Land Cover Change Analysis Research Articles

Deep Learning Techniques for Enhanced Mangrove Land use and Land change from Remote Sensing Imagery: A Blue Carbon Perspective

Deep learning techniques are revolutionizing the analysis of remote sensing imagery for mangrove land use and land change studies. These methods improve the accuracy and efficiency of identifying changes in mangrove forests, providing valuable insights for environmental monitoring and conservation efforts. This study presents an in-depth analysis of land cover changes on Hainan Island, China, over 30 years from 1993 to 2023, using Landsat 5 imagery. Through the classification of land cover into six distinct categories—barren/agriculture, urban/infrastructure, high vegetation, low vegetation, water, and clouds—we examined the temporal shifts and trends in the island's landscape. An accuracy assessment using high-resolution Landsat Satellite imagery provided validation for the classification method, achieving an accuracy of over 70%. The results revealed a notable urban expansion and recuperation of high vegetation areas, indicating a strong interaction between human activities and natural ecosystems. The study underscores the dynamic nature of land cover influenced by both anthropogenic and natural factors and highlights the importance of remote sensing in environmental monitoring. Future work will involve the adoption of newer satellite technologies and machine learning techniques to enhance the precision of land cover classification, integrate additional ecological data, and employ predictive modeling for sustainable land management strategies. The continuation of these efforts is crucial for informing policy decisions to ensure the preservation of Hainan's rich biodiversity and natural resources.

Spatiotemporal analysis of land cover change, projection, and fragmentation: an application of Google earth engine and machine learning approach on Baraitali forest, Bangladesh

ABSTRACT Understanding the spatiotemporal dynamics of land cover/use change and forest fragmentation from 1981 to 2021 across the Baraitali Forest was very crucial for modeling future land cover, formulation of sustainable and robust forest/land-management strategies, and policy. The study adopted a mixed modern tool of Google Earth Engine platform, Machine Learning Algorithms, ArcGIS software, Land Change Modeller of TerrSet software, and FRAGSTATS programs. The study revealed that total land cover from 1981 to 2021 found a negative change in vegetative coverage, and a positive change in settlement which was more visible within the 6 land cover classes. Projections of future land cover for the next 10 years anticipate that vegetation coverage will be reduced by 65.96 ha, while settlement will be enhanced by 41.91 ha in 2031. The study also found conversions/transition of land cover throughout the research area. Overall change of spatiotemporal patterns was leading to substantial forest fragmentation. The overall accuracy and Kappa statistics for all the supervised land cover classifications were satisfactory. The study will fill an information gap by providing the revealed information at national and regional scales and may contribute to understanding of global change.

Analysis of land cover changes on the Sumatran Orangutan (Pongo abelii) habitat due to forest fires in Gunung Leuser National Park

Gunung Leuser National Park (GLNP) is home to Indonesia’s native species, the Sumatran orangutan (Pongo abelii). The GLNP in some of its areas has experienced forest degradation, one of which is by land clearing activities by burning. The purpose of this study is to analyze land cover changes due to forest fires in the habitat of Sumatran orangutans in GLNP, Langkat Regency, North Sumatera Province. This study used temporal and spatial analysis of fire hotspot data with land cover. Temporal analysis of land cover changes was conducted between before and after forest and land fires in the study area. There are at least 6,783 ha of forest fires, of which 1,541 ha were habitat distribution areas for Sumatran orangutans. From 2000-2019, 158 hotspots were detected, of which 36 hotspots were in the distribution area of Sumatran orangutan habitat. Land cover in the study area has mostly changed from primary dryland forest to plantations. The primary dryland forest that remained in the study area only 483.51 Ha in 2019 after converting since 2000. Communities around GLNP need to be assisted in the process of ecosystem restoration through a conservation partnership pattern to restore the function of the area, one of which is as a habitat for Sumatran orangutans.

