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Lakes are essential to global freshwater systems, supporting ecosystem services and ecological processes, but they are increasingly impacted by climate change and human activities. This study examined the long-term dynamics of Lakes Abaya and Chamo in the Ethiopian Rift Valley using Landsat images, altimetry-derived lake level data, and a machine learning method within Google Earth Engine, specifically a Multi-Index-based Random Forest (MIRF) classifier. The MIRF classifier achieved high accuracy, ranging from 97.58% to 99.13%, in extracting lake surface water. Substantial fluctuations in lake areas and lake levels were observed: Lake Abaya's area decreased from 2000 to 2005 at a rate of 6.67 km²/year, then expanded until 2022 at the same increasing rate; Lake Chamo's area decreased from 2000 to 2010 at a rate of 1.62 km²/year, then expanded until 2022 at a rate of 2.88 km²/year. The correlation analysis between lake areas and environmental factors such as Rainfall (RF), Temperature (TEMP), Normalized Difference Vegetation Index (NDVI), Groundwater Storage (GWS), Terrestrial Water Storage (TWS), and Soil Moisture (SM) revealed important associations. For Lake Abaya, strong correlations were identified with NDVI and TWS, suggesting that vegetation cover and terrestrial water significantly influence its area changes. In contrast, for Lake Chamo, NDVI emerged as the key driver, indicating that vegetation dynamics play a crucial role in the lake's fluctuations. Furthermore, higher-order polynomial regression models were developed to better capture the complex relationships between lake area and water levels for both lakes. In general, this study integrates remote sensing, machine learning, and cloud computing, offering valuable insights into the lakes' long-term characteristic and providing critical information for future water resource management strategies. [ABSTRACT FROM AUTHOR]

Environmental crises in Iran, including lack of rainfall and drying rivers, wetlands, and lakes, have caused natural and human hazards and vulnerabilities. Meanwhile, the drying of Urmia Lake as a national crisis can significantly create natural and human problems and risks. In this paper, based on annual Landsat satellite images and long-term changes in land cover using the output of the GEE system, we investigate shifts in the water level of Lake Urmia. The effects of its drying on the trend of population changes in surrounding settlements (cities and villages) from 1986 to 2021 indicate that Urmia Lake's water level is expected to be 1271.39 m in 2021. The lake's salt area has increased by 465 Km2 due to its drying trend from 1986 to 2021. The water level estimate for the lake in November 1986 was 1275 m, indicating a 3.7-meter drop in Urmia Lake's water level. Indeed, from 1995 to 2021, the lake lost approximately 48% of its area and 89% of its volume. It was observed that water reduction has a negative effect on the level of Urmia Lake. Modis imagery -using the Linear Fit algorithm- shows an unbalanced relationship in lake water use between 1986 and 2021. The trend of the water area decreasing in Urmia Lake caused five centers of salt dust in the lake. The dust from the five centers significantly impacted the surrounding settlements (cities and villages). Most of the towns in a county like Tabriz (-3.3%), Azarshahr (-0.7%), and Malekan (-0.6%) have negative population growth instead, in urban areas like Urmia (4.3%), Osku (7%), and Azarshahr (3.4%). Increasing in the population happened. Thus, drying lakes and dust and salt centers have a terrible effect on more than 7 million people in villages and cities and intensified migration from 1986 to 2021. [ABSTRACT FROM AUTHOR]

Al- Dalmaj area is a large water body in Iraq in the Afak district, which feeds from the general downstream between the Tigris and the Euphrates. The present study focused mainly on the changes detection in Al-Dalmaj area through analysis of the Landsat images for four selected years (2000, 2002, 2013 and 2017) to determine the changes in marsh waters, wetlands area, drylands areas and other land classes, The Landsat images of Al-Dalmaj area were analysed with the scene that includes the water surface and the area surrounded by the fixed geographical boundaries. The combination of remote sensing and GIS techniques have been found to be successful and effective in monitoring the spatial and temporal changes in land cover. These changes in the land cover of Al-Dalmaj area were detected using various analyses, including Normalised Difference Vegetation (NDVI), the Normalised Difference Water Index (NDWI), the Salinity Index(SI), and the Salinity ratio using Geographic Information Systems (GIS) programs v.10, using ERDAS Imagine v. 9.2 and spectral index techniques were used in this study to determine the land changes, to analyse Landsat images for the years (2000,2002,2013 and 2017). From the application of the equations of evidence referred to, it was found that the salinity (%) increased in the study area. 2002 represents the most extreme stage of the lack of natural conditions in Al- Dalmaj area. It was concluded that the water change was 83.8% for 2002 as 2000 as a reference. As for the natural vegetation, the percentage of change was 84.5% for the same year. The results showed that the percentage change in salinity was 127.5%, a high percentage compared to the rest of the study years. The bulk density values range between (0.70 - 0.89) Mg/m³. [ABSTRACT FROM AUTHOR]

Soil salinization is one of the most widespread soil degradation processes, especially in arid and semi-arid regions. In such climatic conditions, soluble salts accumulate in the soil, leading to deterioration in soil properties and ultimately reduced crop yield. The purpose of this study was to analyse the relationship between the level of soil salinity and the main spectral indicators obtained from Landsat satellite data. The studied area was the Maisky district, which is located in the southeastern part of the Pavlodar region of Kazakhstan. The variants of the research were agricultural lands using sprinkler irrigation and flood irrigation, as well as sites without irrigation. To analyse the relationships, we used the normalized difference vegetation index (NDVI), salinity indices (SI) and soil indices such as SI 1, SI 2, SI 3, SI 4, normalized difference salinity index (NDSI), soil adjusted vegetation index (SAVI), and brightness index (BI). The normalized difference salinity index (R-NIR)/(R + NIR), using a quadratic statistical relationship, showed the best correlation with the laboratory data. The vegetation index NDVI showed the weakest correlation due to dryness or poor crop growth. As a result of the lack of clear control over irrigation and agrotechnical measures, the indicators of cation exchange capacity in irrigated plots using the flooding method were higher than in other irrigation methods. During irrigation, it is necessary to ensure clear rules, according to which the supplied water and fertilizers will have a positive effect on the soil and the entire agroecosystem. The methods used in this research can be useful in mapping and studying saline soils using satellite data in natural and climatic conditions of arid and semi-arid regions.

Rapid urban expansion in Vietnam presents significant management and environmental challenges. This study quantifies urban growth in the Ha Long City area using a time series of six Landsat images acquired from 2000 to 2023. To address the heterogeneity of the urban class, an object-based (OB) image analysis approach was employed. Instead of relying on spectral channels, Principal component analysis (PCA) was utilized during segmentation, followed by classification using the Random Forest (RF) algorithm. For post-classification processing, a logical filter was applied to confirm the classification results at a detailed level, using additional information from the general classification results. Unconfirmed objects were subsequently verified through visual interpretation. The accuracy of the post-classified process results of this study ranges from 83.44% to 96.64% (producer accuracy) and from 83.48% to 91.27% (user accuracy). The results show that the urban area expanded more than four times, with the most significant growth occurring between 2000 and 2005, and between 2015 and 2020. Notably, land reclamation contributed significantly to urban growth. Understanding these trends is crucial for informed urban planning and environmental management in the region.

