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Aim To map and interpret floristic and geoecological patterns across the Amazon basin by combining extensive field data with basin‐wide Landsat imagery and climatic data. Location Amazonia. Taxon Ground truth data on ferns and lycophytes; remote sensing results reflect forest canopy properties. Methods We used field plot data to assess main ecological gradients across Amazonia and to relate floristic ordination axes to soil base cation concentration, Climatologies at High Resolution for the Earth's Land Surface Areas (CHELSA) climatic variables and reflectance values from a basin‐wide Landsat image composite with generalized linear models. Ordination axes were then predicted across all Amazonia using Landsat and CHELSA, and a regional subdivision was obtained using k‐medoid classification. Results The primary floristic gradient was strongly related to base cation concentration in the soil, and the secondary gradient to climatic variables. The Landsat image composite revealed a tapestry of broad‐scale variation in canopy reflectance characteristics across Amazonia. Ordination axis scores predicted using Landsat and CHELSA variables produced spatial patterns consistent with existing knowledge on soils, geology and vegetation, but also suggested new floristic patterns. The clearest dichotomy was between central Amazonia and the peripheral areas, and the available data supported a classification into at least eight subregions. Main conclusions Landsat data are capable of predicting soil‐related species compositional patterns of understorey ferns and lycophytes across the Amazon basin with surprisingly high accuracy. Although the exact floristic relationships may differ among plant groups, the observed ecological gradients must be relevant for other plants as well, since surface reflectance recorded by satellites is mostly influenced by the tree canopy. This opens exciting prospects for species distribution modelling, conservation planning, and biogeographical and ecological studies on Amazonian biota. Our maps provide a preliminary geoecological subdivision of Amazonia that can now be tested and refined using field data of other plant groups and from hitherto unsampled areas.

High frequency and spatially explicit irrigated land maps are important for understanding the patterns and impacts of consumptive water use by agriculture. We built annual, 30 m resolution irrigation maps using Google Earth Engine for the years 1986–2018 for 11 western states within the conterminous U.S. Our map classifies lands into four classes: irrigated agriculture, dryland agriculture, uncultivated land, and wetlands. We built an extensive geospatial database of land cover from each class, including over 50,000 human-verified irrigated fields, 38,000 dryland fields, and over 500,000 km 2 of uncultivated lands. We used 60,000 point samples from 28 years to extract Landsat satellite imagery, as well as climate, meteorology, and terrain data to train a Random Forest classifier. Using a spatially independent validation dataset of 40,000 points, we found our classifier has an overall binary classification (irrigated vs. unirrigated) accuracy of 97.8%, and a four-class overall accuracy of 90.8%. We compared our results to Census of Agriculture irrigation estimates over the seven years of available data and found good overall agreement between the 2832 county-level estimates (r 2 = 0.90), and high agreement when estimates are aggregated to the state level (r 2 = 0.94). We analyzed trends over the 33-year study period, finding an increase of 15% (15,000 km 2 ) in irrigated area in our study region. We found notable decreases in irrigated area in developing urban areas and in the southern Central Valley of California and increases in the plains of eastern Colorado, the Columbia River Basin, the Snake River Plain, and northern California.

Studying and evaluating desertification is essential due to its potential occurrence as a result of both natural and anthropogenic processes. Precise forecasting of forthcoming climate change perils is crucial for devising policies, action strategies, and mitigation measures at both the local and global scales. Remote sensing facilitates the examination, monitoring, and forecasting of several aspects of desertification. Throughout the years, many methodologies have been employed to investigate desertification through the utilization of Remote Sensing (RS). This study investigated the worldwide prevalence and temporal sequence of research that utilized remote sensing (RS) to investigate desertification. In addition, the study assessed the primary approaches and factors employed in the examination of desertification through the analysis of remote sensing data. The application of remote sensing (RS) in the investigation of desertification can be traced back to 1991. Between 2015 and 2020, an annual average of over 40 publications were published, indicating a substantial rise in the utilization and accessibility of remote sensing (RS) technology to monitor desertification. However, there is a significant disparity in the amount of research conducted in different fields. Asia demonstrates a substantially higher quantity of studies in contrast to America or Africa. China has conducted the highest number of research on desertification using remote sensing (RS) techniques. The Thematic Mapper (TM) sensor is the principal source of satellite data, specifically Landsat pictures. The primary techniques utilized for studying desertification are classification and monitoring of alterations. Furthermore, remote sensing methods commonly employ land cover/land use change and vegetation, together with its attributes such as the Normalised Difference Vegetation Index (NDVI), as the primary factors for studying desertification.

