Landsat-8 Operational Land Imager Research Articles

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.

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.

Fuzzy machine learning model to detect transition building footprints using multi-sensor multi-temporal images

The research study proposes a fuzzy machine learning based approach for the detection of transitioning building footprints in urban area using temporal high and medium resolution datasets acquired by multi-sensors. Remote sensing dataset from Planetscope, Sentinel-2A/2B and Landsat-8 Operational Land Imager were used to generate the multi sensor based temporal indices database. The class-based sensor independent – normalized difference vegetation index (CBSI-NDVI) was used to reduce spectral dimensionality and to generate temporal database, which was given as an input in MPCM classifier with two training approaches, namely, mean training approach and Individual Sample as Mean (ISM) training approach. The training was performed using single and multiple phases of change in urban area in different time periods. The results were validated using high resolution Google Earth imagery as well as ground observations at five different testing sites. The kappa coefficient was calculated as 0.78 for the fuzzy MPCM with ISM training approach. Also, the MMD and variance were calculated to be 0.006 and 0.002 respectively. The study concluded that the MPCM classifier with ISM training approach could address the heterogeneity in urban area and provides classified images considering each transition trend as mean input for classification, thus improving the classification accuracy.

Monitoring global cement plants from space

As an energy-intensive industry, cement production can emit significant air pollutants that negatively impact human health and the environment. Geospatial data on cement plants are crucial for managing the cement industry and designing effective policies to mitigate ever-increasing air pollution. However, the available fundamental asset-level datasets of global cement plants are generally incomplete and may not adequately satisfy the above requirements. To address this gap, (i) we proposed an innovative semi-automatic framework, which is based on the high-temperature signal from active kilns in time-series Landsat-8 Operational Land Imager (OLI) and Sentinel-2 Multispectral Instrument (MSI) imagery and the iconic characteristics of cement processing equipment in high-resolution imagery, to identify global cement plants, assess their operational status, and evaluate relevant activity levels. (ii) We compiled a state-of-the-art inventory of global cement plants, enriched with detailed attributes, using multi-source spaceborne observations/products. We identified 3075 cement plants with rotary/shaft kilns and pinpointed 1960 locations for cement grinding and storage facilities. (iii) We investigated the status of global cement plants using time-series Sentinel-2 MSI imagery. Our results show that from 2016 to date, 69.3% of cement plants are continuously operating, 4.3% were newly constructed, and 26.4% were decommissioned. (iv) We proposed two normalized metrics, namely the occurrence rate and the mean detected area, to assess the activity level of cement plants. The results showed that the rotary kilns in wet-process cement plants had the highest activity level due to the higher energy consumption. The activity level of global cement plants increased by ∼9% during 2016–2021 and then decreased by ∼4% in 2022, possibly due to the influence of COVID-19 and weak market demand. The proposed framework provides valuable insights for investigating the locations and dynamics of other industrial hot sources, such as those in steel mills, coke ovens, refineries, and oil/gas development areas, to improve the estimates of CO2 emissions and air pollutants and then to promote climate action.

Development of Soil-Suppressed Impervious Surface Area Index for Automatic Urban Mapping

Expanding urban impervious surface area (ISA) mapping is crucial to sustainable development, urban planning, and environmental studies. Multispectral ISA mapping is challenging because of the mixed-pixel problems with bare soil. This study presents a novel approach using spectral and temporal information to develop a Soil-Suppressed Impervious Surface Area Index (SISAI) using the Landsat Operational Land Imager (OLI) data set, which reduces the soil but enhances the ISA signature. This study mapped the top 12 populated megacities using SISAI and achieved an over-all accuracy of 0.87 with an F1-score of 0.85. It also achieved a higher Spatial Dissimilarity Index between the ISA and bare soil. However, it is limited by bare gray soil and shadows of clouds and hills. SISAI encourages urban dynamics and inter-urban compari- son studies owing to its automatic and unsupervised methodology.

