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Abstract: The future of coastal wetlands will depend on the combined effects of climate change and human impacts from urbanization and coastal management. Disentangling the effects of these factors is difficult, but satellite imagery archives provide a way to track biological and physical changes in wetlands over recent decades to reveal how coastal wetlands have been changing in response to climate and human drivers. In this study, we used Landsat to monitor the conditions of 32 coastal wetlands in southern California from 1984 to 2019 and identify environmental and human drivers of these trends. Wetland conditions were characterized by vegetation greenness, using the normalized difference vegetation index (NDVI), and by habitat composition, derived from areal estimates of wetland and subtidal habitats. Overall, wetlands displayed three types of long‐term response: greening and gaining wetland (10), greening and losing wetland (16), and browning and losing wetland (6). Regional environmental drivers with overall positive effects on wetland NDVI were sea level, wave height, and precipitation, whereas stream discharge, vapor pressure deficit, and air temperature had negative or nonlinear effects. Wetland area change was primarily correlated with sea level, but response was highly contextual among sites. Negative trends in wetland NDVI and area were more common in larger sites with low elevations and in sites with open inlets. Restoration had mixed effects, with only half of the restored sites showing positive changes in NDVI and wetland area post‐restoration. The important work of managing and restoring urban coastal wetlands is complicated by variability and context and requires us to account for the influence of humans and climate as we build a regional understanding of historic, present, and future wetland health.

Spatially explicit, near real time information on surface water dynamics is critical for understanding changes in water resources, and for long-term water security planning. The distribution of surface water across the African continent since 1984 and updated as every new Landsat scene becomes available is presented here, and validated for the continent for the first time. We applied the Water Observations from Space (WOfS) algorithm, developed and well-tested in Australia, to every Landsat scene acquired over Africa since the mid 1980s to provide spatial information on surface water dynamics over the past 30+ years. We assessed the accuracy of WOfS using aerial and satellite imagery. Four regional geospatial organisations, coordinated through the Digital Earth Africa Product Development Task Team, conducted the validation campaign and provided both the regional expertise and experience required for a continental-scale validation effort. We assessed whether the point was wet, dry, or cloud covered, for each of the 12 months in 2018, resulting in 34,800 labelled observations. As waterbodies larger than 100 km² are easy to identify with Landsat resolution data and can thus boost accuracy, these were masked out. The resulting overall accuracy of the water classification was 82%. WOfS in Africa is expected to be used by ministries and departments of agriculture and water across the continent, by international organisations, academia, and the private sector. A large-scale collaborative effort, which included regional and technical skills spanning two continents was required to create a service that is regionally accurate and is both hosted on, and implemented operationally from, the African continent. [ABSTRACT FROM AUTHOR]

Water quality is deteriorating in the world’s freshwater bodies, and Lake Tana in Ethiopia is becoming unpleasant to biodiversity. The objective of this study is to retrieve non-optical water quality data, specifically total nitrogen (TN) and total phosphorus (TP) concentrations, in Lake Tana using Machine Learning (ML) techniques applied to Landsat 8 OLI imagery. The ML methods employed include Artificial Neural Networks (ANN), Support Vector Regression (SVR), Random Forest Regression (RF), XGBoost Regression (XGB), AdaBoost Regression (AB), and Gradient Boosting Regression (GB). The XGB algorithm provided the best result for TN retrieval, with determination coefficient (R²), mean absolute error (MARE), relative mean square error (RMSE) and Nash Sutcliff (NS) values of 0.80, 0.043, 0.52, and 0.81 mg/L, respectively. The RF algorithm was most effective for TP retrieval, with R² of 0.73, MARE of 0.076, RMSE of 0.17 mg/L, and NS index of 0.74. These methods accurately predicted TN and TP spatial concentrations, identifying hotspots along river inlets and northeasters. The temporal patterns of TN, TP, and their ratios were also accurately represented by combining in-situ, RS and ML-based models. Our findings suggest that this approach can significantly improve the accuracy of water quality retrieval in large inland lakes and lead to the development of potential water quality digital services. [ABSTRACT FROM AUTHOR]

Accurate representation of the location and amount of burned areas is vital to the understanding of patterns and impacts of fires. Some extant burned area maps appear to have high commission errors, which lead to an overrepresentation of burned area. The primary research objective of this study was to assess whether region-specific training data used for machine learning routines improve accuracy of burned area products for western San Diego County. We used training data derived from fine-scale aerial orthoimagery to create and compare three training sets, each with a different Landsat scale sub-pixel burn threshold: 20%, 50%, or 80%. Meaning either 20%, 50%, or 80% of a 30 m × 30 m pixel had to burn for the entire pixel to be classified as burned. High-resolution orthoimagery was used for the creation of the training/testing data as well as determining which sub-pixel threshold leads to more accurate burned area representation. These training data were input into a gradient-boosted regression model. We compared the burned area product from the region-specific gradient boosted model (L-GBRM) to the three products: Monitoring Trends in Burn Severity, Fire and Resource Assessment Program, and the Landsat Burned Area product. We found >20% sub-pixel burn threshold of a Landsat pixel yielded the most accurate classification results. We used a 50% sub-pixel burn threshold for the reference data to compare the results to since the burned area associated with it is closely aligned with the high-resolution orthoimagery determined burn area. The L-GBRM was the most accurate product while also mapping the smallest area burned, suggesting that the extant products have relatively high commission errors. Using region-specific training data achieved a higher accuracy than nationwide training data. Looking at sub-pixel burn thresholds for creating a burned area map could prove to make a more accurate map in terms of area burned represented. [ABSTRACT FROM AUTHOR]

• Landsat bands alone may not give adequate quality of LULC mapping in a heterogeneous landscape. • Remote sensing-derived indices and auxiliary data improve the accuracy of LULC mapping. • LULC maps classified under different classification algorithms have shown a significant discrepancy. Land use/land cover (LULC) information is regarded as one of the most important variables in global change studies. Several LULC classification algorithms are available with different levels of accuracy in LULC mapping under different geographical setups. Furthermore, different remote sensing (RS) based indices and auxiliary data have been reported to have contributed to the accuracy of LULC mapping. Hence, the main aim of this study was to compare the different ML algorithms for the LULC mapping and explicitly assess the potential contribution of different RS-derived indices and auxiliary data on classification accuracy in the Upper Tekeze-Atbarah (UTA) river basin in Ethiopia. The overall classifiers evaluation result revealed that random forest (RF) performed best and followed by K-Nearest Neighbour (KNN), Support Vector Machine (SVM), Artificial Neural Network (ANN), Maximum Likelihood Classification (MLC), and Classification and Regression Tree (CART). Compared to RF, the MLC method showed less accuracy by 16.2% (19.7%) in OA (kappa coefficient) respectively, which showed that the currently emerging ML classifiers contributed to an improvement in LULC mapping. The use of RS-derived indices and DEM as predictors together with the Landsat bands improved the overall accuracy and kappa coefficient by 5.5% and 7% respectively. Furthermore, the discrepancy in the classified LULC map from RF and other classifiers was compared at pixel-level and the result showed a considerable disagreement between the LULC maps. For instance, when the LULC map from RF and KNN are compared, only 80.6% of the area has the same LULC condition. This result reflects the potential discrepancy that could exist in LULC maps from different classifiers. The hydrological simulation under each of the classified map also resulted in a considerable difference in the major water balance components. In a conclusion, given the considerable difference in classifier performance and the potential propagation of errors in LULC maps to further applications such as water resources assessment, it is highly recommended to explicitly evaluate the available classifiers. Users should also harness the power of RS indices and auxiliary datasets for improved LULC mapping. [ABSTRACT FROM AUTHOR]

