Land Cover Changes in Oil-Spill Affected Area: A Case Study in Pulau Rambut Wildlife Sanctuary

Introduction Although numerous land cover datasets can act as references for understanding land cover change in China, the inconsistencies between the datasets can also provide understanding. Previous studies on the consistency between land cover datasets have mostly focused on land cover type consistencies and have ignored data consistencies in land cover change. Outcomes Therefore, we aim to analyse the consistencies in land cover changes through likelihood assessment methods. We compared the spatiotemporal changes in forest, grassland, cropland, and bare land in the Climate Change Initiative land cover dataset (CCI-LC), Moderate-resolution Resolution Imaging Spectroradiometer land cover dataset (MCD12Q1), China’s National Land Use and Cover Change (CNLUCC), Globeland30 and Global Land Cover Fine Surface Covering 30 (GLC-FCS30) datasets in 2010. The results showed that the percentages and changes in each land cover type in MCD12Q1 were different from those in the other datasets. Discussion For example, the proportion of grassland in MCD12Q1 was the highest, reaching 48.04%. The places with high consistency were the places where the land cover types were concentrated, and the bare land had the highest consistency. However, the consistency of China’s land cover change was quite low, and the percentage of low consistency was more than 87% from 2000-2018. Comparison of the data with the global artificial impervious area (GAIA) and Hansen-Global Forest Change (Hansen-GFC) datasets showed that the percentage of high construction gain consistency (38.83%) was higher than the forest change consistency, and the percentage forest loss high consistency (8.85%) was lower than the forest gain high consistency (12.76%). Conclusion The results not only provide a basis for the use of land cover datasets but also give a clearer understanding of the pattern of land cover changes.

Characterization of climate uncertainties due to different land cover maps in regional climate models is essential for adaptation strategies. The spatiotemporal heterogeneity in surface characteristics is considered to play a key role in terrestrial surface processes. Here, we quantified the sensitivity of model results to changes in land cover input data (GlobCover 2009, GLC 2000, CCI, and ECOCLIMAP) in the regional climate model (RCM) COSMO-CLM (v5.0_clm16). We investigated land cover changes due to the retrieval year, number, fraction and spatial distribution of land cover classes by performing convection-permitting simulations driven by ERA5 reanalysis data over Germany from 2002 to 2011. The role of the surface parameters on the surface turbulent fluxes and temperature is examined, which is related to the land cover classes. The bias of the annual temperature cycle of all the simulations compared with observations is larger than the differences between simulations. The latter is well within the uncertainty of the observations. The land cover class fractional differences are small among the land cover maps. However, some land cover types, such as croplands and urban areas, have greatly changed over the years. These distribution changes can be seen in the temperature differences. Simulations based on the CCI retrieved in 2000 and 2015 revealed no accreditable difference in the climate variables as the land cover changes that occurred between these years are marginal, and thus, the influence is small over Germany. Increasing the land cover types as in ECOCLIMAP leads to higher temperature variability. The largest differences among the simulations occur in maximum temperature and from spring to autumn, which is the main vegetation period. The temperature differences seen among the simulations relate to changes in the leaf area index, plant coverage, roughness length, latent and sensible heat fluxes due to differences in land cover types. The vegetation fraction was the main parameter affecting the seasonal evolution of the latent heat fluxes based on linear regression analysis, followed by roughness length and leaf area index. If the same natural vegetation (e.g. forest) or pasture grid cells changed into urban types in another land cover map, daily maximum temperatures increased accordingly. Similarly, differences in climate extreme indices are strongest for any land cover type change to urban areas. The uncertainties in regional temperature due to different land cover datasets were overall lower than the uncertainties associated with climate projections. Although the impact and their implications are different on different spatial and temporal scales as shown for urban area differences in the land cover maps. For future development, more attention should be given to land cover classification in complex areas, including more land cover types or single vegetation species and regional representative classification sample selection. Including more sophisticated urban and vegetation modules with synchronized input data in RCMs would improve the underestimation of the urban and vegetation effect on local climate.

