Classification Of Satellite Imagery Research Articles

Artificial Intelligence for Computational Remote Sensing: Quantifying Patterns of Land Cover Types around Cheetham Wetlands, Port Phillip Bay, Australia

This paper evaluates the potential of using artificial intelligence (AI) and machine learning (ML) approaches for classification of Landsat satellite imagery for environmental coastal mapping. The aim is to identify changes in patterns of land cover types in a coastal area around Cheetham Wetlands, Port Phillip Bay, Australia. The scripting approach of the Geographic Resources Analysis Support System (GRASS) geographic information system (GIS) uses AI-based methods of image analysis to accurately discriminate land cover types. Four ML algorithms are applied, tested and compared for supervised classification. Technical approaches are based on using the ‘r.learn.train’ module, which employs the scikit-learn library of Python. The methodology includes the following algorithms: (1) random forest (RF), (2) support vector machine (SVM), (3) an ANN-based approach using a multi-layer perceptron (MLP) classifier, and (4) a decision tree classifier (DTC). The tested methods using AI demonstrated robust results for image classification, with the highest overall accuracy exceeding 98% and reached by the SVM and RF models. The presented scripting approach for GRASS GIS accurately detected changes in land cover types in southern Victoria over the period of 2013–2024. From our findings, the use of AI and ML algorithms offers effective solutions for coastal monitoring by analysis of change detection using multi-temporal RS data. The demonstrated methods have potential applications in coastal and wetland monitoring, environmental analysis and urban planning based on Earth observation data.

The decline, fall, and rise of a large urban colonising bird

Context The process of urbanisation results in dramatic landscape changes with long-lasting and sometimes irreversible consequences for the biota as urban sensitive species are eliminated. The Australian brush-turkey (Alectura lathami) is a recent urban colonist despite atypical traits for an urban adapter. Contrary to observed range declines and initial reports of decreased reproductive success in cities, Australian brush-turkeys have increased their range in urban areas. Aims Historical atlas and present citizen science data were used to examine the changing distribution of the Australian brush-turkey at continental and city scales, and the changing land use in urban areas occupied by the species. We assess which environmental and landscape features are driving observed distribution changes over time. Methods We describe and map changes at the continental scale between 1839–2019. We then assessed colonisation of the cities of Brisbane and Sydney (located 900 km apart) over the period 1960–2019. At the city scale, we quantified the changing land use within Australian brush-turkey occupied areas over time using classification of satellite imagery. Key results The Australian brush-turkey’s geographical range has shifted over the last century, with the species receding from the western and southwestern parts of their range, while expanding in the northwest. Areas occupied in the cities of Brisbane and Sydney have expanded, with more recently occupied areas containing less vegetation and more developed land than previously occupied areas. Conclusions Our results confirm that Australian brush-turkeys are successfully colonising urban areas, including major cities, and are likely to continue moving into urban areas, despite declines elsewhere in their natural range. The species is not limited to suburbs with a high proportion of greenspace, as Australian brush-turkeys are increasingly occurring in highly developed areas with limited vegetation. Implications This study highlights that species which were locally extirpated from urban areas, and thought to be unlikely candidates for recolonisation, can successfully occupy human modified habitats. Successful expansion is likely to be associated with key behavioural traits, urban greening, and legal protection from human persecution.

The decline, fall, and rise of a large urban colonising bird

Context The process of urbanisation results in dramatic landscape changes with long-lasting and sometimes irreversible consequences for the biota as urban sensitive species are eliminated. The Australian brush-turkey (Alectura lathami) is a recent urban colonist despite atypical traits for an urban adapter. Contrary to observed range declines and initial reports of decreased reproductive success in cities, Australian brush-turkeys have increased their range in urban areas. Aims Historical atlas and present citizen science data were used to examine the changing distribution of the Australian brush-turkey at continental and city scales, and the changing land use in urban areas occupied by the species. We assess which environmental and landscape features are driving observed distribution changes over time. Methods We describe and map changes at the continental scale between 1839–2019. We then assessed colonisation of the cities of Brisbane and Sydney (located 900 km apart) over the period 1960–2019. At the city scale, we quantified the changing land use within Australian brush-turkey occupied areas over time using classification of satellite imagery. Key results The Australian brush-turkey’s geographical range has shifted over the last century, with the species receding from the western and southwestern parts of their range, while expanding in the northwest. Areas occupied in the cities of Brisbane and Sydney have expanded, with more recently occupied areas containing less vegetation and more developed land than previously occupied areas. Conclusions Our results confirm that Australian brush-turkeys are successfully colonising urban areas, including major cities, and are likely to continue moving into urban areas, despite declines elsewhere in their natural range. The species is not limited to suburbs with a high proportion of greenspace, as Australian brush-turkeys are increasingly occurring in highly developed areas with limited vegetation. Implications This study highlights that species which were locally extirpated from urban areas, and thought to be unlikely candidates for recolonisation, can successfully occupy human modified habitats. Successful expansion is likely to be associated with key behavioural traits, urban greening, and legal protection from human persecution.

