULC maps of both years. Machine learning algorithm-based spatial models were developed for mapping the poor landscape condition of the existing wetland parts. Finally, semi-subjective analytic hierarchy approach (AHP)-based models were developed for assessing waterbird habitat suitability. Results demarcated more than 48% area belonging primarily to edges and tiny patches of wetlands under a poor state in 2020. Although the total wetland area was reduced between 2016 and 2020, the wetland area found to be highly suitable habitat increased from 25.5 to 59.44% of the total area during that period. The suitability of edge-preferring bird habitat showed a 10% increase. The increasing poverty of the landscape was caused by declining edge-preferring bird habitat suitability. From 1990 to 2020, 27% of wetlands were converted to single-cropped lands, and 5% were converted to multi-cropped agricultural land. Since the study spatially identified the potential suitable area and trend of wetland habitat transformation, this could help policymakers define suitable planning for the restoration and conservation of such promising bird habitat.

Assessing open-pit mining impacts on semi-arid grassland: A framework for boundary and intensity quantification

Coal mining in northern China's grasslands faces significant environmental challenges due to the fragile semi-arid ecosystem and the abundance of mineral resources. However, current research lacks clarity in both theoretical understanding and effective quantification of its impacts. Insufficient distinction between direct and indirect environmental effects, the oversight of interactions from neighboring activities, and inaccurate measurements of impact intensity all hinder a comprehensive understanding. These limitations also prevent the precise delineation of ecological protection compensation areas. In this study, we developed a comprehensive framework utilizing Landsat time-series images (1989–2017) and field sampling data (228 sample locations in 2017) to assess the boundaries and intensities of mining impacts. First, the k-means clustering algorithm was applied to identify the Normalized Difference Vegetation Index (NDVI) time-series trajectories and map the spatial information of direct impacts. Then, a buffer zone was established and an amplitude model was proposed for determined the cross-impacts and their intensities based on the volatility of NDVI trajectories. Accordingly, a Trend-thresholds model was proposed to automatically extract the indirect impacts boundary of mining-dominated on vegetation under varying intensities. The Soil Quality Index (SQI) ∼ distance curves were fitted and a derivative model was employed for determine the boundary of indirect impacted on soil. We applied this assess framework in Baorixile coalfield (i.e., Baorixile coal mine (BM), DongMing coal mine (DM), Shunxing coal mine (SX)) a typical semi-arid grassland mining area, and quantified three types of impacts: direct, indirect, and cross-impacts. Findings reveal negative coal mining impacts primarily confined to permitted mining areas and decreasing over time. Direct impacts boundary limited in the mining sites and its impact intensity shows a dual nature, with a negative weakening and a positive strengthening trend. Widespread negative indirect impacts exist outward the mining area displays spatial heterogeneity, and its intensity attenuation with distances. In there, the very severe effect (D1) is predominantly focusing on the grassland where near by the mining work face (BM maximum range 1.2 km, DM maximum range 0.55 km, SX maximum range 0.4 km). Moreover, our study indicated that vegetation and soil exhibited consistent responses to coal mining impacts within 1 km, demonstrating the feasibility of using remote sensing data to qualify the impact boundary on the grassland environment. Besides, we further identify significant cross-impacts surrounding the mining areas, characterized by strong negative effects, particularly in the southeastern region of BM. This research offers valuable insights for effective ecological compensation governance and scientific environmental management in similar global contexts.

Spatiotemporal variation in determinants of cropland abandonment across Yangtze River Economic Belt, China

Cropland abandonment is a widespread land-change phenomenon globally, driven by complex social, economic, and political transformations, with significant implications for the environment and society. However, the inconsistent definitions of cropland abandonment and the bias in the selection of study areas hinder the comparison of identified determinants of cropland abandonment. In this study, we defined cropland abandonment from the perspective of vegetation succession. By employing land-cover change detection with Landsat time-series images from 1990 to 2021, we analyzed the spatiotemporal patterns of cropland abandonment across the cities of Yibin, Huangshi, and Chaohu within the Yangtze River Economic Belt. Furthermore, we utilized the Gradient-Boosting Decision Tree model to identify the primary spatial determinants and their relative importance in each city. Our findings showed significant variations in abandonment rates by 2021. Huangshi exhibited the highest rate of cropland abandonment, leading with 53.81±2.1 %, followed by Yibin with 45.36±2.18 %, and Chaohu with the lowest at 36.94±2.21 %. Interestingly, a consistent spatiotemporal trend, areas with higher abandonment rates tended to be abandoned earlier, emerged in abandonment determinants across cities. Areas with lower bulk density, higher soil clay content, and greater organic carbon contents exhibited higher abandonment rates. However, the most influential determinants varied: the distance from water bodies was the most important in Yibin and Huangshi, while the distance from the forest had a greater impact in Chaohu. This study offers a more nuanced perspective on defining cropland abandonment and can be applied to other regions after some adjustment. Our results provide valuable insights for cropland abandonment management by protecting high-value croplands and setting aside some areas for environmental amenities.

