Assessment Of Land Cover Changes Research Articles

Analysis of land cover dynamics in Mozambique (2001–2016)

Land cover change (LCC) is a complex and dynamic process influenced by social, economic, and biophysical factors that can cause significant impacts on ecological processes and biodiversity conservation. The assessment of LCC is particularly relevant in a country like Mozambique where livelihood strongly depends on natural resources. In this study, LCC was assessed using a point-based sampling approach through Open Foris Collect Earth (CE), a free and open-source software for land assessment developed by the Food and Agriculture Organization of the United Nations. This study aimed to conduct an LCC assessment using CE for the entire Mozambique, and according to three different land classifications: administrative boundaries (provinces), ecoregions, and protected vs unprotected areas. A set of 23,938 randomly selected plots, with an area of 0.5 hectares, placed on a 4 × 4 km regular grid over the entire country, was assessed using CE. The analysis showed that Mozambique has gone through significant loss of forest (− 1.3 Mha) mainly to the conversion to cropland. Deforestation is not occurring evenly throughout the country with some provinces, such as Nampula and Zambezia, characterized by higher rates than others, such as Gaza and Niassa. This result can be explained considering a combination of ecological and socio-economic factors, as well as the conservative role played by the protected areas. Our study confirmed that LCC is a complex phenomenon, and the augmented visual interpretation methodology can effectively complement and integrate the LCC analyses conducted using the traditional wall-to-wall mapping to support national land assessment and forest inventories and provide training data for environmental modeling.

An Assessment of Land Cover Change in Gashaka-Gumti National Park, Nigeria

An Assessment of Land Cover Change in Gashaka-Gumti National Park, Nigeria

GIS AND REMOTE SENSING IN ESTIMATION OF THE AGRICULTURE LANDS INFRINGEMENT, CASE STUDY: KOM HAMADA, BEHIERA

The paper aims to assess agricultural land infringement using satellite images by applying three methods of classification: supervised (maximum likelihood), unsupervised and normalized difference vegetation index. To determine which sets of remote sensing satellite images were the best, they were compared. During the monitoring periods (2010–2011 and 2020–2021), Beban village is used as a study area. Landsat 8, Sentinel 2, Aster, and Modes satellite images are used to generate the remote sensing data. This period has been selected to classify images in order to assess land cover changes and the infringement of agricultural lands within Beban village and Kom Hamada center. The proposed methods employ the multi-spectral remote sensing data technique for land cover classification, with the selection of a satellite image dependent on the comparisons between the data quality of each satellite image downloaded for the study area. For land cover classification, some band combinations of the remotely sensed data are exploited, and the spatial distributions such as urban areas, agricultural land, and water resources are interpreted. The results give two important points: the Landsat 8 OLI/TIRS sensor is the best when compared with the other satellite, and the second point for the percentage of agricultural land in the study area in 2020, 2015, and 2010 was estimated to be 77.76%, 78.88%, and 84.04%, respectively. That is, agricultural land infringement accounted for 6.28% of Beban Village's total area.

Urbanization and Other Land Use Land Cover Change Assessment in the Greater Kumasi Area of Ghana

Urbanization and Other Land Use Land Cover Change Assessment in the Greater Kumasi Area of Ghana

Assessment of Land Use and Land Cover Changes (LULC) in the North Talihya River Watershed (Lubero Territory, Eastern DR Congo)

On the Equator, the Talihya North watershed is a vast area of nearly 581.7 km2 that extends from the Cool Highlands on the Congo-Nile Crest in Lubero Territory to the Rift Valley in the Virunga National Park in Beni Territory. This vast territory has an agropastoral vocation. Indeed, agricultural activities combined with the high population density in this watershed generate modifications in the landscape structure. The objective of this paper is to study the dynamics of land use from 1987 to 2020. To achieve this, two Landsat TM+ and ETM+ images and one Sentinel-2 image were analyzed. After the classification of the images based on the Maximum Likelihood algorithm, this study shows that two processes are evident in the landscape of the North Talihya watershed: deforestation and savannization. Forests that occupied 253.11 km2 in 1987 have decreased to 201.12 km2 in 2001 and to 123.04 km2 in 2020. These area balances indicate that the natural forest formations in the North Talihya watershed have been converted primarily to croplands and fallows. The estimated annual deforestation rate between 1987 and 2020 is 2.18%. With this high rate of deforestation, mechanisms to restore degraded forest ecosystems in this watershed will need to be put in place. The sustainable management of residual forest ecosystems that have escaped human pressure is necessary for the conservation of biodiversity for future generations.

