Search Results (441)

Palm trees, referred to here as vegetation cover (VC), provide essential ecosystem services in an arid Oasis. However, because of socioeconomic transformation, the rapid urban expansion of major cities and villages at the expense of agricultural lands of the Al-Ahsa Oasis, Saudi Arabia, has placed enormous pressure on the palm-growing area and led to the loss of productive land. These challenges highlight the need for robust, integrative methods to assess their impact on the agroecosystem. Here, we analyze spatiotemporal fluctuations in vegetation cover and its effect on the agroecosystem to determine the potential influencing factors. Data from Landsat satellites, including TM (Thematic mapper of Landsat 5), ETM+ (Enhanced Thematic mapper plus of Landsat 7), and OIL (Landsat 8) and Sentinel-2A imageries were used for analysis, while GeoEye-1 satellite images as well as socioeconomic data were applied for result validation. Principal Component Analysis (PCA) was applied to extract pure endmembers, facilitating Spectral Mixture Analysis (SMA) for mapping vegetation and urban fractions. The spatiotemporal change patterns were analyzed using time- and space-oriented detection algorithms. Results indicated that vegetation fraction patterns differed significantly; pixels with high fraction values declined significantly from 1990 to 2020. The mean vegetation fraction value varied from 0.79 to 0.37. This indicates that a reduction in palm trees was quickly occurring at a decreasing rate of −14.24%. Results also suggest that vegetation fractions decreased significantly between 1990 and 2020, and this decrease had the greatest effect on the agroecosystem situation of the Oasis. We assessed urban sprawl, and our results indicated substantial variability in average urban fractions: 0.208%, 0.247%, 0.699%, and 0.807% in 1990, 2000, 2010, and 2020, respectively. Overall, the data revealed an association between changes in palm tree fractions and urban ones, supporting strategic vegetation and/or agricultural management to enhance the agroecosystem in an arid Oasis. Full article

Against the backdrop of the continuous growth in global demand for mineral resources, efficient and accurate mineral exploration technologies are of paramount importance. Therefore, utilizing remote sensing technology, which features wide coverage, a non-contact nature, and multi-source data acquisition, is of great significance for conducting mineral resource exploration and prospecting research. This study focuses on the Tsagaankhairkhan copper–gold mining area in Mongolia and proposes a structural-alteration anomalies coupling mining prediction method based on the collaboration of multi-source remote sensing data. By comprehensively utilizing multi-source image data from Landsat-8, GF-2, and Sentinel-2, and employing methods such as principal component analysis (PCA), band ratio, and texture analysis, we effectively extracted structural information closely related to mineralization, as well as alteration anomaly information, including hydroxyl alteration anomalies and iron-staining alteration anomalies. Landsat-8 and Sentinel-2 data were employed to extract and mutually validate hydroxyl and iron-staining alteration anomaly information in the study area, thereby delineating alteration anomaly zones. By integrating the results of structural interpretation, the distribution of alteration anomaly information, and their spatial coupling characteristics, we explored the spatial coupling relationship between structural and alteration anomalies, analyzed their mineral control patterns, and identified 7 prospecting target areas. These target areas exhibit abundant mineral anomalies and favorable structural settings, indicating high metallogenic potential. The research findings provide crucial clues for the exploration of the Tsagaankhairkhan copper–gold mine in Mongolia and can guide future mineral exploration and development efforts. Full article