Surface water monitoring from 1984 to 2021 based on Landsat time-series images and Google Earth Engine

Dynamic monitoring of surface water bodies is essential for understanding global climate change and the impact of human activities on water resources. Satellite remote sensing is characterized by large-scale monitoring, timely updates, and simplicity, and it has become an important means of obtaining the distribution of surface water bodies. This study is based on a long time-series Landsat satellite images and the Google Earth Engine (GEE) platform, focusing on Anhui Province in China, and proposes a method for extracting surface water that combines water indices, Bias-Corrected Fuzzy Clustering Method (BCFCM), and OTSU threshold segmentation. The spatial distribution of surface water in Anhui Province was obtained from 1984 to 2021, and further analysis was conducted on the spatiotemporal characteristics of surface water in each city and three major river basins within the province. The results indicated that the overall accuracy of water extraction in this study was 94.06 %. Surface water in Anhui was most abundant in 1998 and least in 2001, with more distribution in the south than in the north. Northern Anhui is dominated by rivers, while southern Anhui has more lakes. Permanent surface water with an inundation frequency of above 75 % covered approximately 4341 km2, accounting for 32.03 % of the total water, while seasonal water with an inundation frequency between 5 % and 75 % covered about 6661 km2, accounting for 49.15 % of the total water, others were considered temporary surface water. By comparing our results with the global annual surface water released by the Joint Research Centre (JRC), we found that our study performed better in extracting lakes and rivers in terms of completeness, but the extraction results for aquaculture areas were slightly less than the JRC dataset. Overall, the long-term surface water dataset established in this study can effectively supplement the existing datasets and provide important references for regional water resource investigation, management, as well as flood monitoring.

Effects of Deforestation and Anthropogenic Aspects on Streamflow in the Mau Forest Catchments in Kenya: Hydrological Modelling of Surface Water Yields from Sondu River Basin

Climatic factors determine the amount and distribution of atmospheric water received at the land surface while the land cover conditions determine the partitioning of this water into different hydrological components and ultimately the catchment surface water yields. This study assessed the effects of deforestation of a tropical catchment on surface water yields to address fluctuating flows of the rivers emanating from Mau Forest, the largest water tower in Kenya. Sondu basin traverses Southwest Mau Forest covering an area of 3500 km2. The main channel in the basin flows in a southwest direction into Lake Victoria in an altitudinal range of 2900 to 1130 m a.s.l over a length of 173 km. Different deforestation scenarios over the basin were integrated with climate data to form inputs to a hydrologic model, Soil and Water Assessment Tool (SWAT). Using model outputs, the effects of deforestation on annual and seasonal surface water yields, represented by changes in streamflow volumes under different deforestation scenarios, were evaluated. Deforestation scenarios were derived from a supervised classification scheme of time series of LANDSAT images (1970-2020) to show deforestation trends. Effects of deforestation on the catchment water-yielding capacity were estimated as the ratio of the difference between simulated yields under different deforestation scenarios and those simulated under the pre-deforestation scenario of the 1970s decade. Results show that forest cover declined by 21% and a corresponding growth in land under agriculture by 26% in the period 1970-2020. The decline in forest coverage resulted in an increase in the annual surface water yields of about 23% (from 152 to 187 MCM/year) throughout the period of study. This implies that there is less recharge of groundwater due to decreased infiltration and subsequent storage leading to lower flows during the dry seasons and increased flood frequencies in the basin during the wet seasons. The study has therefore, demonstrated that deforestation has reduced the stability of Mau Forest as a water tower and conservation of the forest will enhance its water-holding capacity thereby ensuring a stable water supply to rivers emanating from it as a way of combating floods and low flows in the basin. The resultant impacts on environment and society are displacements from floods and destruction of properties.

