Cover Change Detection Research Articles

Analysis of Land Use and Land Cover Dynamics in the Highlands of Northern Ethiopia

In the highlands of northern Ethiopia, many land use and land cover changes (LULCC) have occurred during different activities of the populations. Despite its coverage and unwise use of natural resources, these land uses and land covers are facing increasing or decreasing their area coverage. Information derived from land use and land cover change detection is important to land conservation, sustainable development, and management of Natural resources. This purpose of this study is therefore concerned with identifying the change in land use and land cover detection of the Wejic watershed. To identify land cover changes detection; remote sensing data, satellite imagery and image processing techniques had done within two dates of 1998 and 2017 using Land sat TM 30 m resolution images using Arc GIS 10.1. Hence, a field survey was carried out in 2017 to identify the major land cover types in the watershed. Detection Analysis of land use and land cover dynamics were calculated from the selected watershed in Southern Tigray, Northern Ethiopia. These land use types were cultivated, forest, bush, shrub, and homestead lands. Furthermore, a normalized difference vegetation index (NDVI) was developed for both years. The land use and land cover change results indicated that forest, cultivated land, and bush land decreased by 4.4%, 2.2%, and 2.4%, respectively. However, shrub land and homestead increased by 5.5% and 3.6%, respectively. Therefore The change detection analysis using GIS and remote sensing could deliver useful information to understand the seasonal patterns of land use dynamics for planners and decision makers; consequently, sustainable land management planning is possible.

A fast hybrid approach for continuous land cover change monitoring and semantic segmentation using satellite time series

Land cover change detection and classification, including both inter-class changes (land cover conversion, LCC) and intra-class changes (land cover modification, LCM), is critical for understanding the Earth’s dynamic processes and promoting sustainability. However, previous studies have predominantly focused on LCC, with less emphasis on LCM. Land cover classification remains challenging, and its mapping results are often affected by salt and pepper noise. Here, we propose a hybrid approach for continuous change detection and classification of LCC and LCM using Jinchang City, China, as a case study. Firstly, we combined the Continuous Change Detection and Classification (CCDC) and the Bayesian Estimator of Abrupt change, Seasonal change, and Trend (BEAST) algorithms to identify LCC and LCM using all available Landsat time series (TS) data from 2000 to 2020. Then, the harmonic regression coefficients and RMSE values derived from CCDC (hereafter called CCDC features) were fed into the DCNN model for LCC classification. Our findings indicate: (1) For LCC and LCM accuracy assessment, the CCDC and BEAST ensemble achieved a spatial F1 score of 82.7% and an average temporal F1 score of 79.7%. (2) In LCC classification, the DCNN model with CCDC features, particularly DeepLabV3+, outperformed the pixel-based XGBoost and other multi-year land cover products, with frequency-weighted intersection over union (FWIoU), overall accuracy, and Kappa scores of 88.7%, 94%, and 0.87, respectively. (3) Seasonal LCM showed a more concentrated distribution than trend LCM. (4) In Jinchang City, LCM larger than LCC in area, and grassland and cultivated land are the most distributed. Our approach can be contributed to wall-to-wall land surface monitoring and enhance land management capabilities.

Land use land cover change detection in Baghdad city for the years 2000 and 2024 using random forest algorithm

Land use land cover change detection in Baghdad city for the years 2000 and 2024 using random forest algorithm

Temporal dynamics of land use land cover (LULC) changes and assessment of environmental and climatic parameters using remote sensing and GIS: A case study in Limboti Reservoir, Loha Taluka, Maharashtra, India

In this study, multispectral satellite data from Landsat 4-5 TM and Landsat 8 OLI/TIRS was used to analyse Land Use Land Cover (LULC) change detection and to retrieve environmental parameters. The LULC change detection was done between 2007 and 2021. This study used the supervised classification-maximum likelihood approach in ArcGIS 10.3 software to detect LULC in Loha Taluka, Maharashtra. The taluka was classified into four major LULC classes, viz. water, vegetation, settlement and barren-land. Changes in LULC directly or indirectly will also induce a change in the environmental parameters. Environmental parameters (water surface temperature, chlorophyll-a concentration and total suspended solids) were analysed for Limboti Reservoir. Data were predicted for next two years (2022 and 2023) using multiplicative seasonal decomposition and Holt-Winter’s multiplicative method and trends of each parameter were analysed using Mann-Kendall trend method. For both years, vegetative land was the most extensive LC in Loha Taluka, accounting for more than 60% of the total land area. However, throughout these years, urbanisation was rampant and vegetative land was converted to settlement. In these years, open water resources such as reservoirs, lakes and rivers covered a very small percentage of the total area, which is a serious threat to the ecosystem. As a result, proper water body management is essential; otherwise, these resources will be destroyed and will no longer be able to contribute to the area’s socioeconomic growth. Keywords: Change detection, Chlorophyll content, Land use land cover, Water quality, Water surface temperature