Mangroves in Sri Lanka provide critical ecosystem services, yet they have undergone significant changes due to anthropogenic and natural drivers. This study presents the first national-scale assessment of mangrove dynamics in Sri Lanka using remote sensing techniques. A total of 4670 Landsat images from Landsat 5, 7, 8, and 9 were selected to detect mangrove distribution, changes in extent, and structure and stability patterns from 1987 to 2022. A Random Forest classification model was applied to elucidate the spatial changes in mangrove distribution in Sri Lanka. Using national-scale data enhanced mapping accuracy by incorporating region-specific spectral and ecological characteristics. The average overall accuracy of the maps was over 96.29%. The total extent of mangroves in 2022 was 16,615 ha, representing 0.25% of the total land of Sri Lanka. The results further indicate that, at the national scale, mangrove extent increased from 1989 to 2022, with a net gain of 1988 ha (13.6%), suggesting a sustained and continuous recovery of mangroves. Provincial-wise assessments reveal that the Eastern and Northern Provinces showed the largest mangrove extents in Sri Lanka. In contrast, the Colombo, Gampaha, and Kalutara districts in the Western Province showed persistent declines. The top mangrove spatial structure and stability districts were Jaffna, Trincomalee, and Gampaha, while the most degraded mangrove districts were Batticaloa, Colombo, and Kalutara. This study offers critical insights into sustainable mangrove management, policy implementation, and climate resilience strategies in Sri Lanka. Full article

Avocado orchards are among the most profitable and fastest-growing commodity crops in Mexico, especially in the area known as the “Avocado Belt”. Several efforts have been made to monitor their expansion; however, there is currently no method that can be easily updated to track this expansion. The main objective of this study was to monitor the expansion of avocado orchards from 1993 to 2024, using the Continuous Change Detection and Classification (CCDC) algorithm and Landsat 5, 7, 8, and 9 imagery. Presence/absence maps of avocado orchards corresponding to 1 January of each year were used to perform a trajectory analysis, identifying eight possible change trajectories. Finally, maps from 2020 to 2023 were verified using reference data and very-high-resolution images. The maps showed a level of agreement = 0.97, while the intersection over union for the avocado orchard class was 0.62. The main results indicate that the area occupied by avocado orchards more than tripled from 1993 to 2024, from 64,304.28 ha to 200,938.32 ha, with the highest expansion occurring between 2014 and 2024. The trajectory analysis confirmed that land conversion to avocado orchards is generally permanent and happens only once (i.e., gain without alternation). The method proved to be a robust approach for monitoring avocado orchard expansion and could be an attractive alternative for regularly updating this information. Full article

Tea is a prominent cash crop in global agriculture, and it is Sri Lanka’s top agricultural export known as ‘Ceylon Tea,’ employing nearly one million people, with land covering an area of 267,000 ha. However, over the past decade, many tea lands in Sri Lanka have been abandoned, leading to a gradual decline in production. This research aims to identify, map, and verify tea land abandonment over time and space by identifying and analyzing a series of land use trajectories with Landsat, Google Earth, and PlanetScope imageries to provide a substantial knowledge base. The study area covers five Divisional Secretariats Divisions in Kandy District, Central Highlands of Sri Lanka: Delthota, Doluwa, Udapalatha, Ganga Ihala Korale, and Pasbage Korale, where around 70% of the tea lands in Kandy District are covered. Six land use/cover (LULC) classes were considered: tea, Home Garden and Other Crop, forest, grass and bare land, built-up area, and Water Body. Abandoned tea lands were identified if the tea land was converted to another land use between 2015 and 2023. The results revealed the following: (1) 85% accuracy in LULC classification, revealing tea as the second-largest land use. Home Garden and Other Crop dominated, with an expanding built-up area. (2) The top 22 trajectories dominating the tea trajectories were identified, indicating that tea abandonment peaked between 2017 and 2023. (3) In total, 12% (5457 ha) of pixels were identified as abandoned tea lands during the observation period (2015–2023) at an accuracy rate of 94.7% in the validation. Significant changes were observed between the two urban centers of Gampola and Nawalapitiya towns. (4) Tea land abandonment over 7 years was the highest at 35% (1892.3 ha), while 5-year and 3-year periods accounted for 535.4 ha and 353.6 ha, respectively, highlighting a significant long-term trend. (5) The predominant conversion observed is the shift in tea towards Home Garden and Other Crop (2986.2 ha) during the timeframe. The findings underscore the extent and dynamics of tea land abandonment, providing critical insights into the patterns and characteristics of abandoned lands. This study fills a key research gap by offering a comprehensive spatial analysis of tea land abandonment in Sri Lanka. The results are valuable for stakeholders in the tea industry, providing essential information for sustainable management, policy-making, and future research on the spatial factors driving tea land abandonment. Full article