Enhanced Detection of Artisanal Small-Scale Mining with Spectral and Textural Segmentation of Landsat Time Series

Artisanal small-scale mines (ASMs) in the Amazon Rainforest are an important cause of deforestation, forest degradation, biodiversity loss, sedimentation in rivers, and mercury emissions. Satellite image data are widely used in environmental decision-making to monitor changes in the land surface, but ASMs are difficult to map from space. ASMs are small, irregularly shaped, unevenly distributed, and confused (spectrally) with other land clearance types. To address this issue, we developed a reliable and efficient ASM detection method for the Tapajós River Basin of Brazil—an important gold mining region of the Amazon Rainforest. We enhanced detection in three key ways. First, we used the time-series segmentation (LandTrendr) Google Earth Engine (GEE) Application Programming Interface to map the pixel-wise trajectory of natural vegetation disturbance and recovery on an annual basis with a 2000 to 2019 Landsat image time series. Second, we segmented 26 textural features in addition to 5 spectral features to account for the high spatial heterogeneity in ASM pixels. Third, we trained and tested a Random Forest model to detect ASMs after eliminating irrelevant and redundant features with the Variable Selection Using Random Forests “ensemble of ensembles” technique. The out-of-bag error and overall accuracy of the final Random Forest was 3.73 and 92.6%, which are comparable to studies mapping large industrial mines with the normalized difference vegetation index (NDVI) and LandTrendr. The most important feature in our study was NDVI, followed by textural features in the near and shortwave infrared. Our work paves the way for future ASM regulation through large area monitoring from space with free and open-source GEE and operational satellites. Studies with sufficient computational resources can improve ASM monitoring with advanced sensors consisting of spectral narrow bands (Sentinel-2, Environmental Mapping and Analysis Program, PRecursore IperSpettrale della Missione Applicativa) and deep learning.

Mapping the dynamics of intensive forage acreage during 2008–2022 in Google Earth Engine using time series Landsat images and a phenology-based algorithm

The increasing demand for livestock feed in China has led to a remarkable increase in forage acreage, particularly in forage fields under intensive agricultural management. However, there are no maps currently available to demonstrate and document the spatial and temporal patterns of the continuous expansion of intensive forage. Here we proposed a pixel- and phenology-based approach to map intensive forage by utilizing time series Landsat images and the Google Earth Engine platform. Given the challenge of accurately identifying intensive forage fields in diverse regions using fixed multi-temporal Landsat data, along with the distinct phenological characteristics of intensive forage fields that undergo multiple annual harvests, we proposed an innovative data mining approach based on dynamic monthly composite images (DMCI). This approach aims to enhance the algorithm’s efficiency and precision in identifying intensive forage. As a pilot study, we analyzed all available Landsat images (1732 scenes) from 2008 to 2022 in a county located in the farming-pastoral ecotone of northern China and tracked the historical dynamics of intensive forage expansion over five epochs at three-year intervals. The accuracy assessment of the intensive forage maps for the five epochs using validation sample points showed that the overall accuracy and Mathews correlation coefficient ranged from 97.1 % to 98.9 % and 0.93 to 0.98 respectively. The intensive forage acreage in the study area exhibited a dramatic expansion from 2008 to 2022, particularly after the 2012s. This study demonstrates the potential of our DMCI- and phenology-based algorithm and time series Landsat images for tracking the dynamics of intensive forage acreage at 30-m resolution in temperate single-cropping regions.

Deep Siamese Network for annual change detection in Beijing using Landsat satellite data