Assessment of land use and land cover changes of Damdama wetland, Gurugram using various change detection techniques

Deterioration of water quality, habitat loss, and degradation of terrestrial and aquatic ecosystems, are serious global environmental concerns. Over the years, wetland ecosystems have undergone significant changes because of human activities particularly urbanization, agriculture, and industrial expansion. The principal objective of this study was to monitor the in and around changes over Damdama wetland using remote sensing and geographic information systems. The Land use and land cover changes in Damdama wetland were analyzed for the period of 2010-2020 for two seasons i.e., pre-monsoon and post-monsoon using ERDAS Imagine version 2014. The classification was based on six different classes i.e., agriculture, barren land, built-up, fallow land, vegetation and water body using maximum-likelihood supervised classification. The resulted Land use and land cover changes map showed an increase of 9.41%, 14.83%, 3.95% and 0.15% in agriculture, barren land, built-up and water body respectively from 2010 to 2020 in pre-monsoon season whereas the fallow land and vegetation decreased by 15.15% and 13.20%. In post-monsoon season barren land, built-up and fallow land increased by 9.6%, 4.13%, and 1.38% respectively whereas agriculture, vegetation and waterbody decreased by 6.5%, 8.42% and 0.19%, respectively. Overall accuracy was found to be 90%, 92% for pre-monsoon and post-monsoon season respectively in the year 2010, while 94% for both the seasons in the year 2020. The results indicated that three different analytical techniques applied to assess the changes showed similar trend. Overall, such transformation may pose a serious threat to wetland ecosystem in future.

Assessment of Land Use, Cover Changes, and Fire Hotspots in a Conservation Unit: A 20-Year Analysis

Assessment of Land Use, Cover Changes, and Fire Hotspots in a Conservation Unit: A 20-Year Analysis

Use of Spatial Technology for Land Cover Change Assessment in Protected Area Management

Land cover assessment and monitoring of land cover dynamics are important to understand social and ecological processes in protected areas and it is helpful in monitoring, modeling and detection of environmental changes. Land cover is the characteristics of the physical material on the ground surface. Change in land cover is among the most important human alteration affecting the surface of earth that directly impacts on biological diversity. Such changes affect and increase the vulnerability of population, environment and ecosystem to global phenomena of climate change. This study has assessed the changes in land cover in Shivapuri Nagarjun National Park (SNNP) along with its Buffer Zone during twenty-seven years. The main objective of this study is to identify the changed scenario on land cover condition of the study area based on available topographic maps (1994), Google Earth images (2021) and Key Informant Interview (KII). The study has adopted Remote Sensing (RS), Geographic Information System (GIS) and Global Positioning System (GPS) approach supported by secondary data and focus group discussion for classification and mapping of land cover. Among the different land cover classes, forest land, sandy area, water bodies and others land particularly built up area have been increased whereas, the bush/shrub land, cultivated land and grass land have been found decreased in the park area. On the other hand, the cultivated land, grass land and water bodies have been found decreased, while the bush/ shrub land, forest land, sandy area and others land particularly built up areas have been found significantly increased during 1994 to 2021 in the BZ area. It will provide a baseline for planners and policy makers to make proper plans to sustainably manage the protected area’s land cover.

Assessment of Land use and Land Cover Change in Shallabugh Wetland of Kashmir Himalaya using Landsat Tm and Liss Iv Satellite Datasets

