Landsat-8 Operational Land Imager Research Articles

Comparative use of UAV and Satellite Images in discrimination and estimation of cashew plantation areas

Cashew plantations generate significant interest in Benin due to their high socioeconomic value for the population. A thorough understanding of the spatial distribution of these plantations is crucial for comprehending their environmental and socioeconomic impacts. In this study, various types of multi-sensor imagery were compared to assess each sensor's capabilities in mapping plantation areas. The study was conducted in the Savè commune, a major industrial cashew-producing region. Multispectral sensors from Landsat-8 Operational Land Imager (OLI), Sentinel-2A, and UAV multispectral platforms, along with ground surveys, were fused and classified using the Random Forest algorithm. The study results allowed for the assessment of uncertainties associated with different platforms in detecting cashew plantations in the test area. Classification using Random Forest algorithms on UAV, Sentinel, and Landsat platform images yielded overall accuracies of 83%, 65%, and 48%, respectively. Producer and user accuracies were 94% and 75% for the UAV platform, 98% and 71% for the Sentinel platform, and 91% and 77% for the Landsat platform in cashew tree detection. This study demonstrates the complementarity among various platforms in detecting and mapping cashew plantations.

Cloud removal in landsat images based on spatial-spectral-temporal random forests

Abstract The Landsat-8 Operational Land Imager (OLI), together with the Landsat-9 OLI2 sensors, provides important optical data for various applications. Due to frequent cloud cover, the obtained Landsat images always contain different degrees of spatial information loss. Although great efforts have been made to remove clouds in remote sensing images in recent decades, there still exists a large challenge, that is, the land cover changes between the target cloudy and the auxiliary cloud-free images, which limits greatly the accuracy of cloud removal. To cope with this issue, a spatial-spectral-temporal random forest (SSTRF) algorithm was proposed in this paper. The SSTRF model aims at removing clouds in Landsat images with the assistance of a remote sensing image with spatially complete cover from another satellite, that is, the 8-day MODIS surface reflectance product (MOD09A1). By compositing the valid data from 8 separate days, the MOD09A1 product has a great possibility to provide cloud-free land cover change information for the cloud removal of Landsat images. Through experiments with simulated and real clouds, SSTRF was demonstrated to produce greater accuracy than two benchmark cloud removal methods and has great potential to be applied to practical use.

Spatio-temporal change and driving mechanisms of land use/cover in Qarhan Salt Lake area during from 2000 to 2020, based on machine learning

The significance of land use classification has garnered attention due to its implications for climate and ecosystems. This paper establishes a connection by introducing and applying automatic machine learning (Auto ML) techniques to salt lake landscape, with a specific focus on the Qarhan Salt Lake area. Utilizing Landsat-5 Thematic Mappe (TM) and Landsat-8 Operational Land Imager (OLI) imagery, six machine learning algorithms were employed to classify eight land use types from 2000 to 2020. Results show that XGBLD performed optimally with 77% accuracy. Over two decades, salt fields, construction land, and water areas increased due to transformations in saline land and salt flats. The exposed lakes area exhibited a rise followed by a decline, mainly transforming into salt flats. Agricultural land areas slightly increased, influenced by both human activities and climate. Our analysis reveals a strong correlation between salt fields and precipitation, while exposed lakes demonstrate a significant negative correlation with evaporation and temperature, highlighting their vulnerability to climate change. Additionally, human water usage was identified as a significant factor impacting land use change, emphasizing the dual influence of anthropogenic activities and natural factors. This paper addresses the void in the application of Auto ML in salt lake environments and provides valuable insights into the dynamic evolution of land use types in the Qarhan Salt Lake region.

