Identification Of Land Cover Changes Research Articles

Identification of land cover change and spatial distribution based on topographic variations in Java Island

Population growth and rapid development can increase the need for land, especially for settlements which impacts the reduced land availability on Java Island. This impact is unavoidable Land Cover Change (LCC). The LCC that occurs needs special attention as it can significantly impact climate change, contributing to 33% of global carbon emissions. One of the supporting factors of LCC is topographic variation. No studies on priority areas for settlement management consider the LCC results based on topographic variations. Thus, this study aims to model the priority areas for handling LCC residential areas based on topographic variations by considering the 2000–2018 Land Cover data on the island of Java, Indonesia, and to compare the residential priority area results with the 2030 land cover prediction model using the Markov Chain-Cellular Automata method. Topographic variations that cause LCC can be determined by looking at the threshold in each land cover change class with its elevation class, using a mathematical approach to obtain an average value in each land cover change. The results showed that areas with low topography change more rapidly than those with high topography. The modeling results will also be validated with Land Cover data in 2020 by looking at changes from 2018 on topographic variations and whether they are following the predetermined threshold. The validation results show an overall accuracy value of 0.796. This study hopes it can be considered for policy making in development planning, especially in Java, Indonesia.

Identification of Land Cover Changes and its Impact on Biodiversity in Kalimantan Island

The land cover change that happens on Kalimantan Island has the biggest contribution to forest area reduction. High population density, clean water crisis, and land carrying capacity degradation in Java Island are among the reasons for the Indonesian National Capital City relocation from DKI Jakarta to East Kalimantan. New capital city development is inseparable from land acquisition activities which can escalate land conversion. The land cover change that is already occurring, coupled with the National Capital City (IKN) development plan, are threats to biodiversity in Kalimantan. To minimize the negative impacts, it is necessary to plan for flora and fauna conservation programs in Kalimantan. This study aims to identify land cover change patterns on biodiversity on Kalimantan and analyze suitable areas for conservation sites based on ecosystem services index, topography, and accessibility. In this study, biodiversity is represented by the ecosystem services index, while the land cover change pattern on biodiversity is represented by changes in the ecosystem services index for biodiversity. Analysis of suitable areas for conservation is determined based on several criteria, (1) very high and high class of ecosystem service indexes, (2) located at more than 9 km from arterial and collector roads, and (3) considering the adjacent grid area. The ecosystem services index in high, and moderate classes has decreased since 1990 – 2020, and land cover changes that dominate this change occur in the conversion of forest class to dryland agriculture, plantations, and other vegetation with lower ecosystem service values.

Identification of land cover change and spatial distribution based on slope variation in java island

The phenomenon of land cover change in Java Island is an important issue today because of its impact on the environment and human life. Rapid population growth in Java is one of the causes of land conversion. However, industrialization, urbanization and agricultural intensification are also major factors in land cover change. Java Island has land slope characteristics that vary from 0° to 30°. The large amount of land conversion that occurs in areas with steep slopes causes many landslides and floods in Java. The methodology used in this research is spatial analysis to identify land cover change in Java Island in 2000-2018 based on its land slope. Meanwhile, to determine the pattern of land cover change in Java Island, identification was carried out by dividing it into 3 periods with an interval of 6 years. Each land cover class that changes is determined by the land slope threshold with average central tendency statistical analysis. Each area of change that occurs in a class within its land slope class is averaged to get a reference value for its land slope threshold. Land cover change in Java Island was visualized in 3D based on land slope using Rstudio. The results showed that the phenomenon of land cover change on the island of Java is dominant on a gentle slope, which is around 0°-3°. Forest and wetland agriculture experienced the largest decrease in area. While the built-up area class experienced the largest area increase of 404300 Ha or 35.37%. Overall accuracy of this research is 73% of 400 sample points randomly scattered on the island of Java.

Identification of land cover changes before and after forest and land fires in conservation areas of North Sumatra, Indonesia

Damage to conservation areas in North Sumatra can be mitigated by understanding the pattern of land cover change, which can be performed by detecting hotspots and their temporal and spatial patterns. This study aimed to explore land cover spatially and temporally before and after forest fires in the conservation areas in North Sumatra. Data on the hotspots, satellite imagery, land cover maps, and field verification were used to see the transition of land cover changes before and after forest and land fires. Temporal and spatial analysis was employed to see the trend of land cover changes of the land before and after the fires. Field verification was conducted through observations and interviews related to land cover conditions in the field caused by forest and land fires. This study found three conservation areas with the highest number of hotspots in 2014 in the period 2001-2019, including Gunung Leuser National Park (GLNP), Dolok Surungan Wildlife Reserve, and Barumun Wildlife Reserve during the 2001-2019 period. In 2010 and 2014 there were strong indications of a large area of land burning in three conservation areas. The burned land was near the outer boundary of the conservation areas and continued to expand into the areas over time. The area of the non-forest cover was relatively stable between periods prior to the discovery of several hotspots. Changes in forest cover to non-forest have become more widespread after the highest number of hotspots were detected in 2010. Conservation area management and various parties need to prevent the expansion of forest clearing considering the strategic role especially in GNLP as a world heritage.

