Land Cover Change Analysis Research Articles

Drivers of land use land cover change and intensity analysis of land transformation in the moist semi-deciduous forest zone of Ghana: Case of Bobiri Forest Reserve and its environs

Understanding land cover transformation over time and space is crucial for comprehending ecological challenges, particularly in environmentally vulnerable areas. In Sub-Saharan Africa, the demand for fuel, food, and fibre is expected to significantly expand croplands, resulting in the degradation of natural ecosystems. This research presents a spatiotemporal analysis of land-cover change in the Bobiri forest and its surrounding areas in Ghana's moist semi-deciduous forest zone. The study’s main objectives were to identify the drivers of land-cover change, and to analyse the intensity of land transformation from 1986 to 2022 in the study area. The methodology used measured land-cover changes at different levels, including intervals, categories, and transitions. The findings indicate that the annual rate of land change was higher from 1986 to 2007 (1.64%) compared to the period from 2007 to 2022 (1.04%). Additionally, the analysis at the category level reveals that the increase in cropland and non-vegetated areas during both time intervals was higher than the uniform intensity. The analysis at the transition level suggests that most of the cropland gains originated from non-vegetated areas during both periods. There is also evidence of localized increases in closed forests, likely due to afforestation policies implemented by the Ghanaian government. However, overall, there has been a decline in closed forests over the entire timeframe, with an annual rate of change of -1.25%. This study contributes valuable data for enhancing our understanding of the scale and direction of land-cover change, which is essential for developing policies aimed at mitigating the impact on local livelihoods and the environment at national and sub-national levels.

Analysis of land use and land cover change using machine learning algorithm in Yola North Local Government Area of Adamawa State, Nigeria.

The dynamic use of land that results from urbanization has an impact on the urban ecosystem. Yola North Local Government Area (Yola North LGA) of Adamawa state, Nigeria, has experienced tremendous changes in its land use and land cover (LULC) over the past two decades due to the influx of people from rural areas seeking for the benefits of its economic activities. The goal of this research is to develop an efficient and accurate framework for continuous monitoring of land use and land cover (LULC) change and quantify the transformation in land use and land cover pattern over a specific period (between 2002 and 2022). Land sat images of 2002, 2012, and 2022 were obtained, and the Support Vector Machine classification method was utilized to stratify the images. Land Change Modeler (LCM) tool in Idrissi Selva software was then used to analyze the LULC change. SVM produced a good classification result for all three years, with 2022 having the highest overall accuracy of 95.5%, followed by 2002 with 90% and 2012 with 87.7% which indicates the validity of the algorithm for future predictions. The results showed that severe land changes have occurred over the course of two decades in built-up (37.32%), vegetation (forest, scrubland, and grassland) (-3.27%), bare surface (-33.47%), and water bodies (-0.59%). Such changes in LULC could lead to agricultural land lost and reduced food supply. This research develops a robust framework for continuous land use monitoring, utilizing machine learning and geo-spatial data for urban planning, natural resource management, and environmental conservation. In conclusion, this study underscores the efficacy of support vector machine algorithm in analyzing complex land use and land cover changes.

Exchange means change: An unsupervised single-temporal change detection framework based on intra- and inter-image patch exchange

Change detection is a critical task in studying the dynamics of ecosystems and human activities using multi-temporal remote sensing images. While deep learning has shown promising results in change detection tasks, it requires a large number of labeled and paired multi-temporal images to achieve high performance. Pairing and annotating large-scale multi-temporal remote sensing images is both expensive and time-consuming. To make deep learning-based change detection techniques more practical and cost-effective, we propose an unsupervised single-temporal change detection framework based on intra- and inter-image patch exchange (I3PE). The I3PE framework allows for training deep change detectors on unpaired and unlabeled single-temporal remote sensing images that are readily available in real-world applications. The I3PE framework comprises four steps: (1) intra-image patch exchange method is based on an object-based image analysis (OBIA) method and adaptive clustering algorithm, which generates pseudo-bi-temporal image pairs and corresponding change labels from single-temporal images by exchanging patches within the image; (2) inter-image patch exchange method can generate more types of land-cover changes by exchanging patches between images; (3) a simulation pipeline consisting of several image enhancement methods is proposed to simulate the radiometric difference between pre- and post-event images caused by different imaging conditions in real situations; (4) self-supervised learning based on pseudo-labels is applied to further improve the performance of the change detectors in both unsupervised and semi-supervised cases. Extensive experiments on two large-scale datasets covering Hong Kong, Shanghai, Hangzhou, and Chengdu, China, demonstrate that I3PE outperforms representative unsupervised approaches and achieves F1 value improvements of 10.65% and 6.99% to the state-of-the-art method. Moreover, I3PE can improve the performance of the change detector by 4.37% and 2.61% on F1 values in the case of semi-supervised settings. Additional experiments on a dataset covering a study area with 144 km2 in Wuhan, China, confirm the effectiveness of I3PE for practical land-cover change analysis tasks.