Soil salinization is one of the most widespread soil degradation processes, especially in arid and semi-arid regions. In such climatic conditions, soluble salts accumulate in the soil, leading to deterioration in soil properties and ultimately reduced crop yield. The purpose of this study was to analyse the relationship between the level of soil salinity and the main spectral indicators obtained from Landsat satellite data. The studied area was the Maisky district, which is located in the southeastern part of the Pavlodar region of Kazakhstan. The variants of the research were agricultural lands using sprinkler irrigation and flood irrigation, as well as sites without irrigation. To analyse the relationships, we used the normalized difference vegetation index (NDVI), salinity indices (SI) and soil indices such as SI 1, SI 2, SI 3, SI 4, normalized difference salinity index (NDSI), soil adjusted vegetation index (SAVI), and brightness index (BI). The normalized difference salinity index (R-NIR)/(R + NIR), using a quadratic statistical relationship, showed the best correlation with the laboratory data. The vegetation index NDVI showed the weakest correlation due to dryness or poor crop growth. As a result of the lack of clear control over irrigation and agrotechnical measures, the indicators of cation exchange capacity in irrigated plots using the flooding method were higher than in other irrigation methods. During irrigation, it is necessary to ensure clear rules, according to which the supplied water and fertilizers will have a positive effect on the soil and the entire agroecosystem. The methods used in this research can be useful in mapping and studying saline soils using satellite data in natural and climatic conditions of arid and semi-arid regions. [ABSTRACT FROM AUTHOR]

The changes in land cover and usage at different scales have significant environmental consequences. Human influences on the environment are happening at magnitudes, rates, and spatial scales that were previously unheard of. The study indicates that the built-up area was he most dominant land use land cover (LULC) class from 1991 to 2021. Agriculture, bare land, vegetation and water have decreased by 41.6%, 50.5%, 47.9%, and 17.84% respectively, while built-up have increased by 141.2%. Agriculture decreased by 142.95 ha/year while bare land, vegetation and water decreased by 243.1 ha/year, 83.11 ha/year and 18.21 ha/year respectively. In contrast, the study showed that the built-up area increased by rate of 487.4 ha/year. All types of land features were converted to built-up land class, with bare land conversion being the most dominant. Agriculture was the second highest area of land that converted to built-up. Vegetation was also converted to built-up, bare land and agriculture. On the other hand, the Normalized Difference Vegetation Index (NDVI) result show that resultant minimum and maximum values ranged from -0.136 to 0.667 in 1991 while it is from -0.12 to 0.56 in 2021. The reduction in vegetation and water areas has led to environmental concerns, such as increased heat island effects, loss of biodiversity, and reduced ecosystem services. Additionally, the Normalized Difference Vegetation Index (NDVI) result shows a decrease in vegetation cover, which can further impact the environment. Therefore, it is crucial to implement effective governance policies that consider the impact of land use changes on the environment and promote sustainable urbanization practices to ensure the preservation of urban green infrastructure. [ABSTRACT FROM AUTHOR]

Study Region: Central Rift Valley (CRV) region, Ethiopia. Study Focus: The study aims to investigate the spatiotemporal change in lake water surface extents over 35 years (1986–2020) and identify potential causes. Three alternative multispectral water indices—Normalized Difference Water Index (NDWI), Automatic Water Extraction Index (AWEI), and Modified Normalized Difference Water Index (MNDWI)—derived from Landsat sensors (TM, ETM+, OLI) were utilized to extract and analyze the lake surface area. The Mann-Kendall correlation was applied to examine whether hydro-meteorological and anthropogenic variables are possible causes for changes. New Hydrological Insights for the Region: MNDWI outperforms other indices with over 96 % accuracy in mapping lake surface area. Lake Abiyata experienced the most significant reduction in surface area from 1986 to 2020 (>50 %), followed by Lake Ziway (

Land-use change is one of the main threats to the environment and natural resources nowadays. Dealing with the impacts of landscape change and its better management needs up-to-date information and data about the rate and amount of changes in different land-use types and spatial configurations. Remote sensing and landscape assessment provide diverse metrics for analyzing spatiotemporal changes over different scales. The purpose of this study is to quantifying the spatiotemporal analysis of landscape change index (LCI) during 1984 to 2016, and predicting the trend of land-use in 2030 using CA–Markov model. Toward this attempt, the Meshgin-Shahr area was chosen as a region with high potential in agriculture and tourism that has undergone many changes in recent decades. Landsat images were used to change detection, and landscape indices were calculated. Also, the LCI has been quantified in different time intervals under study. Also, the CA–Markov model was employed to predict the trend of changes in 2032. The values of CONTAG_MN, AREA_MN, CONNECT, and edge density (ED) landscape metrics have been increased at the class level. Assessing the trend of changes in the landscape metrics showed that the number of patch (NP), patch density (PD), largest patch index (LPI), ED, and total edge (TE) had a decreasing trend at the landscape level, which shows a significant decrease from 2008 to 2016. Accordingly, the value of the LCI was 6.67 in 2008–2016 as the most considerable value among consequent periods. Eventually, the LCI for the predicted period (2016–2032) equals 4.97. The result provides a basis for predicting the landscape characteristics in the future and will help managers to develop effective policies for the conservation of landscape integrity and connectivity. [ABSTRACT FROM AUTHOR]

Coastal erosion is a natural process, that contributed to shaping the Nile Delta shoreline in Egypt over history. The objective of this research is to investigate shoreline changes, accretion, and erosion and to detect particularly vulnerable locations that require immediate attention. Another goal is to assess the efficiency of coastal installations that have been recently implemented along the study area and determine whether they have performed their role to the fullest or need additional modifications. Several Landsat images (TM, ETM+, and OLI) were utilized over 37 years to track the shoreline changes and were analysed using remote sensing (RS) and Geographic Information System (GIS). The digital shoreline analysis system (DSAS) was integrated with the LRR model for assessing historical changes for shorelines from 1985 to 2022 and forecasting future shoreline positions in 2030, 2050, and 2100. Most of the eight zones that make up the Delta region have lately seen the establishment of coastal projects, such as the development of the Abu Qir port's breakwater in 2021. From the results, it was found that the areas around Rosetta promontory, Burullus headland, and Damietta promontory experienced a significant and rapid retreat and with large rates over the study period, with average values of –15.7, –3.25, and –16.8 m/y, respectively. However, both the coast of Alexandria, and Gamasa embayment were subjected to accretion as a prevailing case, with average rates of 2.85, and 4.03 m/y, respectively. Many groins were installed in the east of the Rosetta promontory (zone 3) in 2016 to decrease the erosion process; however, it didn't pay off and could not solve the problem. In contrast, the groins system, which was implemented at the East Kitchener Drain (zone 5) in the same year, lowered erosion rates from 17.6 m/y from 2000 to 2010 to 7 m/y from 2010 to 2022. In 2019, Y-groins built in zone 7, east of Damietta Port, succeeded in slowing rates of erosion. Finally, inlet jetties at El-Gamil (zone 8) were constructed in 2016, resulting in the coastline advancing at 14.7 m/y on average in the period of 2010–2022. The findings of this study confirmed that hard structures are dangerous because they exacerbate the problem of shoreline erosion by disseminating it to the neighbouring beaches and acting as an impediment to the movement of longshore sediments. According to the expected future shoreline patterns, it is necessary for authorities to implement both short-term and long-term protective measures to stop the erosion of several areas of the beach.