The detection of spatiotemporal changes in land use/land cover (LULC-SC) plays a paramount role in the analysis of smart cities, it can describe complex urban distribution, functions, and patterns. Chinas capital city has been developing rapidly in the past two decades, however, there are only few long-term studies on an annual scale of LULC-SC. To fill this research gap, we propose a remote sensing parallel framework for the detection of LULC-SC based on the combination of the Deep Siamese Network and long time series, which focuses on the spatial semantic information at the object level. A Landsat time series from 2002 to 2022 serves as input satellite data. First, we focus on building graph constructions at the object level and then use an autonomously constructed deeper-feature graph convolutional network (DF-GCN) to mine deeper features, spatial semantic, and relationships at the object level. Finally, the Siamese Network recognizes the changes in the spatial semantic tensors of long time series of Landsat images and quickly maps LULC-SC. The results prove that the proposed spatiotemporal change detection framework is effective in LULC-SC in Beijing. Compared with other networks, the optimal accuracy of semantic mining based on DF-GCN can reach about 90%. Over the past two decades, the LULC-SC of Beijing has changed in a complex way, with urbanization occurring primarily through the replacement of farmland. Consequently, the proposed framework can generate accurate LULC change maps at high temporal frequencies, which can contribute to a better comprehension of sustainable urban development and planning.

Dispossessed lands and land-use change in the Colombian armed conflict: Exploring a link through a regional case study

Land dispossession in the Colombian armed conflict is a critical form of victimization and a trigger of territorial configurations that shape the rurality of the country. The consequences of the illegal appropriation of land by means of usurpation or forced displacement result in a humanitarian drama and various socio-spatial and socio-economic transformations. This research analyzes the intricate dynamics of land use and cover changes in four municipalities within the Cesar department and its relationship with instances of land abandonment and dispossession over a 29-year period (1991–2020). Landsat satellite image time series of TM, ETM+ and OLI data were used to determine land use and land cover (LULC). The classification process was carried out using the Random Forest model. The dynamics of land dispossession and forced abandonment were identified by analyzing 195 land restitution court rulings. Geo-referenced properties were subsequently overlaid on the created LULC maps. As a result, a pattern indicating a direct relationship between both variables was found. Consequently, the phenomenon of land dispossession in the context of this internal armed conflict has contributed to the transition from the traditional agrarian model to the agro-industrial one.

Monitoring the Invasion of S. alterniflora on the Yangtze River Delta, China, Using Time Series Landsat Images during 1990–2022

Spartina alterniflora (S. alterniflora) has grown rapidly in China since its introduction in 1979, showing the trend of alien species invasion, which has seriously affected the ecosystem balance of coastal wetlands. The temporal and spatial expansion law of S. alterniflora can be obtained through remote sensing monitoring, which can provide a reference and basis for S. alterniflora management. This paper presents a method for extracting and mapping S. alterniflora based on phenological characteristics. The coastal areas of the Yangtze River Delta Urban Agglomeration are selected as the research area, and the Landsat time series data from 1990 to 2022 on the Google Earth Engine (GEE) platform are used to support the experiment in this paper. Firstly, the possible growing area of S. alterniflora was extracted using the normalized differential moisture index (NDMI), normalized differential vegetation index (NDVI), and normalized differential water index (NDWI); Then, the time series curve characterizing the phenological characteristics of vegetation was constructed using the vegetation index to determine the difference phase of phenological characteristics between S. alterniflora and other vegetation. Finally, a decision tree was constructed based on the phenological feature difference phase data to extract S. alterniflora, and it is applied to the analysis of temporal and spatial changes of S. alterniflora in the study area from 1990 to 2022. The results show that the area of S. alterniflora increased from ~1426 ha in 1990 to ~44,508 ha in 2022. However, the area of S. alterniflora began to show negative growth in 2015 due to the construction of nature reserves and ecological management. The results of correlation analysis showed that the growth of C. japonicum was significantly affected by temperature stress and weakly affected by precipitation. This study verified that Landsat time series images can effectively extract vegetation phenological information, which has strong feasibility for extraction and dynamic monitoring of S. alterniflora and provides technical support for the management and monitoring of invasive plants in coastal wetlands.

Ecological restoration trajectory of the Taitema Lake wetland in arid northwest China: A 36-year wetland health assessment using Landsat time series data