Leveraging a decade of Landsat-8 spectral records for mapping blue carbon storage in tidal salt marshes

Tidal salt marsh ecosystems are known to accumulate and store large amounts of “blue” carbon, making them an important component of regional carbon cycle processes and a potential target for ecosystem-based carbon crediting efforts. However, blue carbon content in salt marshes can vary substantially at relatively small spatial scales. Understanding spatial variations of blue carbon storage at the landscape or local scale is important for developing carbon inventories, guiding ecological restorations, and informing habitat management strategies. We investigated the potential of spectral index records from the Landsat-8 Operational Land Imager spanning from 2014 to 2023 for mapping blue carbon storage in the soils of two tidal salt marsh systems in the Mid-Atlantic United States. The decadal mean of a non-photosynthetic vegetation index and standard deviations of a normalized difference vegetation index and a modified normalized difference water index were identified as predictors of blue carbon. These predictors were used to train a gradient boosted trees model for predicting soil organic matter content that achieved a testing set r-squared value of 0.67. We estimated that the two study marshes stored a combined 133-208 gigagrams of organic carbon in the top 30 cm of soil. We emphasize the need for better quantification of deep soil carbon in tidal salt marsh systems, which is likely quite high, and demonstrate the potential for satellite-based mapping of blue carbon within individual tidal wetland systems.

Normalized Difference Red-NIR-SWIR: A new Sentinel-2 three-band spectral index for mapping freshly-opened swiddens in the tropics

Swidden agriculture is undergoing a rapid but overlooked transition and transformation in the tropics, complicating global carbon budgeting and sustainable livelihood assessment of swiddeners. Remotely sensed algorithms for accurately detecting swiddening practices have been slowly developed to generate annual updates on their dynamics. This is primarily because, using medium spatial resolution imagery (≥30 m), it is challenging to identify the exact boundary of swidden patches. Spectral-based approaches have by far dominated the detection and mapping of swidden agriculture, but the potential of Sentinel-2 has not been examined. To reconstruct annual information of swidden agriculture, a new Sentinel-2 three-band spectral index, i.e., the Normalized Difference Red, Near-infrared (NIR), and Shortwave-infrared (SWIR), or NDRII, has been developed to map freshly-opened swiddens in tropical regions. As Red (visible), NIR, and SWIR spectral band combinations (i.e., the VNIR-SWIR spectroscopy) are sensitive to vegetation-moisture variations and thermal anomalies caused by slash and burn in tropical uplands during the dry season, NDRII delineates exact patches of freshly opened swiddens. The latest 20-m map facilitates probing into the landscape patterns of newly opened swiddens and underlines their prevalence in Laos for the first time. Established within the VNIR-SWIR spectroscopy of Sentinel-2 and Landsat-8 Operational Land Imager, the NDRII algorithm contributes to reconstructing historical datasets of tropical swiddens via integrating state-of-the-art approaches that use temporally stacked observations with available VNIR/SWIR satellite imagery and further understanding the dynamics of landscape pattern and disturbance due to rapid transition and transformation.

Impact of land use and land cover change on land surface temperature: Comparative studies in four cities in southwestern Ethiopia

The urban climate has undergone significant changes due to rapid population growth, leading to a decline in vegetation cover and an increase in land surface temperature (LST). This study aims to assess the influence of land use land cover (LULC) changes on LST in four major urban areas in southwestern Ethiopia, namely Jimma, Bonga, Mattu and Nekemte, during the period from 2002 to 2024. To investigate the impact of LULC dynamics on LST, 30 m spatial resolution images from Landsat were utilized, including Thematic Mapper (TM) for the year 2002 and Landsat Operational Land Imager (OLI) and Thermal Infrared (TIRS) for the years 2014 and 2024. Over the past 22 years, the mean LST has increased by 2.81 °C, 2.94 °C, 3.37 °C, and 3.96 °C for Bonga, Nekemte, Mattu, and Jimma, respectively. The increase in LST can be attributed to various factors, but one of the primary reasons is linked to the rapid urbanization and decrease in forest cover. Changes in LULC triggered by rapid urbanization significantly influences LST in major cities. The results highlight the increment of impervious surface and the decline in vegetation cover as key factors contributing to the upward trend in LST. The results indicate that urban centers with less vegetation cover experienced higher LST compared to their surroundings. The results of this study indicate the necessity of effective urban planning to increase vegetation cover through urban greenery and parks to mitigate the increasing trends of LST, which can improve urban thermal comfort levels.