Information about the state of vegetation is very important for environmental planning, land preparation and achieving sustainable development. In this study normalized differential vegetation index (NDVI) values were calculated based on Landsat 8 satellite images in order to show temporal and spatial changes in the vegetation cover of agricultural lands in Sistan plain over ten years (2011 to 2020) using the Google Earth Engine platform. Additionally, the NDVI index were classified using decision tree algorithm in order to analyze vegetation changes using thematic change workflow method. By comparing classified images with reference samples which collected from ground sampling, validation was carried out. Then, in order to assess accuracy of vegetation maps, the error matrix was prepared, the overall accuracy and kappa indices were determined. The values of overall accuracy and kappa indices indicated optimal accuracy and it can be stated that there is moderate agreement between ground samples and the classified images (i.e., kappa index is 0.48 to 0.7). The central areas of Sistan plain have a decline in vegetation, whereas areas in northern and eastern have an increase. The cover vegetation on lands of Sistan plain decreased in 19260.4 ha while increased over 25633.2 ha throughout ten years. Examination of NDVI index shows instability of production in this area due to aforementioned factors.

Land use/land cover (LU/LC) change has a considerable effect on the runoff characteristics of any watershed. LU/LC change studies are necessary for policymakers to identify the challenges and adopt the course of action such as soil and water conservation measures for improvement. The current research is undertaken for the upper Shetrunji River basin using a hydrological model linked with GIS. Input data like LU/LC, meteorological, and soil elements are necessary to execute watershed modeling. The model was calibrated and validated in SWAT-CUP using the SUFI-2 algorithm. The data from 2000 to 2010 were utilized for calibration, whereas the data from 2011 to 2020 were used for validation with a three-year warm-up period from 1997 to 1999. LU/LC change detection has been accomplished using ERDAS IMAGINE 2015 via a supervised classification approach. Close agreement was obtained between simulated and observed streamflow data during model calibration (NSE = 0.74 and R2 = 0.78) and validation (NSE = 0.70 and R2 = 0.73). LU/LC change detection shows that agricultural, urban, and water body areas increased between 2000–2010 and 2010–2020, and forest areas increased in 2010 and decreased in 2020. Thus, it is concluded that the change in LU/LC significantly influenced the runoff of the upper Shetrunji River basin. HIGHLIGHTS The study has been made for a water-scarce area of India.; Modern technique of data collection in fastest manner (remote sensing) is used for analysis to get quick and reliable results.; The study relates to the problems of man-made changes to the Earth's surface, thereby tilting the balance of water.;

Forest is one of the key precious resources that support human well-being by providing ecosystem services. Unfortunately, the forest cover has decreased over time due to natural and anthropogenic factors. The present study aims to assess the spatiotemporal forest cover changes and its implication on environmental sustainability in Dedo district in southwestern parts of Ethiopia. We used three Landsat images from 1985, 2002 and 2019. The results show that forest cover areas declined from 64,872 ha in 1985 to 53,805 ha in the year 2019. The study area lost about 11,067 ha (7.7%) of forest over the past 34 years. In contrast, the farmland was increased from 38,801 ha (27.06%) in 1985 to 54,917 ha (38.3%) in 2019. The increasing trend of cultivated land is associated with the increment of human population. Forest cover has been declined in the study area for a variety of reasons, including fire wood collection, charcoal and timber extraction, semi-forest and khat investments, and settlements. Loss of forest cover can have significant implications for environmental sustainability, as forests have played an important role in ecosystem services, such as climate regulation, clean air, flood control, carbon sequestration, soil protection against soil erosion, and increased environmental resilience to the impacts of climate change. The existence of forest resources can contribute to sustainable development of local communities, as the majority of agricultural communities are directly or indirectly dependent on forest products. The findings of this study can be used to improve forest conservation and protection at different scales.

The environmental legacy of post-closure mine sites poses a significant risk to the sustainability of mining operations and natural resource development. This study aims to advance the understanding of sustainable mine site reclamation behavior in Canada by using multi-temporal Landsat satellite images to examine the long-term land cover changes at post-closure mine sites. Six representative post-closure mine sites were selected for the evaluation and comparison. The Normalized Difference Vegetation Index (NDVI) analysis, Landsat image classification, post-classification change detection, and Regrowth Index (RI) analysis were conducted to assess the speed and extent of landscape and vegetation recovery at the target mine sites. A significant vegetation recovery was quantified for the mine sites that have experienced active reclamation activities. In contrast, the post-closure mine area undergoing only passive revegetation typically demonstrated a slow and minor increase in vegetation over time. The actively revegetated mine sites can typically be restored to a vegetation cover level that equals or is better than the pre-mining situation. This work confirms that active reclamation and revegetation at post-closure mine sites is critically important in sustainable mining. The quantified mine site reclamation behavior and the relevant sustainable practices would be useful for evidence-based sustainable resource management in Canada. [ABSTRACT FROM AUTHOR]

This study was designed to examine the trends of LULC transformation and driving forces over the last 35 years (1986 – 2020) in Bako Tibe District, Ethiopia. Results showed that cultivation and shrubland were dominant covering more than two-thirds of the total area. Trend analysis showed that cultivated land (33.26%) and built-up area (2.71%) increased over the years. In contrast, wetland, shrub and grassland had declined by 13.04%, 13.7% and 7.35%, respectively. The majority (96%) of respondents perceived expansion of cultivated land (81.1%) and wood extraction (73.6%) as principal proximate drivers while population growth (92.6) as the underlying factor. [ABSTRACT FROM AUTHOR]

Information about the state of vegetation is very important for environmental planning, land preparation and achieving sustainable development. In this study normalized differential vegetation index (NDVI) values were calculated based on Landsat 8 satellite images in order to show temporal and spatial changes in the vegetation cover of agricultural lands in Sistan plain over ten years (2011 to 2020) using the Google Earth Engine platform. Additionally, the NDVI index were classified using decision tree algorithm in order to analyze vegetation changes using thematic change workflow method. By comparing classified images with reference samples which collected from ground sampling, validation was carried out. Then, in order to assess accuracy of vegetation maps, the error matrix was prepared, the overall accuracy and kappa indices were determined. The values of overall accuracy and kappa indices indicated optimal accuracy and it can be stated that there is moderate agreement between ground samples and the classified images (i.e., kappa index is 0.48 to 0.7). The central areas of Sistan plain have a decline in vegetation, whereas areas in northern and eastern have an increase. The cover vegetation on lands of Sistan plain decreased in 19260.4 ha while increased over 25633.2 ha throughout ten years. Examination of NDVI index shows instability of production in this area due to aforementioned factors. [ABSTRACT FROM AUTHOR]