Spatial analysis of land cover changes in mangrove and peatland forests in North Sumatra, Indonesia

Forest management in Indonesia can be observed through land cover change as a macro indicator for forest loss or gains. In order to have a clear understanding of the trend of the land cover change in peatland and mangroves, we conduct the land cover change analysis using the last decadal or 10-year period. The land typologies used as analysis units are peat and mangrove ecosystem. The research area is located in North Sumatra Province, occupying a peat area of 50,089.9 ha and mangrove area of 86,907 ha. The spatial analysis includes land cover maps overlay and classification using GIS. The 2012-2022 analysis results indicate a trend of an increase in peat reforestation by 819.6 ha and deforestation by 5,043.5 ha, while the trend of mangrove reforestation increased by 12,801.5 ha and deforestation by 4,669.5 ha. Land cover changes in the peatland are predominantly derived due to conversion of plantation to secondary swamp forest and conversion of secondary swamp forest to bare land. In the mangrove ecosystem land cover changes are mainly derived due to the regrowth of shrubs to secondary mangrove forests and due to conversion or degradation of secondary mangrove forests to shrubs.

Analysis of Land Cover Changes Impact on Design Flood Estimation Case study: Upper Cimanuk Watershed in Garut City

The unexpected flooding events that occurred in the years 2021 and 2022 in the city of Garut became unforeseen incidents. According to the National Disaster Management Agency (BNPB), the flood disasters were caused by several factors, one of which was changes in land cover. Based on this background, this research is conducted to examine the causes of changes in flood discharge that have occurred in the city of Garut due to land cover changes from the years 2000, 2010, and 2020. The evaluation of the factors causing the flood magnitude is performed by conducting a hydrological analysis in the Cimanuk Watershed. An analysis of land cover changes using the ArcGIS application to create land cover samples and HEC-HMS application was conducted to determine the percentage increase in discharge and to understand the percentage of land cover changes that occurred in the Cimanuk Watershed. The data used included satellite image maps, digital elevation models (DEM), rainfall intensity, and cross-sectional data. The accuracy of the simulation was assessed using the Nash-Sutcliffe Efficiency (NSE). The simulation results show an increase in the inflow discharge into Garut city ranging from 23.7% to 32.2% for each return period. The NSE value obtained for the model was 0.83, indicating that the HEC-HMS hydrological model performed well.

SPATIAL ANALYSIS OF LAND COVER CHANGES IN DEPOK CITY USING GEOGRAPHIC INFORMATION SYSTEMS

Depok City is an area undergoing significant changes in land cover due to rapid urban development. Additionally, population growth is contributing to these land cover changes. These factors exert pressure on land use changes, with implications for land cover. Therefore, it is necessary to conduct a spatial analysis of land cover changes in Depok City. This research employs Geographic Information System (GIS) methods and spatial analysis to examine land cover changes in Depok from 2017 to 2022. The objectives of this study are to identify the land cover conditions in Depok from 2017 to 2022 and analyze the changes in land cover during this period. The data source used is Landsat 8 satellite imagery. These satellite images were interpreted to produce land cover maps for the years 2017 and 2022. Subsequently, these two maps were overlaid to assess changes in land use. The research results revealed seven land cover classes in 2017, with the largest in terms of area being Residential/Built-up Areas (12,383.43 hectares), Green Open Spaces (3,479.66 hectares), Dryland Agriculture (2,218.65 hectares), Services/Industry (885.41 hectares), Paddy Fields (414.59 hectares), Open Land (373.66 hectares), and Water Bodies (239.23 hectares). Spatial analysis indicated a decrease in land cover for Green Open Spaces (238.84 hectares), Dryland Agriculture (122.67 hectares), and Paddy Fields (31.89 hectares). Conversely, an increase in land cover was observed for Residential/Built-up Areas (266.01 hectares), Services/Industry (91.84 hectares), and Open Land (35.55 hectares).

Changes In Greenness Level In PT Arumin Indonesia's RPT NPLCT Area With Normalized Difference Vegetation Index (NDVI)