An iterative and subsequent proximation method to map historical crop information with satellite images

Mapping agricultural information such as cropping area and type is of great significance for land use and food security and a common method to retrieve such information is satellite imagery classification. The current image classification techniques require a statistically large number of training samples to obtain representative spectral signatures, ideally collected during the satellite overpass time/date. Although this prerequisite sounds simple and easy in principle, it is extremely challenging and sometimes impossible when classifying historical satellite images, as one simply cannot go back in time to collect training datasets or ground truthing. In this paper, we introduce an iterative and subsequent proximation (ISP) method to circumvent this problem for historical image classifications for historical crop mapping. This method assumes that the same crops grown two years apart have similar spectral properties within the same growing season, with limited variation. This allows the use of the crop information classified in any given year (n) to be used as training samples for the next year (n + 1) or the previous year (n-1). Iteratively repeating n-1, n-2… process leads to the mapping of historical cropping information without a priori ground truthing data. To demonstrate the ISP feasibility, we first used the historical Landsat time-series data to map four major stable crops (rice, maize, soybean, and wheat) in Hailun County, Heilongjiang Province, China, and then further expanded the ISP application to the entire Heilongjiang Province to map cropping areas from 1982 to 2020, using the classification and regression trees (CART) algorithm. The results were validated for those years when actual cropping measurement data were available and further verified with statistical data for other years. The results indicated that the proposed ISP method was appropriate for the historical mapping of four major crops, with a mapping accuracy of approximately 80 % when validated with field data, and correlation coefficients of 0.86, 0.85, 0.91, and 0.94, respectively when compared with historical statistical cropping data. The crop mapping results showed distinct trends of northward expansion in each of the four crops in Heilongjiang Province, which agreed well with previous studies. In conclusion, the ISP method is quick, easy, and convenient for historical crop mapping, which is important in understanding the agricultural production history.

Spatio-temporal classification of land use and land cover and its changes in Kerala using remote sensing and machine learning approach.

Land use and land cover (LULC) analysis gives important information on how the region has evolved over time. Kerala, a land with an extensive and dynamic history of land-use changes, has, until now, lacked comprehensive investigations into this history. So the current study focuses on Kerala, one of the ecologically diverse states in India with complex topography, through Landsat images taken from 1990 to 2020 using two different machine learning classifications, random forest (RF) and classification and regression trees (CART) on Google Earth Engine (GEE) platform. RF and CART are versatile machine learning algorithms frequently employed for classification and regression, offering effective tools for predictive modelling across diverse domains due to their flexibility and data-handling capabilities. Normalised Difference Vegetation Index (NDVI), Normalised Differences Built-up Index (NDBI), Modified Normalised Difference Water Index (MNDWI), and Bare soil index (BSI) are integral indices utilised to enhance the precision of land use and land cover classification in satellite imagery, playing a crucial role by providing valuable insights into specific landscape attributes that may be challenging to identify using individual spectral bands alone. The results showed that the performance of RF is better than that of CART in all the years. Thus, RF algorithm outputs are used to infer the change in the LULC for three decades. The changes in the NDVI values point out the loss of vegetation for the urban area expansion during the study period. The increasing value of NDBI and BSI in the state indicates growth in high-density built-up areas and barren land. The slight reduction in the value of MNDWI indicates the shrinking water bodies in the state. The results of LULC showed the urban expansion (158.2%) and loss of agricultural area (15.52%) in the region during the study period. It was noted the area of the barren class, as well as the water class, decreased steadily from 1990 to 2020. The results of the current study will provide insight into the land-use planners, government, and non-governmental organizations (NGOs) for the necessary sustainable land-use practices.