Water is scarce in semi-arid regions due to environmental limitations; this situation is aggravated by changes in land use and land cover (LULC). In this respect, the basic ecological functions of Permanent Preservation Areas (PPAs) help to maintain water resources. The aim of this study was to evaluate the relationship between the LULC and water quality in PPAs in a semi-arid watershed, from 2009 to 2016. The following limnological data were analyzed: chlorophyll-a, transparency, total nitrogen and total phosphorus. The changes in LULC were obtained by classifying images from Landsat 5, 7 and 8 into three types: Open Dry Tropical Forest (ODTF), Dense Dry Tropical Forest (DDTF) and Exposed Soil (ES). Spearman correlation and principal component analysis were applied to evaluate the relationships between the parameters. There was a significant positive correlation between DDTF and the best limnological conditions. However, ES showed a significant negative relationship with transparency and a positive relationship with chlorophyll-a, indicating a greater input of sediments and nutrients into the water. The PCA corroborated the results of the correlation. It is therefore essential to prioritize the preservation and restoration of the vegetation in these sensitive areas to ensure the sustainability of water resources. Future studies should assess the impact of specific human activities, such as agriculture, deforestation and livestock farming, on water quality in the PPAs. Full article

Drought, a natural phenomenon intricately intertwined with the broader canvas of climate change, exacts a heavy toll by ushering in acute terrestrial water scarcity. Its ramifications reverberate most acutely within the agricultural heartlands, particularly those nestled in arid regions. To address this pressing issue, this study harnesses the temperature vegetation dryness index (TVDI) as a robust drought indicator, enabling a granular estimation of land water content trends. This endeavor unfolds through the sophisticated integration of geographic information systems (GISs) and remote sensing technologies (RSTs). The methodology bedrock lies in the judicious utilization of 72 high-resolution satellite images captured by the Landsat 7 and 8 platforms. These images serve as the foundational building blocks for computing TVDI values, a key metric that encapsulates the dynamic interplay between the normalized difference vegetation index (NDVI) and the land surface temperature (LST). The findings resonate with significance, unveiling a conspicuous and statistically significant uptick in the TVDI time series. This shift, observed at a confidence level of 0.05 (Z_S = 1.648), raises a crucial alarm. Remarkably, this notable surge in the TVDI exists in tandem with relatively insignificant upticks in short-term precipitation rates and LST, at statistically comparable significance levels. The implications are both pivotal and starkly clear: this profound upswing in the TVDI within agricultural domains harbors tangible environmental threats, particularly to groundwater resources, which form the lifeblood of these regions. The call to action resounds strongly, imploring judicious water management practices and a conscientious reduction in water withdrawal from reservoirs. These measures, embraced in unison, represent the imperative steps needed to defuse the looming crisis. Full article

In recent years, with the development of sensor technology, the volume of spatial data has grown exponentially. However, this data is often unevenly distributed, and traditional indexing methods cannot predict the overall data distribution when data are continuously inserted into the database. This makes them inefficient for indexing large-scale, unevenly distributed spatial data. This paper proposes a hybrid indexing method based on the grid-indexing and R-tree methods, called R-MLGTI (R-Multi-Level Grid–Tree Index). The method first divides the two-dimensional space using the Z-curve to form multiple sub-grid regions. When incrementally inserting data, R-MLGTI calculates the grid encoding of the data and computes the $c\left(G\right)$ of the corresponding grid G to measure the sparsity or density within the grid region, where $c\left(G\right)$ is a metric that quantifies the data density within grid G. All data in sparse grids are indexed by R-trees associated with grid encodings. In dense grid areas, a finer-grained space-filling curve is recursively applied for further spatial division. This process forms multiple sub-grids until the data within all sub-grids becomes sparse, at which point the original data is re-indexed according to the sparse grids. Finally, this paper presents a prototype system of the in-memory R-MLGTI and conducts benchmark tests for incremental data import and range queries. The incremental data insertion performance of R-MLGTI is lower than that of the grid-indexing and R-tree methods; however, on various unevenly distributed simulated datasets, the average query time for different query regions in R-MLGTI is about 6.49% faster than that of the grid-indexing method and about 51.78% faster than that of the R-tree method. On a real dataset, Landsat 7 EMT, which contains 2,585,203 records, the average query time for various query ranges is 61.39% faster than that of the grid-indexing method and 17.01% faster than that of the R-tree method. Experiments show that R-MLGTI performs better than the traditional R-tree and grid-indexing methods in large-scale, unevenly distributed spatial data query requests. Full article