Remote sensing is an invaluable tool for tracking decadal‐scale changes in vegetation greenness in response to climate and land use changes. While the Landsat archive has been widely used to explore these trends and their spatial and temporal complexity, its inconsistent sampling frequency over time and space raises concerns about its ability to provide reliable estimates of annual vegetation indices such as the annual maximum normalised difference vegetation index (NDVI), commonly used as a proxy of plant productivity. Here we demonstrate for seasonally snow‐covered ecosystems, that greening trends derived from annual maximum NDVI can be significantly overestimated because the number of available Landsat observations increases over time, and mostly that the magnitude of the overestimation varies along environmental gradients. Typically, areas with a short growing season and few available observations experience the largest bias in greening trend estimation. We show these conditions are met in late snowmelting habitats in the European Alps, which are known to be particularly sensitive to temperature increases and present conservation challenges. In this critical context, almost 50% of the magnitude of estimated greening can be explained by this bias. Our study calls for greater caution when comparing greening trends magnitudes between habitats with different snow conditions and observations. At a minimum we recommend reporting information on the temporal sampling of the observations, including the number of observations per year, when long‐term studies with Landsat observations are undertaken. [ABSTRACT FROM AUTHOR]

The urban heat island (UHI) effect has become increasingly prevalent and significant with the accelerated pace of urbanization, posing challenges for urban planners and policymakers. To reveal the spatiotemporal variations of the urban heat island effect in Jinan City, this study utilized Landsat satellite images from 2009, 2014, and 2019, employing the classic Mono-Window algorithm to extract land surface temperature (LST). Additionally, Geodetector was introduced to conduct a detailed analysis of the relationship between LST in Jinan City and land cover types (vegetation, water bodies, and buildings). The results indicate a significant increase in the severity of the urban heat island effect in Jinan from 2009 to 2019, with the central urban area consistently exhibiting a high-intensity core heat island. Suburban areas of Jinan show a clear trend of merging their heat island effects with the central urban area. The combined area of strong cool island effect zones and cool island effect zones within water bodies reaches 89.7%, while the combined proportion of heat island and strong heat island effect zones in building areas is 62.2%. Vegetation cover (FVC) exerts the greatest influence among all factors on the intensity level of the urban heat island effect. These findings provide a reliable basis for decision-making related to urban planning and construction in Jinan City. [ABSTRACT FROM AUTHOR]

A method for estimating river water surface elevation (WSE) from Landsat imagery using the river inundation area–water surface elevation (RIA-WSE) rating curve constructed from the U.S. Geological Survey Topobathymetric Elevation Model (TEM) data was developed and tested at six gauging stations along the Upper Mississippi River. Otsu's automatic threshold selection algorithm was employed for the image classification and estimation of inundation areas within each predefined polygon around each gauging station. In addition to the commonly used green-band-based water indices, Landsat 8 and 9 OLI's ultra-blue, blue, and red band-based water indices were also tested in this study, which resulted in twenty different water indices: NDWIv (Normalized Difference Water Index), MNDWI1v and MNDWI2v (Modified Normalized Difference Water Index), AWEIsv (Automatic Water Extraction Index with shadows), and AWEInsv (AWEI without shadows), where v represents the visible light band used in the water index. At each station, about 60–80 Landsat 8 or 9 images during 2013–2023 were used to assess the performances of the twenty water indices by comparing the estimated WSEs with the measured WSEs. The results showed that the ultra-blue or red band-based AWEIs yielded the most accurate estimations of WSEs among the twenty tested water indices. [ABSTRACT FROM AUTHOR]

Regular detection of land-use and land-cover (LULC) changes with high accuracy is necessary for natural resources management and sustainable urban planning. The produced LULC maps from Google Earth Engine (GEE) also illustrate the transformation of the LULC for the respective landscape over time. The selected study area, Cottbus City and the Spree-Neisse district in northeastern Germany, has undergone significant development over the past decades due to various factors, including urbanization and industrialization; also, the landscape has been converted in some areas for post-mining activities. Detection of LULC changes that have taken place over the last few decades thus plays a vital role in quantifying the impact of these factors while improving the knowledge of these developments and supporting the city planners or urban management officials before implementing further long-term development initiatives for the future. Therefore, the study aims to (i) detect LULC changes for the time slices 2002 and 2022, testing machine learning (ML) algorithms in supervised and unsupervised classification for Landsat satellite imageries, and (ii) validate the newly produced LULC maps with the available regional database (RDB) from the federal and state statistical offices, Germany, and the Dynamic World (DW) near real-time 10 m global LULC data set powered by artificial intelligence (AI). The results of the Random Forest (RF) and the Smilecart classifiers of supervised classification using Landsat 9 OLI-2/TIRS-2 in 2022 demonstrated a validation accuracy of 88% for both, with Kappa Index (KI) of 83% and 84%, respectively. Moreover, the Training Overall Accuracy (TOA) was 100% for both years. The wekaKMeans cluster of the unsupervised classification also illustrated a similar transformation pattern in the LULC maps. Overall, the produced LULC maps offered an improved representation of the selected region's various land-cover classes (i.e., vegetation, waterbodies, built areas, and bare ground) in the last two decades (20022 to 2022). [ABSTRACT FROM AUTHOR]

Water temperature is a fundamental parameter of aquatic ecosystems. It directly influences most processes occurring within them. Hence, knowledge of this parameter's behavior, based on long-term (reliable) observations, is crucial. Gaps in these observations can be filled using contemporary methodological solutions. Difficulties in reconstructing water temperature arise from the selection of an appropriate methodology, and overcoming them involves the proper selection of input data and choosing the optimal modeling approach. This study employed the air2water model and Landsat satellite imagery to reconstruct the water temperature of Lake Miedwie (the fifth largest in Poland), for which field observations conducted by the Institute of Meteorology and Water Management—National Research Institute ended in the late 1980s. The approach based on satellite images in this case yielded less accurate results than model analyses. However, it is important to emphasize the advantage of satellite images over point measurements in the spatial interpretation of lake thermal conditions. In the studied case, due to the lake's shape, the surface water layer showed no significant thermal contrasts. Based on the model data, long-term changes in water temperature were determined, which historically (1972–2023) amounted to 0.20 °C per decade. According to the adopted climate change scenarios by the end of the 21st century (SSP245 and SSP585), the average annual water temperature will be higher by 1.8 °C and 3.2 °C, respectively. It should be emphasized that the current and simulated changes are unfavorable, especially considering the impact of temperature on water quality. From an economic perspective, Lake Miedwie serves as a reservoir of drinking water, and changes in the thermal regime should be considered in the management of this ecosystem. [ABSTRACT FROM AUTHOR]

Soils play a central role in ecosystem functioning, and thus, mapped soil property information is indispensable to supporting sustainable land management. Digital Soil Mapping (DSM) provides a framework to spatially estimate soil properties. However, broad-scale DSM remains challenging because of non-purposively sampled soil data, large data volumes for processing extensive soil covariates, and high model complexities due to spatially varying soil–landscape relationships. This study presents a three-dimensional DSM framework for Switzerland, targeting the soil properties of clay content (Clay), organic carbon content (SOC), pH value (pH), and potential cation exchange capacity (CECpot). The DSM approach is based on machine learning and a comprehensive exploitation of soil and remote sensing data archives. Quantile Regression Forest was applied to link the soil sample data from a national soil data base with covariates derived from a LiDAR-based elevation model, from climate raster data, and from multispectral raster time series based on satellite imagery. The covariate set comprises spatially multiscale terrain attributes, climate patterns and their temporal variation, temporarily multiscale land use features, and spectral bare soil signatures. Soil data and predictions were evaluated with respect to different landcovers and depth intervals. All reference soil data sets were found to be spatially clustered towards croplands, showing an increasing sample density from lower to upper depth intervals. According to the R2 value derived from independent data, the overall model accuracy amounts to 0.69 for Clay, 0.64 for SOC, 0.76 for pH, and 0.72 for CECpot. Reduced model accuracies were found to be accompanied by soil data sets showing limited sample sizes (e.g., CECpot), uneven statistical distributions (e.g., SOC), and low spatial sample densities (e.g., woodland subsoils). Multiscale terrain covariates were highly influential for all models; climate covariates were particularly important for the Clay model; multiscale land use covariates showed enhanced importance for modeling pH; and bare soil reflectance was a major driver in the SOC and CECpot models. [ABSTRACT FROM AUTHOR]