Use of Landsat Imagery Time-Series and Random Forests Classifier to Reconstruct Eelgrass Bed Distribution Maps in Eeyou Istchee

The eastern coastline of James Bay is known to have been home to sizeable eelgrass beds (Zostera marina L.) which thrived in the bay’s shallow, subarctic waters. The region was subjected to substantial hydroelectric dams, large fires, and other human activities in the past half-century. To assess the impact of these factors on eelgrass beds, a historical reconstruction of eelgrass bed distribution was performed from images acquired by Landsat-5 Thematic Mapper (TM) in 1988, 1991, and 1996 and images of the Landsat-8 Operational Land Imager (OLI) in 2019. All the images were classified using the Random Forests classifier (RF) and assessed for accuracy each year on a bay-wide scale using an independent field validation dataset. The validation data were extracted from an eelgrass bed map established using aerial photos and field surveys in 1986, 1991, and 1995 and from a field survey in 2019. The overall validation accuracy of the classified images (between 72% and 85%) showed good agreement with the other datasets for most locations, providing reassurance about the reliability of the research. This makes it possible to use satellite imagery to detect past changes to eelgrass distribution within a bay. The classified images of 1988 and 1996 were also compared to aerial photos taken in years close to each other at ten sites to determine their ability to assess small eelgrass beds’ shape and presence. Such a comparison revealed that the classified images accurately portrayed eelgrass distribution even at finer scales.

Quantifying forest degradation rates and their drivers in Alle district, southwestern Ethiopia: Implications for sustainable forest management practices

Forest ecosystems contribute significantly to global climate regulation. Nonetheless, vulnerability has emerged as a multifaceted topic in the scientific world. Despite the importance of forest ecosystem services, there has been little quantification of worldwide forest change. The main objective of this study was to quantify forest degradation rates and drivers in the Alle district in Southwest Ethiopia. A mixed-research design was used to collect data on forest degradation rates and drivers in Alle district, incorporating both quantitative and qualitative method. The Land Use/Cover (LULC) for 1990, 2010, and 2022 derived from Landsat Thematic Mapper (TM) and Landsat Operational Land Imager (OLI) were used to detect changes and rates of forest degradation. Using a simple random sampling technique, 284 respondents were selected and questionnaires, interviews, and field observations were used to collect survey data. The results indicated that the forest cover of Abidibor per hectare was 2467.5 ha (97.2 %), 2268.3 ha (89.4 %) and 2203.9 ha (86.8 %) during 1990, 2010, and 2022, respectively. The forest coverage of Aba Gamta was 8296.7 ha (96.7 %), 6796.9 ha (79.2 %), and 6654.5 ha (77.6 %) in 1990, 2010, and 2022, respectively. Agricultural and grazing land increased, whereas forests and wetlands decreased during the respective years. The majority (39.36 %) of the sampled respondents reported that the conversion of forest land to agriculture by a rapidly growing population resulted in the expansion of agricultural land and rural settlements, resulting in forest degradation manifested by deforestation, overgrazing, and overexploitation. Thus, the forest coverage of the area decreased rapidly with time. As a solution to the devastating problems of diminishing forests, the local government and other stakeholders should consider conserving and managing depreciating forests by controlling direct drivers and determinants through participatory and institutionalized mechanisms.

Methods for detecting green tide in the Yellow Sea using Google Earth Engine platform