The increase in human activities has highlighted the need to assess land use and land cover in the Shallabugh wetland using LANDSAT TM and LISS IV satellite datasets over a 28-year period from 1990 to 2018. After ground truthing, the images were visually evaluated on screen and seven land use and land cover classes, viz. macrophytic vegetation, mixed plantation, open water, grazing land, agriculture field, built-up area, and vegetable field, were demarcated using Arc-GIS software. Out of a total area of 2552.62 ha, the maximum area was covered by macrophytic vegetation (33.87% and 32.36%) and the minimum area by vegetable field (0.77% and 1.51%) in both the years 1990 and 2018. Over the 28 year time period, land use land cover pattern showed increase in mixed plantation (2.28%), built-up (86.8%) and vegetable field (96.1%), whereas decrease was observed in macrophytic vegetation (4.4%), open water (10.3%), grazing land (28.6%), and agriculture field (2.6%). These remarkable changes were largely attributed to natural and human activities, viz., siltation, plantation and crop cultivation by local people and the construction of residential houses. Shallabugh wetland plays a role as breeding ground for a variety of waterfowl and serves as an important staging and wintering location for migratory avifauna. Land use and land cover change pose a severe threat to the Shallabugh wetland which is one of the most economically important wetland of Kashmir valley.

Assessing Changes in Boreal Vegetation of Kola Peninsula via Large-Scale Land Cover Classification between 1985 and 2021

The effective monitoring of boreal and tundra vegetation at different scales and environmental management at latitudes above 50 degrees North relies heavily on remote sensing. The vastness, remoteness and, in the case of Russia, the difficulty of access to boreal–tundra vegetation make it an ideal technique for vegetation monitoring in the Kola peninsula, located predominantly beyond the Arctic circle in the European part of Russia. Since the 1930s, this area has been highly urbanised and exposed to strong influence by a number of different types of human impact, such as toxic pollutions, fires, mineral excavation, grazing, logging, etc. Extensive open archives of remote sensing imagery as well as recent advances in machine learning further enable the efficient use of remote sensing methods for assessing land cover changes. Here, we present the results of mapping northern vegetation land cover and changes in it over a large territory, in time and under human impact based on remote imagery from Landsat TM, ETM+ and OLI. We study the area of about 37,000 km2 located in the central part of the Kola peninsula in the boreal, pre-tundra and tundra between 1985 and 2021 with a time interval of approximately 5 years and confirm the correlations between the human pressure and the level of vegetation changes. We put those into the perspective of year-on-year changes in the temperature and precipitation regimes and describe the recovery of the damaged original boreal vegetation (dominated by spruce) through pine and deciduous vegetation. As a by-product of this study, we develop and test an approach for the semi-automated processing and classification of Landsat images using the novel TensorFlow machine learning technique (widely spread across other disciplines) that enables high-throughput classification, even on conventional hardware.

Assessment of Land Use Land Cover Changes for Predicting Vulnerable Agricultural Lands in River Basins of Bangladesh Using Remote Sensing and a Fuzzy Expert System

The aim of this study was to assess the LULC changes over 26 years from 1995 to 2021 to find the most changed land use conditions within the 25 km territory of the main river systems of Bangladesh. In addition, the prediction of vulnerable areas for agricultural land use in terms of inundation by river water was also analyzed. The study area includes river networks distributed through eight administrative divisions (Rangpur, Rajshahi, Mymensingh, Sylhet, Dhaka, Khulna, Barishal and Chittagong) of Bangladesh, covering an area of 64,556 km2. The study was conducted by identifying permanent water bodies from NDWI indices and preparing LULC maps that include the five main land use classes (water body, bare land, vegetation, agricultural land, and urban area) in the Google Earth Engine platform using supervised classification. The LULC maps were then analyzed in the ArcGIS® environment. A vulnerability map for agricultural land use was also prepared using a fuzzy expert-based system applying multicriteria analysis. From the land use land cover map of the study area, it was found that among the five land use classes, water bodies, bare land, vegetation, and urban areas increased in size by 3.65%, 2.18%, 3.31% and 2.55%, respectively, whereas agricultural land use significantly decreased by 11.68%. This decrease in agricultural land use was common for the analyzed area of all administrative divisions. According to the vulnerable area map of the eight divisions, more than 50% of the analyzed area of the Khulna and Dhaka divisions and more than 40% of the analyzed area of the Rajshahi, Mymensingh, Sylhet, Barishal and Chittagong divisions were highly vulnerable to agricultural land use due to the possibility of inundation by water. However, approximately 44% of the analyzed area of the Rangpur division was not vulnerable for agricultural land use. The prepared LULC and vulnerability maps can be helpful for the future land use planning of Bangladesh to meet the increasing demand for food production and livelihoods for increasing populations.