Land Use and Land Cover Change Detection Using Remote Sensing in the Kal River Basin, Raigad District, Maharashtra, India

Land use and land cover (LULC) are distinct yet interrelated concepts that describe the characteristics and utilization of land. Land cover refers to the physical surface, such as vegetation, water bodies, or man-made features, while land use denotes the purpose of land utilization. Changes in LULC significantly impact water resources, making it a critical component in water resource studies. This study aims to detect changes in LULC over a five-year period (2017–2021) in the Kal River basin, a sub-basin of the Savitri River in Maharashtra, India. Sentinel-2 satellite imagery with a 10 m resolution was used, and the supervised classification method, specifically Maximum Likelihood Classification (MLC), was applied to classify LULC into six categories: cropland, bare ground, built-up areas, trees, rangeland, and water bodies. The results show that in 2017, the areas under cropland, tree cover, bare ground, built-up areas, and water bodies were 35.20 km², 187.61 km², 0.133 km², 7.77 km², and 1.89 km² respectively. By 2021, cropland, tree cover and bare ground decreased by 3.82%, 4.85%, and 0.01% respectively while water bodies, rangeland, and built-up areas increased by 0.02%, 8.34%, and 0.32% respectively. The overall accuracy of LULC classification was 77% for 2017 and 91% for 2021 with validation using the Kappa coefficient indicating good to excellent accuracy. This study highlights the importance of monitoring LULC changes for understanding the impacts of human activities and climate on watershed development and water resource management. Such analysis provides valuable insights for decision-makers to plan for sustainable development, land conservation and environmental management, ensuring balanced growth while protecting natural resources in the Kal River basin.

Forest Cover Change Detection Over North Eastern Ghat Zone of Odisha, India Using Multi-Year Landsat Data

Forest Cover Change Detection Over North Eastern Ghat Zone of Odisha, India Using Multi-Year Landsat Data

SFA-Net: Semantic Feature Adjustment Network for Remote Sensing Image Segmentation

Advances in deep learning and computer vision techniques have made impacts in the field of remote sensing, enabling efficient data analysis for applications such as land cover classification and change detection. Convolutional neural networks (CNNs) and transformer architectures have been utilized in visual perception algorithms due to their effectiveness in analyzing local features and global context. In this paper, we propose a hybrid transformer architecture that consists of a CNN-based encoder and transformer-based decoder. We propose a feature adjustment module that refines the multiscale feature maps extracted from an EfficientNet backbone network. The adjusted feature maps are integrated into the transformer-based decoder to perform the semantic segmentation of the remote sensing images. This paper refers to the proposed encoder–decoder architecture as a semantic feature adjustment network (SFA-Net). To demonstrate the effectiveness of the SFA-Net, experiments were thoroughly conducted with four public benchmark datasets, including the UAVid, ISPRS Potsdam, ISPRS Vaihingen, and LoveDA datasets. The proposed model achieved state-of-the-art accuracy on the UAVid, ISPRS Vaihingen, and LoveDA datasets for the segmentation of the remote sensing images. On the ISPRS Potsdam dataset, our method achieved comparable accuracy to the latest model while reducing the number of trainable parameters from 113.8 M to 10.7 M.