Mapping alien and native forest dynamics in Chile using Earth observation time series analysis

Chile is a global biodiversity hotspot and hosts a large proportion of the southern hemisphere’s temperate forests. The Chilean Valdivian temperate forest is a vulnerable ecosystem containing a highly ecologically valuable species assemblage. Productive forest plantations have involved deforestation of, and alien species introduction into, this ecosystem. This process has already severely impacted the western part of Chile (the Coastal Range) and is now occurring in the eastern part (the Andes), with forestry plantations promoted by government subsidies between 1974 and 2012. Archive Landsat satellite imagery classification and Google Earth Engine are used to assess land cover change over a 31-year period with a focus on alien species (Pinaceae and Eucalyptus spp.) spread and native (Nothofagus spp. and Araucaria araucana) deforestation. Results show a clear land cover pattern based on elevation: a higher altitude, relatively undisturbed area dominated by native forest (the Andes), and a lower altitude area where most human activity and related land covers are located (the valley area). The valley is highly dynamic because of constant land cover change due to forestry. Overall, Araucaria araucana cover has decreased over the study period, while Nothofagus spp. has remained relatively stable. Alien Pinaceae has decreased, while Eucalyptus spp. has remained stable. However, the results indicate that change analysis over long periods conceal dynamism. For example, Eucalyptus spp. sharply decreased between the 1980s and the 1990s and surged afterwards. Also, even though Nothofagus spp. cover dominates throughout the study period, change analysis shows a high degree of change in the valley area, indicating newly established Nothofagus spp. patches. Over the study period, long rotation Pinaceae plantations for timber have given way to shorter rotation forestry (alien Eucalyptus spp., native Nothofagus spp.) for pulp and local uses resulting from discontinuation of forest subsidies. In the absence of subsidies, only large-scale plantations can engage in long rotation forestry, as smallholders need the more stable income provided by shorter rotation forestry. Although higher elevations (the Andes) are dominated by native forest, several abandoned alien forest plantations may be the source of biological invasion. In addition, native forest degradation as a result of Araucaria araucana loss and shrub encroachment is occurring. Earth observation methods are key for forest and alien species monitoring and landscape management. They can enhance traditional, ground-based forest surveys and provide continuous and even retrospective monitoring of forest change thanks to the wide availability of current and historical satellite data.

Primena metoda mašinskog učenja pri klasifikaciji satelitskih snimaka

Machine learning, as a specific domain within artificial intelligence, opens new horizons for both theoretical and experimental research in remote sensing, particularly in satellite imagery classification. This study focuses on applying machine learning methods, specifically decision trees and support vector machines, to classify satellite images. The analysis uses the SAGA GIS software on LANDSAT 8 OLI Level 2A satellite images. Satellite image classification encompasses two primary groups of computer operations: unsupervised (automatic or formal) and supervised (semi-automatic or logical) classification. This research executes the practical classification of satellite images by applying the aforementioned machine learning methods. The results indicate that the obtained classified rasters not only align with but also fully replace existing classification and identification methods of geospatial objects. Consequently, this research contributes to a significant advancement in collecting and analysing geospatial data.

Lightweight Parallel Convolutional Neural Networkwith SVM classifier for Satellite Imagery Classification

Lightweight Parallel Convolutional Neural Networkwith SVM classifier for Satellite Imagery Classification

Remote sensing-based assessment of mangrove ecosystems in the Gulf Cooperation Council countries: a systematic review