The spatial and temporal variations of glacier surface area, annual equilibrium line altitude (ELA), and accumulation area ratio (AAR) in Mago basin, Eastern Himalaya, are studied over 1988–2019 using Landsat data. The glacier surface area was mapped using automatic glacier extraction index (AGEI) and 49 glaciers named as G1–G49 were found in 2019. Fifteen glaciers with area greater than 1 km2 in 2019 were selected for analysis. An automatic GIS tool comprising area altitude balance ratio (AABR), AAR, and mean glacier elevation (MGE), and combination of NIR band and Otsu threshold, were used for determining ELA. All glaciers experienced area shrinkage with a total loss of 15.22 km2 (28.48%) at 0.49 km2 (0.92%) per annum. G1, G9, G15, G35, G41, and G42 showed significant decreasing trend in area. Glaciers facing South-West showed highest deglaciation. For the annual ELA, all glaciers showed increasing trend and significant increasing trend was observed in G2, G15, and G40. Overall, there was an increase in ELA by 137.3 m at 4.43 m/year. The average AAR varied from 0.44 to 0.97. Average AAR of about 0.8 was observed in South, South-East, and North facing, about 0.6 in South-West and West facing, and about 0.7 in East facing glaciers. HIGHLIGHTS The spatial and temporal variations of glacier surface area, annual equilibrium line altitude (ELA), and accumulation area ratio (AAR) in an Eastern Himalayan basin throughout for 1988–2019 were studied.; The glacier dynamics of Eastern Himalayan basins, which is very different from well-studied Western Himalayan basins, have been observed and discussed.;

Reconstructing the missing data for cloud/shadow-covered optical satellite images has great significance for enhancing the data availability and multi-temporal analysis. In this study, we proposed a deep-learning-based method for cloud/shadow-covered missing data reconstruction for time-series Landsat images. Central to this method is the combined use of autoencoder, long-short-term memory (AE-LSTM)-based similar pixel clustering and for backward LSTM-based time-series prediction. First, manually delineated cloud/shadow-covered masks were overlaid onto multi-temporal satellite images to produce pixel-wise time-series data with masking values. Second, these pixel-wise time series were clustered by an AE-LSTM-based unsupervised method into multiple clusters, for searching similar pixels. Third, for each cluster of target images, a for-backward-LSTM-based model was established to restore missing values in time series data. Finally, reconstructed data were merged with cloud-free (image) regions to produce cloud-free time-series images. The proposed method was applied onto three datasets of multi-temporal Landsat-8 OLI images to restore cloud/shadow-covered images. The reconstruction results, showing an improvement of greater than 10% in normalized mean-square error compared to the state-of-the-art methods, demonstrated the effectiveness of the proposed method in time-series reconstruction for satellite images.

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Secchi depth (SD) is a valuable and feasible water quality indicator of lake eutrophication. The establishment of an automated system with efficient image processing and an algorithm suitable for the inversion of transparency in lake-rich regions could provide sufficient temporal and spatial information for lake management. These are especially critical for lake-rich regions where in situ monitoring data are scarce. This study demonstrated the implementation of an atmospheric correction algorithm (ACOLITE algorithm) in conjunction with the Google Earth Engine platform to generate remote-sensing reflectance products of specific points efficiently. The study also evaluated the performance of an algorithm for inverting lake SDs in Yunnan Plateau lakes, which is one of the five lake districts in China, since there is a lack of in situ data for most of the lakes in the region. The in situ data from four lakes with large SD ranges and imagery from Landsat Operational Land Imager were used to train and evaluate the performance of two algorithms: an empirical algorithm (stepwise regression) and machine learning (support vector machines and multi-layer perception). The results revealed that the retrieval accuracy of models with bands and band ratio combinations could be substantially improved compared with models with a single band or band combinations. A negative correlation was also observed between the temporal match between observations and the model accuracy. This study found that the MLP model with sufficient training data was more suitable for transparency estimation of lakes belonging to the dataset; the SVM model was more suitable for transparency prediction outside the training set, regardless of the adequacy of the training data. This study provides a reference for monitoring lakes within the Yunnan region using remote sensing. [ABSTRACT FROM AUTHOR]

Abstract: In recent years, Kani Borazan wetland in Mahabad has been at risk due to the drought crisis in the watershed of Urmia Lake, which mostly connected with human societies. Considering the importance of this environmental issue as well as the lack of grounded data using remote sensing technology and geographic information system, changes can be made in the region. Four Landsat 8 satellite images during 2013 to 2016 were investigated.for this purpose. The NDVI maps analysis was done within ArcGIS. Finally, small changes in the water range, dense vegetation, poor vegetation (marshland) have been estimated over the past four years. The results showed that vegetation cover increased in 2016 compared to 2013, while wetland area has decreased in recent years. Wetlands are indicator of climate change in a region. Due to the decrease in the extent and water level of the wetland, it is suggested that more precise conservation plans be applied to reduce and control water consumption in the catchment area of the area. It is also necessary to provide sustainable vegetation monitoring in the wetland.

Coastline changes include accretion (raised soil) and abrasion. This study aims to analyze coastline changes in the Indramayu Regency between 1989 and 2019, as well as the causing factors. The secondary data were obtained from Landsat 5 Satellite Imagery in 1989, 1999, 2009, and Landsat 8 in 2019. This analysis used BILKO method, which is a Landsat image analysis to separate land and sea areas. Then overlay method was used to delineate boundary. Spatial analysis showed coastline changes in the Indramayu regency of both accretion and abrasion. In the 1989–1999 period, there was an accretion of 319 ha, while there was an abrasion 1.291 ha. Furthermore, in the 1999–2009 period, there was an accretion of 349 ha, and an abrasion of 1.125 ha. Also, in the 2009–2019 period, there was an accretion of 698 ha, and abrasion of 358 ha. Meanwhile, the factors that cause coastline changes are natural such as wind, waves, and currents, as well as human factors, such as the conversion of mangrove land to ponds and also planting activities. Therefore, efforts made by the government are making wave containment tools and planting mangrove vegetation. [ABSTRACT FROM AUTHOR]