The temporal behaviors of land surface temperature (LST) coupled with its associated parameters play a crucial role in determining the microclimate at the city scale. The increasing pattern of LST and consequent changes in biophysical parameters (parameters specify the amalgamation of living system with their physical characteristics including vegetation, water, built-up, bareness and drought parameters) at monthly, seasonal and annual time spans and from regional to global scale need to be comprehensively evaluated. The present study deals with LST estimation along with other spectral indices including Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI), Normalized Difference Built-up Index (NDBI), Normalized Difference Bareness Index (NDBaI), and Normalized Multi-band Drought Index (NMDI) using Landsat series datasets from 1991 to 2022 of Aligarh city, Uttar Pradesh, India. The spatial pattern of LST indicates that the areas having water bodies and dense vegetation are colder such as the Aligarh Muslim University Campus and the catchment of Ganga canal areas, whereas the areas of high urbanization and bare grounds reflect high LST trends. Study finds a positive correlation of LST with NDBI (R2-0.56), NDBaI (R2-0.22) and NDWI (R2-0.22), whereas a negative correlation with NDVI (R2-0.35), MNDWI (R2-0.36) and NMDI (R2-0.41). Land use land cover (LULC)-based change detection in land cover classes was found consistent with the obtained results for spectral indices and LST patterns in the study area. Finally, the cross-validation using Modern-Era Retrospective analysis for Research and Applications (MERRA) reanalysis-based products of earth skin temperature and rainfall showed a good fit between observed and reanalysis products. [ABSTRACT FROM AUTHOR]

Surface urban heat island intensity (SUHII), which is the difference in land surface temperature (LST) between urban and rural areas, depends on selection of rural reference and studies that have analyzed the impacts of different methods on estimating SUHII are few. This study aims to analyze impact of two different methods for defining rural areas, buffer area around city, and non-urban areas within city, on estimating SUHII in five major cities of South Korea using satellite measurements for period 2018–2022. Using a buffer region around city to define rural reference area results in a higher estimate of SUHII compared to using rural areas within city limits. The largest differences in SUHII were found in Seoul (both day and night) and Busan (night), with Seoul exhibiting a difference of 1.14°C (day) and 1.51°C (night), and the differences are mostly driven by the physical characteristics of the city and geographical location. [ABSTRACT FROM AUTHOR]

The article focuses on how meteorological factors, such as rainfall and temperature, influence changes in land use within the marshes of the Al-Majar Al-Kabir region in southern Iraq. Topics include the impact of temperature and rainfall on land cover types, the use of Landsat satellite data for monitoring these changes, and the implications for soil conservation and marshland management.

Land use and land cover changes (LULC) are one of the main factors in global environmental change, as well as one of the main causes of soil and biodiversity loss. The main objective of this research was to determine the dynamics of land use changes in the Bustillos basin located in the municipality of Cuauhtémoc, Chihuahua, Mexico. The research consisted of the processing and analysis of satellite images from Landsat Thematic Mapper (TM5) and Landsat Operational Land Imager (OLI8). From the classifications obtained through satellite images, six categories of land use were obtained and later were compared through the use of a cross-tabulation matrix. The results showed that the use of remote sensors applied to the evaluation of the dynamics of land use changes allowed for knowing the changes that occurred in a period of 43 years. To compare the land use of the years 1974 and 2016, maps were obtained with soil covers. This served as the basis for the analysis of the changes that occurred. In this way, it was possible to determine the growth of the urban area (7851.48 hectares) due to the development of economic activity and the increase in population. The category that had a more significant increase was the agricultural areas with a gain in surface of the sub-basin of 28,334.23 hectares. The grassland class lost 21,385.28 hectares; this area was associated with the class of agricultural areas and urban areas. The oak–pine forest had losses of 9150.03 hectares, as well as the pine forest (586.06 hectares). Finally, the class of water bodies lost 228.02 hectares. The results showed that the implementation of dynamic LULC using geographic information systems could be adopted as a planning tool to manage LULC in the Bustillos basin in the future. [ABSTRACT FROM AUTHOR]

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Modeling the impacts of Land Use/Land Cover changes (LULCC) on Land Surface Temperature (LST) is crucial in understanding and managing urban heat islands, climate change, energy consumption, human health, and ecosystem dynamics. This study aimed to model past, present, and future LULCC on Land Surface Temperatures in the Greater Amman Municipality (GAM) in Jordan between 1980 and 2030. A set of maps for land cover, LST, Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), and topography was integrated into the Cellular Automata-Artificial Neural Network (CA-ANN) and the Long-Short-Term Model (LSTM) models to predict the LULC and LST for 2030. The results showed an expansion of urban areas in GAM from 54.13 km2 (6.6%) in 1980 to 374.1 km2 (45.3%) in 2023. However, agricultural areas decreased from 152.13 km2 (18.5%) in 1980 to 140.38 km2 (17%) in 2023, while barren lands decreased from 54.44 km2 (6.6%) in 1980 to 34.71 km2 (4.22%) in 2023. Forested areas declined from 4.58 km2 (0.56%) in 1980 to 4.35 km2 (0.53%) in 2023. Rangelands/ sparsely vegetated areas declined from 557 km2 (67.7%) in 1980 to 270.71 km2 (32.9%) in 2023. The results of modeling LST showed an increase in average LST for all land cover types, with the most significant increases evident within urban areas and Rangelands/Sparsely vegetated areas. The slightest increase in LST was within forested areas as the average LST increased from 28.42 °C in 1980 to 34.16 °C in 2023. The forecasts for the future showed a continuous increase in LST values in all land cover types. These findings highlight the impact of land surface dynamics and their impact on increasing land surface temperature, which urges the adoption of more sustainable planning policies for more livable and thermally comfortable cities. [ABSTRACT FROM AUTHOR]

E'bian Yi Autonomous County is a mineral-rich area located in a complex geological structure zone. The region experiences frequent geological disasters due to concentrated rainfall, steep terrain, and uneven vegetation cover. In particular, during the rainy season, large amounts of rainwater rapidly accumulate, increasing soil moisture and slope pressure, making landslides and debris flows more likely. Additionally, human activities such as mining, road construction, and building can alter the original geological structure, exacerbating the risk of geological disasters. According to publicly available data from the Leshan government, various types of geological disasters occurred in 2019, 2020, 2022, and 2023, resulting in economic losses and casualties. Although some studies have focused on geological disaster issues in E'bian, these studies are often limited to specific areas or types of disasters and lack comprehensive spatial and temporal analysis. Furthermore, due to constraints in technology, funding, and manpower, geophysical exploration, field geological exploration, and environmental ecological investigations have been challenging to carry out comprehensively, leading to insufficient and unsystematic data collection. To provide data support and monitoring for regional territorial spatial planning and geological disaster prevention and control, this paper proposes a new method to study the correlation between soil moisture changes and geological disasters. Six high-resolution Landsat remote sensing images were used as the main data sources to process the image band data, and terrain factors were extracted and classified using a digital elevation model (DEM). Meanwhile, a Normalized Difference Vegetation Index–Land Surface Temperature (NDVI-LST) feature space was constructed. The Temperature Vegetation Drought Index (TVDI) was calculated to analyze the variation trend and influencing factors of soil moisture in the study area. The research results showed that the variation in soil moisture in the study area was relatively stable, and the overall soil moisture content was high (0.18 < TVDI < 0.33). However, due to the large variation in topographic relief, it could provide power and be a source basis for geological disasters such as landslide and collapse, so the inversion value of TVDI was small. The minimum and maximum values of the correlation coefficient (R2) were 0.60 and 0.72, respectively, indicating that the surface water content was relatively large, which was in good agreement with the calculated results of vegetation coverage and conducive to the restoration of ecological stability. In general, based on the characteristics of remote sensing technology and the division of soil moisture critical values, the promoting and hindering effects of soil moisture on geological hazards can be accurately described, and the research results can provide effective guidance for the prevention and control of geological hazards in this region. [ABSTRACT FROM AUTHOR]