The study of the spatio-temporal distribution and evolution trends of water resources in large regions plays an important role in the study of regional water resource planning, regional economic and social development, and water disasters. In this study, a Landsat multi-index relationship and water probability thresholding method is developed based on the Google Earth Engine (GEE) platform, which can support the integration of multiple Landsat satellites. The algorithm jointly combines multiple remote sensing metrics along with the calculation of water probability to produce an interannual water body product for the Jianghan Plain on a 20-year time series. The results indicate that the Landsat multi-index relationship algorithm used in this study has high accuracy in extracting long-term water bodies in extensive, flat terrain areas such as the Jianghan Plain, with an overall accuracy (OA) of 97.23%. By analyzing the water body products and landscape patterns, we have identified the following features: (1) From 2002 to 2021, the changes in river water bodies in the Jianghan Plain were relatively small, and some lakes experienced a shrinkage in area. Overall, there is a strong correlation between water distribution and precipitation. (2) The complexity index of water bodies shows a strong negative correlation with effective irrigation area and population, indicating a strong mutual influence between water bodies and socio-economic activities. (3) Through the study of the distribution characteristics of built-up areas and the water system, it was found that for large rivers, the larger the size of the river, the more built-up areas are nearby. Most extensive built-up areas are located near large rivers. This study contributes to providing methods and data support for urban planning, water resource management, and disaster research in the Jianghan Plain.

The formation and occurrence of urban heat island (UHI) is a result of rapid urbanization and associated concretization. Due to intensification of heat combined with high pollution levels, urban areas expose humans to unexpected health risks. In this context, the study aims at comparing the UHI in the two largest metropolitan cities of India, i.e., Delhi and Mumbai. The presence of surface UHI is analyzed using the Landsat 5 TM image of 5 May 2010 for Delhi and the 17 April 2010 image for Mumbai. The validation of the heat island is done in relation to the Normalized Difference Vegetation Index (NDVI) patterns. The study reveals that built-up and fallow lands record high temperatures, whereas the vegetated areas and water bodies exhibit lower temperatures. Delhi, an inland city, possesses mixed land use and the presence of substantial tree cover along roads; the Delhi Ridge forests and River Yamuna cutting across the city have a high influence in moderating the surface temperatures. The temperature reaches a maximum of 35 °C in West Delhi and a minimum of 24 °C in the east at the River Yamuna. Maximum temperature in East Delhi goes to 30 °C, except the border areas. North, Central and south Delhi have low temperatures (28 °C–31 °C), but the peripheral areas have high temperatures (36 °C–37 °C). The UHI is not very prominent in the case of Delhi. This is proven by the correlations of surface temperature with NDVI. South Delhi, New Delhi and areas close to River Yamuna have high NDVI and, therefore, record low temperatures. Mumbai, on the other hand, is a coastal city with lower tree cover than Delhi. The Borivilli National Park (BNP) is in the midst of dense horizontal and vertical growth of buildings. The UHI is much stronger where the heat is trapped that is, the built-up zones. There are four small rivers in Mumbai, which have low carrying capacity. In Mumbai suburban district, the areas adjoining the creeks, sea and the lakes act as heat sinks. The coastal areas in South Mumbai record temperatures of 28 °C–31 °C; the Bandra-Kurla Complex has a high range of temperature i.e., 31 °C–36 °C. The temperature witnessed at Chattrapati Shivaji International Airport is as high as 38 °C. The temperature is nearly 37 °C–38 °C in the Dorai region in the Mumbai suburban district. The BNP has varied vegetation density, and therefore, the temperature ranges from 27 °C–31 °C. Powai Lake, Tulsi Lake and other water bodies record the lowest temperatures (24 °C–26 °C). There exists a strong negative correlation between NDVI and UHI of Mumbai, owing to less coverage of green and vegetation areas.

Albedo tests the total surface reflectance, offering plenty of useful details on the environment and a deeper understanding of the balance of environment features. Yet usually different sunlight wavelengths are not reflected equally, resulting in a variable surface color and variations in the absorption of certain wavelengths due to changes in the surfaces physical or chemical characteristics. Surface albedo differences can be measured using radiometers, or by using the general equation to extract the value of the albedo for surfaces if all the variables are available. Every space agency uses this equation according to its measured wavelengths. So, the general equation of the satellite landsat was chosen to extract the values of albedo. Surface whiteness is a modulus, and it represents a portion of the incident sunlight reflected by the surface of any feature, and the surface absorbs radiation that is not reflected. The study examined the effect of albedo on the climate of Fallujah and the thermal composition of that urban area. The study used a method that combines remote sensing and geographic information systems to achieve this. Basic samples representing the ten landmarks of Fallujah were taken, the coordinates of these points were measured, and simulated with the satellite images of Landsat 7 and 8 and the sensor (ETM +, OLI) to find the wavelengths of reflectance of these features. When applying the general equation, the albedo values were shown as follows (Buildings with a value of 0.19, Streets with a value of 0.20, Rivers and canals with a value of 0.16, Sand plains with a value of 0.16, River islands with a value of 0.16, Low density arable land with a value of 0.18, High density arable land with a value of 0.15, Gravel land and its value. 0.23, Industrial areas with a value of 0.20, Abandoned land and saline, a value of 0.21).