The Taitema Lake wetland in the lower reaches of the Tarim River (northwest China) is sensitive to hydrological changes and provides necessary ecosystem functions for biodiversity conservation. Monitoring and evaluating the long-term dynamics of the Taitema Lake wetland is essential for conserving and restoring regional wetland ecosystems. This paper used a dense time series of Landsat images from 1986 to 2021 to map the Taitema Lake wetlands. It analyzed the annual and seasonal variations of the wetlands by ten ecological indicators, which include area extent, vegetation, hydrology, and landscape features. A systematic assessment of the ecological quality of the wetlands over the past 36 years, along with their influencing factors, was conducted from the perspective of wetland health. The results showed that (1) the recovered water areas and the wetlands showed a clear trend of ecological restoration since 2000, accompanied by an increased fragmentation. The total wetland area increased from 69.95 km2 in 1986 to 1164.47 km2 in 2021, with a 15.6-fold increase, and the vegetation area increased twentyfold to 639.84 km2. (2) The wetness in the core zone increased consistently across seasons, coupled with a simultaneous vegetation expansion surrounding the core zone. The areas with increasing wetness levels account for 48 % during winter and spring, and the areas with vegetation expansion account for 81 % and 72 % in summer and autumn, respectively. (3) The wetland ecological health status recovered to a “Good” level in 2021 and has not reached the “Excellent” status. The expansion of the water body is the primary driver behind the promotion of wetland ecological recovery. Small water bodies within the area range of 0.05 to 0.1 km2 notably exhibited the most stimulating effects on the wetlands. Thus, increasing the count of smaller water bodies helps rehabilitate vegetation growth and mitigating the water supply’s stress to sustain large lacustrine bodies.

High spatiotemporal-resolution mapping for a seasonal erosion flooding inundation using time-series Landsat and MODIS images

Seasonal erosion flooding events present a significant challenge for effective disaster monitoring and land degradation studies. This research addresses this challenge by harnessing the combined capabilities of time-series Landsat and MODIS images to achieve high spatiotemporal-resolution mapping of flooding during such events. The study underscores the critical importance of precise flood monitoring for disaster mitigation and informed land management. To overcome the limitations posed by the trade-off between spatial and temporal resolution in current satellite sensors, we emplyedand theflexible spatiotemporal data fusion (FSDAF) methods to produce synthetic flood images with enhanced spatiotemporal resolutions for mapping by using MODIS and Landsat data from August 29 to September 3, 2016. A comparison was made between flood maps from several post-disaster forecasts based on ground-obtained time-series images of the Tumen River flood in China. According to the FSDAF approach, the input Landsat image of March 25, 2016, and the fused results had a root mean square error (RMSE) of 0.0301, average difference of 0.001, r of 0.941, and structure similarity indexof 0.939, indicating that temporal variation data had been effectively incorporated into a forecast on August 16, 2016. Results also indicated that the FSDAF forecast values are lower than those from the actual Landsat image. The results of the study also showed that the generated images could be effectively used for flood mapping. By using our newly developed simulation model, we were able to produce a comprehensive map of the inundated areas during the event from August 29 to September 3, 2016. This shows that FSDAF holds great potential for flood prediction and study and has the potential to benefit further disaster-related land degradation by combining multi-source images to provide high temporal and spatial resolution remote sensing information.

Examining the Effects of Soil and Water Conservation Measures on Patterns and Magnitudes of Vegetation Cover Change in a Subtropical Region Using Time Series Landsat Imagery

Soil and water erosion has long been regarded as a serious environmental problem in the world. Thus, research on reducing soil erosion has received continuous attention. Different conservation measures such as restoring low-function forests, closing hillsides for afforestation, planting trees and grass, and constructing terraces on slope land have been implemented for controlling soil erosion problems and promoting vegetation cover change. One important task is to understand the effects of different conservation measures on reducing water and soil erosion problems. However, directly conducting the evaluation of soil erosion reduction is difficult. One solution is to evaluate the patterns and magnitudes of vegetation cover change due to implementing these measures. Therefore, this research selected Changting County, Fujian Province as a case study to examine the effects of implementing conservation measures on vegetation cover change based on time series Landsat images and field survey data. Landsat images between 1986 and 2021 were used to produce time series vegetation cover data using the Google Earth Engine. Sentinel-2 images acquired in 2021 and Landsat images in 2010 were separately used to develop land cover maps using the random forest method. The spatial distribution of different conservation measures was linked to annual vegetation cover and land cover change data to examine the effects on the change in vegetation cover. The results showed a significant reduction in bare lands and increase in pine forests. The vegetation coverage increased from 42% in 1986 to 79% in 2021 in the conservation region compared with an increase from 73% to 87% in the non-conservation region during the same period. Of the different conservation measures, the change magnitude was 0.44 for restoring low-function forests and closing hillsides for afforestation and 0.65 for multiple control measures. This research provides new insights in terms of understanding the effects of taking proper measures for reducing soil and water erosion problems and provides scientific results for decisionmaking for soil erosion controls. The strategy and method used in this research are valuable for other regions in understanding the roles of different conservation measures on vegetation cover change and soil erosion reduction through employing remote sensing technologies.