The green tide was a blooming phenomenon consisting of outbreaks and aggregations of marine macroalgae, causing severe damage to the ecosystem of the Yellow Sea in China. Remote sensing technology was considered an effective means of green tide detection. Currently, commonly used low spatial resolution satellites cannot satisfy the green tide detection in the fine-scale range. To better improve the accuracy of green tide identification, based on the Commission Internationale del'éclairage (CIE) system, two new green tide detection algorithms (CIE xy, hue angle α) were established using Sentinel-2 MultiSpectral Instrument (MSI) and Landsat-8 Operational Land Imager (OLI) data on the Google Earth Engine (GEE) platform. These algorithms were validated by introducing the quantitative index Dgw from "Interclass Distance". The CIE xy algorithm and the hue angle α algorithm based on Sentinel-2 MSI exhibited higher Dgw values of 10.29 and 5.87, respectively, surpassing the performance of the two algorithms based on the Landsat-8 OLI in green tide identification (Dgw values of 3.36 and 5.17, respectively). Meanwhile, the proposed algorithms were compared with normalized difference vegetation index (NDVI), difference vegetation index (DVI), ratio vegetation index (RVI), floating algae index (FAI), enhanced vegetation index (EVI), and algal bloom detection index (ABDI) algorithms using Sentinel-2 MSI images. The results showed that the CIE xy algorithm and the NDVI algorithm outperformed the other algorithms. Especially in turbid water, the CIE xy algorithm exhibited significant advantages. However, the hue angle α algorithm still showed some potential in identifying green tide, particularly in the presence of thin cloud interference. These findings can provide effective information support for the monitoring of green tide in the Yellow Sea.

Evaluating Machine-Learning Algorithms for Mapping LULC of the uMngeni Catchment Area, KwaZulu-Natal

Analysis of land use/land cover (LULC) in catchment areas is the first action toward safeguarding freshwater resources. LULC information in the watershed has gained popularity in the natural science field as it helps water resource managers and environmental health specialists develop natural resource conservation strategies based on available quantitative information. Thus, remote sensing is the cornerstone in addressing environmental-related issues at the catchment level. In this study, the performance of four machine learning algorithms (MLAs), namely Random Forests (RFs), Support Vector Machines (SVMs), Artificial Neural Networks (ANNs), and Naïve Bayes (NB), were investigated to classify the catchment into nine relevant classes of the undulating watershed landscape using Landsat 8 Operational Land Imager (L8-OLI) imagery. The assessment of the MLAs was based on a visual inspection of the analyst and commonly used assessment metrics, such as user’s accuracy (UA), producers’ accuracy (PA), overall accuracy (OA), and the kappa coefficient. The MLAs produced good results, where RF (OA = 97.02%, Kappa = 0.96), SVM (OA = 89.74%, Kappa = 0.88), ANN (OA = 87%, Kappa = 0.86), and NB (OA = 68.64%, Kappa = 0.58). The results show the outstanding performance of the RF model over SVM and ANN with a significant margin. While NB yielded satisfactory results, its sensitivity to limited training samples could primarily influence these results. In contrast, the robust performance of RF could be due to an ability to classify high-dimensional data with limited training data.

More Accurately Estimating Aboveground Biomass in Tropical Forests With Complex Forest Structures and Regions of High‐Aboveground Biomass

AbstractAccurately estimating aboveground biomass (AGB) in tropical forests is vital for managing the threats posed by deforestation, degradation, and climate change. However, challenges persist in accurately estimating AGB in high AGB regions. This study aims to accurately estimate the AGB of regions with high AGB by using spatial statistical analyses based on AGB estimates made by machine‐learning fusion of multisource data. We hypothesize that incorporating dominant auxiliary factors in the analysis increases the estimation accuracy. This study focuses on tropical forests located in Longyan, Fujian Province, China, covering an area of 19,028 km2. Multisource data are used, including airborne laser scanning, the Shuttle Radar Topography Mission digital elevation model, the Landsat Operational Land Imager, and the National Forest Inventory. Based on GeogDetector's spatial covariance matrix and the spatial similarity principle, we identify key auxiliary factors (dominant tree species, canopy closure, and herbaceous cover) and investigated how auxiliary variables can improve estimation accuracy. Empirical Bayesian kriging regression prediction introduces the main auxiliary factors to refine AGB estimates. These refinements significantly enhance the accuracy of AGB estimates, particularly for high AGB, resulting in a 0.1 increase in R2, a 7.0% reduction in root mean square error, a 13.5% reduction in mean square error, and a 6.6% reduction in mean absolute error when compared with the AGB estimates obtained by using machine learning to fuse multisource data. Thus, incorporating spatial statistical analysis into the integration of multisource data and machine learning for AGB estimation can enhance the accuracy of high‐AGB estimates in intricate forest structures, resulting in precise AGB maps.