An accurate depiction of wildfire, harvesting, and insect outbreak disturbances is essential for sustainable ecosystem management of forests in Canada. Even though the advent of temporally consistent 30‐m resolution Landsat data has enabled the detailed mapping of forest disturbances in Canada from 1985 onward, the disturbance record prior to 1985 remains sparse. This study aimed to extend the existing pre‐1985 disturbance history record by mapping wildfire, harvest, and insect outbreaks in Canadian forests between 1965 and 1984. Our geospatial data processing methodology relied on multilayer perceptrons (MLP) trained on spectral recovery signatures to map and age these disturbances. Our model detected approximately 4.8, 7.3, and 3.8 million ha of burnt, harvested, and insect‐ravaged forest areas, respectively, that were absent from national and provincial disturbance databases and forest inventories. Results were validated using both internal and external validation datasets. Our disturbance detection methodology was highly effective, with an internal validation kappa score of 0.91 and an external score of 0.81. The fire and harvest age disturbance MLPs, whose predictions can also be used as a proxy of forest stand age, performed adequately on the internal (fire R2 = 0.675; root mean squared error [RMSE] = 4.42; harvest R2 = 0.723; RMSE = 3.17) and external validation datasets (fire R2 = 0.242; RMSE = 4.69; harvest R2 = 0.257; RMSE = 5.46), outperforming existing forest age disturbance products. Finally, we relied on several open data products, such as provincial forest inventories, to correct our disturbance type and year prediction whenever these more reliable, but incomplete, data sources were available. Specific years were not assigned to insect outbreaks due to the lack of dependable training and validation data. We also illustrate how extending the existing forest disturbance record by 20 years may provide a more in‐depth understanding of landscape‐disturbance dynamics with a case study of the 2023 Canadian wildfire season. [ABSTRACT FROM AUTHOR]

Currently, the global climate system is complex and ever-changing, with multiple factors influencing climate change. The Qinghai–Tibet Plateau, known as the "Third Pole" of the Earth, is particularly sensitive to global climate change. Without timely and scientific research on the ecological environment of the Qinghai–Tibet Plateau and without summarizing relevant adaptive strategies, global climate change will impact the sustainable development of the plateau. This study utilized Landsat remote sensing images from 2000 to 2020 to extract the snow cover area and snow temperature of the Qinghai–Tibet Plateau using the snow frequency threshold method. The study analyzed the spatiotemporal characteristics of snow cover and temperature over the 20-year period and investigated some of the climate and topographical driving factors influencing their changes. The results showed that from 2000 to 2020, the permanent snow cover area in the Qinghai–Tibet Plateau region showed a fluctuating decreasing trend, reducing from approximately 12.34 thousand km2 to around 9.01 thousand km2; the permanent snow temperature showed an initial increase followed by a decrease during the same period. The highest annual average snow temperature was approximately −3.478 °C, while the lowest annual average temperature was around −8.150 °C. Over the 20-year period, the snow cover area in the plateau was negatively correlated with temperature and precipitation, while snow temperature was positively correlated with temperature and precipitation. The snow cover in the weak wind areas of the plateau showed a significant reduction. Areas with higher average wind speeds, such as shaded slopes and semi-shaded slopes, had larger snow cover areas. These research findings provide important insights into the protection and management of the ecological environment of the Qinghai–Tibet Plateau. [ABSTRACT FROM AUTHOR]

Soil salinity plays an important role in agriculture production and land degradation, especially in semi-arid and arid regions. Accurate prediction of soil salinity requires evaluating crop yield, native vegetation situations, and irrigation command area management. In this study, MLR (multiple linear regression), SVMs (support vector machines) and ANNs (artificial neural networks) models were employed by using Landsat-8 OLI and GIS (Geographical Information Systems) techniques for predicting soil salinity in northwest Iran. Soil salinity was measured at 92 points (in a depth of 0–20 cm). The vegetation and soil salinity spectral indices, extracted from Landsat-8 OLI, were employed as input data. The results of this study indicated that the best-developed model for predicting soil salinity was the SVM-based model with R2 (0.874) and RPD (2.32) and the lowest RMSE (11.20 dS m−1). Moreover, the performance of developed models under different vegetation coverage showed that the SVM-based model yielded the best result. It was concluded that the SVM-based model is reliable for quantifying soil salinization. [ABSTRACT FROM AUTHOR]

The long-term monitoring of wetland ecosystem functioning is critical because wetlands, which provide multiple services, can be affected by human activities and climate change. The aim of this study was to monitor wetland ecosystem functioning in the long term using the Landsat archive. Four contrasting, Ramsar wetlands were selected in boreal, temperate, arid, and tropical areas. First, the annual sum of the normalized difference vegetation index (NDVI-I) was calculated as an indicator of annual net primary productivity for the period 1984–2021 using the continuous change detection and classification (CCDC) algorithm. Next, the influence of the number of Landsat images and class of land use and land cover (LULC) on the accuracy of the CCDC was investigated. Finally, correlations between annual NDVI-I and climate were analyzed. The results revealed that NDVI-I accuracy was influenced mainly by the LULC class and to a lesser extent by the number of cloud-free Landsat observations. Infra- and inter-site variations in NDVI-I were high and showed an overall increasing trend. NDVI-I was positively correlated with the mean temperature. This study shows that this approach applied in contrasting sites is robust for the long-term monitoring of wetland ecosystem functioning and can be used to improve the implementation of international biodiversity conservation policies. [ABSTRACT FROM AUTHOR]

Land use land cover plays an essential role in altering regional micro and macro climate, diminishing biodiversity, and significantly influences Land Surface Temperature (LST). However, no research has been conducted in the Shigar district to investigate LULC changes, particularly the correlation between LST and vegetation/snow cover at higher elevated regions. Therefore, this study was carried out in the Shigar district of Gilgit Baltistan Region, Pakistan, due to its extreme climatic variability. In this study, we investigated the spatiotemporal variability of LULC and its impacts on LST in the Shigar district, utilizing Landsat data from 1993 to 2020. To study the variable LST, thermal bands of Landsat imagery were used. LULC was determined by classifying the images using the supervised maximum likelihood classification method. The vegetation and snow cover were also extracted by using the indices of the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Snow Index (NDSI), respectively. The results showed that the Shigar district had experienced severe land cover variations in the years of 1993, 2003, 2010, and 2020. Specifically, the agricultural area gradually increased from 1993 to 2020. The forest/vegetation cover also increased in the study area. In contrast, snow cover and water bodies were drastically reduced during this period. The overall vegetation increased within the region and the LST values were found to be decreased in 2003 and 2010, whereas the LST rose in 2020. In addition, the correlation between NDVI and LST was found to be significant negative in areas with high vegetation cover. Conversely, it was weakly positive in other areas with minor vegetations. These findings suggest that green cover plays a crucial role in regulating temperature. Additionally, a negative correlation between LST and NDSI was observed with rising elevation. This study will be helpful in landscape and urban planning, watershed management, and policymaking for climate change variability. [ABSTRACT FROM AUTHOR]

Paramos, unique and biodiverse ecosystems found solely in the high mountain regions of the tropics, are under threat. Despite their crucial role as primary water sources and significant carbon repositories in Colombia, they are deteriorating rapidly and garner less attention than other vulnerable ecosystems like the Amazon rainforest. Their fertile soil and unique climate make them prime locations for agriculture and cattle grazing, often coinciding with economically critical deposits such as coal which has led to a steady decline in paramo area. Anthropic impact was evaluated using multispectral images from Landsat and Sentinel over 37 years, on the Guerrero and Rabanal paramos in central Colombia which have experienced rapid expansion of mining and agriculture. Our analysis revealed that since 1984, the Rabanal and Guerrero paramos have lost 47.96% and 59.96% of their native vegetation respectively, replaced primarily by crops, pastures, and planted forests. We detected alterations in the spectral signatures of native vegetation near coal coking ovens, indicating a deterioration of paramo health and potential impact on ecosystem services. Consequently, human activity is reducing the extent of paramos and their efficiency as water sources and carbon sinks, potentially leading to severe regional and even global consequences. [ABSTRACT FROM AUTHOR]