Today, open-source Cloud Computing platforms are valuable for geospatial image analysis while the combination of the Google Earth Engine (GEE) platform and new satellite launches greatly facilitate the monitoring of national-scale lake Water Quality (WQ). The main aim of this research is to assess the transferability and performance of published general, natural-only and artificial-only lake WQ models (Chl-a, Secchi Disk Depth-SDD- and Total Phosphorus-TP) across Greece’s WFD (Water Framework Directive) lake sampling network. We utilized Landsat (7 ETM +/8 OLI) and Sentinel 2 surface reflectance (SR) data embedded in GEE, while subjected to different atmospheric correction (AC) methods. Subsequently, Carlson’s Trophic State Index (TSI) was calculated based on both in situ and modelled WQ values. Initially, WQ models employed both DOS1-corrected (Dark Object Subtraction 1; manually applied) and GEE-retrieved respective SR data from the year 2018. Double WQ values per lake station were inserted in a linear regression analysis to harmonize the AC differences, separately for Landsat and Sentinel 2 data. Yielded linear equations were accompanied by strong associations (R² ranging from 0.68 to 0.98) while modelled and GEE-modelled TSI values were further validated based on reference in situ WQ datasets from the years 2019 and 2020. The values of the basic statistical error metrics indicated firstly the increased assessment’s accuracy of GEE-modelled over modelled TSIs and then the superiority of Landsat over Sentinel 2 data. In this way, the hereby adopted methodology was evolved into an efficient lake management tool by providing managers the means for integrated sustainable water resources management while contributing to saving valuable image pre-processing time. Full article

Wall rock alteration is an important geological marker in prospecting, which can indicate the existence and location of ore bodies. Extracting mineralization and alteration information through remote sensing data and obtaining the spatial distribution characteristics of altered rocks have always been an important research content of remote sensing prospecting. Satellite remote sensing data, such as Landsat 8, has become a common tool for extracting altered mineral information due to its easy access, low cost, and high efficiency. To enhance the accuracy of extracting information on mineralized alterations, this study employs remote sensing technology to propose an Alteration Information Extraction Network combining Residual Attention and Hybrid Convolution (RAHC-AIE). Taking the Beitashan area in Xinjiang, China, as an example, the application of this method is studied. Firstly, the characteristics of the Landsat 8 OLI data are analyzed. Each mineralized alteration information characteristic bands were selected, and training samples were extracted via principal component analysis for the combined bands. We used band 2, band 5, band 6, and band 7 to extract hydroxyl alteration information, and band 2, band 4, band 5, and band 6 for iron-stained alteration information. Subsequently, the RAHC-AIE model is used to train the training samples. Finally, the trained RAHC-AIE model is used to extract the alteration information. The results reveal that the RAHC-AIE model’s overall accuracy (PA) in alteration information extraction is 98.61%. The category average pixel accuracy (MPA) is 95.76%, the Kappa coefficient (Kappa) is 85.79%, the average F1 score (Mean_F1) is 92.90%, and the frequency-weighted intersection and union ratio (FWIoU) is 97.47%. These metrics indicate the model’s strong performance. To validate these results, we conducted field validation and a laboratory analysis based on alteration mapping. The extraction result is good. This study shows that the RAHC-AIE model is an effective method for alteration information extraction. This can guide ore deposit searches and provide an important reference for accurate and rapid ore searching in the Beitashan area, as well as predicting and evaluating ore deposits in similar areas. Full article