Mangrove forests in the Gulf Cooperation Council (GCC) countries are facing multiple threats from natural and anthropogenic-driven land use change stressors, contributing to altered ecosystem conditions. Remote sensing tools can be used to monitor mangroves, measure mangrove forest-and-tree-level attributes and vegetation indices at different spatial and temporal scales that allow a detailed and comprehensive understanding of these important ecosystems. Using a systematic literature approach, we reviewed 58 remote sensing-based mangrove assessment articles published from 2010 through 2022. The main objectives of the study were to examine the extent of mangrove distribution and cover, and the remotely sensed data sources used to assess mangrove forest/tree attributes. The key importance of and threats to mangroves that were specific to the region were also examined. Mangrove distribution and cover were mainly estimated from satellite images (75.2%), using NDVI (Normalized Difference Vegetation Index) derived from Landsat (73.3%), IKONOS (15%), Sentinel (11.7%), WorldView (10%), QuickBird (8.3%), SPOT-5 (6.7%), MODIS (5%) and others (5%) such as PlanetScope. Remotely sensed data from aerial photographs/images (6.7%), LiDAR (Light Detection and Ranging) (5%) and UAV (Unmanned Aerial Vehicles)/Drones (3.3%) were the least used. Mangrove cover decreased in Saudi Arabia, Oman, Bahrain, and Kuwait between 1996 and 2020. However, mangrove cover increased appreciably in Qatar and remained relatively stable for the United Arab Emirates (UAE) over the same period, which was attributed to government conservation initiatives toward expanding mangrove afforestation and restoration through direct seeding and seedling planting. The reported country-level mangrove distribution and cover change results varied between studies due to the lack of a standardized methodology, differences in satellite imagery resolution and classification approaches used. There is a need for UAV-LiDAR ground truthing to validate country-and-local-level satellite data. Urban development-driven coastal land reclamation and pollution, climate change-driven temperature and sea level rise, drought and hypersalinity from extreme evaporation are serious threats to mangrove ecosystems. Thus, we encourage the prioritization of mangrove conservation and restoration schemes to support the achievement of related UN Sustainable Development Goals (13 climate action, 14 life below water, and 15 life on land) in the GCC countries.

Геоинформационное картографирование урбанизированных территорий с использованием данных дистанционного зондирования (на примере г. Волгограда)

The article examines the dynamics of land cover change in the territory of Volgograd city within the administrative districts from 2017 to 2021. The research objective is to analyze the changes in urban development areas of Volgograd over a five-year period using remote sensing data and geoinformation technologies. The data sources used are global land use maps provided by ESRI, based on the automatic classification of Sentinel-2 satellite imagery with a spatial resolution of 10 m/pixel. The creation and analysis of electronic maps were performed using QGIS 3 software. As a result of the conducted research, the areas of major land cover types were obtained, and their dynamics were studied, identifying areas of new urban development. The three most developed districts are Central, Voroshilovsky, and Krasnooktyabrsky. The built-up area has increased by 325 hectares from 2017 to 2021, with the majority of this increase occurring in the Soviet district of the city. The growth of built-up areas mostly happens at the expense of vacant lots and abandoned arable land. From 2012 to 2021, the population of Volgograd was decreasing by an average of 2,500 people per year. However, the construction of new residential complexes has not ceased. In 2022, the population sharply increased to a record level for the past decade, which may intensify new construction. The city’s forested areas are decreasing, which is characteristic of all administrative districts. According to ESRI data, in 2021, the average forest coverage was 5.3% with a forest area of 4.6 thousand hectares, while in 2017, the average forest coverage was 5.6%. The highest forest coverage is observed in the Kirovsky district, at 16.4%. Sarpa Island, which was included within the administrative boundaries of the district in 2008, also has a high forest coverage of 30.8%.

Improving the Accuracy of Land Use and Land Cover Classification of Landsat Data in an Agricultural Watershed

Classification of remotely sensed imagery for reliable land use and land cover (LULC) remains a challenge in areas where spectrally similar LULC features occur. For example, bare soils of harvested crop fields in agricultural watersheds exhibit spectral characteristics similar to high-intensity developed regions and impede an accurate classification. The goal of this study is to improve the accuracy of LULC classification of satellite imagery for the Big Sunflower River Watershed, Mississippi using ancillary data, multiple classification methods, and a post-classification correction (PCC). To determine the best approach, the methodology was applied to Landsat 8 Operational Land Imager (OLI) imagery during the growing season and post-harvest. Imagery for the growing season was acquired on 25 August 2015, and post-harvest was acquired on 7 January 2018. Three classification methods were applied: maximum likelihood (ML), support vector machine (SVM), and random forest (RF). LULC imagery was classified as open water, woody wetlands, harvested crop, rangeland, cultivated crop, high-intensity developed, and mid-low intensity developed areas. Ancillary data such as normalized difference vegetation index (NDVI), thematic maps of urban areas, river networks, transportation networks, high-resolution National Agriculture Imagery Program (NAIP) imagery, Google Earth time-series data, and phenology were used to determine the training dataset. Initially none of the three classification methods performed adequately. Hence, a post-classification correction (PCC) was implemented by masking and applying a majority filter using thematic maps of urban areas. Once PCC was implemented, the accuracies from each of the classification methods increased significantly with the SVM classification method performing best in both the growing season and post-harvest with an overall classification accuracy of 93.5% with a Kappa statistic of 0.88 in the post-harvest imagery and an overall classification accuracy of 84% with a Kappa statistic of 0.789 in the imagery from the growing season. It was found that SVM was the best classification method while PCC is an effective strategy to implement when dealing with spectrally similar LULC features. The use of SVM together with PCC increased the reliability of the information extracted. Strategies from this study can help to evaluate the LULC in agricultural and other watersheds.