Monitoring and providing accurate land use and land cover (LULC) change information is vital for sustainable environmental planning. This study used Landsat imagery from 2002 to 2022 to create updated LULC change maps for the eThekwini Municipality. Random Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost) were used to conduct these LULC classifications, with XGBoost achieving the highest accuracy (80.57%). The generated maps revealed a significant decrease in cropland and an increase in impervious surfaces. As such, this research established a framework for continuous LULC mapping and highlighted Landsat 9’s potential in LULC classifications. [ABSTRACT FROM AUTHOR]

The population of Africa is expected to rise to 2.5 billion by 2050, with more than 80% of this increase concentrated in cities. Africa's anticipated population growth has serious implications for urban resource utilization and management, necessitating multi-level monitoring efforts that can inform planning and decision-making. Commonly, broad extent (e.g., country level) urban change analyses only examine a homogenous "developed" or "built-up" area, which may not capture patterns influenced by the heterogeneity of landscape features within urban areas. Contrarily, studies examining landscape heterogeneity at a finer resolution are typically limited in spatial extent (e.g., single city level). The goal of this study was to develop and test a hierarchical integrated mapping framework using globally available Earth Observation data (e.g., Landsat, Sentinel-2, Sentinel-1, and nightlight imagery) and accessible methodologies to produce national-level land use (LU) and urban-level land cover (LC) map products which may support a range of global and local monitoring and planning initiatives. We test our multi-tier methodology across three rapidly urbanizing African countries for the 2016–2020 period: Ethiopia, Nigeria, and South Africa. The initial output of our methodology includes annual national land use maps (Tier 1) for the purpose of delineating the dynamic boundaries of individual urban areas and monitoring national LU change. To complement Tier 1 LU maps, we detailed urban heterogeneity through LC classifications within urban areas (Tier 2) delineated using Tier 1 LU maps. Based on country-optimized sets of selected features that leverage spatial/texture and temporal dimensions of available data, we obtained an overall map accuracy of between 65 and 80% for Tier 1 maps and between 60 and 80% for Tier 2 maps, dependent on the evaluation country, although with consistent performance across study years providing a solid foundation for monitoring changes. We demonstrate the potential applications for our products through various analyses, including urbanization-driven LU change, and examine LC urban patterns across the three African study countries. While our findings allude to general differences in urban patterns across national scales, further analyses are needed to better understand the complex drivers behind urban LC configurations and their change patterns across different countries, city sizes, and rates of urbanization. Our multi-tier mapping framework is a viable strategy for producing harmonious, multi-level LULC products in developing countries using publicly available data and methodologies, which can serve as a basis for a wide range of informative and insightful monitoring analyses. [ABSTRACT FROM AUTHOR]

Landsat data have been used to derive forest canopy structure, height, and volume using machine learning models, i.e., giving computers the ability to learn from data and make decisions and predictions without being explicitly programmed, with training data provided by ground measurement or airborne lidar. This study explored the potential use of Landsat reflectance and airborne lidar data as training data to estimate canopy heights in the Brazilian Amazon forest and examined the impacts of Landsat reflectance products at different process levels and sample spatial autocorrelation on random forest modeling. Specifically, this study assessed the accuracy of canopy height predictions from random forest regression models impacted by three different Landsat 8 reflectance product inputs (i.e., USGS level 1 top of atmosphere reflectance, USGS level 2 surface reflectance, and NASA nadir bidirectional reflectance distribution function (BRDF) adjusted reflectance (NBAR)), sample sizes, training/test split strategies, and geographic coordinates. In the establishment of random forest regression models, the dependent variable (i.e., the response variable) was the dominant canopy heights at a 90 m resolution derived from airborne lidar data, while the independent variables (i.e., the predictor variables) were the temporal metrics extracted from each Landsat reflectance product. The results indicated that the choice of Landsat reflectance products had an impact on model accuracy, with NBAR data yielding more trustful results than the other products despite having higher RMSE values. Training and test split strategy also affected the derived model accuracy metrics, with the random sample split (randomly distributed training and test samples) showing inflated accuracy compared to the spatial split (training and test samples spatially set apart). Such inflation was induced by the spatial autocorrelation that existed between training and test data in the random split. The inclusion of geographic coordinates as independent variables improved model accuracy in the random split strategy but not in the spatial split, where training and test samples had different geographic coordinate ranges. The study highlighted the importance of data processing levels and the training and test split methods in random forest modeling of canopy height. [ABSTRACT FROM AUTHOR]

Land surface temperature (LST) plays a crucial role in characterizing land surface processes and the energy balance. Accurate monitoring of spatial–temporal variations in LST holds great significance for global and regional climate change research. However, with the increasing of multi-source remote sensing data, it has become a challenging task to construct an LST model that reduces dependence on auxiliary data (such as land surface emissivity (LSE) and atmospheric water vapor content (WVC) synchronized with satellite observations. This study proposed an LST retrieval model based on random forest model (RFLSTR). The results of 10-fold cross-validation showed that the RFLSTR model had high modelling accuracy, the root mean square error (RMSE) and mean absolute error (MAE) were lower than 2 K. Landsat 8 images from the Beijing area and South Korea, were utilized to assess the spatial–temporal migration performance of the RFLSTR model. Additionally, Landsat 9 images from South Korea were employed to analyse the sensor migration performance of the RFLSTR model. In conclusion, the findings highlight that the proposed RFLSTR model achieve high retrieval accuracy without relying on auxiliary data (such as LSE and WVC) synchronized with satellite observations. Moreover, the RFLSTR model exhibits robust spatial–temporal capabilities, enhancing the efficiency of LST retrieval and facilitating the utilization of satellite data. [ABSTRACT FROM AUTHOR]

Investigation of cities' spatiotemporal dynamics, including vegetation and urban areas, is of utmost importance for understanding ecological balance, urban planning, and sustainable development. This study investigated the dynamic interactions between vegetation, settlement patterns, and surface urban heat islands (SUHIs) in Istanbul using spatiotemporal hotspot analysis. Utilizing Landsat satellite imagery, we applied the Getis-Ord Gi* statistic to analyze Land Surface Temperature (LST), Urban Index (UI), and Normalized Difference Vegetation Index (NDVI) across the city. Using satellite images and the Getis-Ord Gi* statistic, this research investigated how vegetation and urbanization impact SUHIs. Based on the main results, mean NDVI, UI, and LST values for 2009 and 2017 were analyzed, revealing significant vegetation loss in 37 of Istanbul's 39 districts, with substantial urbanization, especially in the north, due to new infrastructure development. On the other hand, hotspot analysis was conducted on normalized NDVI, UI, and LST images by analyzing 977 neighborhoods. Results showed a significant transformation of green areas to non-significant classes in NDVI, high urbanization in UI, and the formation of new hot areas in LST. SUHIs were found to cluster in areas with increasing residential and industrial activities, highlighting the role of urban development on SUHI formation. This research can be applied to any region since it offers crucial perspectives for decision-makers and urban planners aiming to mitigate SUHI effects through targeted greening strategies and sustainable urban development. By integrating environmental metrics into urban planning, this study underscores the need for comprehensive and sustainable approaches to enhance urban resilience, reduce environmental impact, and improve livability in Istanbul. [ABSTRACT FROM AUTHOR]

Algeria, the main fire hotspot on the southern rim of the Mediterranean Basin, lacks a complete fire dataset with official fire perimeters, and the existing one contains inconsistencies. Preprocessed global and regional burned area (BA) products provide valuable insights into fire patterns, characteristics, and dynamics over time and space, and into their impact on climate change. Nevertheless, they exhibit certain limitations linked with their inherent spatio-temporal resolutions as well as temporal and geographical coverage. To address the need for reliable BA information in Algeria, we systematically reconstructed, validated, and analyzed a 40-year (1984–2023) BA product (NEALGEBA; North Eastern ALGeria Burned Area) at 30 m spatial resolution in the typical Mediterranean ecosystems of this region, following international standards. We used Landsat data and the BA Mapping Tools (BAMTs) in the Google Earth Engine (GEE) to map BAs. The spatial validation of NEALGEBA, performed for 2017 and 2021 using independent 10 m spatial resolution Sentinel-2 reference data, showed overall accuracies > 98.10%; commission and omission errors < 8.20%; Dice coefficients > 91.90%; and relative biases < 3.44%. The temporal validation, however, using MODIS and VIIRS active fire hotspots, emphasized the limitation of Landsat-based BA products in temporal fire reporting accuracy terms. The intercomparison with five readily available BA products for 2017, by using the same validation process, demonstrated the overall outperformance of NEALGEBA. Furthermore, our BA product exhibited the highest correspondence with the ground-based BA estimates. NEALGEBA currently represents the most continuous and reliable time series of BA history at fine spatial resolution for NE Algeria, offering a significant contribution to further national and international fire hazard and impact assessments and acts as a reference dataset for contextualizing future weather extremes, such as the 2023 exceptional heat wave, which we show not to have led to the most extreme fire year over the last four decades. [ABSTRACT FROM AUTHOR]