This paper analyzes the spatio-temporal change of land use and the regression of agricultural and forest areas in the capital of Algeria "Algiers." A methodology based on a Geographic Information System (GIS) environment and remote sensing to process and analyze a series of satellite images from 1987 to 2018 was used to categorize the soil into urban, agricultural, forestry, and water areas. In addition, a spatio-temporal simulation was performed to predict and assess changes in each land use class by 2040. These approaches were supported by a field survey of the entire study area to determine the price and availability of buildable land. Results indicated an increase in urban areas at the expense of agricultural and forest areas. This expansion is randomly driven by poor management and the need to meet increasing housing demand. Also, land prices are lower on the periphery than in the center, while the population is increasing remarkably from year to year and requires both public (roads, schools, hospitals) and private (housing, investments) facilities. The prediction model revealed a 69% urbanization of the study area by 2040 and an abusive consumption of agricultural and forest areas in urban zones. In short, this rate threatens the remaining agricultural and forest areas in the study area. These predicted results are only valid if the studied variables used in this research (demographic evolution, development of infrastructures, and socio-economic facilities) remain unchanged over the next 20 years, since the scenario considered in this research is based on a linear logic linked to past trends. [ABSTRACT FROM AUTHOR]

Changes in land use/land cover (LULC) are a global environmental concern that has a significant impact on sustainable land and water resource management and development as well as hydrological processes. The primary driving forces of LULC changes in developing countries are population pressure, demand for firewood collection and construction material, shortage of cultivated land, land tenure insecurity, and deforestation. Focusing on the Ethiopian Birr river watershed, the study investigates changes in LULC trends, extents, and magnitudes over the last 32 years using geospatial technologies such as Landsat images of 1986, 2001, and 2018. The supervised land use land cover classification technique was applied to classify the LULC classes using the maximum likelihood algorithm technique. Between 1986 and 2018, agricultural land increased significantly from 56.39% to 70.19%, while settlements increased from 0.73%) to 1.42%. On the other hand, bushlands, forest areas, and grasslands LULC classes in the study area decreased from 26.18% to 19.30%, 4.94%–1.92%, and 11.77%–7.16%, respectively. The findings suggest that integrated watershed management and land use planning should be concerned and implemented in the Birr River watershed.

In the present day, the acceleration of urban surface heat impacts resulting from urbanization and industrialization is critical for citizens and municipal governments in developing-country cities. The previous key findings have indicated the association between urban surface heat and the following areas: forests, mixed agricultural land, built-up area, and water bodies. This study was motivated by a lack of knowledge regarding the variation of temperature, evaporation, and humidity in Central Vietnam's major region. The non-parametric Mann–Kendall test, Sen's slope estimator, and Landsat image analysis were employed to determine the trend and statistical significance of the variables across the 42-year study period for Da Nang city and Quang Nam province. Our results show that Da Nang city has a consistent trend with a high correlation between temperature, evaporation, and relative humidity, whereas Quang Nam province showed an inverse relationship between temperature and relative humidity since the beginning of the regional urbanization. The maximum, minimum, and mean temperatures have increased by at least 0.29 °C in Quang Nam province and 0.71 °C in Da Nang city since 2000. Between 1979 and 2021, the frequency of days with temperatures exceeding 35 °C has increased by two and seven days during the past decade at the meteorological stations in Da Nang and Tam Ky, respectively. The temperature in Da Nang city varied from 31.80 °C to 32.82 °C with high temperatures concentrated in urbanized regions with less coverage of small trees, plants, and water bodies. Thus, the results of this study will serve as a scientific basis for decision-makers and regional officials for land-use management and to increase community awareness of sustainable planning, particularly in Da Nang city and Quang Nam province in Central Vietnam. [ABSTRACT FROM AUTHOR]

This study aimed to predict some soil water contents and soil erodibility indices with a multilayer perceptron (MLP) artificial neural network (ANN) using remote sensing data (Landsat 8 OLI TIRS) and topographic variables from a digital elevation model (DEM) in a semi-arid ecosystem. In models, the input variables were derived from remote sensing imaging and DEM. The output variables were field capacity, wilting point, aggregate stability index, structural stability index, dispersion ratio, and clay flocculation index. This study was realized in the watersheds of the Koruluk dam, the Kızlarkalesi, and the Telme ponds built for agricultural irrigation in Gümüşhane-Şiran. The soil samples were obtained from two depths (0–10 cm and 10–20 cm) from 59 soil profiles. Besides field capacity, wilting point, and aggregate stability analysis, undispersed/dispersed sand, silt, clay contents, and organic matter analysis were performed due to their strong effect on soil moisture, soil water content, and erodibility indices. The correlation analysis results showed significant relationships between soil characteristics and soil water contents/soil erodibility indices. The remote sensing variables were derived from three Landsat images of 2015 (June, July, and September). The performance results of MLP ANN models predicted for soil water contents and erodibility indices ranged from 0.75 to 0.90 for R2, 0.046–4.115 for root mean square error (RMSE), 4.46–6.54 for normalized root mean square error (NRMSE), and 0.042–0.186 for mean absolute error (MAE). Topography was a more significant group of variables that affected soil water contents and soil erodibility indices and the feature importance of topography in the prediction was over 55%. The results showed that the use of topographic variables together with remote sensing variables in MLP ANN modeling increased the performance of the models. [ABSTRACT FROM AUTHOR]

The spatiotemporal changes of inundation frequency in the Yellow River Delta (YRD) have profound influences on sustainable ecological protection and are also closely relevant to economic development scarcity on the coast of China. However, long-term changes of inundation frequency have remained poorly characterized. Using the Google Earth Engine (GEE) cloud platform, this study processed Landsat images to explore inundation frequency changes from 1990 to 2020 in the YRD. The results indicated that (1) The existing water index combining the classification results based on the MLM (Maximum Likelihood Method) is suitable for the mapping of the long-term water bodies, especially in the coastal regions; (2) The inundation frequency showed a clearly uneven temporal–spatial distribution. The low inundation area (LIA) is mainly intertidal natural wetlands with a declining trend, while the high inundation area (HIA) is dominated by constructed wetlands with a rising trend; (3) The use frequency of artificial ponds determines the inundation frequency of the constructed wetland. The industry development has gradually matured, causing the inundation frequency from dispersion to concentration in the constructed wetland; and (4) In the natural wetland, the LIA have increased since 2010 and have accounted for 30% in 2020. The large-scale appearance of LIA occurs with the emergence of high vegetation abundance. Spartina alterniflora salt marshes with strong reproductive ability and high abundance lead to the difficulty of wetland flooding and reduce the inundation frequency. [ABSTRACT FROM AUTHOR]