Understanding saline lake sand dunes dynamics: Coupling remote sensing techniques and field studies

Large saline lakes, a benchmark for natural and anthropogenic fluctuations in the Earth's climate system, are drying up worldwide at accelerated rates. This study focuses on Lake Urmia, the world's second-largest hypersaline lake that indicates significant environmental challenges as it approaches near-complete dryness. Over the last decade, new aeolian features, including sand dunes, have emerged due to anthropogenic and natural factors. The present study documents the dynamics of recent aeolian processes on exposed dried-up lakebeds of saline lakes environments by investigating their mineralogy, geochemistry, and sedimentology to understand better dune morphology, evolution and migration rate. Quantitative analyses are performed on the dunes of Lake Urmia's western shores using ancillary data (meteorological and climate), field data, X-Ray Diffraction, Scanning Electron Microscopy and Energy Dispersive X-rays Spectroscopy (SEM - EDX), petrographic analysis, and supervised classification and change detection of satellite imagery from 2003 to 2019. The migration rates yielded high values, approximately above 20 m yr−1 on average, mostly related to (1) the rising drift potential (DP), (2) the percentage of wind speeds that are above the threshold velocity (V > Vt%), (3) decline in precipitation and unidirectional wind regime and (4) atmospheric circulations over the Mediterranean Sea. The results infer that local and global-scale factors significantly impact the migration rate of Lake Urmia dunes. The study outcomes are widely applicable to drying up saline lakes worldwide, enhancing the understanding of current aeolian processes and the evolution of dune fields/sand seas, building towards sustainable land management and policy-making.

Graph Neural Network-Based Method of Spatiotemporal Land Cover Mapping Using Satellite Imagery.

Multispectral satellite imagery offers a new perspective for spatial modelling, change detection and land cover classification. The increased demand for accurate classification of geographically diverse regions led to advances in object-based methods. A novel spatiotemporal method is presented for object-based land cover classification of satellite imagery using a Graph Neural Network. This paper introduces innovative representation of sequential satellite images as a directed graph by connecting segmented land region through time. The method's novel modular node classification pipeline utilises the Convolutional Neural Network as a multispectral image feature extraction network, and the Graph Neural Network as a node classification model. To evaluate the performance of the proposed method, we utilised EfficientNetV2-S for feature extraction and the GraphSAGE algorithm with Long Short-Term Memory aggregation for node classification. This innovative application on Sentinel-2 L2A imagery produced complete 4-year intermonthly land cover classification maps for two regions: Graz in Austria, and the region of Portorož, Izola and Koper in Slovenia. The regions were classified with Corine Land Cover classes. In the level 2 classification of the Graz region, the method outperformed the state-of-the-art UNet model, achieving an average F1-score of 0.841 and an accuracy of 0.831, as opposed to UNet's 0.824 and 0.818, respectively. Similarly, the method demonstrated superior performance over UNet in both regions under the level 1 classification, which contains fewer classes. Individual classes have been classified with accuracies up to 99.17%.

Multi-Branch Deep Learning Framework for Land Scene Classification in Satellite Imagery

Land scene classification in satellite imagery has a wide range of applications in remote surveillance, environment monitoring, remote scene analysis, Earth observations and urban planning. Due to immense advantages of the land scene classification task, several methods have been proposed during recent years to automatically classify land scenes in remote sensing images. Most of the work focuses on designing and developing deep networks to identify land scenes from high-resolution satellite images. However, these methods face challenges in identifying different land scenes. Complex texture, cluttered background, extremely small size of objects and large variations in object scale are the common challenges that restrict the models to achieve high performance. To tackle these challenges, we propose a multi-branch deep learning framework that efficiently combines global contextual features with multi-scale features to identify complex land scenes. Generally, the framework consists of two branches. The first branch extracts global contextual information from different regions of the input image, and the second branch exploits a fully convolutional network (FCN) to extract multi-scale local features. The performance of the proposed framework is evaluated on three benchmark datasets, UC-Merced, SIRI-WHU, and EuroSAT. From the experiments, we demonstrate that the framework achieves superior performance compared to other similar models.