A GIS Approach to Land Use and Land Cover Change Assessment from 2016 to 2020 in Teknaf and Ukhiya Upazila of Cox’s Bazar district of Bangladesh due to Rohingya Intrusion

A GIS Approach to Land Use and Land Cover Change Assessment from 2016 to 2020 in Teknaf and Ukhiya Upazila of Cox’s Bazar district of Bangladesh due to Rohingya Intrusion

Study on the Evolution Law of Surface Landscape Pattern in Earthquake-Stricken Areas by Remote Sensing: A Case Study of Jiuzhaigou County, Sichuan Province

Earthquakes not only cause the destruction of surface buildings and a large number of casualties, but also have an important impact on regional land-use change. Timely understanding of land cover and its changes before and after earthquakes is of great scientific significance for studying the restoration and reconstruction of the natural ecosystem and the sustainable development of the social economy in disaster areas. At present, there are few studies on land cover changes before and after earthquakes in the earthquake-stricken areas, especially the quantitative assessment of land cover changes before and after earthquakes at the micro scale of landscape spatial distribution. Therefore, this article is based on remote sensing data and uses an earthquake in Jiuzhaigou, Sichuan, as an example. By calculating the land-use degree comprehensive index and its rate of change, the transfer matrix of land-use, quantitative expression methods of the landscape via the landscape pattern index are analyzed from the points of view of the spatial–temporal evolution law of the landscape pattern of land cover before and after the earthquake. The experimental results showed that the area of forest, cropland, shrub. and grassland types in the study area changed greatly, and the area of grassland types in Shuanghe Township changed most obviously, as they increased from 6.57 km2 in 2015 to 7.43 km2, and then decreased to 6.21 km2 in 2020. During the earthquake recovery period, the comprehensive index of land-use degree for most towns in the study area showed an upward trend, and the land cover types were improved. After 2017, the PD and LSI of the study area showed a downward trend, among which the bare land type changed greatly, with the PD value decreasing from 0.38 in 2017 to 0.21 in 2020, and the LSI value decreasing from 58.29 in 2017 to 40.69 in 2020. This indicates that the degree of landscape fragmentation and the spatial heterogeneity of the land surface in the study area are reduced in the later stage of earthquake recovery. After 2017, the AI value of the study area showed an upward trend, and the AI value of the bare land type increased from 63.97 in 2017 to 78.25 in 2020, indicating that the connectivity between landscape patches in the study area was enhanced in the later stage of the earthquake. This also reflects that the ecological environment of the study area gradually showed a good trend in the later stage of the earthquake. The results of this paper not only reveal the spatial–temporal evolution of land cover in Jiuzhaigou County before and after the earthquake, but also help the relevant national decision-making departments to formulate corresponding policies and measures.

Spatial Assessment of Temperature, Rainfall and Land Cover Change as Climate Change Monitoring Techniques in Okigwe and Its Environs

Climate change is an alarming global environmental change phenomena that is constituting a serious threat to natural, social, cultural, and human systems. Its associated risks require a wide range of policy responses and techniques at all levels from local to global. This study sought to evaluate the changes in climatic parameters over three decades as monitoring techniques in Okigwe, Imo State, Nigeria, based on evaluated land cover and temperature variation using Landsat 5 TM, Landsat 7 ETM+ and Landsat 8 ETM+ satellite data of 1986, 2003, and 2020 respectively. Precipitation data from 1990-2020 of the study area was used to evaluate rainfall variation as climatic change parameters over three decades. Results of the geospatial analysis indicates that climate change has become a fallout from developmental activities taking place in Okigwe, which obviously is impacting on all facets of human activities thereby militating against sustainable development. Implicitly, changes in built-up areas or surfaces significantly produced a corresponding effect in escalating urban heat in the city. This study recommends the enforcement of dequate land use planning via adopting green city planning techniques in infrastructural development.