Chattanooga, Tennessee is one of many cities in the Southeastern United States that is experiencing rapid urban growth. As these metropolitan areas continue to grow larger, more and more of Earth's unique temperate forest, an ecosystem of enormous cultural, ecological, and recreational significance in the Southeastern United States, is destroyed to make way for new urban development. This research takes advantage of the extensive temporal archive of multispectral satellite imagery provided by the Landsat program to conduct a 37-year analysis of urban forest canopy cover across the City of Chattanooga. A time series of seven Landsat 5 scenes and three Landsat 8 scenes were acquired between 1984 and 2021 at an interval of five years or less. Each multispectral image was processed digitally and classified into a four-class thematic raster using a supervised hybrid classification scheme with a support vector machine (SVM) algorithm. The obtained results showed a loss of up to 43% of urban forest canopy and a gain of up to 134% urban land area in the city. Analyzing the multidecade spatiotemporal forest canopy in a rapidly expanding metropolitan center, such as Chattanooga, could help direct sustainable development efforts towards areas urbanizing at an above-average rate. [ABSTRACT FROM AUTHOR]

The monitoring of Poyang Lake water area and storage changes using remote sensing and satellite gravimetry techniques is valuable for maintaining regional water resource security and addressing the challenges of global climate change. In this study, remote sensing datasets from Landsat images (Landsat 5, 7, 8 and 9) and three Gravity Recovery and Climate Experiment (GRACE) and Gravity Follow-on (GRACE-FO) mascon solutions were jointly used to evaluate the water area and storage changes in response to global and regional climate changes. The results showed that seasonal characteristics existed in the terrestrial water storage (TWS) and water area changes of Poyang Lake, with nearly no significant long-term trend, for the period from April 2002 to December 2022. Poyang Lake exhibited the largest water area in June and July every year and then demonstrated a downward trend, with relatively smaller water areas in January and November, confirmed by the estimated TWS changes. For the flood (August 2010) and drought (September 2022) events, the water area changes are 3032 km2 and 813.18 km2, with those estimated TWS changes 17.37 cm and −17.46 cm, respectively. The maximum and minimum Poyang Lake area differences exceeded 2700 km2. The estimated terrestrial water storage changes in Poyang Lake derived from the three GRACE/GRACE-FO mascon solutions agreed well, with all correlation coefficients higher than 0.92. There was a significant positive correlation higher than 0.75 between the area and TWS changes derived from the two independent monitoring techniques. Therefore, it is reasonable to conclude that combined remote sensing with satellite gravimetric techniques can better interpret the response of Poyang Lake to climate change from the aspects of water area and TWS changes more efficiently. [ABSTRACT FROM AUTHOR]

Soil erosion represents a complex ecological issue that is present on a global level, with negative consequences for environmental quality, the conservation and availability of natural resources, population safety, and material security, both in rural and urban areas. To mitigate the harmful effects of soil erosion, a soil erosion map can be created. Broadly applied in the Balkan Peninsula region (Serbia, Bosnia and Herzegovina, Croatia, Slovenia, Montenegro, North Macedonia, Romania, Bulgaria, and Greece), the Erosion Potential Method (EPM) is an empirical erosion model that is widely applied in the process of creating soil erosion maps. In this study, an innovation in the process of the identification and mapping of erosion processes was made, creating a coefficient of the types and extent of erosion and slumps (φ), representing one of the most sensitive parameters in the EPM. The process of creating the coefficient (φ) consisted of applying remote sensing methods and satellite images from a Landsat mission. The research area for which the satellite images were obtained and thematic maps of erosion processes (coefficient φ) were created is the area of the Federation of Bosnia and Herzegovina and the Brčko District (situated in Bosnia and Herzegovina). The Google Earth Engine (GEE) platform was employed to process and retrieve Landsat 7 Enhanced Thematic Mapper plus (ETM+) and Landsat 8 Operational Land Imager and Thermal Infrared Sensor (OLI/TIRS) satellite imagery over a period of ten years (from 1 January 2010 to 31 December 2020). The mapping and identification of erosion processes were performed based on the Bare Soil Index (BSI) and by applying the equation for fractional bare soil cover. The spatial–temporal distribution of fractional bare soil cover enabled the definition of coefficient (φ) values in the field. An accuracy assessment was conducted based on 190 reference samples from the field using a confusion matrix, overall accuracy (OA), user accuracy (UA), producer accuracy (PA), and the Kappa statistic. Using the confusion matrix, an OA of 85.79% was obtained, while UA ranged from 33% to 100%, and PA ranged from 50% to 100%. Applying the Kappa statistic, an accuracy of 0.82 was obtained, indicating a high level of accuracy. The availability of a time series of multispectral satellite images for each month is a crucial element in monitoring the occurrence of erosion processes of various types (surface, mixed, and deep) in the field. Additionally, it contributes significantly to decision-making, strategies, and plans in the domain of erosion control work, the development of plans for identifying erosion-prone areas, plans for defense against torrential floods, and the creation of soil erosion maps at local, regional, and national levels. [ABSTRACT FROM AUTHOR]

Sandy coastlines are very dynamic spaces affected by a variety of natural and human factors. In Central Chile, changes in oceanographic and wave conditions, modes of inter-annual climate variability such as El Niño Southern Oscillation (ENSO), and extreme events such as earthquakes and tsunamis condition the beach morphology. At the same time, direct human actions alter the arrival of sediments to the coast and their alongshore distribution. Despite the relevance of the beaches for this coastal region and the interesting relationship their morphology has with the aforementioned factors, there is a lack of robust morphological datasets to provide a deep characterization and understanding of the dynamism of the Chilean coast. Based on the information provided by satellite-derived shorelines (SDSs) defined by using the SHOREX algorithm, this paper characterizes the morphological changes of Playa Grande in Cartagena Bay (Central Chile) during the period 1985–2019. The shoreline position data are analyzed in the context of changing beach transforming elements, allowing for a better understanding of the changes according to multiple drivers. While some of these factors, such as earthquakes or coastal storms, have a punctual character, changes in wave patterns vary at different time scales, from seasonal to multi-annual, linked to climate phases such as ENSO. Its effects are translated into shoreline erosion and accretion conditioned by the morphology and orientation of the coast while influenced by the availability of sediment in the coastal system. According to that, a conceptual model of the dynamism and redistribution of sediment in the Bay of Cartagena is proposed. The work proves the high utility that the systematic analysis of multi-decadal SDS datasets obtained from the images acquired in the optical by the Landsat and Sentinel-2 offer for beach monitoring and understanding the coastal dynamism. [ABSTRACT FROM AUTHOR]