Support vector machine-based spatiotemporal land use land cover change analysis in a complex urban and rural landscape of Akaki river catchment, a Suburb of Addis Ababa, Ethiopia

Intense level of land use land cover (LULC) changes has been observed in Sub-Saharan Africa, particularly in the central highlands of Ethiopia, due to rapid population growth and urbanization process. However, quantifying and identifying the rural-urban landscape changes are challenging. In this study, LULC changes during the years 1984, 1990, 2000, 2010, and 2021 have been analyzed using satellite imageries and Support Vector Machine (SVM) algorithms in a heterogenous rural and urban landscape of the Akaki river catchment, central highlands of Ethiopia. The LULC change drivers were evaluated by applying LULC thematic change analysis combined with key informants’ interviews. Seven LULCs that include: Built-up area (BTA), Cropland (CL), Grassland (GL), Waterbody (WB), Plantation Forest (PF), Woodland (WL), and Bareland (BL) were detected. The result shows that 51.3 % of the catchment area has been transformed into other land uses. BTA increased by 24.7 % while GL and WL reduced by 18.1 % and 5.9 % respectively. Large areas of CL (61 %) and GL (22 %) were changed into an urban landscape. The spatial and non-spatial analysis revealed that the major spatiotemporal LULC change drivers between 1984 and 2005 were land use policy and legislation change and the Eucalyptus tree plantation campaign. Whereas, low-cost housing programs, informal settlers, market opportunity, and real estate development were the main drivers for the LULC changes between 2006 and 2021. The study also found the key informant observation and SVM image classification results are aligned and therefore, we found the SVM-based classifications are suited for such complex rural-urban landscape change and pattern analysis. The outcome of this research can contribute to improving land use policy, its management, and public understanding of the LULC dynamics and its implications.

Spatial Analysis Of Land Cover Change In Wae Batu Gajah Watershed, Ambon City In 2013, 2018 And 2023 Using Geographic Information System

This research presents a spatial analysis of land cover change in the Wae Batu Gajah Watershed, Ambon City, over three time periods, namely 2013, 2018, and 2023, using Geographic Information Systems (GIS). The data used for land cover change analysis are Landsat 7 satellite images for 2013 and satellite images for 2018 and 2023. Land cover is classified based on SNI 7465:2010 on Land Cover Classification which consists of built-up land, open land, non-agricultural land and water bodies. The analysis showed significant changes in land use patterns, including rapid urbanization, loss of natural habitats, and transformation of agricultural land. This information provides an important basis for sustainable regional planning and natural resource management. In addition, this study provides insights into the socioeconomic impacts of land cover change on local communities, and makes an important contribution to the understanding of environmental dynamics and better decision-making for the future of the Wae Batu Gajah watershed.

An assessment of spatial pattern of urban sprawl in JOS metropolis of plateau state, Nigeria: Using remote sensing and GIS techniques