Development of an algal bloom satellite and in situ metadata hub with case studies in Canada

Satellite remote sensing of algal blooms has been in use for almost five decades and has advanced with an increasing number of sensors, improvements in sensor performance, and developments in algorithms. This progress has enabled the detection and monitoring of blooms in increasingly optically complex water bodies and small lakes, and at finer spatial and temporal resolutions. With climatic and anthropogenic stressors impacting bloom occurrence, duration, and severity, it is critical to understand and characterize blooms across a wide range of spatial and temporal scales. However, resolving blooms at both fine spatial and temporal scales, and simultaneously across broad spatial extents, provides challenges to the limitations of current sensors. This study introduces the Algal Bloom Metadata Hub, an application that can be used to investigate freely available remote sensing data from contemporary satellite sensors to streamline data management and acquisition, particularly when interested in data from multiple satellite sensors. Optical sensors hosted by the hub include the Sentinel-3 Ocean and Land Colour Instrument (OLCI), Sentinel-2 MultiSpectral Instrument (MSI), LANDSAT-8 Operational Land Imager (OLI) and LANDSAT-9 OLI-2; SAR sensors include Sentinel-1, RADARSAT-2, and RADARSAT Constellation Mission (RADARSAT-CM). The application also includes an opportunity for in situ reporting of blooms, providing the potential to improve understanding of bloom occurrence distribution at a wide spatial scale. Case studies in the Western Basin of Lake Erie and Ramsey Lake in Ontario, Canada, demonstrate its utility in identifying suitable remote sensing data. Particularly in Ramsey Lake, the opportunity to leverage scenes from multiple sensors, and the integration of synthetic aperture radar (SAR) data on cloudy days, improved the temporal resolution of bloom monitoring at higher spatial resolutions. This finding was corroborated by performing an investigation into data availability for all small water bodies in Ontario. Further work can be done to integrate data from additional satellite sensors, update the application to add data in real-time, and to quality test in situ data submitted by (citizen) scientists.

Automated mapping of regolith units with support vector machine and artificial neural network using data from Landsat-8 OLI, ALOS PALSAR DEM, and Sentinel-1A radar images: the case of the Sissingué Gold Project, Northern Côte d’Ivoire

ABSTRACT The Sissingué Gold Project is in Northern Côte d’Ivoire and part of the southern extension, a large peneplain of the landscape in South Mali. The area has experienced extensive weathering and erosion over a long time, resulting in a widespread and complex regolith cover. This diverse and vast regolith cover poses considerable difficulties for exploration. To overcome this problem, we adopted mapping of regolith units using machine learning (ML) with support vector machine (SVM) and artificial neural network (ANN) algorithms. The main objectives were to (1) make the predictive regolith map of the study region using these algorithms with data obtained from Landsat-8 operational land imager, Advanced Land Observing Satellite Phased Array Type L-Band Synthetic Aperture Radar digital elevation model and Sentinel-1A; (2) show the method of pre-processing and processing the required data and (3) develop the regolith landform unit (RLU) map from the predictive regolith map and collected data based on the Relict – Erosional – Depositional model. Tests using the SVM and ANN algorithms showed that both ML tools could accurately map regolith landforms. An innovative method of optimizing data using parameters is presented. The results showed that ANN outperformed SVM with an overall accuracy of 87.01% and a kappa coefficient of 0.84, whereas the corresponding values for SVM were 86.69% and 0.84, respectively. However, the validation data obtained from SVM-based prediction exhibited a better score than that of validation data obtained from ANN-based prediction. The Sentinel-1A radar band combined with Landsat-8 data reduced the vegetation-masking effect and improved the classification results. The RLUs of this area are composed primarily of relicts at 24.91%, including lateritic residuum and soil, depositional material at 36.39% with exotic sediments and ferrierite and the remaining 38.7% of erosional material made of saprolite, colluvial fragments, and mottled zones on flanks.

Comparative Study on Object-Oriented Identification Methods of Plastic Greenhouses Based on Landsat Operational Land Imager

The rapid and precise acquisition of the agricultural plastic greenhouse (PG) spatial distribution is essential in understanding PG usage and degradation, ensuring agricultural production, and protecting the ecological environment and human health. It is of great practical significance to realize the effective utilization of remote sensing images in the agricultural field and improve the extraction accuracy of PG remote sensing data. In this study, Landsat operational land imager (OLI) remote sensing images were used as data sources, and Shandong Province, which has the largest PG distribution in China, was selected as the study area. PGs in the study area were identified by means of contour recognition, feature set construction of the spatial structure, and machine learning. The results were as follows. (1) Through an optimal segmentation parameter approach, it was determined that the optimal segmentation scale for size, shape, and compactness should be set at 20, 0.8, and 0.5, respectively, which significantly improved PG contour recognition. (2) Among the 72 feature variables for PG spatial recognition, the number of features and classification accuracy showed a trend of first gradually increasing and then decreasing. Among them, fifteen feature variables, including the mean of bands 2 and 5; six index features (NDWI, GNDVI, SWIR1_NIR, NDVI, and PMLI); two shape features, the density and shape index; and two texture features, the contrast and standard deviation, played an important role. (3) According to the recall rate, accuracy rate, and F-value of three machine learning methods, random forest (RDF), CART decision tree (CART), and support vector machine (SVM), SVM had the best classification effect. The classification method described in this paper can accurately extract continuous plastic greenhouses through remote sensing images and provide a reference for the application of facility agriculture and non-point-source pollution control.