A remote sensing based index for assessing long-term ecological impact in arid mined land

Large-scale mining operations in western China would accelerate the fragility and degradation to arid ecological environments. Remotely sensed monitoring and assessing of mining effects in long term change could provide sound understanding to guide mine ecological restoration and local ecosystem sustainability. This study's primary goal is to develop an arid mined land status (AMLS) index based on remote sensing data to assess ecological effects in mining areas of arid land. Furthermore, the performance of AMLS was contrasted with that of the remote sensing ecological index (RSEI) and the land surface ecological status composition index (LSESCI) in arid mines under various landscapes conditions. Eventually, the Sen + Mann–Kendall method was used to analyze the temporal and spatial land status variations in the mining effect between 2005 and 2020 based on Landsat time-series images. The following was revealed by the results: (1) AMLS had high correlation (r > 0.65) with each factor, including bare soil flatness, dryness, land surface temperature, and slope. (2) Comparing with LSESCI and RSEI, the proposed AMLS could characterize the mining area and surrounding natural bare soil and rock with different ecological status levels in three mines, hence indicated the mining directional heterogeneity and distance attenuation of ecological status. (3) Based on time series Landsat data, the AMLS demonstrated long-term ecological variability from 2005 to 2020 due to mining and reclamation in three mines.

The analysis of the spatio-temporal changes and prediction of built-up lands and urban heat islands using multi-temporal satellite imagery

Irregular and rapid shifts in built-up lands are among the prime causes of increased Land Surface Temperature (LST) and the subsequent formation of Urban Heat Islands (UHIs). Timely and accurate delineation of built-up lands is fundamental to the understanding of LST variation and environmental changes; however, most of the built-up land maps have coarse resolutions. The present study attempts to develop a combinational index representing the biophysical characteristics of the land surface using exponential functions and Landsat time-series images for accurate built-up land maps on a fine scale. The developed index was used along with the Otsu threshold to categorize different built-up from 2001 to 2021 and then utilized as an input to the CA-Markov model to predict built-up lands in 2031. The corresponding relations between changes in built-up lands and its effects on LST and UHI were also analyzed via partial correlation analysis and bivariate LISA. The accuracy assessments of the proposed model for 2001–2021 revealed an accuracy value of 78 for the period (2001–2021) and a value of 77 when the CA-Markov model was utilized for 2021. The correlation values connecting the built-up land status and LST were also significantly strong and positive (0.86–0.89). The apparent similarities and patterns of clusters linking the built-up lands to UHI were also verified by global bivariate Moran's I index of greater than 0.328, showing how an increase in built-up lands leads to increasing the UHI. These findings are helpful for urban construction planning to minimize the negative environmental impacts of urbanization.

Higher temperature sensitivity of retrogressive thaw slump activity in the Arctic compared to the Third Pole

Climate change exacerbates permafrost thawing, resulting exceptionally intense retrogressive thaw slump (RTS) activity in the Arctic and Third Pole. However, comparative assessments of permafrost characteristics and RTS sensitivity under warming climate at both poles are still lacking. Here, the severity and temperature sensitivity of RTS were presented and compared using Tasselled Cap (TC) trend analysis of time-series Landsat images and Interferometric Synthetic Aperture Radar (InSAR) measurement. RTS has a more severe growth trend in the Arctic cold permafrost region, also with a deformation rate of approximately 70 mm/year and cumulative displacement up to 120 mm. In comparison, the deformation rate in the Third Pole is approximately 50 mm/year. The RTS severity in the Arctic is about 1.5 times higher than in the Third Pole, primarily owing to different sensitivities of cold and warm permafrost under warming climate. The intensification and vulnerability of RTS have global implications on climatological processes, hydrology, carbon release and ground stability, thus calling for attention and effective governance action.