Early warning systems rely on statistical prediction models, with environmental risks and remote sensing data serving as essential sources of information for their development. The present work is focused on the use of remote sensing for the estimation of transmission risk and the prediction of malaria cases in northwest Argentina. This study was conducted in the city of San Ramón de la Nueva Orán, where cases of the disease have been reported from 1986 to 2005. The relationship between reported malaria cases and climatic/environmental variables—including the normalized difference vegetation index (NDVI), normalized difference water index (NDWI), and land surface temperature (LST)—obtained from Landsat 5 and 7 satellite images was analyzed using multilevel Poisson regression analyses. An increased abundance of reported malaria cases was observed in summer. An ARIMA (autoregressive integrated moving average) temporal series model incorporating environmental variables was developed to forecast malaria cases in the year 2000. The analysis of the relationship between malaria cases and environmental and climatic factors showed that malaria cases were associated with increases in LST and mean temperature and a decrease in the NDVI. Early warning systems that provide information about spatial and temporal predictions of epidemics could help to control and prevent malaria outbreaks. Based on these findings, this study is expected to support the development of future prevention and control measures by health officials. Full article

This study addresses the critical need for radiometrically accurate and consistent hyperspectral data as the remote sensing community moves towards a hyperspectral world. Previous calibration efforts on Hyperion, the first on-orbit hyperspectral sensors, have exhibited temporal stability and absolute accuracy limitations. This work has developed and validated a novel cross-calibration methodology to address these challenges. Also, this work adds two other hyperspectral sensors, DLR Earth Sensing Imaging Spectrometer (DESIS) and Earth Surface mineral Dust Source Investigation instrument (EMIT), to maintain temporal continuity and enhance spatial coverage along with spectral resolution. The study established a robust approach for calibrating Hyperion using DESIS and EMIT. The methodology involves several key processes. First is a temporal stability assessment on Extended Pseudo Invariant Calibration Sites (EPICS) Cluster13–Global Temporal Stable (GTS) over North Africa (Cluster13–GTS) using Landsat Sensors Landsat 7 (ETM+), Landsat 8 (OLI). Second, a temporal trend correction model was developed for DESIS and Hyperion using statistically selected models. Third, absolute calibration was developed for DESIS and EMIT using multiple vicarious calibration sites, resulting in an overall absolute calibration uncertainty of 2.7–5.4% across the DESIS spectrum and 3.1–6% on non-absorption bands for EMIT. Finally, Hyperion was cross-calibrated using calibrated DESIS and EMIT as reference (with traceability to ground reference) with a calibration uncertainty within the range of 7.9–12.9% across non-absorption bands. The study also validates these calibration coefficients using OLI over Cluster13–GTS. The validation provided results suggesting a statistical similarity between the absolute calibrated hyperspectral sensors mean TOA (top-of-atmosphere) reflectance with that of OLI. This study offers a valuable contribution to the community by fulfilling the above-mentioned needs, enabling more reliable intercomparison, and combining multiple hyperspectral datasets for various applications. Full article