SPATIAL DISTRIBUTION OF MALARIA AND LAND COVER PATTERNS IN OGONI LAND, RIVERS STATE, NIGERIA

The distribution of malaria is characterised by microgeographic variations determined by a range of factors, including the local environment. A study on the spatial distribution of malaria about land cover patterns was carried out by sampling Primary Health Centres in Ogoni Land. Nine Primary Healthcare Centres (PHCs) were selected across four local government areas (LGA) using Systematic Grid Point Sampling. Human blood samples were obtained from 318 consented individuals, and questionnaires were administered to obtain demographic data. Plasmodium species were identified through microscopy using thick and thin blood films. A geodatabase was created and imported into ArcGIS 10.7 to produce a thematic map of the study area. A cloud-free Landsat-8 Operational Land Imager (OLI) was employed for land cover analysis. Both supervised and unsupervised classifications of land cover were performed to generate the land cover classes. Pearson correlation was carried out to determine the significance between malaria distribution and land cover. Of the 318 individuals, 169 were infected with an overall prevalence of 53.1%.Only P. falciparum was identified and malaria distribution showed spatial variations. Across the PHCs sampled, the highest point prevalence was recorded in Model Primary Health Centre Koroma in Tai LGA whereas the lowest was recorded in MPHC Okwale in Khana LGA. Cumulatively, Kwawa PHC recorded the highest malaria prevalence whereas MPHC Bunu in Tai recorded the lowest prevalence. The highest prevalence was recorded in Khana LGA while the lowest was recorded in Eleme LGA. Land cover analysis revealed that Ogoni Land has a total land cover mass of 982.97km.2 Sparse vegetation dominated the study area (471.06km2) while dense vegetation covers a total mass of 213.1km2. Bivariate analysis showed a significant correlation between malaria prevalence and dense vegetation (p<0.05, 0.952). Dense vegetation played a significant role in malaria transmission in Ogoni Land. The study concludes that the presence of dense vegetation is associated with high malaria prevalence in the study area.

Lithological Mapping Research Based on Feature Selection Model of ReliefF-RF

Multispectral satellite data allow experts to discriminate rock units based on their spectral signature characteristics. Here, Sentinel-2, ASTER and the Landsat-8 Operational Land Imager (OLI) were assessed for lithological mapping by using a random forest (RF) classifier for a study area located in Xitieshan, Northwest China. The classification accuracy of Sentinel-2 was 60.71%, which was 5.24% and 4.77% higher than the accuracies for ASTER and the Landsat-8 OLI, respectively. Three image enhancement techniques, namely, principal component analysis (PCA), independent component analysis (ICA) and minimum noise fraction (MNF), were used with grey-level cooccurrence matrices (GLCMs) to increase the quality of the input datasets. The ICA could discriminate between rock unit datasets better than the other approaches. In contrast, GLCM performed poorly when used independently. The overall classification accuracies were 60.71%, 62.63%, 64.34%, 65.21% and 58.87% for the 10 bands of Sentinel-2, PCA, MNF, ICA and GLCM, respectively. Then, five datasets were combined as a single group and applied in RF classification. Sentinel-2 obtained an overall accuracy of 73.96% and performed better than the other single-dataset approaches used in this study. Furthermore, the classification result of RF was achieved better performance than that of the support vector machine algorithm (SVM). During feature selection processing, ReliefF, the most successful pre-processing algorithm, was employed to preliminarily perform feature screening. Then, the optimal dataset was selected on the basis of the importance ranking of RF. A total of 20 more important predictors were selected from 114 original features using the ReliefF-RF model. These predictors were used in the lithological mapping, and an overall accuracy of 77.63% was reached.