Rangeland ecosystems in the western United States are vulnerable to climate change, fire, and anthropogenic disturbances, yet classification of rangeland areas remains difficult due to frequently sparse vegetation canopies that increase the influence of soils and senesced vegetation, the overall abundance of senesced vegetation, heterogeneity of life forms, and limited ground-based data. The Rangeland Condition Monitoring Assessment and Projection (RCMAP) project provides fractional vegetation cover maps across western North America using Landsat imagery and artificial intelligence from 1985 to 2023 at yearly time-steps. The objectives of this case study are to apply hyperspectral data from several new data streams, including Sentinel Synthetic Aperture Radar (SAR) and Earth Surface Mineral Dust Source Investigation (EMIT), to the RCMAP model. We run a series of five tests (Landsat-base model, base + SAR, base + EMIT, base + SAR + EMIT, and base + Landsat NEXT [LNEXT] synthesized from EMIT) over a difficult-to-classify region centered in southwest Montana, USA. Our testing results indicate a clear accuracy benefit of adding SAR and EMIT data to the RCMAP model, with a 7.5% and 29% relative increase in independent accuracy (R2), respectively. The ability of SAR data to observe vegetation height allows for more accurate classification of vegetation types, whereas EMIT's continuous characterization of the spectral response boosts discriminatory power relative to multispectral data. Our spectral profile analysis reveals the enhanced classification power with EMIT is related to both the improved spectral resolution and representation of the entire domain as compared to legacy Landsat. One key finding is that legacy Landsat bands largely miss portions of the electromagnetic spectrum where separation among important rangeland targets exists, namely in the 900–1250 nm and 1500–1780 nm range. Synthesized LNEXT data include these gaps, but the reduced spectral resolution compared to EMIT results in an intermediate 18% increase in accuracy relative to the base run. Here, we show the promise of enhanced classification accuracy using EMIT data, and to a smaller extent, SAR. [ABSTRACT FROM AUTHOR]

Changes in policy and new plans can significantly influence land use and trigger land use change in the long term. The data for pre- and post-policy implementation is necessary to assess the specific policy's impact on land use. In the early nineties, Germany started promoting renewable energy production, including bioenergy, which changed the agricultural landscape. Remote sensing (RS) image-based machine learning models can be beneficial for mapping agricultural land use in the present and the past. However, machine learning classification models trained on RS data from specific training sites and time may not be able to predict data for unknown sites and unknown temporal points due to changes in crop phenology, field features, or ecological site circumstances because most of the models are limited in their performances according to variations of the training data set. Therefore, this study aims to assess the spatial–temporal transferability of Landsat-based agricultural land use type classification. The study was developed to map agricultural land cover (5 classes: maize, grasslands, summer crops, winter crops, and mixed crops) in two regions in Germany (North Hesse and Weser-Ems) between the years 2010 and 2018 using Landsat archive data (i.e., Landsat 5, 7, and 8). Two machine learning models (random forest − RF and 2D convolution neural network – 2DCNN) were trained and evaluated according to no transferability (reference) scenario and three spatial–temporal scenarios using mF1 and class level F1 values. Three model transferability scenarios were evaluated: a) temporal – S1, b) spatial – S2, and c) spatiotemporal – S3. The reference scenario, without transferability, achieved an overall accuracy of 89.1% and a macro F1 score of 0.74 for RF and 89.9% and 0.75 for CNN, respectively. Under three transferability scenarios (S1, S2, and S3), the macro F1 scores decreased to 0.67, 0.66, and 0.62 for RF, and 0.68, 0.62, and 0.58 for CNN, respectively. The dissimilarity between the data employed to train the model and data from the new domain indicated a clear link that could explain the reduction in model predictability. Moreover, the performance degradation could be attributed to the disparity in environmental, climatic, and crop calendar conditions between the two domains. Understanding the extent of model performance degradation during transferability is crucial for developing effective strategies to mitigate these issues and enhance the generalisability of machine learning models for agriculture land cover mapping. [ABSTRACT FROM AUTHOR]

Accurate and fine-scale forest data are essential to improve natural resource management, particularly in the face of climate change. Here, we present SCANFI, the Spatialized CAnadian National Forest Inventory, which provides coherent, 30 m resolution 2020 wall-to-wall maps of forest attributes (land cover type, canopy height, crown closure, aboveground tree biomass, and main species composition). These maps were developed using the NFI photo-plot dataset, a systematic regular sample grid of photo-interpreted high-resolution imagery covering all of Canada's non-arctic landmass. SCANFI was produced using temporally harmonized summer and winter Landsat spectral imagery along with hundreds of tile-level regional models based on a multiresponse k-nearest neighbours and random forest imputation method. This tile-level approach revealed the importance of radiometric variables in predicting vegetation attributes, namely winter radiometry, as the large-scale climate gradients were controlled at the tile-level. SCANFI was validated with rigorous cross-validation analyses, which revealed robust model performance for structural attributes (biomass R2 = 0.76; crown closure R2 = 0.82; height R2 = 0.78) and tree species cover (e.g., Douglas fir R2 = 0.60). SCANFI attributes were also validated with several independent external products, ranging from ground plot-based tree species cover (e.g., black spruce R2 = 0.53) to satellite LiDAR height data products (e.g., crown closure R2 = 0.71). SCANFI total aboveground biomass trends also followed those published by other studies. The methodology presented herein can be used to map time series of these attributes, identify the original training points used to make any given prediction, as well as map additional variables associated with the NFI photo-plots that are challenging to map using traditional remote sensing approaches. [ABSTRACT FROM AUTHOR]

Land cover change represents one of the most significant global transformations, which has profound impacts on ecosystems, biological diversity, and the ongoing climate crisis. In this study, our objective was to analyse land cover transformation in the Valencian Community over the last four decades. Utilising Landsat 5, 8, and 9 summer images, a Random Forest algorithm renowned for its ability to handle large datasets and complex variables, was employed to produce land cover classifications consisting of five categories: 'Urban Areas', 'Dense Vegetation', 'Sparse Vegetation', 'Water Bodies', and Other'. The results were validated through in situ measurements comparing with pre-existing products and utilising a confusion matrix. Over the study period, the urban area practically doubled, increasing from approximately 482 to 940 square kilometres. This expansion was concentrated mainly in the proximity of the already existing urban zone and occurred primarily between 1985 and 1990. The Dense and Sparse Vegetation classes exhibit substantial fluctuations over the years, displaying a subtle trend towards a decrease in their cumulative value. Water bodies and Other classes do not show substantial changes over the years. The Random Forest algorithm showed a high Overall Accuracy (OA) of 95% and Kappa values of 93%, showing good agreement with field measurements (88% OA), ESA World Cover (80% OA), and the Copernicus Global Land Service Land Cover Map (73% OA), confirming the effectiveness of this methodology in generating land cover classifications. [ABSTRACT FROM AUTHOR]

The identification and systematic monitoring of water spread regions using remote sensing techniques necessitates precise temporal resolution datasets. Landsat data are one of the most popular data sources that provides satellite derived images of the Earth's surface for examining the satellite-based indices of a specific region. In the present research study, Normalized Difference Water Index is employed to identify the surface area of 11 system and non-system lakes in a part of Coimbatore region over the time span of 32 years (1990–2022). Based on the analysis, the water spread region in 1990 is seen to be 13.01 ha which gradually increased to 435.17 ha in 2022. This clearly indicates that there is substantial difference in the rainfall statistics over the years. Computations for cross-validation are done using other indices such as Normalized Difference Vegetation Index (NDVI) and Normalized Difference Build-up Index (NDBI). A 500 m buffer is created and calculated for vegetation and built-up indices like Chinna kulam 1 and 2, Kumaraswamy Lake, Valankulam, Singanallur and Sengulam lakes which show the decreasing trend of NDVI. Simultaneously, NDBI increased in 2000, which shows that measures were taken to tackle the encroachment of lakes in the city and the concerted efforts of the Governmental authorities and Non-governmental organizations (NGOs) were fruitful. A decreasing trend is noted in NDBI during 2011 and 2020. The microclimatic conditions and advection current of waterbodies along with boating facilities and parks in some lakes offer city dwellers a tranquil recreational area with respite from vehicular pollution and heat generation from the concrete jungle. This study will be helpful in facilitating policy decisions for future planning and sustainable development of water bodies. It will also be beneficial in devising rehabilitation measures for irrigation tanks and will aid in bringing about agrarian stability of tanks in urban locales. [ABSTRACT FROM AUTHOR]

Efforts made to restore the degraded landscape of the Tigray region, Northern Ethiopia, over the last three decades have been relatively successful. However, an armed conflict that broke out in the region in November 2020 has significantly destroyed the restored vegetation, either directly associated with conflict (environment, pollution, fire) or indirectly (agricultural abandonment). This study aimed at assessing spatio-temporal changes in vegetation cover in a 50 km radius zone centered on Mekelle city, Tigray. Vegetation cover dynamics was evaluated using Landsat Enhanced Thematic Mapper Plus (ETM+) and Operational Land Imager (OLI) datasets for the years 2000, 2020, and 2022 and analysed using ENVI 5.3 and ArcGIS 10.8.1 software. These data were analysed using the Modified Normalized Difference Vegetation Index (MNDVI), Optimized Soil Adjusted Vegetation Index (OSAVI), and Moisture Adjusted Vegetation Index (MAVI). Based on the MNDVI, results show that vegetation cover increased in the period 2000–2020 by 179 km2 or 2% of the area, whereas in the period 2020–2022, there was a decrease in vegetation cover by 403 km2 or 5% of the area. This was accompanied by a decrease in vegetation density. These vegetation changes in 2020–2022 are attributed to the impact of armed conflict on the land surface which can include farmlands and village abandonment, spread of weeds and scrub vegetation, or failure to harvest crops. Monitoring vegetation change using Landsat data can help understand the environmental impacts of armed conflict in rural agricultural landscapes, including potential food security risks.