In Jos metropolis, urban sprawl debate has closely paralleled on urban growth trends over the past few decades. These studies indicate that it is the pattern, types, density and rate of new urban growth that creates the appearance of sprawl. Population dynamics are often cited as a driving force behind urban sprawl. This paper uses Geographic Information Systems (GIS) mapping and land cover change analysis, neighbourhood statistics, cross change detection techniques, field work on Urban sprawl areas in Jos metropolis. The paper also highlights the characteristics, types, rates, pattern and density of sprawl to examine the spatial pattern of sprawl in Jos North and Jos South. Linear sprawl pattern can only be visually seen by the road, the roads in Jos play important role in linear pattern of urban sprawl formation through road data, it helped to visualize clearly the areas of linear sprawl in Jos metropolis. An example can be visually seen in Naraguta area, part of a large high residential development of Jos north. Cluster types of urban sprawl settlement development in Jos metropolis. From the map, it shows that cluster types of sprawls in study area are found in the core city of Jos metropolis like Jos city and Bukuru in the study area. Cluster type of urban sprawl also appeared to grow out of what was once a leapfrog pattern of development in some area in the study area. Leapfrog patterns of urban sprawl appeared to be somewhat disconnected from other areas of existing developed settlement land. Leapfrog types of urban sprawl in Jos metropolis can be seen in Kuru North, Dan Miangu, Barakin Kuru and Kuru Hills.After the map of the densities urban sprawl for new growth areas has been created. The study reveals that settlement area has increase from 125.45 to 208.16 sq.km between 2002-2022 in twenty years period. It also revealed that water bodies have also drastically reduced by 3.33 sq.km and open space land use contributed about 127.57 km2 land converted to settlement within the time under study. Further analysis reveals that the urban settlement land of the area increased approximately 82.71 km2 in the twenty years’ time period of the study (2002 to 2022). Next, the patterns of urban expansions were analyzed by using mapping capabilities within the GIS and neighborhood statistics in order to show the density and connectivity of patches of new growth. Based on the density and connectivity of new growth areas, the patterns of growth were classified as: linear along highways, cluster, and leapfrog. The threshold densities are; 0 to 2000 as 30-meter pixels per km2 for low density, 2000 to 25000 for medium density, and 25000 to 130000 for high density. The results indicate that Jos metropolis did experience an increase in urban growth from 2002 to 2022 and that urban growth in the study area can be classified as urban sprawl. The density of new development is more in Jos North, but the pattern and character with which development has occurred in Jos North is synonymous with sprawl in Jos South. the type, rate and patterns of urban expansions were analyzed by using mapping capabilities within the GIS and neighborhood statistics in order to show the density and connectivity of patches of new growth in the study area.

ANALISIS PERUBAHAN TUTUPAN LAHAN DI AREAL KONSESI IUPHHK-HA PT BINTANG LIMA MAKMUR KABUPATEN MALUKU TENGAH

This study aims to analyze land cover in the concession area of IUPHHK-Ha PT. Bintang Lima Makmur, Analyzing land cover changes in the IUPHHK-Ha concession area of PT. BLM 2010-2020, and Identifying the factors causing land cover changes in the IUPHHK-Ha concession area of PT. BLM. This research uses Landsat 7 ETM satellite imagery in 2010 and Landsat 8 OLI in 2015, 2020 (Path 108, Row 62). Downloaded directly from the United States Geological Survey (USGS) website (https://glovis.usgs.gov/). This study uses a guided classification, the results of land cover classification in 2010, 2015, and 2020 consisting of primary dry land forest, secondary dry land forest, shrubs, dry land mixed bush agriculture, open land, and bodies of water, from 2010 to 2020 the land cover class that experienced the largest change was secondary dryland forest at 25.25%, and the smallest was water bodies at 0.10%, and factors that influence land cover changes, namely; production activities of PT. BLM and also the livelihoods of local residents

Effect of Urbanization Through Land Coverage Classification

AbstractLand cover changes from rural or natural habitats to impervious surfaces that support various human activities are called urbanization. The evaluation of policy alternatives for future expansion and the promotion of sustainable urban development therefore place great emphasis on the mapping, analysis, and monitoring of land cover changes in metropolitan environments. By assessing changes in relevant environmental indicators at a range of scales, from local to regional, this study seeks to determine the rate of urbanization and analyze its impact on the environment in and around two key cities in Tamilnadu. Chennai and Madurai are the urban areas under investigation. The results are based on classifications of medium to high spatial resolution optical satellite images from 1995 to 2020. Various classification approaches with combinations of spectral, shape, and texture input information were combined to achieve high classification accuracy. Based on the categories, environmental indicators were created and used to calculate the impact of urbanization on the environment. The findings show that each research area experienced varying degrees of urban expansion and environmental impact. Urban areas grew tenfold between 1995 and 2015, according to a study comparing Chennai and Madurai. To evaluate the spatiotemporal dynamics of urbanization and its environmental impact in various metropolitan regions, this research shows the value of combining urban and environmental indicators with remote sensing data.