The observation focused on PT Arutmin Indonesia's NPLCT RPT area of 98 hectares. These observations are viewed with the help of remote sensing technology and geographic information systems (GIS). This observation aims to analyze changes in greenness levels that occur from 2019 to 2023 using the normalized difference vegetation index (NDVI) method and the materials used sentinel-2A imagery. The goal is to evaluate changes in greenness levels and their interpolation from 2019 to 2023. An area of 98 hectares was monitored using sentinel-2A satellite imagery with a pixel sharpness of 10 meters x 10 meters. The results show sentinel imagery from 2019 to 2020, 38.41 ha experienced reforestation, 1.65 ha experienced deforestation and 58.14 ha did not change. Sentinel-2A image from 2020 to 2021, 79.09 ha experienced reforestation, 1.35 ha experienced deforestation and 17.68 ha did not change. Sentinel-2A image from 2021 to 2022, 1.08 ha experienced reforestation, 79.48 ha experienced deforestation and 17.68 ha did not change. Sentinel-2A imagery from 2022 to 2023, 0.47 ha experienced reforestation, 40.65 ha experienced deforestation and 57.17 ha did not change. The sentinel-2A image changes from 2019 to 2023, 2.27 ha reforested, 20.35 ha deforested and 75.64. These observations highlight the importance of remote sensing technology and geographic information systems (GIS) in monitoring and analyzing land cover change to support conservation, rehabilitation and natural resource management policies in Kotabaru District, South Kalimantan.

Land cover change analysis in Majalengka Regency using the pan-sharpening method and random forest machine learning algorithm

The ever-increasing population has accelerated the need for housing and supporting facilities. Further, this growing number of residences and life support facilities resulted in changes in land cover. For instance, the construction of the West Java International Airport and the increase in the population resulted in land cover changes in Majalengka Regency, Indonesia. This study aims to analyze changes in land cover during 2014, 2018, and 2022 in the Majalengka district using Landsat 8 satellite imagery. We used the pan-sharpening research method, while for the closure classification, we used machine-learning random forest algorithms on the google earth engine platform. The land cover classification classes adopted in this study were natural vegetation, cultivated vegetation, open land, built-up land, and bodies of water. The obtained land cover classification results suggested overall accuracy values of 0.96, 0.94, and 0.93 in 2014, 2018, and 2022 respectively, with kappa index values of 0.950, 0.925, and 0.91667. The results indicate a trend of changes in the land cover in the Majalengka district. From 2014-2022, the trend of increasing land cover area was observed in open land and built-up land, while the decreasing land cover area was found in natural vegetation and cultivated vegetation areas. Using both the pan-sharpening method and the machine learning random forest algorithm, we established images with a more detailed appearance with an outstanding kappa accuracy value (above 0.85). Therefore, the developed algorithm can be used in land cover change mapping analysis.

Demi-decadal land use land cover change analysis of Mizoram, India, with topographic correction using machine learning algorithm.

Mizoram (India) is part of UNESCO's biodiversity hotspots in India that is primarily populated by tribes who engage in shifting agriculture. Hence, the land use land cover (LULC) pattern of the state is frequently changing. We have used Landsat 5 and 8 satellite images to prepare LULC maps from 2000 to 2020 in every 5years. The atmospherically corrected images were pre-processed for removal of cloud cover and then classified into six classes: waterbodies, farmland, settlement, open forest, dense forest, and bare land. We applied four machine learning (ML) algorithms for classification, namely, random forest (RF), classification and regression tree (CART), minimum distance (MD), and support vector machine (SVM) for the images from 2000 to 2020. With 80% training and 20% testing data, we found that the RF classifier works best with the most accuracy than other classifiers. The average overall accuracy (OA) and Kappa coefficient (KC) from 2000 to 2020 were 84.00% and 0.79 when the RF classifier was used. When using SVM, CART, and MD, the average OA and KC were 78.06%, 0.73;78.60%, 0.72;and73.32%, 0.65, respectively. We utilised three methods of topographic correction, namely, C-correction, SCS (sun canopy sensor) correction, and SCS + C correction to reduce the misclassification due to shadow effects. SCS + C correction worked best for this region; hence, we prepared LULC maps on SCS + C corrected satellite image. Hence, we have used RF classifier for LULC preparation demi-decadal from 2000 to 2020. The OA for 2000, 2005, 2010, 2015, and 2020 was found to be 84%, 81%, 81%, 85%, and 89%, respectively, using RF. The dense forest decreased from 2000 to 2020 with an increase in open forest, settlement, and agriculture; nevertheless, when Farmland was low, there was an increase in the barren land. The results were significantly improved with the topographic correction, and misclassification was quite less.