In this paper, we observed the tectono-geomorphological changes along the Dawki Fault and adjoining areas of Sylhet, Bangladesh using multitemporal Landsat satellite imageries from 1980 to 2020. The 40 years time lapse images were used to derive geomorphometric such as sinuosity index (SI), hydraulic sinuosity index (HSI), topographic sinuosity index (TSI), and lineament, directional and lineament density (LD) change analyses. To support these changes, other important parameters, i.e., Bouguer gravity anomaly (BGA) and digital elevation model (DEM) were considered based on their data availability. The present research observed spatiotemporal patterns in these parameters using remote sensing and GIS techniques, where multiple software platforms (i.e., ENVI 5.3, PCI Geomatica Banff 2020, Rockworks 16, ArcGIS 10.8 and MS Excel 2019) were utilized for mapping and data visualization. The study results from multiple parameters such as prominent lineament changes towards NE–SW directions matched well with the Dawki and nearby Disang faults, BGA data variations based on multiple transects, and elevation changes confirmed the increasing intensity of tectonic activity in the study area. Moreover, the lineament distributions, their concentrations, CV, and CSD have also confirmed the present tectonic movements which are especially observed in central, southern and northeastern directions. Research highlights: Tectono-geomorphological changes of the Dawki Fault and adjoining areas of Sylhet, Bangladesh were investigated. Geomorphological river sinuosity such as SSI, HSI, and TSI were measured based on the multi-temporal Landsat satellite data. Among the river sinuosity indices, TSI values were dominated over the HSI indicating the presence of tectonic activity in the study area. The study observed the lineament density change and its directional movements and increased tectonic activity along the Dawki Fault and adjoining areas. The digital elevation model (DEM) supports the tectonic activities and the change results indicating the subsidence and upliftment across the study area. [ABSTRACT FROM AUTHOR]

In order to analyze the impact of land use and land cover change on land surface temperature (LST), remote sensing is the most appropriate tool. Land use/cover change has been confirmed to have a significant impact on climate through various aspects that modulate LST and precipitation. However, there are no studies which illustrate this link in the Fez-Meknes region using satellite observations. Thus, the aim of this study was to monitor LST as a function of the land use change in the Saïss plain. In the study, 12 Landsat images of the year 2019 (one image per month) were used to represent the variation of LST during the year, and 2 images per year in 1988, 1999 and 2009 to study the interannual variation in LST. The mapping results showed that the land use/cover in the region has undergone a significant evolution; an increase in the arboriculture and urbanized areas to detriment of arable lands and rangelands. On the basis of statistical analyses, LST varies during the phases of plant growth in all seasons and that it is diversified due to the positional influence of land use type. The relationship between LST and NDVI shows a negative correlation (LST decreases when NDVI increases). This explains the increase in LST in rangelands and arable land, while it decreases in irrigated crops and arboriculture.

Secchi depth (SD) is a valuable and feasible water quality indicator of lake eutrophication. The establishment of an automated system with efficient image processing and an algorithm suitable for the inversion of transparency in lake-rich regions could provide sufficient temporal and spatial information for lake management. These are especially critical for lake-rich regions where in situ monitoring data are scarce. This study demonstrated the implementation of an atmospheric correction algorithm (ACOLITE algorithm) in conjunction with the Google Earth Engine platform to generate remote-sensing reflectance products of specific points efficiently. The study also evaluated the performance of an algorithm for inverting lake SDs in Yunnan Plateau lakes, which is one of the five lake districts in China, since there is a lack of in situ data for most of the lakes in the region. The in situ data from four lakes with large SD ranges and imagery from Landsat Operational Land Imager were used to train and evaluate the performance of two algorithms: an empirical algorithm (stepwise regression) and machine learning (support vector machines and multi-layer perception). The results revealed that the retrieval accuracy of models with bands and band ratio combinations could be substantially improved compared with models with a single band or band combinations. A negative correlation was also observed between the temporal match between observations and the model accuracy. This study found that the MLP model with sufficient training data was more suitable for transparency estimation of lakes belonging to the dataset; the SVM model was more suitable for transparency prediction outside the training set, regardless of the adequacy of the training data. This study provides a reference for monitoring lakes within the Yunnan region using remote sensing.

Background: Land use/land cover assessment and monitoring of the land cover dynamics are essential to know the ecological, physical and anthropogenic processes in the landscape. Previous studies have indicated changes in the landscape of mid-hills of Nepal in the past few decades. But there is a lack of study in the Chitwan Annapurna Landscape; hence, this study was carried out to fill in study gap that existed in the area. Methods: This study evaluates land use/land cover dynamics between 2000 to 2020 in the central part of the Chitwan Annapurna Landscape, Nepal by using Landsat images. The Landsat images were classified into eight different classes using remote sensing and geographic information system (GIS). The accuracy assessment of classified images was evaluated by calculating actual accuracy, producer's accuracy, user's accuracy and kappa coefficient based on the ground-truthing points for 2020 and Google Earth and topographic maps for images of 2010 and 2000. Results: The results of land use/land cover analysis of Landsat image 2020 showed that the study area was composed of grassland (1.73%), barren area (1.76%), riverine forest (1.93%), water body (1.97%), developed area (4.13%), Sal dominated forest (15.4%), cropland (28.13%) and mixed forest (44.95%). The results of land cover change between 2000 to 2020 indicated an overall increase in Sal dominated forest (7.6%), developed area (31.34%), mixed forest (37.46%) and decrease in riverine forest (11.29%), barren area (20.03%), croplands (29.87%) and grasslands (49.71%). The classification of the images of 2000, 2010 and 2020 had 81%, 81.6% and 84.77% overall accuracy, respectively. This finding can be used as a baseline information for the development of a proper management plan to protect wildlife habitats and forecasting possible future changes, if needed. [ABSTRACT FROM AUTHOR]

【Background】 Inner Mongolia, located in the arid and semi-arid region of central Eurasia, is the home to numerous lakes, rivers and reservoirs. These surface water resources are of great significance to ecosystem functions but have dwindled significantly over the past few decades due to the combined impacts of anthropogenic activities and climate change, leading to environmental deterioration. Understanding spatiotemporal change in these surface waters and their determinants is hence indispensable for improving water management and restoring functions of ecosystems in this region. 【Objective】 This paper is to analyze the spatiotemporal changes in surface water in Inner Mongolia from 1990 to 2015, and the impact of climate change and anthropogenic activities. 【Method】 We divided the whole region into four sub-regions and compared the performance and stability of six spectral indexes: MNDWI, NDWI, AWEI, MBWI, WI2015 and TCW, from which we derived an optimal water-body index most suitable for identifying and extracting surface water bodies for evaluation. 【Result】 The surface water in the region had dwindled steadily from 1990 to 2010 and increased from 2010 to 2015. Except Xing’an League, Hohhot and Wuhai which had seen an increase in surface water resources, surface water in other areas had been in decline during this period. The AWEI water body index threshold was stable and had a good stability and practicality; it was more suitable for identifying and monitoring surface water in this region. 【Conclusion】 Factors impacting surface waters in Inner Mongolia are complex and diverse, but overall, they can be classified as climatic factors and anthropogenic factors, especially the latter which were the main forces driving down the waters in both rivers and lakes.