Principal Component Analysis of Selective Bands for Digital Satellite Imagery Classification

Principal Component Analysis technique was applied to correlated multispectral data to convert it into uncorrelated data for the purpose of benefiting from its use in the automatic classification process.This project aims to classify multispectral satellite images using selective thematic Mapper (TM) spectral bands, and principal component (PC) images. The supervised classification method with maximum likelihood is adopted to perform the classification process.This technique was implemented using banding images and PC’s images. The results showed that the classification accuracy of the three spectral bands with the highest contrast was 86.9%, compared to the classification accuracy of the first three PC’s (89.08%). This value represents the highest classification accuracy obtained among the classification accuracy values for the spectral bands or PC images alone or in combination.The results show the advantages of feature selection in the PC’s, for every value of n-components, the 1stprincipal is the best choice. Moreover at low values of n-PC’s contain more information about the discriminability of classes than for any combination of n-original spectral channels.

ADU-Net: Semantic segmentation of satellite imagery for land cover classification

Semantic Segmentation is an important problem in many vision related tasks. Land use and land cover classification involves semantic segmentation of satellite imagery and plays a vital role in many applications. In this paper, we propose an extended U-Net architecture with dense decoder connections and attention mechanism for pixel wise classification of satellite imagery named Attention Dense U-Net (ADU-Net). We further evaluate the effect of different upsampling strategies in the decoder part of the U-Net architecture. We evaluate our models on the Gaofen Image Dataset (GID) for landcover classification consisting of five classes: built-up, forest, farmland, meadow and water. The experiments on the GID dataset show better performance than the previous approaches. Our proposed architecture delivers more than 4% higher mIoU and F1-score than the baseline U-Net. Moreover, our proposed architecture achieves an F1-score of 87.21% and mIoU of 77.66% on the GID dataset. Our evaluations shows that data-dependent upsampling layer achieves higher accuracy than the Transposed Convolution, Pixel Shuffle and Bilinear upsampling layers.

Deep ResU-Net Convolutional Neural Networks Segmentation for Smallholder Paddy Rice Mapping Using Sentinel 1 SAR and Sentinel 2 Optical Imagery

Rice is a globally significant staple food crop. Therefore, it is crucial to have adequate tools for monitoring changes in the extent of rice paddy cultivation. Such a system would require a sustainable and operational workflow that employs open-source medium to high spatial and temporal resolution satellite imagery and efficient classification techniques. This study used similar phenological data from Sentinel-2 (S2) optical and Sentinel-1 (S1) Synthetic Aperture Radar (SAR) satellite imagery to identify paddy rice distribution with deep learning (DL) techniques. Using Google Earth Engine (GEE) and U-Net Convolutional Neural Networks (CNN) segmentation, a workflow that accurately delineates smallholder paddy rice fields using multi-temporal S1 SAR and S2 optical imagery was investigated. The study′s accuracy assessment results showed that the optimal dataset for paddy rice mapping was a fusion of S2 multispectral bands (visible and near infra-red (VNIR), red edge (RE) and short-wave infrared (SWIR)), and S1-SAR dual polarization bands (VH and VV) captured within the crop growing season (i.e., vegetative, reproductive, and ripening). Compared to the random forest (RF) classification, the DL model (i.e., ResU-Net) had an overall accuracy of 94% (three percent higher than the RF prediction). The ResU-Net paddy rice prediction had an F1-Score of 0.92 compared to 0.84 for the RF classification generated using 500 trees in the model. Using the optimal U-Net classified paddy rice maps for the dates analyzed (i.e., 2016–2020), a change detection analysis over two epochs (2016 to 2018 and 2018 to 2020) provided a better understanding of the spatial–temporal dynamics of paddy rice agriculture in the study area. The results indicated that 377,895 and 8551 hectares of paddy rice fields were converted to other land-use over the first (2016–2018) and second (2018–2020) epochs. These statistics provided valuable insight into the paddy rice field distribution changes across the selected districts analyzed. The proposed DL framework has the potential to be upscaled and transferred to other regions. The results indicated that the approach could accurately identify paddy rice fields locally, improve decision making, and support food security in the region.