Analysis of Land use and Land Cover Change, Causes and Effects on Agricultural Productivity: The Case of Anlemo District in Hadiya zone, Central Ethiopia

Analysis of Land use and Land Cover Change, Causes and Effects on Agricultural Productivity: The Case of Anlemo District in Hadiya zone, Central Ethiopia

Analysis of Land Cover Changes and Sustainable Reforestation in Sianjur Mula-Mula District, Samosir Regency, Indonesia

Analysis of Land Cover Changes and Sustainable Reforestation in Sianjur Mula-Mula District, Samosir Regency, Indonesia

Analysis of landcover changes and carrying capacity of coastal cities North Java of Central Java Province, Indonesia

North Coastal Central Java has been a strategic zone from the colonial era to the present because it supports the national socio-economic sector. Urbanization has threatened the development of districts/cities on the northern coast of Central Java. The physiographical conditions of coastal districts/cities along Pantura are formed from alluvial plains, making the environment's carrying capacity more vulnerable to changes in global conditions and pressures from the socio-economic activities of the population. This study aims to identify changes in land cover, its relationship to road infrastructure development, and its implications for environmental carrying capacity. The method used in this study uses GIS to determine changes in land cover and settlement growth patterns and calculates biocapacity and ecological footprint to obtain environmental carrying capacity conditions. The results show that land cover changes mainly occur in agricultural land, plantations, and settlements. In addition, the environment's carrying capacity experienced a deficit/overshoot in 2010, which continued until 2020. The result indicates the problem in the environmental dimension of sustainable development of coastal cities and requires serious intervention to increase environmental quality.

Intensity analysis applied to land use and land cover change and transitions in a fragile tropical mountain environment: a case of sironko catchment on Mt. Elgon, Eastern Uganda

ABSTRACT Intensity Analysis (IA) of land use and land cover change (LULCC) is important to support policy and practice. We applied IA to analyse LULCC in Sironko catchment, Uganda. Results show that agricultural land, forest, and wetland reduced by 8%; 32%; and 20% between 1986-2000. Between 2000-2016, forest, wetland, and built-up areas increased by 84%, 5%, and 57%. Active gainers between 1986-2000 were bare land, built-up areas, grassland and woodland. Between 2000-2016, bare lands and woodlands were the active losers. Transition to agricultural land in both intervals suggests that subsistence agriculture is fuelling deforestation and wetland encroachment in the region.

Modeling Land Cover Change Using MOLUSCE in Kahayan Tengah Forest Management Unit, Kalimantan Tengah

A management unit-based land cover change analysis was examined in Kahayan Tengah Forest Management Unit (FMU) to understand past, present, and future land cover to assist forest management planning in Kahayan Tengah FMU. This study aims to model land cover change in 2011 and 2016, predict 2021, and simulate land cover in 2026 in Kahayan Tengah FMU. Modeling land cover prediction and simulation using MOLUSCE from the QGIS plugin. The results revealed that agricultural land experienced significant increase in total area during 2011–2016. Modeling potential land cover transitions in 2011 and 2016 with the Artificial Neural Network method showed a Kappa coefficient of 0.701 in the good category, and simulation of land cover in 2021 with the Cellular Automata method showed a Kappa coefficient of 0.672 in the good category. By 2026, the agricultural land will continue to increase while forest land tends to remain stable in its total area. This study managed to predict land cover in 2021 and simulated 2026 with good accuracy. Thus, this data and information can support forest management planning in Kahayan Tengah FMU. Keywords: forest management unit, Kahayan Tengah, land cover change, land cover prediction, MOLUSCE

Can land-use and land-cover change explain reduced resilience in forests?