Monitoring the Spatio-Temporal Distribution of Soil Salinity Using Google Earth Engine for Detecting the Saline Areas Susceptible to Salt Storm Occurrence

Recent droughts worldwide have significantly affected ecosystems in various regions. Among these affected areas, the Lake Urmia Basin (LUB) has experienced substantial effects from both drought and human activity in recent years. Lake Urmia, known as one of the hypersaline lakes globally, has been particularly influenced by these activities. The extraction of water since 1995 has resulted in an increase in the extent of salty land, leading to the frequent occurrence of salt storms. To address this issue, the current study utilized various machine learning algorithms within the Google Earth Engine (GEE) platform to map the probability of saline storm occurrences. Landsat time-series images spanning from 2000 to 2022 were employed. Soil salinity indices, Ground Points (GPs), and Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol products were utilized to prepare the training data, which served as input for constructing and running the models. The results demonstrated that the Support Vector Machine (SVM) performed effectively in identifying the probability of saline storm occurrence areas, achieving high R2 values of 91.12%, 90.45%, 91.78%, and 91.65% for the years 2000, 2010, 2015, and 2022, respectively. Additionally, the findings reveal an increase in areas exhibiting a very high probability of saline storm occurrences from 2000 to 2022. In summary, the results of this study indicate that the frequency of salt storms is expected to rise in the near future, owing to the increasing levels of soil salinity resources within the Lake Urmia Basin.

Mapping multi-decadal wetland loss: Comparative analysis of linear and nonlinear spatiotemporal characterization

Wetlands provide critical habitat for birds and endangered species, and can be influenced by shifts in water availability and land use. The Landsat image archive can help both document historical wetland change and monitor current dynamics, but multi-decadal retrospective studies face two important challenges: 1) many important ecohydrological processes occur at scales finer than the field of view of a 30 m Landsat pixel, and 2) ecologically meaningful wetland response units frequently exhibit spatiotemporal complexity in both vegetation and surface water which may not be well-represented by a simple study area spatial mean. Here we explore a novel method for contending with these challenges with application to the rapidly changing Andrade Mesa wetlands along the US-Mexico border. Using a 26-year (1995–2021) Landsat image time series, we track changes in surface water and vegetation using green vegetation (V) and dark (D) fractions estimated from spectral mixture analysis (SMA). Spatiotemporal dynamics in both V and D fractions were characterized using two techniques: linear Principal Component Analysis (PCA) and nonlinear manifold learning (via Uniform Manifold Approximation and Projection; UMAP). Bounding temporal endmembers were identified from PCA and used to construct a temporal mixture model which quantified total area of surface water and vegetation loss, including an uncertainty estimate. UMAP clearly distinguished areas with differing onset of vegetation and water loss which were not identifiable using PCA. Change maps were then associated with different mechanisms and outcomes, including possible mechanical clearing of vegetation and unexpected greening of woody vegetation. The overall timing of surface water and vegetation decline follows the lining of the All-American Canal, suggesting a potential cross-border hydrologic association between loss of a wetland in Mexico and water efficiency measures implemented in the United States.

Improved Modeling of Gross Primary Production and Transpiration of Sugarcane Plantations with Time-Series Landsat and Sentinel-2 Images

Sugarcane croplands account for ~70% of global sugar production and ~60% of global ethanol production. Monitoring and predicting gross primary production (GPP) and transpiration (T) in these fields is crucial to improve crop yield estimation and management. While moderate-spatial-resolution (MSR, hundreds of meters) satellite images have been employed in several models to estimate GPP and T, the potential of high-spatial-resolution (HSR, tens of meters) imagery has been considered in only a few publications, and it is underexplored in sugarcane fields. Our study evaluated the efficacy of MSR and HSR satellite images in predicting daily GPP and T for sugarcane plantations at two sites equipped with eddy flux towers: Louisiana, USA (subtropical climate) and Sao Paulo, Brazil (tropical climate). We employed the Vegetation Photosynthesis Model (VPM) and Vegetation Transpiration Model (VTM) with C4 photosynthesis pathway, integrating vegetation index data derived from satellite images and on-ground weather data, to calculate daily GPP and T. The seasonal dynamics of vegetation indices from both MSR images (MODIS sensor, 500 m) and HSR images (Landsat, 30 m; Sentinel-2, 10 m) tracked well with the GPP seasonality from the EC flux towers. The enhanced vegetation index (EVI) from the HSR images had a stronger correlation with the tower-based GPP. Our findings underscored the potential of HSR imagery for estimating GPP and T in smaller sugarcane plantations.