Impact analysis of convolutional neural network in classification of satellite imagery

The classification of satellite images is crucial for information extraction and analysis. It is a method for categorizing images based on their features. The classification process involves identifying different details along with satellite imagery patterns. Any decision made in a remote sensing study is primarily determined by how well the method of classification is performing. The process of classifying data or images is difficult. Numerous elements, such as the mixed pixel issue, can have an impact on this process. In this research paper, convolutional neural networks that are used to create remote sensing-based image classification. To determine the precision and effectiveness of the proposed deep neural network model, comparison analysis has been conducted between the proposed model and various other CNN architectures, including LeNet, AlexNet, VGGNet, and ZfNet.

The Cellular Automata Approach in Dynamic Modelling of Land Use Change Detection and Future Simulations Based on Remote Sensing Data in Lahore Pakistan

Rapid urbanization has become an immense problem in Lahore city, causing various socio-economic and environmental problems. Therefore, it is noteworthy to monitor land use/land cover (LULC) change detection and future LULC patterns in Lahore. The present study focuses on evaluating the current extent and modeling the future LULC developments in Lahore, Pakistan. Therefore, the semi-automatic classification model has been applied for the classification of Landsat satellite imagery from 2000 to 2020. And the Modules of Land Use Change Evaluation (MOLUSCE) cellular automata (CA-ANN) model was implemented to simulate future land use trends for the years 2030 and 2040. This study project made use of Landsat, Shuttle Radar Topography Mission Digital Elevation Model, and vector data. The research methodology includes three main steps: (i) semi-automatic land use classification using Landsat data from 2000 to 2020; (ii) future land use prediction using the CA-ANN (MOLUSCE) model; and (iii) monitoring change detection and interpretation of results. The research findings indicated that there was a rise in urban areas and a decline in vegetation, barren land, and water bodies for both the past and future projections. The results also revealed that about 27.41% of the urban area has been increased from 2000 to 2020 with a decrease of 42.13% in vegetation, 2.3% in barren land, and 6.51% in water bodies, respectively. The urban area is also expected to grow by 23.15% between 2020 and 2040, whereas vegetation, barren land, and water bodies will all decline by 28.05%, 1.8%, and 12.31%, respectively. Results can also aid in the long-term, sustainable planning of the city. It was also observed that the majority of the city's urban area expansion was found to have occurred in the city's eastern and southern regions. This research also suggests that decision-makers and municipal Government should reconsider city expansion strategies. Moreover, the future city master plans of 2050 must emphasize the relevance of rooftop urban planting and natural resource conservation.

Trends and population estimate of the threatened Buff-breasted SandpiperCalidris subruficolliswintering in coastal grasslands of southern Brazil

SummaryInformation about population sizes, trends, and habitat use is key for species conservation and management. The Buff-breasted SandpiperCalidris subruficollis(BBSA) is a long-distance migratory shorebird that breeds in the Arctic and migrates to south-eastern South America, wintering in the grasslands of southern Brazil, Uruguay, and Argentina. Most studies of Nearctic migratory species occur in the Northern Hemisphere, but monitoring these species at non-breeding areas is crucial for conservation during this phase of the annual cycle. Our first objective was to estimate trends of BBSA at four key areas in southern Brazil during the non-breeding season. We surveyed for BBSA and measured vegetation height in most years from 2008/09 to 2019/20. We used hierarchical distance sampling models in which BBSA abundance and density were modelled as a function of vegetation height and corrected for detectability. Next, we used on-the-ground surveys combined with satellite imagery and habitat classification models to estimate BBSA population size in 2019/20 at two major non-breeding areas. We found that abundance and density were negatively affected by increasing vegetation height. Abundance fluctuated five- to eight-fold over the study period, with peaks in the middle of the study (2014/15). We estimated the BBSA wintering population size as 1,201 (95% credible interval [CI]: 637–1,946) birds in Torotama Island and 2,232 (95% CI: 1,199–3,584) in Lagoa do Peixe National Park during the 2019/20 austral summer. Although no pronounced trend was detected, BBSA abundance fluctuated greatly from year to year. Our results demonstrate that only two of the four key areas hold high densities of BBSA and highlight the positive effect of short grass on BBSA numbers. Short-grass coastal habitats used by BBSA are strongly influenced by livestock grazing and climate, and are expected to shrink in size with future development and climatic changes.