Integrated Geomatics and Remote Sensing Analysis of Forest Fire Propagation and Land Cover Change in Berkane, Morocco

Integrated Geomatics and Remote Sensing Analysis of Forest Fire Propagation and Land Cover Change in Berkane, Morocco

Catchment-Scale Land Use and Land Cover Change Analysis in Two Coastal Ramsar Sites in Ghana, Using Remote Sensing

Coastal wetlands are complex ecosystems that support biodiversity. They provide many benefits, including flood mitigation and sustenance for communities. The unique characteristics of wetlands make them vulnerable to natural and human-induced disturbances. Numerous factors, including industrialisation, urbanisation, and climate change, add to this phenomenon. The activities that threaten coastal wetlands in the world are relevant to coastal wetlands in Ghana. The Songor and Sakumo wetland catchments are international ecosystems endangered by land modifications and sea level rise. There are gaps in the body of knowledge that need investigation as regards underlying processes and transformation. This study assessed land use and land cover (LULC) changes between 1990 and 2020. The study used geospatial techniques and intensity analysis. LULC change results were from Landsat images (1990, 2000, 2011, and 2020). These changes were attributed to an increase in human activities. Changes in the Sakumo wetland catchment fell more into human-induced LULC categories, and vice versa for the Songor wetland catchment. The study recommends comprehensive methods of LULC change analysis. This would enhance biodiversity and allow the sustainable usage of wetland resources.

Mangrove forest encroachment by Nypa frutican, Derris trifoliata, and Acanthus spp. in Segara Anakan Lagoon

Based on phytosociological methods and land cover change analysis, previous studies in Segara Anakan Lagoon (SAL) indicated the spreading of mangrove species living in the forest margin ecotone into the forest interior, encroaching the other mangrove communities. This current study aims to quantify the spreading pattern of those mangrove species, assess the effect of the spreading of those mangrove species to the other mangrove communities, and identify the location of the spreading pattern relative to the newly emerged islands. The spreading pattern can be demonstrated by analyzing landscape structures quantified by landscape metrics. It was done using multi-temporal mangrove maps, representing the changes in mangrove communities over four periods with a decade interval. The landscape metrics used were the aggregation index, the size of community patches, and the distance between the patches. These metrics were extracted by using landscape metrics, an R package. The changes in the metrics over time were compared namely 1990, 2000, 2010, and 2020. The results showed that the small patches of the forest margin species became more aggregated into a larger patch, while the mangrove forests became more fragmented. This condition occurred most in the west part of SAL where the new islands emerged.

Temporal Analysis of Land Use and Land Cover Changes in Vizianagaram District, Andhra Pradesh, India using Remote Sensing and GIS Techniques

Land use and land cover change (LULCC) has become a significant global concern due to its wide-ranging environmental, social and economic impacts. This literature review aims to provide a comprehensive overview of the key ideas, drivers, consequences and approaches to studying LULCC. By synthesizing various research articles, this review offers insights into the causes and impacts of LULCC, as well as the methods used to analyze and monitor these changes. The review also highlights the importance of understanding LULCC dynamics for sustainable land management and policy making. Between 2017 and 2022, the LULC categories underwent several changes. Data acquisition process for satellite imagery combining Sentinel-2 digital remote sensing data digital remote sensing data through the Copernicus Open Access Hub. The spectral resolution is 10, 20, and 30 meters respectively, while the spatial resolution is 10 meters which was used for the LULC analysis of the study area. This analysis underscores the importance of LULCC monitoring to inform sustainable land management practices and conservation efforts. The trends identified provide a basis for further investigation into the underlying drivers of these changes and their potential impacts on ecosystems, water resources and human well-being. Continued monitoring and proactive measures are essential to mitigate adverse impacts and promote sustainable land use in the future.