Vegetation extraction from Landsat8 operational land imager remote sensing imagery based on Attention U-Net and vegetation spectral features

Vegetation extraction from Landsat8 operational land imager remote sensing imagery based on Attention U-Net and vegetation spectral features

National-scale nighttime high-temperature anomalies from Landsat-8 OLI images

Identifying and characterizing the distribution, abundance, and structural composition of national high-temperature anomalies (HTAs) are essential for mitigating natural disasters, regulating industrial emissions, and assessing societal impacts. The Landsat-8 Operational Land Imager (OLI) provides a unique capability for monitoring nighttime HTAs (N-HTAs) at high spatial resolutions without contamination from reflected solar radiation. However, the extraction of N-HTAs from nighttime OLI imagery poses significant challenges due to the interference of bright up-light, especially for smaller and cooler hotspots, and the absence of spectral information on backgrounds complicates the classification of N-HTAs at night. Here, we explored the background noise characteristics of Bands 2–7 in nighttime OLI imagery and developed an object-oriented approach for the enhanced detection of small and cool N-HTAs. Furthermore, we introduced a deep learning model that leverages high-resolution aerial imagery and land cover data to categorize N-HTAs into industrial sources, living sources, vegetation fires, and others. Applying these methodologies to nighttime OLI imagery from 2013 to 2020 allowed us to map and classify N-HTAs across the contiguous United States (CONUS), facilitating the creation of a detailed spatiotemporal inventory of industrial N-HTAs. Our analysis revealed that N-HTAs from living sources dominate the total N-HTAs, with a pronounced holiday effect, and gas flaring activities in oil and gas development areas account for >85 % of industrial N-HTAs. The outcomes of this national-scale investigation underscore the significant potential and practical utility of nighttime OLI imagery for N-HTA monitoring and advocate for expanding the spatial and temporal coverage of nighttime OLI observations globally.

Land Use-Land Cover Changes and Associated Drivers: A Case of Salooni Block of Chamba

Land Use-Land Cover (LULC) changes are the anthropogenic alteration of the natural landscape. The LULC changes have been generated by a variety of factors, including natural, demographic, and economic ones, which put pressure on the land and had a variety of environmental effects. The transformation in Landscape is affecting the livelihoods of people in numerous ways. Therefore, it is crucial to understand the patterns in land use in order to determine how land should be developed, managed, and planned in order to understand the way it will be utilised in the future. In the present study, Salooni Block of Chamba district, Himachal Pradesh was subjected to a first-ever attempt at land-use change detection for the years 2002 and 2022. Satellite imageries of Landsat-7 Enhanced Thematic Mapper (ETM+) Landsat-8 Operational Land Imager (OLI) were used for landuse maps of 2002 and 2022 respectively. The classification was completed using a supervised classification technique and the USGS Land use Classification Scheme at Level I classes, including Agricultural Land, Barren Land, Built-up, Forest, Pasture Land, Snow Cover and Waterbody. Furthermore, fifty people aged 50 and above were interviewed to understand the local factors responsible for LU/LC change. The result indicates a rise in the forest cover, which now accounts for 48.2% of the total area. Since 2002, there has been a 2.3-fold rise in built-up land, which is a glaring indication of the pressure on land. The total area of agricultural and pasture land is decreasing, while the percentage of barren land has increased by 2%. To preserve the sustainability and food security of the ecologically vulnerable environment, stringent land use planning needs to be enforced in the mountainous region. The results of the LU/LC dynamics discussed above can be used to establish policies and strategies for restoring and enhancing the sustainability of the environment as well as the carrying capacity of the land.