Application of geospatial techniques for land use land cover change detection in Pairi river subbasin

Application of geospatial techniques for land use land cover change detection in Pairi river subbasin

Change detection due to ISPAAD Programme using geospatial techniques: A case study of Dinogeng Agricultural Extension Area of Kgatleng District, Botswan

Dinogeng Agricultural Extension Area (DAEA), located in the south eastern part of Botswana has witnessed tremendous land use changes due to the Integrated Support Programme for Arable Agriculture Development (ISPAAD). For over a decade, crop cultivation has been relatively small both in size and population. But today, Dinogeng is one of the fastest growing agricultural areas. Therefore, it is paramount to detect the nature and magnitude of land use changes for planning purpose. Remotely Sensed data from Landsat 5 and Landsat 8 were utilized for the purpose of Land Use Land Cover (LULC) change detection. Geographical Information Systems (GIS) and Remote Sensing (RS) were used to produce LULC maps for 2006 and 2020 for assessing the severity of land degradation. In a 14-year span (2006-2020), LULC of DAEA changed markedly. Cultivated land and bare areas increased by 19.4 and 18.3 % whereas shrub land and forest areas decreased by 36.9 and 0.7 %, respectively. Supervised classification algorithms and stratified random sampling design were adopted for the accuracy assessment. The classification process produced good results with overall accuracies of 93% and 94% for the 2006 and 2020 maps, respectively. The findings could be useful to guide the development of functional land use plan for DAEA.

Time series land use/land cover mapping and change detection to support policies on sustainable environmental and economic management

The upper Tekeze River Basin is facing challenges of widespread deforestation and natural vegetation cover degradation that could exacerbate the water scarcity, food insecurity and extreme poverty in the region. Using remote sensing and GIS, this study quantified the land use land cover change trend in the last three decades and analyzed the current land use / cover statues in the basin. A hybrid classification technique is applied to obtain better classification accuracy. Moreover, for automated cloud and cloud shadow detection the newly developed Mountainous Fmask is used. Using post classification change detection technique, seven major land use/cover classes were identified. These classes remained the dominant classes during the study period, showing marked changes in the area coverage within them. Based on the error matrix statistical indices, the classification accuracies of each class are found to be strong. The overall accuracy and the kappa coefficient for the 2021 map are 91% and 89%, respectively. The techniques used have contributed to improving the accuracy of the classification process and helped the classified images to practically match the ground truths. The analysis revealed settlement expansion by 570.31% in parallel with the expansion of farmland by 52.32% during the period 1991–2021. In contrast, the forestland decreased significantly, by 75.55%. The environmental degradation and unplanned use of land resources could have contributed to why the upper Tekeze basin is experiencing worsening poverty, water scarcity and food insecurity. Thus, land use/cover time series modeling is essential for various purposes, including land use planning and, managing natural resources. In this regards this study provides basic information for implementing sustainable environmental conservation strategies in the area. Furthermore, the applied methodologies may have practical applications in other similar areas.

Towards Robust Pansharpening: A Large-Scale High-Resolution Multi-Scene Dataset and Novel Approach

Pansharpening, a pivotal task in remote sensing, involves integrating low-resolution multispectral images with high-resolution panchromatic images to synthesize an image that is both high-resolution and retains multispectral information. These pansharpened images enhance precision in land cover classification, change detection, and environmental monitoring within remote sensing data analysis. While deep learning techniques have shown significant success in pansharpening, existing methods often face limitations in their evaluation, focusing on restricted satellite data sources, single scene types, and low-resolution images. This paper addresses this gap by introducing PanBench, a high-resolution multi-scene dataset containing all mainstream satellites and comprising 5898 pairs of samples. Each pair includes a four-channel (RGB + near-infrared) multispectral image of 256 × 256 pixels and a mono-channel panchromatic image of 1024 × 1024 pixels. To avoid irreversible loss of spectral information and achieve a high-fidelity synthesis, we propose a Cascaded Multiscale Fusion Network (CMFNet) for pansharpening. Multispectral images are progressively upsampled while panchromatic images are downsampled. Corresponding multispectral features and panchromatic features at the same scale are then fused in a cascaded manner to obtain more robust features. Extensive experiments validate the effectiveness of CMFNet.