Identification of Land Cover Changes in The MK-PHR Conservation Area Associated With Fire Events

Peat fire is one of the most environmental issues in Indonesia, particularly in the region of South Sumatra. The fires usually occur during the dry season from Juni to November. This study is designed to evaluate the impact of peat fires on changes in land cover in the Merang Kepahyang Peat Hydrological Region (MK-PHR) for the period of 2002 to 2015. The changes identified by the Normalized Difference Vegetation Index (NDVI) method using Landsat satellite imagery. The results of the study show that extreme peat fire associated with climate anomalies events occurred in 2006, 2012, and 2015. The largest peat fires during the observation period occurred in 2015 coincided with extreme climate anomalies generating negative changes in land cover in the most of MK-PHR areas. The changes occurred in almost half (49%) of the MK-PHR area, where it moved from the dense to moderate vegetation class. The spatial and temporal land cover changes will be discussed in more detail.

Multiscale remote sensing of urbanization in Ho Chi Minh city, Vietnam - A focused study of the south

This study focuses on monitoring and mapping the urbanization process in the south of Ho Chi Minh City, Vietnam. Among the most vulnerable cities worldwide due to climate change, Ho Chi Minh city is experiencing the fast urbanization in last decades, especially in the south, a wetland area. The severe environmental impact such as flood has occurred more frequently implying the critical infrastructures development is far behind the expansion of newly built-up residences and hence, growing population. To what extent, the newly built-up areas have contributed to accelerating the environmental impacts in the context of climate change? The aim of this study is twofold. Firstly, we experiment three different levels of spatial resolution, sub-meter, 2.5 m and 10–15 m in mapping the landcover changes of a selected typical fast urbanization area in 2000–2010. With all about 80% classification accuracy achievement, we found that sub-meter resolution is the best for a reliable identification of land cover changes in such complex and fast-growing built-up areas whereas 2.5 m resolution is sufficient to identify the extent of built-up areas and 10–15 m is unsuitable. We also confirmed a consistent expansion, over 25%, of built-up area in our study area, using three different resolutions in spite of small differences in other landcover types. Secondly, in comparison of ground data, we revealed that most of the newly built-up areas in 2000–2010 were built on the higher land and no flood incident was recorded obviously. However, the nearby lowland experienced more floods due to the changes. A slight increase of sea level and the sinking of the city due to the groundwater extraction may also contribute to more flood incidents. Remote sensing proved to be effective in revealing the temporal urbanization and its possible consequences. However, a holistic approach to investigation needs to be developed to better understand the contribution of anthropogenic activities to environmental impacts in the climate change context.

Identification of land cover changes in the coastal area of Dakshina Kannada district, South India during the year 2004–2008

This study investigates land cover (LC) changes in the coastal area of Dakshina Kannada district in the state of Karnataka, South India, during the years 2004–2008 as a case study. IRS P-6, Linear Imaging Self Scanning sensor (LISS-IV) satellite images were used in the present work. Classification was carried out using artificial bee colony algorithm and support vector machine (SVM) which gave a better result compared to other traditional classification techniques. The best overall classification accuracy for the study area was achieved with an ABC classifier with an OCA of 80.35% for 2004year data and OCA of 80.40% for 2008year data, whereas the OCA in SVM, for the same training set is 71.42% for 2004 data and 71.38% for 2008 data on study area 1 and the results were optimised with respect to multispectral data. In study area 2, ABC algorithm achieved an OCA of 78.17% and MLC of 62.63% which was used to check the universality of the classifier. The classification results with post-classification technique for study area 1 indicate that urbanisation in the study area has almost increased twice. During the same time there is an increase in the forest plantation, agricultural plantation and a decrease in crop land and land without scrubs, indicates rapid changes in the coastal environment.