Thermal pollution reduces water quality through any process that leads to a change in the water’s ambient temperature. Karun is one of the most relevant sources of water supply in Iran, and its pollution, created by industrial, urban, and agricultural issues, has been one of the most critical challenges throughout the last few years. As the water temperature rises, the amount of dissolved oxygen in it decreases, thereby affecting the entire ecosystem associated with it. Drainage of urban and industrial runoff into surface water sources can increase the water temperature. Dams also constitute a significant part, modifying spatial patterns of temperature along river routes and causing thermal contamination. In this paper, the thermal pollution of the Karun River was assessed, and regions along this river with unusually raised water temperatures were identified and compared over 20 years. By analyzing the results, it can be found that the thermal pollution from dams has a significant impact on the downstream river environment and ecology that is considerably relevant during summer periods, showing average decreases of 3 degrees Celsius immediately beyond the dams’ locations (from 41 degrees Celsius upstream dams to 38 degrees Celsius beyond them) or even bigger (reductions of 13 degrees Celsius in one of the studied dams). Hence, our results showed that water temperature is colder downstream in the hot seasons of the year than upstream of the dams. The results suggest that the usage of remote sensing data effectively could complement collected data from ground-based sensors to estimate water temperature and to identify pollution areas. It provides experts with spatially extensive and highly synchronized data.

Overdependence on and exploitation of forest resources have significantly transformed the natural reserve forest of Sundarban, which shares the largest mangrove territory in the world, into a great degradation status. By observing these, a most pressing concern is how much degradation occurred in the past, and what will be the scenarios in the future if they continue? To confirm the degradation status in the past decades and reveal the future trend, we took Sundarban Reserve Forest (SRF) as an example, and used satellite Earth observation historical Landsat imagery between 1989 and 2019 as existing data and primary data. Moreover, a geographic information system model was considered to estimate land cover (LC) change and spatial health quality of the SRF from 1989 to 2029 based on the large and small tree categories. The maximum likelihood classifier (MLC) technique was employed to classify the historical images with five different LC types, which were further considered for future projection (2029) including trends based on 2019 simulation results from 1989 and 2019 LC maps using the Markov-cellular automata model. The overall accuracy achieved was 82.30%~90.49% with a kappa value of 0.75~0.87. The historical result showed forest degradation in the past (1989–2019) of 4773.02 ha yr−1, considered as great forest degradation (GFD) and showed a declining status when moving with the projection (2019–2029) of 1508.53 ha yr−1 and overall there was a decline of 3956.90 ha yr−1 in the 1989–2029 time period. Moreover, the study also observed that dense forest was gradually degraded (good to bad) but, conversely, light forest was enhanced, which will continue in the future even to 2029 if no effective management is carried out. Therefore, by observing the GFD, through spatial forest health quality and forest degradation mapping and assessment, the study suggests a few policies that require the immediate attention of forest policy-makers to implement them immediately and ensure sustainable development in the SRF.

Monitoring of land use and land cover (LULC) change is fundamental aspect of the landscape dynamics or environmental health evaluation at different spatio-temporal scales. Assessment of LULC change is highly imperative in evaluating the environmental and ecosystem management, conservation, land use planning, resource management and overall sustainable environmental management. The rich natural biodiversity zone of Bhutan–Bengal foothill has been considered to assess the LULC change from 1987 to 2019. The principal objective of this study is to identify the rate of transformation of land use and land cover change along with its causes and consequences. ETM, ETM+ and OLI Landsat satellite images of 1987, 2001 and 2019 are used to find out the magnitude of land use and land cover transformation. Maximum likelihood classifier or maximum likelihood classification method has been applied to classify the attributes of LULC change of Bhutan–Bengal foothill. The LULC components are further verified and rectified by reliable statistical error (confused) matrix accuracy assessment techniques to sort out the error incurred during preparation of final spatio-temporal LULC change maps. The result shows that there is a partial change of LULC during the last 3 decades (1987–2019). LULC data of 3 decades reveal a negative change or reduction of areas like vegetation (− 2.93%), agriculture (− 6.955) and plantation (− 0.5%), whereas other three important LULC components such as built-up area (6.44%), barren land (2.71%) and water body (1.29%) take slightly positive trend. Large-scale human encroachment, natural forest habitat fragmentation and conversion have brought rapid transformation from natural landscape to cultural landscape in Bhutan–Bengal foothill. This fundamental research will definitely help to make policy framing holistic management approach.

This study examined the urbanization, industrialization and land transformation surrounding National Highway-48 in the National Capital Region of Delhi. Over the last 20 years, this region has experienced rapid urban growth, industrialization and land transformation. Its urban expansion has fluctuated over time, indicating future urban dynamics. This highway from Delhi to Mumbai is a part of the Delhi-Mumbai Industrial Corridor (DMIC) project. It is India's most ambitious infrastructure development programme, aiming to transform new industrial cities into \"Smart Cities\" by integrating next-generation technologies. This study covers 2637 sq km area surrounding NH-48 from Gurugram city to Sotanala industrial location in Behror, a 127 km stretch in the NCR with a buffer of 10 km. It covers sixteen urban centres, including Gurugram, Garhi Harshru, Bhondsi, Manesar and Pataudi in Gurgaon District, Rewari, Dharuhera, Bawal, Rampura, Ghatal Mahaniawas, Aakera and Maheshari in Rewari District, Bhiwadi, Shahjahanpur, Neemrana and Behror in Alwar District. Landsat satellite images, census data, state and district industrial profiles of Haryana and Rajasthan and city/regional development plans have been used for meaningful analysis.