Typhoon-Induced Forest Damage Mapping in the Philippines Using Landsat and PlanetScope Images

Forests provide valuable resources for households in the Philippines, particularly in poor and upland communities. This makes forests an integral part of building resilient communities. This relationship became complex during extreme events such as typhoon occurrence as forests can be a contributor to the intensity and impact of disasters. However, little attention has been paid to forest cover losses due to typhoons during disaster assessments. In this study, forest damage caused by typhoons was measured using harmonic analysis of time series (HANTS) with Landsat-8 Operation Land Imager (OLI) images. The ΔHarmonic Vegetation Index was computed by calculating the difference between HANTS and the actual observed vegetation index value. This was used to identify damaged areas in the forest regions and create a damage map. To validate the reliability of the results, the resulting maps produced using ΔHarmonic VI were compared with the damage mapped from PlanetScope’s high-resolution pre- and post-typhoon images. The method achieved an overall accuracy of 69.20%. The accuracy of the results was comparable to the traditional remote sensing techniques used in forest damage assessment, such as ΔVI and land cover change detection. To further the understanding of the relationship between forest and typhoon occurrence, the presence of time lag in the observations was investigated. Additionally, different contributing factors in forest damage were identified. Most of the forest damage observed was in forest areas with slopes facing the typhoon direction and in vulnerable areas such as near the coast and hill tops. This study will help the government and forest management sectors preserve forests, which will ultimately result in the development of a more resilient community, by making it easier to identify forest areas that are vulnerable to typhoon damage.

Land use land cover change detection analysis in kodjai watershed using remote sensing and GIS

Land use land cover change detection analysis in kodjai watershed using remote sensing and GIS

Exploring Google Earth Engine, Machine Learning, and GIS for Land Use Land Cover Change Detection in the Federal Capital Territory, Abuja, between 2014 and 2023

Exploring Google Earth Engine, Machine Learning, and GIS for Land Use Land Cover Change Detection in the Federal Capital Territory, Abuja, between 2014 and 2023

Assessment of forest cover change, key drivers of change and perception of locals in Birendranagar Municipality, Surkhet District, Nepal

Land use and land cover change has an immense impact on the global environment and ecosystem and is mostly connected with human activities such as conversion of forest into agricultural land and settlements. Forests are changing globally in quantity and quality, and in both increasing and decreasing trends. For the Birendra Nagar Municipality, this study evaluated LULC dynamics, the conversion of forest cover from 2000 to 2020, as well as community perceptions on forest cover change, its drivers. Landsat images of 2000 and 2020 were used for LULC dynamics and for forest cover change detection Using software GIS 10.8. Questionnaire survey, key informant survey, field observation and secondary data were acquired from journal articles, reports, District Forest office, books, and various other sources to find out drivers of forest cover change and perception of locals. It was found that 1146 ha of forest area, 280.53 ha of water bodies, 752.62 ha of barren land were lost between 2000 and 2020. However, 1147.74 ha of settlement area and 1032.40 ha of agricultural land was gained.Net forest cover decreased overall by 4.67%, however, some areas within the municipality experienced an increase in forest cover namely Ward 11 and ward 16 of Municipality shows Highest positive and Negative Forest cover change in terms of Area. Expansion of agriculture land and settlements with the encroachment of forest area, haphazard infrastructure development, illegal timber harvesting and low regeneration capacity of land responsible for the reduction of forest, along with Community forestry approach, plantations programs, promotion of agroforestry practices, fuel transition were responsible drivers for improvements of forest cover. Water cycle disruption, Increases Natural hazard, Temperature rises, shortage of wood for fuel, extinction of indigenous species were the most perceived negative impacts of the forest cover reduction whereas income generation Activities, support biodiversity, easy to collect fuel wool and fodder and others including purifying air quality, increasing aesthetic beauty were the most perceived positive impacts of the forest cover improvement in the study area. Most of the locals have a holistic understanding of forest cover change. Strengthening forest protection, improving soil and water conservation structures, awareness creation, enrichment planting, Promote Alternate Energy Resources and Equitable benefit-sharing mechanism are possible solutions perceived by locals.

Transformers for Remote Sensing: A Systematic Review and Analysis.

Research on transformers in remote sensing (RS), which started to increase after 2021, is facing the problem of a relative lack of review. To understand the trends of transformers in RS, we undertook a quantitative analysis of the major research on transformers over the past two years by dividing the application of transformers into eight domains: land use/land cover (LULC) classification, segmentation, fusion, change detection, object detection, object recognition, registration, and others. Quantitative results show that transformers achieve a higher accuracy in LULC classification and fusion, with more stable performance in segmentation and object detection. Combining the analysis results on LULC classification and segmentation, we have found that transformers need more parameters than convolutional neural networks (CNNs). Additionally, further research is also needed regarding inference speed to improve transformers' performance. It was determined that the most common application scenes for transformers in our database are urban, farmland, and water bodies. We also found that transformers are employed in the natural sciences such as agriculture and environmental protection rather than the humanities or economics. Finally, this work summarizes the analysis results of transformers in remote sensing obtained during the research process and provides a perspective on future directions of development.