The use of ASTER and landsat remote sensing data for exploring hydrothermal mineral in Arabian-Nubian Shield

Multispectral remote sensing data provided valuable information for hydrothermal minerals exploration. In this article, Landsat-8 Operational Land Imager (OLI) and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data were processed and analyzed using band ratio and principal component techniques to demarcate areas of hydrothermal mineral resources by highlighting the most influenced zone of hydrothermal alteration (high-grade) in Egypt. The area is a part of the Arabian-Nubian Shield, covering 1068 km2, and comprises wide varieties of Pre- and Pan-African rock assemblages. Preparing and integrating thematic layers consisting of the grade of hydrothermal intensity in a GIS technique supported generating a hydrothermal mineral predictive map (HMPM) using a multi-criteria decision-making technique. The plausible zone of very high mineral resources covers about 6.57 %. Analysis of ASTER data revealed that this zone is reliable with phyllic and argillic minerals and iron-oxides. The results of OLI data helped in demarcating potential areas of hydrothermal alteration areas (Al–OH) mixed with iron oxides. Field observations and investigations enabled the validation of the HMPM. The spatial distribution of hydrothermal zones throughout the study was reliable to that observed in the mine areas. Field and ore microscopic examination verified the existence of Au-sulfide minerals connected with hydrothermal alteration zones (HAZs) in different areas e.g., Abu-Marawat, Gidami, Semna, Abu-Gaharish, and Hamama mines. Overall, integrating multi-spectral data through overlay analysis techniques can provide valuable information about hydrothermal mineral resources in an accurate and cost-effective approach.

Advancing reservoirs water quality parameters estimation using Sentinel-2 and Landsat-8 satellite data with machine learning approaches

Reservoir eutrophication, caused by human activities and climate change, has emerged as a critical environmental concern that has attracted both governmental and public attention. However, accurate measurement of water quality parameters, such as chlorophyll a (CHL-a), water clarity (Secchi depth; SD), and total suspended solids (TSS), in inland waters is challenging due to the optical complexity of individual water bodies, which impedes the optimization of conventional bio-optical algorithms. The aim of this study was to demonstrate the viability of harmonizing Sentinel-2 Multi-Spectral Imager (MSI) and Landsat-8 Operational Land Imager (OLI) satellite imagery surface reflectance (SR) products to facilitate the monitoring of inland reservoir CHL-a, SD, and TSS using the Google Earth Engine (GEE) platform and machine learning algorithms. Machine learning models were trained using Landsat-8 OLI and Sentinel-2 MSI surface reflectance products to identify bands and combinations predicting CHL-a, SD, and TSS. Among the machine learning algorithms tested, random forest (RF) (S-2 MSI: R2 = 0.61, mean absolute error [MAE] = 6.56%, root-mean-square error [RMSE] = 12.51 μg/L, L-8 OLI: R2 = 0.56, MAE = 8.44%, RMSE = 16.01 μg/L) yielded the best results in the test set for CHL-a prediction from the Sentinel-2 MSI and Landsat-8 OLI, outperforming the k-nearest neighbor (KNN), AdaBoost, and artificial neural network (ANN) models. It also showed superior performance for SD and TSS prediction. The feature importance analysis revealed that specific band ratios, such as (red/red edge1)*red edge2 for Sentinel-2 MSI and red/blue for Landsat-8 OLI, were significant predictors of CHL-a and TSS. The red/blue band ratio and the green band were highly predictive for SD in Sentinel-2 MSI and Landsat-8 OLI, respectively. The results of fall CHL-a predictions revealed varying trophic levels in the reservoirs. Landsat-8 OLI indicated that 2% of the reservoirs were oligotrophic, while 46%, 43%, and 9% were mesotrophic, eutrophic, and hypertrophic, respectively. Meanwhile, the Sentinel-2 MSI results showed that 51% of the reservoirs were mesotrophic, while 6%, 35%, and 8% were oligotrophic, eutrophic, and hypertrophic, respectively. Overall, this study demonstrates the effectiveness of Landsat-8 OLI and Sentinel-2 MSI surface reflectance products in estimating and monitoring reservoir water quality parameters using machine learning algorithms. This approach has the potential to yield valuable insights aiding the assessment and management of water quality at the regional, national, and global levels.