An improved semiautomatic segmentation approach to land cover mapping for identification of land cover change and trend

Worldwide, land cover change is monitored by conventional land cover mapping techniques using satellite imagery. Index method ends with assigning positive values to indicate vegetation, wetland and built-up area. However, not all positive values up to a certain threshold specify desired land cover and might indicate other covers erroneously. Therefore, a threshold value must be determined above which land covers are mapped more accurately. In this research, we employed an improved land cover mapping technique to extract vegetation, wetland and built-up area using semiautomatic segmentation approach. We used double-window flexible pace search technique not only for built-up features but also for vegetation and wetland mapping to increase the accuracy. Study is based on Landsat Thematic Mapper images of 1989, 1999 and 2010 with spatial resolution of 30 m. Integration of simple recoding of derived index images prior to threshold identification entails increased accuracy. Accuracy assessment of land cover mapping is done using high-resolution Google Earth satellite image which substitutes expensive aerial photography and time-consuming ground data collection. Error matrix presents 82.46, 96.83 and 90 % user’s accuracy of mapping built-up area, vegetation and wetland, respectively. Trend analysis discloses an average loss of vegetation and wetland by 2,664.6 and 5,328.8 acres per year, respectively, in study area from 1989 to 2010. Expectantly, future land cover mapping in similar researches will be greatly assisted with the diligent technique used in this study.

Identification of potential land cover changes on a continental scale using NDVI time-series from SPOT VEGETATION

The identification of land cover changes on a continental scale is a laborious and time-consuming process. A new methodology is proposed based exclusively on SPOT VGT data, illustrated for the African Continent using GLC2000 as reference to select 26 distinct land cover types (classes). For each class, the normalized difference vegetation index (NDVI) time-series are extracted from SPOT VGT images and a hierarchical aggregation is done using two different methods: one that preserves the initial signatures throughout the hierarchical process, and another that recalculates the signatures for each aggregation level. The average classification agreement was above 89% using 26 classes. Reducing the number of classes improves classification agreement. In order to study the influence of temporal variability in the classification results, the methodology was applied on data from 1999, 2001, 2008, and 2010. With 26 classes, the best average classification agreement obtained was 94.5% with annual data, against 74.1% with interannual data.

Characterizing fragmentation of the collective forests in southern China from multitemporal Landsat imagery: A case study from Kecheng district of Zhejiang province

Tropical and subtropical forests provide important ecosystem goods and services including carbon sequestration and biodiversity conservation. These forests are facing increasing socioeconomic pressures and are rapidly being degraded and fragmented. This analysis focuses on the rate of change and patterns of fragmentation in a collective forest area in Zhejiang province, China, during the time period 1988–2005. The research consisted of two parts. The first was the development of general land cover maps and the identification of land cover changes by interpreting Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) time series imagery. The second part involved the computation and analysis of forest fragmentation metrics. For this portion of the study, fragmentation statistics were analyzed, and images were developed to depict forest fragmentation patterns and trends. Results revealed that there was a net loss of 7.8% in forest coverage, dropping from 66.8% in 1988 to 59.0% in 2005, primarily caused by agricultural expansion and poor forest management practices. An acceleration of forest fragmentation was also witnessed during the time intervals, which was evidenced by a decreasing trend in interior forest (57.2% in 1988, 55.0% in 1996 and 54.8% in 2005 respectively) coupled with the scales of the selected geospatial metrics. Continued forest loss and fragmentation are closely correlated with the existing political, educational, institutional and economic processes of contemporary China. To unlock the developmental potentials of the collective forests and to effectively mitigate the rate of forest loss and fragmentation, reforms of forest tenure and ecological immigration practices are recognized as a prospective alternative. The produced fragmentation maps further illustrates the importance of assessing landscape change history, especially the spatiotemporal patterns of forest fragments, when developing landscape level plans for biodiversity conservation, land use management and ecologically sustainable forestry.

Land cover changes in small catchments in Slovakia during 1990–2006 and their effects on frequency of flood events

Statistical approach to the analysis of the relationship between the frequency of flood events and land cover (LC) changes in small catchments of Slovakia is presented in this paper. The data for identification of LC changes were taken from the 1990 and 2006 CORINE LC (CLC) data layers. They were derived by computer-aided visual interpretation of satellite images under the CLC Projects. The data about frequency of flood events in small catchments are from the period 1996–2006. Two hypotheses were formulated: (1) the greater the area of LC changes, the more frequent flood events; (2) in catchments where LC changes accelerating formation of direct runoff (e.g. urbanization, deforestation, farming) dominates, flood events are more frequent than in catchments where the prevailing LC changes (e.g. afforestation) reduce formation of direct runoff. Validity of hypotheses was tested in the framework of flood potential of catchments by two-factor ANOVA method. The obtained results indicate that (1) flood event frequency increases with the increasing total area of LC changes in a catchment. This tendency clearly manifests itself in catchments with very high flood potential. It is somewhat less distinct in catchments with moderate and high flood potentials. (2) There were no differences in flood event frequency between the group of catchments, where LC changes accelerating the formation of the direct runoff prevailed and the group of catchments where LC changes decelerating the formation of direct runoff were dominated.