A Comparative Study of Land Use Land Cover (LULC) Change Detection: A Case Study of Tehsil Babuzai, Swat (Pakistan)

Tehsil Babuzai, District Swat, Khyber Pakhtunkhwa, Pakistan, has experienced a fast population increase, substantially influencing land use and land cover (LULC), raising ecological and environmental issues. This study examines changes in LULC between 2001 and 2020 using US Geological Survey (USGS) satellite images. The paper analyses six primary land cover classes: farmland, built-up area, woodland, water bodies, rangeland, and barren Land using maximum likelihood supervised classification. The findings show a significant rise in rangeland (from 49.1% to 53.7%), built-up areas (from 8.5% to 13.7%), and barren Land (from 5.1% to 11.1%). On the other hand, there is a noticeable decline in the number of water bodies (from 2.1% to 1.6%), agriculture (from 21.2% to 12.2%), and forest cover (from 13.7% to 7.5%). These alterations underscore the detrimental consequences of urban growth on the area's natural resources, demanding prompt and deliberate planning to protect the ecosystem and guarantee sustainable development. This research is crucial for understanding land use changes' dynamics and impacts, providing valuable insights for policymakers and urban planners. It highlights the need for sustainable land management practices to mitigate adverse environmental effects and promote balanced regional development.

Cross‐attention neural network for land cover change detection with remote sensing images

AbstractLand cover change detection (LCCD) with remote sensing images (RSIs) is important for observing the land cover change of the Earth's surface. Considering the insufficient performance of the traditional self‐attention mechanism used in a neural network to smoothen the noise of LCCD with RSIs, in this study a novel cross‐attention neural network (CANN) was proposed for improving the performance of LCCD with RSIs. In the proposed CANN, a cross‐attention mechanism was achieved by employing another temporal image to enhance attention performance and improve detection accuracies. First, a feature difference module was embedded in the backbone of the proposed CANN to generate a change magnitude image and guide the learning progress. A self‐attention module based on the cross‐attention mechanism was then proposed and embedded in the encoder of the proposed network to make the network pay attention to the changed area. Finally, the encoded features were decoded to obtain binary change detection with the ArgMax function. Compared with five methods, the experimental results based on six pairs of real RSIs well demonstrated the feasibility and superiority of the proposed network for achieving LCCD with RSIs. For example, the improvement for overall accuracy for the six pairs of real RSIs improved by our proposed approach is about 0.72–2.56%.

A Land Cover Change Detection Approach to Assess the Effectiveness of Conservation Projects: A Study Case on the EU-Funded LIFE Projects in São Miguel Island, Azores (2002–2021)

A Land Cover Change Detection Approach to Assess the Effectiveness of Conservation Projects: A Study Case on the EU-Funded LIFE Projects in São Miguel Island, Azores (2002–2021)

Multispectral analysis-ready satellite data for three East African mountain ecosystems

The East African mountain ecosystems are facing increasing threats due to global change, putting their unique socio-ecological systems at risk. To monitor and understand these changes, researchers and stakeholders require accessible analysis-ready remote sensing data. Although satellite data is available for many applications, it often lacks accurate geometric orientation and has extensive cloud cover. This can generate misleading results and make it unreliable for time-series analysis. Therefore, it needs comprehensive processing before usage, which encompasses multi-step operations, requiring large computational and storage capacities, as well as expert knowledge. Here, we provide high-quality, atmospherically corrected, and cloud-free analysis-ready Sentinel-2 imagery for the Bale Mountains (Ethiopia), Mounts Kilimanjaro and Meru (Tanzania) ecosystems in East Africa. Our dataset ranges from 2017 to 2021 and is provided as monthly and annual aggregated products together with 24 spectral indices. Our dataset enables researchers and stakeholders to conduct immediate and impactful analyses. These applications can include vegetation mapping, wildlife habitat assessment, land cover change detection, ecosystem monitoring, and climate change research.