An exponential function model was developed based on the red band from Landsat and in situ data. The model performed well, the calibration determination coefficient (R2) was 0.82 and validation metrics root-mean-square error, mean absolute percent error, and bias was 8.23 mg/L, 30% and −2.35 mg/L, respectively. Then, the time series of total suspended matter (TSM) concentrations for the lakes across Inner Mongolia were presented using the Landsat data from 1984–2019. The results showed that the number of the lakes with TSM decrease was slightly more than that with TSM increase (57% versus 43%). A total of 70.72% of those lakes had a changing rate less than 1 mg/L/yr in TSM, while the other 29.28% had a changing rate more than 1 mg/L/yr. In some lakes (24.2%), the coefficient of variation was greater than 102% indicating significant spatial variation. The TSM less than 20 mg/L mainly appeared in the reservoirs. Based on different environmental backgrounds, this study showed comprehensive estimates (increasing and decreasing regions) of TSM concentrations. Finally, the relative roles of several factors to the TSM changes were quantified and examined at different scales. The responses of TSM changes to NDVI representing vegetation coverage indicated that vegetation played an important role for most lakes. Whilst wind speed and precipitation significantly affected a few lakes. For most lakes, the TSM were affected by multiple factors, where a single factor could not be highlighted. Nevertheless, a comprehensive analysis of the factors surrounding a lake remains relevant for determining the possible trend (better or worse) of its TSM variation.

Background Land use/land cover assessment and monitoring of the land cover dynamics are essential to know the ecological, physical and anthropogenic processes in the landscape. Previous studies have indicated changes in the landscape of mid-hills of Nepal in the past few decades. But there is a lack of study in the Chitwan Annapurna Landscape; hence, this study was carried out to fill in study gap that existed in the area. Methods This study evaluates land use/land cover dynamics between 2000 to 2020 in the central part of the Chitwan Annapurna Landscape, Nepal by using Landsat images. The Landsat images were classified into eight different classes using remote sensing and geographic information system (GIS). The accuracy assessment of classified images was evaluated by calculating actual accuracy, producer’s accuracy, user’s accuracy and kappa coefficient based on the ground-truthing points for 2020 and Google Earth and topographic maps for images of 2010 and 2000. Results The results of land use/land cover analysis of Landsat image 2020 showed that the study area was composed of grassland (1.73%), barren area (1.76%), riverine forest (1.93%), water body (1.97%), developed area (4.13%), Sal dominated forest (15.4%), cropland (28.13%) and mixed forest (44.95%). The results of land cover change between 2000 to 2020 indicated an overall increase in Sal dominated forest (7.6%), developed area (31.34%), mixed forest (37.46%) and decrease in riverine forest (11.29%), barren area (20.03%), croplands (29.87%) and grasslands (49.71%). The classification of the images of 2000, 2010 and 2020 had 81%, 81.6% and 84.77% overall accuracy, respectively. This finding can be used as a baseline information for the development of a proper management plan to protect wildlife habitats and forecasting possible future changes, if needed.

Human-induced land use land cover changes resulted in adverse impacts on the environment at various spatial and temporal scales. The Highland regions of Ethiopia are typical examples of these phenomena. The objective of this study was to analyze spatiotemporal changes in land use/ land cover and their impacts on land surface temperature in the suha watershed, northwestern highlands of Ethiopia. Multi-temporal Landsat images (1985–2019) were used to analyze LU/LC changes and LST using GIS and remote sensing techniques. Image preprocessing, supervised classification, accuracy assessment, and change detection were conducted to identify LU/LC classes, area coverage, and their transitions. Thermal bands of these satellite images were also used to extract LST.Significant changes in land use/land cover (spatial and temporal) were observed in the suha watershed during the study periods. Agricultural land has got the largest proportion in all study periods. The barren land and built area also expanded greatly in 35 years period. Agricultural land has increased by 15417.6 ha (34.8%) and bare land has expanded by 5297.2 ha (373.6%). However, grazing and shrub lands were reduced by18568.4 ha (72.1%) and 3544.2 ha (47.6%), respectively. Spatial and temporal variation of LST was also observed in the same period. The highest mean values were found on impervious surfaces (built-up areas and bare land), and the lowest values were recorded on forest land. A negative correlation was found between LST and NDVI. Undesirable land use and land cover changes put greater pressure on environmental resources, resulting in an adverse effect on them. Therefore, to reverse this situation and create a balanced ecosystem, management strategies should be applied that mainly focus on soil conservation technologies in steep slope areas, improve afforestation and apply proper land-use policies. The outcomes of this research are useful in designing and implementing appropriate strategies that can address critical social and environmental problems. It also provides new knowledge that helps us better understand the spatiotemporal land use land cover changes and their impacts on LST.

The many widely reported significant environmental changes in sub-Saharan communities caused by population change and expansion of built-up areas have seldom been documented in detail. Poor documentation of environmental changes and their effects may contribute to poor governance and planning in parts of the region. This study examined change in the form and attributes of areas under different land cover in a relatively homogenous Yoruba ethnic group community in Southwestern Nigeria. It used a mix-method approach of remote sensing and social surveys analysis. Landsat images covering the area for 1986, 2000 and 2016 were evaluated for land use/cover change, and the impact of the changes was assessed with social surveys (questionnaire administration and key informant interviews). Results showed a 212.2% increase in the built-up area and a corresponding decrease in forest and surface water area (streams) by 74.4% and 66.3%, respectively. Coping strategies were weak and overtly reactive, as there appeared to be no evidence of any previous plan for response to such degrading effects. The study concluded that urbanization exhibits degrading effects on land and water attributes in the area, and therefore recommends improved monitoring of the region through relevant policy and institutional investitures. [ABSTRACT FROM AUTHOR]

Time-series reconstruction for cloud/shadow-covered optical satellite images has great significance for enhancing the data availability and temporal change analysis. In this study, we proposed a superpixel-based prediction transformation-fusion (SPTF) time-series reconstruction method for cloud/shadow-covered optical images. Central to this approach is the incorporation between intrinsic tendency from multi-temporal optical images and sequential transformation information from synthetic aperture radar (SAR) data, through autoencoder networks (AE). First, a modified superpixel algorithm was applied on multi-temporal optical images with their manually delineated cloud/shadow masks to generate superpixels. Second, multi-temporal optical images and SAR data were overlaid onto superpixels to produce superpixel-wise time-series curves with missing values. Third, these superpixel-wise time series were clustered by an AE-LSTM (long short-term memory) unsupervised method into multiple clusters (searching similar superpixels). Four, for each superpixel-wise cluster, a prediction-transformation-based reconstruction model was established to restore missing values in optical time series. Finally, reconstructed data were merged with cloud-free regions to produce cloud-free time-series images. The proposed method was verified on two datasets of multi-temporal cloud/shadow-covered Landsat OLI images and Sentinel-1A SAR data. The reconstruction results, showing an improvement of greater than 20% in normalized mean square error compared to three state-of-the-art methods (including a spatially and temporally weighted regression method, a spectral–temporal patch-based method, and a patch-based contextualized AE method), demonstrated the effectiveness of the proposed method in time-series reconstruction for multi-temporal optical images.