Land Use and Land Cover Change Assessment and Future Predictions in the Matenchose Watershed, Rift Valley Basin, Using CA-Markov Simulation

Land use and land cover change (LULC) is known worldwide as a key factor of environmental modification that significantly affects natural resources. The aim of this study was to evaluate the dynamics of land use and land cover in the Matenchose watershed from the years 1991, 2003, and 2020, and future prediction of land use changes for 2050. Landsat TM for 1991, ETM+ for 2003, and Landsat-8 OLI were used for LULC classification for 2020. A supervised image sorting method exhausting a maximum likelihood classification system was used, with the application using ERDAS Imagine software. Depending on the classified LULC, the future LULC 2050 was predicted using CA-Markov and Land Change Models by considering the different drivers of LULC dynamics. The 1991 LULC data showed that the watershed was predominantly covered by grassland (35%), and the 2003 and 2020 LULC data showed that the watershed was predominantly covered by cultivated land (36% and 52%, respectively). The predicted results showed that cultivated land and settlement increased by 6.36% and 6.53%, respectively, while forestland and grassland decreased by 63.76% and 22.325, respectively, from 2020 to 2050. Conversion of other LULC categories to cultivated land was most detrimental to the increase in soil erosion, while forest and grassland were paramount in reducing soil loss. The concept that population expansion and relocation have led to an increase in agricultural land and forested areas was further reinforced by the findings of key informant interviews. This study result might help appropriate decision making and improve land use policies in land management options.

Assessment of land use and land cover change on Gum Talha (Acacia seyal var. seyal) forest in Bahar Alarab, Sudan

Abstract. Younis AYI, Jonathan BN, Togun AO, Yasin EHE, Omer SO, Hamad GAI, Aissata SD. 2022. Assessment of land use and land cover change on Gum Talha (Acacia seyal var. seyal) forest in Bahar Alarab, Sudan. Biodiversitas 23: 4549-4560. Evaluating land use and land cover change (LULC) is essential for the sustainable management of natural resources, biodiversity conservation, monitoring of food security, and research related to climate change and ecology. A better assessment of land-use changes is highly needed for further investigation due to increasingly rapid changes in LULC in response to human population growth. The emerging climatic change also has a significant effect on LULC. The objective of this study is to assess land use and land cover in Bahar Alarab, East Darfur State, Sudan, using remote sensing data obtained from satellite images. For assessing the LULC changes, Landsat images of the years 1988, 2002, and 2020 were downloaded and analyzed using QGIS 3.22.1 and ERDAS 2014 software, where supervised classification was applied with GPS point verification, change detection, matrix, and accuracy assessment. The analysis on LULC showed considerable changes during the two study periods, where 2020 had a considerable increase in forest cover in which on that year it occupied 26.44% of the area compared to 2002 and 1988 with 21.27% and 21.45%, respectively. Whereas 2002 the area was covered by a vast herbaceous vegetation (33.41%) compared to 1988 and 2002. Moreover, in 1988, shrubland decreased from 31.95% to 29.06% and 23.67% in 2020 and 2002, respectively. Sparse vegetation covered a considerable area in 2020 (23.61%) compared to 2002 and 1988 (21.65% and 17.71%, respectively). The results highlighted that there was statistically significant correlation between climate factors and LULC. Average temperature was highly positively correlated with sparse vegetation at 98.2%, while rainfall was highly negatively correlated with forest (-96.9%) and sparse vegetation (-88%), and highly positively correlated with herbaceous vegetation (83.5%). This study provides a unique understanding of LULC changes and their implications in management and conservation efforts, as well as a road map for decision makers for sustainable development of LULC in the Bahar Alarab.

Assessment of Land Use and Land Cover Change from 2000 to 2019 in East Java Indonesia

This study aims to analyse land use, and land cover (LULC) changes in East Java province in Indonesia. The changes are analysed by comparing two maps (the national digital map and the map interpreted from Landsat-8). Supervised classification of Landsat image using maximum likelihood algorithm done an overall and kappa accuracy of 96.62% and 96.02 %, respectively. The classification produces nine (9) classes, i.e.: (1) the pavement or urban area, (2) heterogeneous agricultural land, (3) paddy field, (4) open water, (5) dense vegetation (forest), (6) sparse vegetation (plantation), (7) shrubland, (8) Wetlands, and (9) Sandy-clay-rock. Furthermore, three subsets areas are explored to study the LULC changes caused by the development of the transportation infrastructure; industrial sites; the agricultural sector; tourism; urbanisation and sub-urbanisation. The LULC change is more marked in the most urbanised areas (in and around the big cities), followed by LULC change in and around medium cities and rural areas. Regional development during the last two decades has increased built-up and plantation areas. Conversely, the development has reduced paddy fields, rural areas, and water bodies. The LULC changes have significantly changed the natural to a human-dominated landscape.