Improved Landsat Operational Land Imager (OLI) Cloud and Shadow Detection with the Learning Attention Network Algorithm (LANA)

Landsat cloud and cloud shadow detection has a long heritage based on the application of empirical spectral tests to single image pixels, including the Landsat product Fmask algorithm, which uses spectral tests applied to optical and thermal bands to detect clouds and uses the sun-sensor-cloud geometry to detect shadows. Since the Fmask was developed, convolutional neural network (CNN) algorithms, and in particular U-Net algorithms (a type of CNN with a U-shaped network structure), have been developed and are applied to pixels in square patches to take advantage of both spatial and spectral information. The purpose of this study was to develop and assess a new U-Net algorithm that classifies Landsat 8/9 Operational Land Imager (OLI) pixels with higher accuracy than the Fmask algorithm. The algorithm, termed the Learning Attention Network Algorithm (LANA), is a form of U-Net but with an additional attention mechanism (a type of network structure) that, unlike conventional U-Net, uses more spatial pixel information across each image patch. The LANA was trained using 16,861 512 × 512 30 m pixel annotated Landsat 8 OLI patches extracted from 27 images and 69 image subsets that are publicly available and have been used by others for cloud mask algorithm development and assessment. The annotated data were manually refined to improve the annotation and were supplemented with another four annotated images selected to include clear, completely cloudy, and developed land images. The LANA classifies image pixels as either clear, thin cloud, cloud, or cloud shadow. To evaluate the classification accuracy, five annotated Landsat 8 OLI images (composed of >205 million 30 m pixels) were classified, and the results compared with the Fmask and a publicly available U-Net model (U-Net Wieland). The LANA had a 78% overall classification accuracy considering cloud, thin cloud, cloud shadow, and clear classes. As the LANA, Fmask, and U-Net Wieland algorithms have different class legends, their classification results were harmonized to the same three common classes: cloud, cloud shadow, and clear. Considering these three classes, the LANA had the highest (89%) overall accuracy, followed by Fmask (86%), and then U-Net Wieland (85%). The LANA had the highest F1-scores for cloud (0.92), cloud shadow (0.57), and clear (0.89), and the other two algorithms had lower F1-scores, particularly for cloud (Fmask 0.90, U-Net Wieland 0.88) and cloud shadow (Fmask 0.45, U-Net Wieland 0.52). In addition, a time-series evaluation was undertaken to examine the prevalence of undetected clouds and cloud shadows (i.e., omission errors). The band-specific temporal smoothness index (TSIλ) was applied to a year of Landsat 8 OLI surface reflectance observations after discarding pixel observations labelled as cloud or cloud shadow. This was undertaken independently at each gridded pixel location in four 5000 × 5000 30 m pixel Landsat analysis-ready data (ARD) tiles. The TSIλ results broadly reflected the classification accuracy results and indicated that the LANA had the smallest cloud and cloud shadow omission errors, whereas the Fmask had the greatest cloud omission error and the second greatest cloud shadow omission error. Detailed visual examination, true color image examples and classification results are included and confirm these findings. The TSIλ results also highlight the need for algorithm developers to undertake product quality assessment in addition to accuracy assessment. The LANA model, training and evaluation data, and application codes are publicly available for other researchers.

Role of remote-sensing techniques in unveiling the spatiotemporal response of vegetation to climate change in the western Makkah Province of Saudi Arabia