Because total suspended sediments (TSS) influence the penetration of light into the water column and are likely to carry pollutants and nutrients, their study is essential for understanding the functioning of African river ecosystems. If the estimation of solid transport is important in the context of hydro-agricultural developments, its quantification often poses a problem. In addition, in situ data for these areas are rare and, as a result, the environmental factors responsible for the variability of TSS can hardly be understood. This work aims to evaluate the spatiotemporal variability of TSS in the surface waters of the Senegal River using satellite data over the 2014-2018 period. The spatio-temporal dynamics of TSS is reconstructed using a relationship established on several West African sites between in situ data from TSS and satellite reflectances from Landsat 8. These data allow analyzing the relationship between TSS and factors such as rainfall and discharge. We found that the TSS peaks in Bakel coincide with the arrival of the first rains and are followed by peaks in discharge with a lag of 2 months. A time lag between TSS and discharge peaks is also observed on its tributaries like the River Falémé. Concerning the spatial variability, TSS generally increase from the river upstream to the downstream and decrease in the Senegal delta after the Diama dam. The analysis of the TSS upstream and downstream of the Manantali dam, in the upstream area, confirms the relatively low sediment deposits in the dam lake.

Green spaces are a significant aspect of a healthy urban environment. Dhaka is losing its greener share for decades. However, a quantitative estimation is needed to evaluate the pattern of changes in green space for proper urban management. This study analyzed green spaces of Dhaka city between 1989 and 2020 (30 years) using GIS and Remote Sensing. 30-meter resolution Landsat satellite images were used for the analysis. Supervised classification was performed to calculate the total change in vegetation and differentiation in healthy and non-healthy green spaces were done using NDVI analysis. Results showed that the total decrease in vegetation of the study area was 56% from 1989 to 2020. However, in 1989 the healthy green space was 17% which decreased to only 2% in 2020. Thus, a severe healthy green space loss was detected (−88.24%). The share of urban land was found to be 82% of the total study area in 2020, which was only 59% in 1989. In conclusion, this study confirms the diminishing state of healthy green space in Dhaka city which is not suitable for a healthy urban environment. It is crucial to properly manage and restore Dhaka city's green spaces and give more consideration to ensure healthy urban area in the future policy making.

Land cover changes occur in Indonesia where one of them is in Kelapa Kampit, East Belitung Regency. This area is very famous for large-scale tin mining activities carried out by tin companies. Along with the policy on mining activities that must care about the surrounding environment and reclamation and revegetation activities carried out in post-mining land, the massive tin mining activities in the region are decreasing. This study aims to analyze changes in land cover and find out the adaptation of the community carried out due to changes in land cover and to know the factors that influence the adaptation strategy. This study uses the mixed methods approach. The data used include in the form of Landsat TM satellite imagery acquisition in 1995 and Landsat 8 acquisition in 2015 and interview data. The method of separating land cover objects using the technique of not guided digital classification. After the reclassification process has been carried out, 8 (eight) classes of land cover are produced. The results of the study show (1) there are contradictory conditions between mining land and forests with oil palm plantations. The condition of the mining area and forests from 1994 - 2015 decreased, while the area of oil palm plantations increased significantly. (2) The community adapts to varying sources of income, changes mining areas and relies on assistance from various parties (3) The factors that determine community adaptation are education, knowledge, environmental conditions, people's understanding of job other than mining, sources of income and assistance provided to the community.

In order to analyze the impact of land use and land cover change on land surface temperature (LST), remote sensing is the most appropriate tool. Land use/cover change has been confirmed to have a significant impact on climate through various aspects that modulate LST and precipitation. However, there are no studies which illustrate this link in the Fez-Meknes region using satellite observations. Thus, the aim of this study was to monitor LST as a function of the land use change in the Saïss plain. In the study, 12 Landsat images of the year 2019 (one image per month) were used to represent the variation of LST during the year, and 2 images per year in 1988, 1999 and 2009 to study the interannual variation in LST. The mapping results showed that the land use/cover in the region has undergone a significant evolution; an increase in the arboriculture and urbanized areas to detriment of arable lands and rangelands. On the basis of statistical analyses, LST varies during the phases of plant growth in all seasons and that it is diversified due to the positional influence of land use type. The relationship between LST and NDVI shows a negative correlation (LST decreases when NDVI increases). This explains the increase in LST in rangelands and arable land, while it decreases in irrigated crops and arboriculture. [ABSTRACT FROM AUTHOR]

Currently, coral reef and seagrass ecosystems in Wakatobi Islands are categorized as 'poor' because the coverage of the healthy coral is less than 50%. Therefore, detailed information about their condition through time is needed to prevent coastal degradation. Time-series analysis of Landsat images recorded in 1990, 2000, 2010, and 2017 is expected to provide information on coral reefs' habitat in Wakatobi Islands over the studied period. This study aims to determine areas that experience drastic changes in benthic habitat cover and analyze the dynamics of the changes. The image processing procedures used in this study are image correction, image classification, and applying the Change Detection Model. The image correction consists of geometric, sunglint, and water column correction, while the change detection model uses SPEAR Tools in ENVI software. The results showed a precise location that experienced a drastic change in benthic habitat cover and the changes in benthic habitat classes every ten years, which started from 1990 to 2017. [ABSTRACT FROM AUTHOR]

The spatiotemporal changes of inundation frequency in the Yellow River Delta (YRD) have profound influences on sustainable ecological protection and are also closely relevant to economic development scarcity on the coast of China. However, long-term changes of inundation frequency have remained poorly characterized. Using the Google Earth Engine (GEE) cloud platform, this study processed Landsat images to explore inundation frequency changes from 1990 to 2020 in the YRD. The results indicated that (1) The existing water index combining the classification results based on the MLM (Maximum Likelihood Method) is suitable for the mapping of the long-term water bodies, especially in the coastal regions; (2) The inundation frequency showed a clearly uneven temporal–spatial distribution. The low inundation area (LIA) is mainly intertidal natural wetlands with a declining trend, while the high inundation area (HIA) is dominated by constructed wetlands with a rising trend; (3) The use frequency of artificial ponds determines the inundation frequency of the constructed wetland. The industry development has gradually matured, causing the inundation frequency from dispersion to concentration in the constructed wetland; and (4) In the natural wetland, the LIA have increased since 2010 and have accounted for 30% in 2020. The large-scale appearance of LIA occurs with the emergence of high vegetation abundance. Spartina alterniflora salt marshes with strong reproductive ability and high abundance lead to the difficulty of wetland flooding and reduce the inundation frequency.

In order to reasonably understand the characteristics of the land expansion form and distribution of resource-exhausted cities, Fuxin city, Liaoning province is used as research place, random forest classification is adopted to interpret Landsat image to extract built-up area land; combining with UEI, AUEI etc to analyze characteristics of land use of urban built-up areas in counties and districts of Fuxin city from 1999 to 2019. The result shows that expansion in eastern counties and districts of Fuxin is stronger than that in the west; expansion center moves from the west towards the east; distribution modes and expansion trends in built-up areas of counties and district of Fuxin have their own characteristics. In the past 20 years, land use advantages, patchy position, connectivity, shape and human disturbance degree in Fuxin built-up areas have changed in different degree. The Conclusion of the research is of significance in interpreting expansion characteristics and development of resource-exhausted city reasonably. [ABSTRACT FROM AUTHOR]