Atmospheric correction algorithm based on deep learning with spatial-spectral feature constraints for broadband optical satellites: Examples from the HY-1C Coastal Zone Imager

Broadband optical satellites have been widely used for fine monitoring of coastal waters and inland lakes for their high spatial resolution. Atmospheric correction (AC) is one of the essential data processing steps for remote sensing of water environments with high spatial resolution satellites. Most broadband optical satellites, such as HY-1C Coastal Zone Imager (CZI) with a spatial resolution of 50 m, lack of near-infrared and shortwave infrared bands needed for atmospheric correction, making accurate AC difficult. Auxiliary aerosol data is often needed for accurate AC of broadband optical satellite data, which suffers from the differences in spatial resolution and imaging time between auxiliary data and satellite data. Furthermore, existing AC algorithms always perform AC pixel by pixel, which ignores the spatial relationship between adjacent pixels. Taking HY-1C CZI as an example, this paper proposes a novel atmospheric correction algorithm based on deep learning (SSACNet). Considering the inherent spatial-spectral features of satellite images, the SSACNet combines 2D and 3D convolution. The 3D convolution was used to mine the spatial and spectral features of the image and 2D convolution was explored to perform spatial information compensation. The SSACNet was trained and evaluated using the spatio-temporally synchronized dataset of HY-1C CZI and Landsat8 Operational Land Imager (OLI). The evaluation results by in-situ data show that the SSACNet has good performance, with the average correlation coefficient of 0.89, and the absolute percentage deviation (APD) of four bands ranging from 21.53 % to 35.41 %. Compared with the quasi-synchronous Landsat8 OLI remote sensing reflectance (Rrs), the APD is less than 10 %. Compared with traditional atmospheric correction algorithms, SSACNet has significantly improved the spatial and spectral information fidelity. In addition, SSACNet also shows good applicability, as it can be used in both clear ocean waters and turbid coastal waters. This study lays a foundation for the quantitative remote sensing of water environment by broadband optical satellites.

Monitoring of chlorophyll-a and suspended sediment concentrations in optically complex inland rivers using multisource remote sensing measurements

In recent decades, phytoplankton proliferation and sediment input to rivers (especially urban rivers) have become more dramatic under the compound pressure of climate change and human activities. Given the generally narrow width of rivers and current high spatial resolution satellites, which are limited by band settings, bandwidth, and the signal-to-noise ratio, UAVs with their exceptional spatiotemporal resolution can be used as a useful tool for river environmental monitoring and inversion uncertainty assessment. In this study, UAV-based hyperspectral (X20P) and multispectral (P4M) images, along with Sentinel-2 MultiSpectral Instrument (MSI), Landsat-8 Operational Land Imager (OLI) and Landsat-9 OLI2 data, were used to assess the uncertainty in retrieving chlorophyll-a (Chla) and suspended sediment (SS) concentrations in rivers. Chla and SS models based on UAV and satellite data were constructed using stepwise multiple regression and typical Chla and SS retrieval algorithms, respectively, and the performance of the models was the focus of our research. The results demonstrated that in the Chla concentration inversion, each sensor performed as follows: X20P > P4M > Landsat9 OLI2 > Sentinel-2 MSI > Landsat8 OLI, and the performance in the SS concentration inversion was as follows: X20P > Sentinel-2 MSI > P4M > Landsat9 OLI2 > Landsat8 OLI. In addition, the uncertainty of high spatial resolution satellite retrievals was analyzed with the assistance of the UAV-based model. Results showed that narrow bandwidths and finely tuned band settings are more essential for the Chla inversion. The typical Chla retrieval algorithm, NDCI, is only effective in certain bands (band 1 from 684 to 724 nm and band 2 from 660 to 680 nm). It is also noted that Landsat8 and Landsat9 lack some key band settings (e.g., the red-edge band of 700–710 nm), severely limiting practical application in relation to Chla. However, specific variances in different sensor bands have a relatively small impact on SS inversion, for example, the correlation between SS and the R/B (a typical SS retrieval algorithm) constructed by each sensor ranged from 0.68 to 0.77. Chla monitoring, on the other hand, necessitates a higher spatial resolution than SS monitoring. The accuracy decreased markedly when UAV images were resampled to 10 m and 30 m spatial resolution. However, it is not as crucial for the SS inversion, images with the original spatial resolution (RMSE<30cm = 6.28 mg/L) were resampled to 10 m resolution (RMSE10m = 5.85 mg/L) and 30 m resolution (RMSE30m = 4.08 mg/L) while using P4M for SS inversion, and the accuracy increased. Our results demonstrated and highlighted various options for future monitoring of Chla and SS, while exploiting the synergy between UAVs and satellites to achieve more precise observations at greater spatial and temporal scales, which will benefit aquatic environment management and protection.