Identification of Land-Cover Changes Through Image Processing and Associated Impacts on Water Reservoir Conditions

A temporal assessment of land-cover changes of the province Beykoz in Istanbul has been documented in this article. The study focuses on the acquisition and analysis of LANDSAT 5 TM images that reflect the drastic land-cover changes between the years 1984 and 2001 utilizing aerial photographs, orthophoto maps, standard topographic maps, and ground truth measurements. The status of the province, including its surrounding villages, were examined together with more specialized maps showing only the city center and part of the province that lies within the watershed of the Elmali Drinking Water Reservoir. The land distribution profiles were also calculated for each of the images to help authorities and decision-makers to better understand the main causes of such remarkable changes and to inform them of the changing quality conditions of the reservoir. Rapid, uncontrolled, and illegal urbanization accompanied by insufficient infrastructure has caused degradation of forests and barren lands in the province, especially within the past two decades. The proximity of the province to the reservoir's watershed, downtown Istanbul, and the transportation network has accelerated the land-cover changes whose adverse impacts on the reservoir water quality are sensed. It is intended that the data gathered and processed in this study will provide a basis for future sustainable urban planning and management activities.

Detection of deforestation and land conversion in Rondônia, Brazil using change detection techniques

Fires associated with tropical deforestation, land conversion and land use greatly contribute to emissions as well as the depletion of carbon and nutrient pools. The objective of this research was to compare change detection techniques for identifying deforestation and cattle pasture formation during a period of early colonization and agricultural expansion in the vicinity of Jamari, Rondônia. Multi-date Landsat Thematic Mapper (TM) data between 1984 and 1992 were examined in a 94 370 ha area of active deforestation to map land cover change. The tasselled cap (TC) transformation was used to enhance the contrast between forest, cleared areas and regrowth. TC images were stacked into a composite multi-date TC and used in a principal components (PC) transformation to identify change components. In addition, consecutive TC image pairs were differenced and stacked into a composite multi-date differenced image. A maximum likelihood classification of each image composite was compared for identification of land cover change. The multi-date TC composite classification had the best accuracy of 0.78 (kappa). By 1984, only 5% of the study area had been cleared, but by 1992, 11% of the area had been deforested, primarily for pasture, and 7% lost due to hydroelectric dam flooding. Finally, discrimination of pasture versus cultivation was improved due to the ability to detect land under sustained clearing opposed to land exhibiting regrowth with infrequent clearing.

Impacts of vegetation dynamics on the identification of land-cover change in a biologically complex community in North Carolina, USA

A land-cover (LC) change detection experiment was performed in the biologically complex landscape of the Neuse River Basin (NRB), North Carolina using Landsat 5 and 7 imagery collected in May of 1993 and 2000. Methods included pixel-wise Normalized Difference Vegetation Index (NDVI) and Multiband Image Difference (MID) techniques. The NDVI method utilized non-normalized (raw) imagery data, while the MID method required normalized imagery. Image normalization techniques included both automatic scattergram-controlled regression (ASCR) and localized relative radiometric normalization (LRRN) techniques. Change/no-change thresholds for each method were optimized using calibration curves developed from reference data and a series of method-specific binary change masks. Cover class-specific thresholds were derived for each of the four methods using a previously developed NRB-LC classification (1998–1999) to support data stratification. An independent set of accuracy assessment points was selected using a disproportionate stratified sampling strategy to support the development of error matrices. Area-weighted conditional probability accuracy statistics were calculated based on the areal extent of change and no change for each cover class. All methods tested exhibited acceptable accuracies, ranging between 80% and 91%. However, change omission errors for woody cover types were unacceptably high, with values ranging between 60% and 79%. Overall commission errors in the change category were also high (42–51%) and strongly affected by the agriculture class. There were no significant differences in the Kappa coefficient between the NDVI, MID ASCR, and LRRN normalization methods. The MID non-normalized method was inferior to both the NDVI and MID ASCR methods. Stratification by major LC type had no effect on overall accuracies, regardless of method.

An investigation of uncertainty in field habitat mapping and the implications for detecting land cover change

Land cover data for landscape ecological studies are frequently obtained by field survey. In the United Kingdom, temporally separated field surveys have been used to identify the locations and magnitudes of recent changes in land cover. However, such map data contain errors which may seriously hinder the identification of land cover change and the extent and locations of rare landscape features. This paper investigates the extent of the differences between two sets of maps derived from field surveys within the Northumberland National Park in 1991 and 1992. The method used in each survey was the ‘Phase 1’ approach of the Nature Conservancy Council of Great Britain. Differences between maps were greatest for the land cover types with the smallest areas. Overall spatial correspondence between maps was found to be only 44.4%. A maximum of 14.4% of the total area surveyed was found to have undergone genuine land cover change. The remaining discrepancies, equivalent to 41.2% of the total survey area, were attributed primarily to differences of land cover interpretation between surveyors (classification error). Differences in boundary locations (positional error) were also noted, but were found to be a relatively minor source of error. The implications for the detection of land cover change and habitat mapping are discussed.