Generating signals of reduced resilience in ecosystems is crucial for conservation and management endeavors. However, the practical implications of such systems are still limited due to the lack of high-frequency data and uncertainties associated with predicting complex systems such as ecosystems. This study aims to investigate the potential of time series analysis of remote sensing data in detecting signals of reduced resilience in mangrove forest ecosystems. Using time series analysis of remote sensing images, the resilience of mangrove forests was explored across two distinct study sites. One site (Qeshm Island) has been adversely affected by land-use and land-cover changes, while the other (Gabrik) serves as a reference ecosystem. The study uses data from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite to quantify three remotely sensed indices: the Normalized Difference Vegetation Index (NDVI), the Modified Normalized Difference Water Index (MNDWI), and the Modified Vegetation Water Ratio (MVWR). In addition, Landsat data has been used to explore temporal alterations in land-use and land-cover change. To identify signals of reduced resilience, trend analyses of indicators such as autocorrelation (acf (1)) and standard deviation (SD) are applied. The findings revealed a notable decrease in resilience, signaled by significant upward trends in NDVI statistical metrics for Qeshm Island (Kendall’s τ of acf (1): 0.50 and SD: 0.90), contrasting with the pattern observed in Gabrik (Kendall’s τ of acf (1): −0.19 and SD: −0.19). These results align with our expectations derived from previous studies. Despite MNDWI significantly indicating reduced resilience in Qeshm Island (Kendall’s τ of acf (1): 0.86 and SD: 0.90), it also signaled decreased resilience in Gabrik (Kendall’s τ of acf (1): 0.79 and SD: 0.90). Moreover, MVWR failed to indicate signals of reduced resilience in both sites, specifically in Qeshm (Kendall’s τ of acf (1): −0.10 and SD: −0.07) and in Gabrik (Kendall’s τ of acf (1): −0.72 and SD: −0.12). These findings may be explained through quantitative analyses of land-use and land-cover change. While Qeshm Island and Gabrik share similarities in climate, geography, and annual rainfall, the analysis of land-use and land-cover change revealed significant differences between the two study areas. Qeshm Island underwent drastic increases in the built-up class by a 64.40% change between 1996 and 2014, whereas the built-up class expanded modestly by a 4.04% change in the Gabrik site. This study contributes to advancing our understanding of ecosystem dynamics. The findings of this study can be integrated with ecosystem management tools to enhance the effectiveness of conservation efforts. This is the first report of the successful application of remote sensing in generating signals of reduced resilience within mangrove forests in the Middle East.

Analysis of land cover change in Sagara Anakan Cilacap, Central Java using principal component analysis (PCA)

Land cover in area Mangrove, an ecosystem with high productivity, is an ecological link that is very important for life in the waters. This study aims to identify land cover vegetation, water bodies, and soil using Principal Component Analysis (PCA) method with imagery using Landsat imagery for 1997 and 2021 carried out by processing Landsat 7 and Landsat 8 OLI images consisting of pre-processing processes and image classification. The results showed that in the mangrove area in Sagara Anakan, there was a significant change in land cover; for the lagoon area, the land cover was overgrown with mangrove forests, but areas far from the lagoon had changed to settlements or sparse vegetation.

Impact of land-use change on salt marsh accretion

Salt marsh vertical accretion that keeps pace with relative sea-level rise (RSLR) promotes habitat resilience and a continuously growing carbon stock. Vertical accretion, however, hinges on various salt marsh biogeomorphic feedback loops that respond to changing sediment supply, storms events, accelerating RSLR, elevation, and vegetation density. During the latter half of the 20th century, a large proportion of watersheds on the lower coastal plain of North America experienced land cover change. Many salt marshes show slow rates of vertical accretion over that period suggesting degradation and destabilization attributed to reduced upstream sediment loads and changing proportions of mineral sediment to organic matter. Here, we investigated a potential dichotomy observed at many sites in coastal North America between previously published accretion rates of the subtidal bay bottoms and adjacent salt marshes that were exceeding and lagging behind RSLR since 1950 CE, respectively. In 12 small coastal watersheds that formed within tributary and coastal prism incised valleys, we coupled 210Pb-derived accumulation rates of fringing salt marshes and previously published data from adjacent tidal creek bay bottoms with geospatial analysis of watershed land cover change since 1959, inundation duration, and vegetation density. Accumulation rates were higher at marsh sites within tributary incised valleys with lower relief watersheds, smaller tidal ranges, shorter inundation times, higher vegetation densities, and where land cover changes were dominated by increased cleared forest area. Additionally, mass accumulation rates (MAR) accelerated at 8 of the 12 marsh sites after a Major Land Cover Change (MLCC). Despite this acceleration in MAR, only 2 marsh sites recorded average sediment accumulation rates (SAR) greater than RSLR after a MLCC, which suggests that most of the marsh sites are drowning or being converted to subtidal habitat. This may be misleading, however, because modern marsh platform elevations at all but one marsh site were above local mean sea level, so the marshes could be accommodation limited. This work supports the disconnect found in previous research suggesting that estuaries are accumulating sediment at sustainable rates while their fringing salt marshes are not, which illuminates the complexities associated with sedimentation regimes in small local watersheds and their fringing salt marshes.