Assessment of Landscape Changes caused by Highway Construction: Case Study of Ruta del Canal Pargua Highway in Chile

The constructed infrastructure that has made cities more livable to their human inhabitants not always has benefited the overall ecosystems, which has leading to some fragile land covers being the most affected (i.e., grasslands, wetlands, etc.). In this article, it is analyzed the relationship between the natural landscape and the highway “Ruta del Canal Pargua” in Puerto Montt, Chile. A comparison was performed/implemented based on a Land Use Land Cover Change (LULCC) analysis from a year before the construction of the highway (2012) to the most updated available image (2021). Three field spots were selected to observe the wetland’s landscape visual qualities, landscape natural features, and road management (2022). The results showed that anthropic interventions have been continuing to transform the natural features of wetland’s landscape, especially the native roadside vegetation, and noise pollution.This change was promoted by the lack of management of the highway inits surroundings, and, by the intrusive LULCC. This paper also discussesthe persistent lack of ecological management for highways in southernChile and sparks the conversation about the necessity to include anecological perspective in road infrastructure design in Chile that couldbe replicated in Latin America.

Land Cover Mapping and Change Analysis Using Optimized Random Forest Classifier Incorporating Fusion of Texture and Gabor Features

Land Cover Mapping and Change Analysis Using Optimized Random Forest Classifier Incorporating Fusion of Texture and Gabor Features

Land cover changes in Bila Watershed, South Sulawesi, Indonesia

Changes in land cover are characterized by changes in land use conversion. One of the factors that cause changes in land closure is the increase in the number of residents so the need for residential land also increases. This study aims to identify changes in land cover in 2017 and 2021 in the Bila River Basin. Land cover change analysis is carried out using the Geographic Information System approach using the overlay analysis method in ArcGIS software. The results showed that in 2017-2021, secondary dryland forest land cover experienced a reduction in the area of 915.96 hectares or from the total area of watersheds converted to mixed dryland agriculture. Meanwhile, the largest addition occurred in the dry land closure class covering an area of 634.08 ha of the total watershed area converted to dryland farming. This is due to the increasing need and dependence of the community on land, with community activities in managing land and utilizing the land to increase agricultural yields as well as a source of livelihood for the community.

Prediction of Land Cover Change in Wae Heru Watershed Ambon City Using Celular Automata Markov Chain

Land cover change in the watershed area in Ambon City has an impact on land degradation, water pollution, flooding and erosion. Therefore, the utilization and efficiency of land cover in the watershed area must be improved based on sustainable land cover planning. This study aims to analyze land cover changes in the Wae Heru watershed, Ambon City in 2013, 2018, and 2023 and predict land cover in 2028. This study used the CA-Markov method to predict land cover in 2028. The results showed that in 2013 the built-up land had an area of 74.25 ha, in 2018 an area of 79.30 ha and in 2023 an area of 88.00 ha and the results of the 2028 prediction of built-up land were 116.96 ha, this is certainly influenced by the increasing number of residents who continue to grow every year. Agricultural land, non-agricultural land and open land continue to decrease in area. The results of this prediction are very useful for the government in making policies related to sustainable spatial planning in the future.

Spatiotemporal changes of coastal land use land cover and its drivers in Shanghai, China between 1989 and 2015

The coastal zone, an ecotone that provides vital diverse ecosystem services, is one of the most ecologically fragile and sensitive areas on the Earth. It has been strongly influenced by human activities and climate change. Thus, understanding the spatiotemporal changes of land use land cover in the coast is an essential prerequisite for the comprehensive evaluation of coastal ecosystems and the promotion of sustainable development. This study investigated the spatial and temporal changes of land use land cover in the coastal zone of mainland Shanghai by using high spatial resolution aerial images, and the associated driving factors obtained from statistical yearbooks between 1989 and 2015. Our results show that the total land area in the coastal zone exhibited an increasing trend at an annual rate of 7.6% on average. The coastal urban land use also experienced substantial increases, with the degree of urban expansion reaching 42.1% in 2015. The coastal urbanization was at the cost of natural and semi-natural lands. Nearly 64.4% of agricultural land, 24.4% of ocean area within the buffer, 84.9% of fresh water, and 92.8% of the tidal flat had been converted to urban land during 1989–2015. The gradient analysis of land use land cover change along the north-to-south coastal line revealed the spatiotemporal patterns of total land, urbanization degree, and natural and semi-natural lands. The majority of the socioeconomic factors influenced land use land cover change in the coastal zone, positively contributing to the increase of public facilities’ use of land, but negatively affecting the freshwater areas. These findings can provide insights for decision-making in the future for coastal land use land cover planning and management in Shanghai.