Land surface temperature (LST) is largely influenced by various biophysical properties of land surface materials, especially in urban areas where multiple spectral bands directly impact LST. The present study compares the seasonal relationship of LST with six vegetation indices, one built-up index, one water index and one soil index in Imphal City, India, using Landsat 8 satellite images from the summer and winter of 2021. The study found that these nine indices exhibit different responses to changes in LST in urban landscapes. Pearson’s correlation coefficient, scatter plot graphs, box plot graphs, and cross-section drawings are used to present the relationships between LST and the indices. The study discovered that all six vegetation indices show a moderate negative relationship with LST in both summer and winter. Additionally, the water index demonstrates a weak negative relation in summer and a moderate negative relation in winter. The built-up index shows a neutral relation in summer and a weak positive relation in winter, while the soil index forms a weak positive relation in summer and a moderate positive relation in winter. Further analysis presents that higher values of vegetation indices correspond to lower LST, while built-up and bare surfaces generate high LST.

Urban expansion constitutes one of the main activities that modify natural environments worldwide; the most vulnerable areas to these effects are peri-urban areas. An example is 59% of the surface of Mexico City, considered a Conservation Zone (CZ). Within the CZ there are natural ecosystems, agricultural and livestock activities take place, which is why it is considered an area that provides a large number of services, including ecosystem services. This study aimed to analyze the rates of urban expansion in the CZ during two periods (2012-2018, 2018-2023) with contrasting environmental policies. Landsat 7 and 8 images were used to perform a supervised classification with Random Forest algorithm with which the surface of different land use classes was estimated for three years 2012, 2018, and 2023. The dynamics of urban expansion in two administrative periods were evaluated with different approaches and budget priorities in the environmental public policy of the CZ. Supervised classification had adequate accuracy (≥89%). The rate of urban expansion during the studied periods was 31.7% (269 ha/year) from 2012 to 2018 and 16.9% (190.3 ha/year) from 2018 to 2023. A clear deceleration of land use change for urban purposes was observed; three out of the nine municipalities comprising the SC exhibited an urban expansion rate close to 0%. Additionally, a “protective” effect was also exhibited towards the agricultural areas and primary forests of the CZ. The budget increase focused on environmental conservation activities and rural development of the CZ appears to have affected the dynamics of urban expansion in the peri-urban area of one of the most complex cities in the world.

Abstract Paramos, unique and biodiverse ecosystems found solely in the high mountain regions of the tropics, are under threat. Despite their crucial role as primary water sources and significant carbon repositories in Colombia, they are deteriorating rapidly and garner less attention than other vulnerable ecosystems like the Amazon rainforest. Their fertile soil and unique climate make them prime locations for agriculture and cattle grazing, often coinciding with economically critical deposits such as coal which has led to a steady decline in paramo area. Anthropic impact was evaluated using multispectral images from Landsat and Sentinel over 37 years, on the Guerrero and Rabanal paramos in central Colombia which have experienced rapid expansion of mining and agriculture. Our analysis revealed that since 1984, the Rabanal and Guerrero paramos have lost 47.96% and 59.96% of their native vegetation respectively, replaced primarily by crops, pastures, and planted forests. We detected alterations in the spectral signatures of native vegetation near coal coking ovens, indicating a deterioration of paramo health and potential impact on ecosystem services. Consequently, human activity is reducing the extent of paramos and their efficiency as water sources and carbon sinks, potentially leading to severe regional and even global consequences.

Long-term mapping of winter wheat is vital for assessing food security and formulating agricultural policies. Landsat data are the only available source for long-term winter wheat mapping in the North China Plain due to the fragmented landscape in this area. Although various methods, such as index-based methods, curve similarity-based methods and machine learning-based methods, have been developed for winter wheat mapping based on remote sensing, the former two often require satellite data with high temporal resolution, which are unsuitable for Landsat data with sparse time-series. Machine learning is an effective method for crop classification using Landsat data. Yet, applying machine learning for winter wheat mapping in the North China Plain encounters two main issues: 1) the lack of adequate and accurate samples for classifier training; and 2) the difficulty of training a single classifier to accomplish the large-scale crop mapping due to the high spatial heterogeneity in this area. To address these two issues, we first designed a sample selection rule to build a large sample set based on several existing crop maps derived from recent Sentinel data, with specific consideration of the confusion error between winter wheat and winter rapeseed in the available crop maps. Then, we developed an optimal zoning method based on the quadtree region splitting algorithm with classification feature consistency criterion, which divided the study area into six subzones with uniform classification features. For each subzone, a specific random forest classifier was trained and used to generate annual winter wheat maps from 2013 to 2022 using Landsat 8 OLI data. Field sample validation confirmed the high accuracy of the produced maps, with an average overall accuracy of 91.1% and an average kappa coefficient of 0.810 across different years. The derived winter wheat area also has a good correlation (R2 = 0.949) with census area at the provincial level. The results underscore the reliability of the produced annual winter wheat maps. Additional experiments demonstrate that our proposed optimal zoning method outperforms other zoning methods, including Köppen climate zoning, wheat planting zoning and non-zoning methods, in enhancing wheat mapping accuracy. It indicates that the proposed zoning is capable of generating more reasonable subzones for large-scale crop mapping.

Land surface temperature (LST) primarily depends on various types of land use (LU). Along with the local warming, land conversion is the key factor in LST rise in an urban body. The present study investigates the LU status and the impact on LST in Imphal city of India using remotely sensed data. The study was performed on both summer and winter Landsat satellite images for 1991, 2001, 2011, and 2021. Results show that most of the lands have been converted into settlements during the period. The average LST of the study area rises at 1.19% rate per decade in summer and 1.03% rate per decade in winter. Forest, agricultural land, and water bodies reduce the rate of LST rise while settlement and fallow lands accelerate the rate. The conversion of some forest and agricultural lands into settlement are quite responsible for the increase in surface temperature. The study reflects the effect of proper land conversion to check the LST rise. It is helpful for environmentalists and LU planners to develop a sustainable urban ecological status.

ABSTRACTThe decline in air quality is thought to be due to rapid land use/land cover (LULC) changes near metropolitan areas that are undergoing rapid urbanisation. Here, we quantify the characteristics and relationship between the aerosol optical depth (AOD) index and LULC changes in Hangzhou, China, since the 21st century using multispectral satellite remote sensing data. The regional study results show that the correlation between the operational AOD observations (MOD04_3K) at 3 km resolution and the in situ AOD measurements available in Hangzhou ranges from 0.48 to 0.80, and their root mean square error (RMSE) < 0.30. During the past two decades, Hangzhou has been in the stage of rapid urbanisation, and the area of construction land has increased significantly, mainly converted from cultivated land and forests. The correlation coefficients between the AOD and the changes of cultivated land, forest land, grassland and construction land in Hangzhou were 0.36, 0.86, 0.61 and 0.63, respectively, indicating a strong correlation except for the cultivated land. Finally, the temporal evolution of the correlation coefficients between the cultivated land and building land are positively correlated with AOD, while from the trend of the correlation curve, grassland, woodland and AOD show negative correlations.