Analyzing land cover change dynamism through a GIS-based method: application to Gran Canaria (Canary Islands, Spain)

Landscapes are dynamic areas which can be studied from the perspective of different, but related, disciplines. The application of GIS methods to study landscape change is an interesting resource to help understand and explain landscape dynamics. Thus, the objective of this paper is to characterize land cover dynamism and identify land cover change ‘hotspots’. The method used here generates raster maps in which each pixel value represents the number of times each pixel has changed land cover between 1990-2018. Furthermore, it provides three of these maps, since it works with the 3 taxonomic levels of Corine Land Cover datasets, giving us different levels of detail in the analysis. On the other hand, the statistical treatment of the data has been done at the municipal level. The most important results reveal that Agüimes is the municipality with the most dynamic land cover change in the three levels, while Tejeda, in level 1, and Valsequillo, in levels 2 and 3, have the least changing land cover. These outputs are complemented with other statistical analyses which allow the integration of different data types such as those related to population, tourism and agriculture. Subsequently, some of the methodological issues and findings are discussed and put in context with the scientific literature.

Land Cover Changes and Driving Factors in the Source Regions of the Yangtze and Yellow Rivers over the Past 40 Years

As a climate-sensitive region of the Tibetan Plateau, the source regions of the Yangtze and Yellow Rivers (SRYYRs) urgently require an analysis of land cover change (LUCCs) over a long period, high temporal resolution, and high spatial resolution. This study utilizes nearly 40 years of land cover, the Normalized Difference Vegetation Index (NDVI), climate, and geomorphological data, applying methods including a land transfer matrix, slope trend analysis, correlation analysis, and landscape pattern indices to analyze the spatial and temporal changes, composition, layout, and quality of the local land cover and the factors. The findings reveal that (1) the land cover area change rate was 8.96% over the past 40 years, the unutilized land area decreased by 24.49%, and the grassland area increased by 6.37%. The changes were obvious at the junction of the two source regions and the southeast side of the source region of the Yellow River. (2) the landscape pattern was more centralized and diversified. The number of low-cover grassland patches increased by 12.92%. (3) The region is still dominated by medium- and low-cover vegetation, with the mean annual NDVI increasing at a rate of 0.006/10a, and the rate of change after 2000 is three times higher than previously. (4) The degree of land cover change is greater in the middle altitudes, semisunny aspects, steepest slopes, and middle-relief mountains. Additionally, 76.8% of the region’s vegetation growth is dominated by mean annual temperatures. This study provides fundamental data and theory for understanding LUCCs and the driving factors in alpine plateau regions.

Spatio-temporal investigation of urbanization and its impact on habitat fragmentation in natural ecosystems of Istanbul using Shannon’s entropy and landscape metrics in GIS

This study aims to investigate the impact of spatio-temporal dynamics of urban sprawl on habitats in Istanbul, using the CORINE 1990 and 2018 land cover datasets. In this context, by means of geographic information system (GIS) tools and techniques, land cover maps are created for land cover change analysis and visualization, the Shannon’s entropy method is performed for measuring urban growth and sprawl, and the landscape metrics method is applied for assessing habitat loss and fragmentation. Results indicate that the rate of urban sprawl in the area increased by 0.1230 as per the Shannon’s entropy index over the 28-year period. Similarly, the Shannon Diversity Index and the Shannon Evenness Index rose from 1.74 to 1.85 and from 0.68 to 0.70, respectively, indicating an increase in urbanization within the area. Moreover, an analysis of patch numbers reveals that habitat fragmentation increased in shrub and/or herbaceous vegetation associations (72.55%), heterogeneous agricultural areas (45.11%), arable lands (42.5%), forests (36.13%) and pastures (15.05%), due to urbanization. Habitat fragmentation has had a detrimental effect on the local biodiversity. While 15 flora species were identified as vulnerable, 13 as endangered and 9 as critically endangered, 19 fauna species were identified as vulnerable and 5 as endangered. This study highlights that the natural habitats and biodiversity of Istanbul will suffer further decline due to urbanization unless sustainable urban planning and management policies are put into practice. It is essential to have controlled urban development to preserve the ecosystem’s carrying capacity, and urbanization decisions must consider this requirement.