Climate change is a global problem that dramatically affects natural resources, resulting in significant changes in temperature, precipitation, and humidity, which affect vegetation cover. Under this light, this study aimed to identify the potential of remote-sensing techniques to reveal the spatiotemporal response of vegetation cover to climate change in the western Makkah Province using Landsat-5 Thematic Mapper, Landsat-8 operational land imager, Global Land Data Assimilation System model, Global Precipitation Measurement, and Famine Early Warning Systems Network Land Data Assimilation System model data from 2000 to 2023. Optimised Soil-Adjusted Vegetation Index (OSAVI), classification, overlay, change detection, and correlation analysis were utilized to process data. Time series analysis of data revealed climate-related changes which were particularly intense in recent years. Specifically, temperature, precipitation, and specific humidity were found to differ depending on the landforms and season. Temperature was higher during the dry season compared to the wet season. A decrease was observed in the overall precipitation rate, which did not exceed 81.39 mm during the wet season and approximately 11.46 mm during the dry season. Additionally, precipitation increased in 2023 but decreased in 2018. Moreover, the study area was located on semi-arid lands for all years except for the wet season of 2023. OSAVI analysis, which is sensitive to climate change, revealed that vegetation coverage can be both positively and negatively affected by climate change. The most profound vegetation coverage in the study region was observed in 2023. A strong correlation was also observed between precipitation and vegetation in the study area, which showed less high-greenness in the dry season and more widespread grasses. The implications of these findings for the development of strategies for biodiversity conservation in semi-arid regions are significant.

Analysis of methods for assessing the degree of water pollution

The most important modern methods for the monitoring of water bodies, their state of pollution and the assessment of water quality are considered. Commonly accepted, standard-ized methods for assessing water quality in water bodies include ground-based sampling, which is untimely and expensive under certain conditions. Best suited for practical use in large areas is the remote approach, which combines ground and satellite measurements to classify changes and detect pollution in closed water bodies. Satellite monitoring is a reliable tool for obtaining operational information on the state of water bodies and their changes with a certain periodicity, thanks to the use of a large set of heterogeneous data. And the availabil-ity of archived data becomes a significant advantage, opening up the possibility of comparing the results obtained and analyzing the factors influencing the state of water bodies and the environment. The paper describes the main methods and datasets that can be used for the ap-plication of remote monitoring methods based on machine learning. The use of Landsat mul-tispectral sensors, including TM (Thematic Mapper), MSS (Multispectral Scanner), ETM (Enhanced Thematic Mapper), and OLI (Operational Land Imager), is proposed as the main data set. Examples of the application of the remote sensing approach to the assessment of wa-ter pollution over large areas will be analyzed. Possible machine learning methods for solv-ing an applied problem are presented.

Physics-Based Satellite-Derived Bathymetry (SDB) Using Landsat OLI Images

The estimation of depth in optically shallow waters using satellite imagery can be efficient and cost-effective. Active sensors measure the distance traveled by an emitted laser pulse propagating through the water with high precision and accuracy if the bottom peak intensity of the waveform is greater than the noise level. However, passive optical imaging of optically shallow water involves measuring the radiance after the sunlight undergoes downward attenuation on the way to the sea floor, and the reflected light is then attenuated while moving back upward to the water surface. The difficulty of satellite-derived bathymetry (SDB) arises from the fact that the measured radiance is a result of a complex association of physical elements, mainly the optical properties of the water, bottom reflectance, and depth. In this research, we attempt to apply physics-based algorithms to solve this complex problem as accurately as possible to overcome the limitation of having only a few known values from a multispectral sensor. Major analysis components are atmospheric correction, the estimation of water optical properties from optically deep water, and the optimization of bottom reflectance as well as the water depth. Specular reflection of the sky radiance from the water surface is modeled in addition to the typical atmospheric correction. The physical modeling of optically dominant components such as dissolved organic matter, phytoplankton, and suspended particulates allows the inversion of water attenuation coefficients from optically deep pixels. The atmospheric correction and water attenuation results are used in the ocean optical reflectance equation to solve for the bottom reflectance and water depth. At each stage of the solution, physics-based models and a physically valid, constrained Levenberg–Marquardt numerical optimization technique are used. The physics-based algorithm is applied to Landsat Operational Land Imager (OLI) imagery over the shallow coastal zone of Guam, Key West, and Puerto Rico. The SDB depths are compared to airborne lidar depths, and the root mean squared error (RMSE) is mostly less than 2 m over water as deep as 30 m. As the initial choice of bottom reflectance is critical, along with the bottom reflectance library, we describe a pure bottom unmixing method based on eigenvector analysis to estimate unknown site-specific bottom reflectance.