Detection of Land Use and Land Cover Change in Jammu District using Remote Sensing and GIS Techniques

Abstract: Jammu District has under gone a massive change in its land use and land cover in last 30 years. It has grown 2.5 times in its population in past 3 decades and simultaneously the infrastructure supporting the population growth has increased altering the overall land cover. With the growing population the demand for arable land, infrastructure development, land resource management becomes an obligation for effective optimization of resources and policy formulation and implementation. In this research the land use land cover change detection and analysis in last 30 years from 1993 to 2023 is attempted for the first time. The classification is achieved by supervised image classification of Landsat 5 and Landsat 9 Level 1 imagery acquired from USGS. The classification scheme is developed for five classes viz. Water Bodies, Built-Up Area, Agriculture, Barren Land and Forests. Change detected is -3% for water bodies, +5% for built-up area, +29 % for agricultural land, -22% for barren land, -9% for forest cover. The percentages are calculated of the total area. The overall accuracy is maintained at 87% for year 1993 and 78% for year 2023.

Trend analysis, past dynamics and future prediction of land use and land cover change in upper Wabe-Shebele river basin

A growing population has led to extensive farming at the expense of a natural environment. Changes in land use and cover have caused land degradation, and problematic groundwater recharge. The objective of this study was to evaluate the historical trend, simulations, and predictions of land use land cover change in the Upper Wabe-Shebele River Basin. The study accounted for 1992, 2007 and 2022 as well as it will predict the change for 2037 and 2052. Landsat TM for 1992, ETM + for 2007, and Landsat-8 OLI for 2022 were used. In QGIS 3.16, the maximum likelihood method was utilized for supervised image classification. Using CA-Markov and the Land Change Modeler land use and land cover change for 2037 and 2052 were predicted. Validity and accuracy of the model was evaluated using actual and predicted land use and land cover changes of 2022. Topography, proximity to a town, stream, roads, and population density were used as input for the model. The results showed that between 1992 and 2007, cultivated land increased by 17.07% on average at a rate of 1.05%, while settlement increased by 17.51% at a rate of 1.08% per year. Agricultural and settlement land increased by 22.97% and 30.12%, respectively. Between 1992 and 2022, the transition area matrix showed 2,330.25 and 1,145.77 km2 of forest and grazing land were changed to settlement and cultivated land, respectively. Meanwhile, from 2022 to 2037, the quantity of land used for cultivated, grazing, and settlement is predicted to increase by 0.19, 3.66, and 23.8% in order. For 2037 and 2052, settlement and cultivated land were increased by 1.3 and 7.32% respectively. Finally, since natural ecosystem had been significantly disturbed by change in the study area, comprehensive rehabilitation and management is demanded.

Analysis of the spatial extension of pineapple monocultures in northern Costa Rica using heterogeneous geographic data

Abstract. In recent years, Costa Rica has become the world's leading producer and exporter of pineapple, with more than 65.000 hectares of its territory dedicated to this crop. The development of these intensive pineapple crops is mainly concentrated in the north of the country, especially in the border area with Nicaragua. This rapid expansion in these regions has many economic, social and environmental consequences. In order to understand the local impact of this phenomenon, a first analysis of land use changes for the benefit of pineapple monocultures must be performed. For this purpose, pineapple crop delineation layers in 2000 and 2019 were provided by MOCUPP, a United Nations agency responsible for monitoring agricultural areas in Costa Rica. Landsat 5 satellite images from 1998 and 2001 were also used to generate land cover classifications. The pineapple crop delineation layers produced by MOCUPP are characterized by heterogeneous granularities and positional accuracies, complicating the analysis of land cover changes through the generation of artefacts. To address this problem, an artefact removal procedure was conducted, based on the use of minimum shape and size indicators. The assignment of land use changes to pineapple crops allowed the assessment of deforested areas between 2000 and 2019. Thus, over this period, more than 6300 hectares of forest were lost to pineapple crops, mainly in the cantons of Los Chiles and San Carlos, in the northern part of the country. Finally, this article illustrates the problems associated with the use of heterogeneous data to analyse land cover changes. To avoid these problems, the use of prior data integration procedures, such as data matching algorithms, is recommended.