Peri-urban area delineation and urban sprawl quantification in Thiruvananthapuram Urban Agglomeration, India, from 2001 to 2021 using geoinformatics

In Southeast Asia, rising population, economic growth, and lack of land supply in the city core have led to the widespread irreversible land cover transformation in peri-urban areas. Such extensive and haphazard urban growth in peri-urban areas raises concern about analyzing and promoting planned urban growth. Therefore, this paper attempts to assess land cover changes from 2001 to 2021 and delineate peri-urban areas of a midsized Indian city, i.e., Thiruvananthapuram Urban Agglomeration (UA) using geoinformatics. The maximum likelihood supervised classification tool in ArcGIS 10.3 was used to prepare land cover maps for 2001, 2007, 2014, and 2021. Further, the presence of urban sprawl in the peri-urban areas was detected through Shannon’s entropy index. The urban sprawl typologies in the peri-urban areas were quantified using the adjacent neighborhood relationships concept. The results revealed rapid growth in built-up land cover and decline in non-built-up land cover within Thiruvananthapuram UA during the study period. Peri-urban areas were delineated based on nine indicators, such as total population, population growth, population density, literacy rate, percentage of the male workforce, percentage of the female workforce, agricultural land cover, distance from urban core, and percentage of cultivators to agricultural workers. A rise in Shannon’s entropy index from 1.59 in 2001 to 2.05 in 2021 exhibited the occurrence of rapid urban sprawl in the peri-urban areas. Dominance of discontinuous low-density development, i.e., scatter development typology of urban sprawl, was observed in peri-urban areas of Thiruvananthapuram UA. Such studies using geoinformatics would assist local governments in scientifically formulating sustainable urban policies and plans.

LUCST: A novel toolkit for Land Use Land Cover change assessment in SWAT+ to support flood management decisions

Land Use Land Cover (LULC) change is widely recognised as one of the most important factors impacting the hydrological response of river basins. SWAT+, the latest version of the Soil and Water Assessment Tool, has been used extensively to assess the hydrological impacts of LULC change. However, the process of making and assessing such changes in SWAT+ is often cumbersome and non-intuitive, thereby reducing its usability amongst a wider pool of applied users. We address this issue by developing a user-friendly toolkit, Land Use Change SWAT + Toolkit (LUCST), that will: (1) allow the end-user to define various LULC change scenarios in their study catchment, (2) run the SWAT+ model with the specified LULC changes, and (3) enable interactive visualisation of the different SWAT+ output variables. A good System Usability Score (79.8) and positive feedback from end-users promises the potential for adopting LUCST in future LULC change studies. Video abstracthttps://youtu.be/QygBidYr4cQ.

Impacts of climate change and evapotranspiration on shrinkage of Aral Sea

The massive desiccation of the Aral Sea, the fourth largest lake in the world, has led to severe ecological problems, expansion of cropland was thought to be the main factor driving that shrinkage. But this study performed a long-term land cover and use change assessment for Aral Sea Basin (ASB) to show that the cropland has stopped expanding in 2000, of which the cropland in the ASB plain area has decreased significantly (−140 km2/year) from 2001 to 2019. By contrast, this study finds the hydrological cycle in the ASB has intensified through a spatial and temporal scale approach based on Earth observation. Specifically, there is a 7.21 % (+304.56 × 108 m3) increase in annual total precipitation and a 10.13 % (+376.21 × 108 m3) increase in annual total actual evapotranspiration (AET) for the whole ASB during 1980–2019. In particular, the total annual AET in the ASB plain area has increased by 37.81 % (+718.92 × 108 m3), which almost depletes the water that should have flowed into the Aral Sea. Therefore, the Aral Sea shrank by 5625 × 108 m3 (or 42,944.32km2) from 1980 to 2019. Changing climate and increasing AET have accelerated the desiccation of the Aral Sea, and the expansion of cropland is no longer the main factor of that shrinkage. After more water was conserved in the ASB plain area, evapotranspiration plays a more vital role in the Aral Sea shrinkage. Reducing AET and unproductive water losses are key initiatives in future projects to save the Aral Sea. This study explores the causes of Aral Sea shrinkage from an integrated perspective of climate-land-water-ecological change across the ASB, bridging the limitations of previous studies that have focused on Aral Sea waters and subbasins.