In this study, we investigated the influence of pre-fire tree mortality on fire behavior. Although other studies have focused on the environmental factors affecting wildfire, the influence of pre-fire tree mortality has not been explored in detail. We used high-spatial-resolution (1.6 m) airborne multispectral orthoimages to detect and map pre-fire dead trees in a portion of the San Bernardino Mountains, where the ‘Old Fire’ burned in 2003, and assessed whether spatial patterns of fire intensity and burn severity coincide with patterns of tree mortality. Dead trees were mapped through a hybrid deep learning classification and manual editing approach and facilitated with Google Earth Pro historical images. Apparent thermal infrared (TIR) brightness temperature captured during the Old Fire was derived from maximum digital number values from FireMapper airborne thermal infrared imagery (7 m) as a measure of fire intensity. Burn severity was analyzed using normalized burn ratio maps derived from pre- and post-fire Landsat 5 satellite imagery (30 m). Pre-fire dead trees were prevalent with 192 dead trees and 108 live trees per ha, with most dead trees clustered near the northwestern part of the study area east of Lake Arrowhead. The degree of spatial correspondence among dead tree density, fire intensity, and burn severity was analyzed using graphical and statistical analyses. The results revealed a significant but weak spatial association of dead trees with fire intensity (R² = 0.31) and burn severity (R² = 0.14). The findings revealed that areas impacted by pre-fire tree mortality were subject to higher fire intensity, followed by severe burn effects, though other biophysical factors also influenced these fire behavior variables. These results contradict a previous study that found no effect of tree mortality on the behavior of the Old Fire. Full article

Soil nitrogen (N) is a crucial nutrient for agricultural productivity and ecosystem health. The accurate and timely assessment of total soil N is essential for evaluating soil health. This study aimed to determine the impact of bootstrapping techniques on improving the predictive accuracy of indirect total soil N in conventional wheat fields in Al-Muthanna, Iraq. We integrated a novel methodological framework that integrated bootstrapped and non-bootstrapped total soil N data from 110 soil samples along with Landsat 9 imagery on the Google Earth Engine (GEE) platform. The performance of the proposed bootstrapping-enhanced random forest (RF) model was compared to standard RF models for soil N prediction, and outlier samples were analyzed to assess the impact of soil conditions on model performance. Principal components analysis (PCA) identified the key spectral reflectance properties that contribute to the variation in soil N. The PCA results highlighted NIR (band 5) and SWIR2 (band 7) as the primary contributors, explaining over 91.3% of the variation in soil N within the study area. Among the developed models, the log (B5/B7) model performed best in capturing soil N (R² = 0.773), followed by the ratio (B5/B7) model (R² = 0.489), while the inverse log transformation (1/log (B5/B7), R² = 0.191) exhibited the lowest performance. Bootstrapped RF models surpassed non-bootstrapped random forest models, demonstrating enhanced predictive capability for soil N. This study established an efficient framework for improving predictive capacity in areas characterized by limited, low-quality, and incomplete spatial data, offering valuable insights for sustainable nitrogen management in arid regions dominated by monoculture systems. Full article

Water temperature, a key environmental factor in river ecosystems, plays an important role in understanding the health of river ecosystems and addressing climate change. The Tibetan Plateau is sensitive to global climate change, and owing to its unique geographic and climatic conditions, the spatiotemporal distribution of water temperature in plateau rivers is highly heterogeneous. However, owing to the complex terrain and harsh climate, traditional water temperature monitoring methods struggle to provide comprehensive coverage. This study focuses on the downstream section of the Yarlung Tsangpo River and uses Landsat 7 and 8 images from 2004–2022. Considering the high water vapor content in the region and the satellite’s inherent system errors, a remote sensing-based model for interpreting water temperature in plateau rivers was developed. This model aims to address the limitations of traditional monitoring methods and provide a new technological approach for studying the spatiotemporal variations in water temperature in plateau rivers. The results show that the model has high accuracy (RMSE ranging from 1.00 °C to 1.85 °C), and regression correction can reduce the relative error by 1.6% to 22.2%. The water temperature downstream of the Yarlung Tsangpo River is influenced by a combination of climate, topography, and runoff inputs, resulting in clear spatiotemporal variation characteristics. Air temperature is the most important factor affecting water temperature, and both the intra-annual variations and spatial distributions of water temperature show significant regional differences. This study provides important data support and technical methods for long-term monitoring and ecological research on water temperature in plateau rivers, as well as scientific evidence for water resource management in plateau regions. Full article