The study aimed to assess the changes that have occurred in land use and land cover within the Maasai Mara landscape using remote sensed data from 1997 to 2017; examine the elephant distribution in relation to land use and land cover changes within the Mara landscape and to determine changes in elephant home ranges in relation to Land use and cover changes in the Mara landscape. In examining the land use and land cover changes on the elephant ranges and distribution, an integrated methodological approach was employed in which the changes that have taken place within the study area over a period of 20 years was determined by analysis involving a 10-year changes in land use and land cover using three epochs from 1997, 2007 and 2017 to generate six land use classes. The Maasai Mara Landscape (MML) supports one of the richest wildlife populations remaining on earth but over the last century, has experienced transformation notably through conversion of former rangelands into croplands. Elephants have both temporal and spatial requirements, which if not provided, render them vulnerable to the land-use practices. The study assessed land use and vegetation cover changes that have occurred and their effects on the elephant movements and distribution within the MML using an integrated methodological approach. The analysis revealed changes in land use and land cover classes over a period of 20 years for the three epochs, from 1997, 2007 and 2017. Elephant’s distribution has been restricted to areas of high vegetation densities within specific habitats hence accelerating the rate of habitat destruction and degradation due to their high densities. These changes have drastically reduced forage for elephants necessitating them to travel longer distances out of their home range in search for food. Human beings have caused land use and cover changes which have detrimental impacts on the ecosystem and ecosystem services. The Maasai Mara landscape supports one of the richest wildlife populations remaining on earth but over the last century, it has experienced land transformation notably through conversion of former rangelands used mainly for tourism and production of grains such as wheat. Land outside the national parks and the reserve is important to the future of elephant existence in Kenya. Little is known about how human occupation on these landscapes negatively affects elephants (Loxodonta africana) habitats, movement and ranges. This has been confirmed by the current continuous demarcation/fencing of land in most areas in Narok County. Elephants like other landscape species, have both temporal and spatial requirements, which if not provided, will render them vulnerable to the land use practices of people. The study aimed to assess the changes that have occurred in land use and land cover within the Maasai Mara landscape using remote sensed data from 1997 to 2017; examine the elephant distribution in relation to land use and land cover changes within the Mara landscape and to determine changes in elephant home ranges in relation to Land use and cover changes in the Mara landscape. The paper describes the different changes that have taken place within the MML and how these changes have affected elephant populations, their trend and distribution within the MML. In examining the land use and land cover changes on the elephant ranges and distribution, an integrated methodological approach was employed in which the changes that have taken place within the study area over a period of 20 years was determined by analysis involving a 10-year changes in land use and land cover using three epochs from 1997, 2007 and 2017 to generate six land use classes. The study found out that there were significant changes of various classes across the years. Forest, water and open shrubs coverages decreased from 1997 to 2017. Classification noted a serious problem within the study area of continuous increase of bare ground coverage across the study years. Elephant populations have been increasing within the area .at an annual rate of 2.69%. The animals are distributed all over the landscape. Distribution of elephants has been restricted to high densities within a specific habitat hence accelerating rate of habitat destruction and degradation due to their high densities within a specific habitat. These changes have reduced drastically foliage for elephants thus necessitating them to travel longer distances in search and as a result increases elephant home ranges.

The European avifauna on agricultural land has been permanently diminished over the past few decades. This phenomenon is clearly connected with agricultural intensification and the recent land cover changes. The main aims of this study were to identify the land cover preferences of a farmland bird species, the Eurasian Skylark Alauda arvensis in Hungary and investigate the link between the recent trend of the abundance of this species and the land-cover change. We employed GIS and statistical methods to assess the link between the abundance of this species based on the Hungarian common bird monitoring database (MMM) and the spatial proportion of the Corine Land Cover (CLC) categories in different buffer zones with 300, 600 and 1200 m from the observation points. Based on the significant statistical connections, we could identify and select land cover categories that serve as habitats and land cover categories that this bird species does not inhabit. The land cover preference of the Eurasian Skylark, in case of some land cover category, is depending on the grain scale (circle radius distance from the observation points). In analyses arable lands has been omitted because this land cover type is the well-known habitat of the species. According to our results, the Eurasian Skylark prefers permanent crops (vineyards, fruit trees and berry plantations) inside 600m and 1200m buffer zones, and pastures inside 1200m buffer zones, while it does not prefer urban fabric areas and heterogeneous agricultural, forests, and wetlands or water bodies inside 300m and 600m, scrub and/or herbaceous vegetation associations (transitional woodland-shrub and natural grassland areas) inside the 600m and 1200m radius buffer areas. The identification of these regional (European level) land-cover categories allowed us to analyse the recent (1990-2012) and the predicted (2006-2050) characteristics of habitat changes of this bird species, associated with land cover change. Based on our results, we could estimate that the Skylark habitat will decrease by 188 560 ha between 2006-2050 in Hungary.

Identification of land use and land cover in forest areas can be challenging due to various land cover types within a forest can be similar, making it hard to differentiate between them using remote sensing techniques. We hypothesized that random forest classification (RF) would outperform maximum likelihood (ML) in the classification of land use and land cover (LULC) in forest areas compared to maximum likelihood (ML). To verify this hypothesis, we conducted a comparative analysis, assessing the accuracy of RF and ML in the classification of LULC within the Taman Negara Endau-Rompin (TNER) region, utilizing Landsat 8 imagery. An accuracy assessment demonstrated that the RF classifier (overall accuracy: 87% kappa coefficient: 0.778, performed better than ML classifying land cover (overall accuracy 77% with kappa accuracy: 0.473) Our results suggest that both methods are able to classify land cover of forest area, but RF is more accurate than ML. From the classification result of RF classification, we calculate the land cover changes of TNER from 2013 to 2022. results showed that there are small changes of forest area were found in TNER. The total forest area decreases from 163250.089 ha to 144765.46 ha during 2013 to 2022. This finding suggests that the effectiveness of the protected area in mitigating deforestation in its surrounding regions may be somewhat limited, as indicated by the observed minor changes.

The European avifauna on agricultural land has been permanently diminished over the past few decades. This phenomenon is clearly connected with agricultural intensification and the recent land cover changes. The main aims of this study were to identify the land cover preferences of a farmland bird species, the Eurasian Skylark Alauda arvensis in Hungary and investigate the link between the recent trend of the abundance of this species and the land-cover change. We employed GIS and statistical methods to assess the link between the abundance of this species based on the Hungarian common bird monitoring database (MMM) and the spatial proportion of the Corine Land Cover (CLC) categories in different buffer zones with 300, 600 and 1200 m from the observation points. Based on the significant statistical connections, we could identify and select land cover categories that serve as habitats and land cover categories that this bird species does not inhabit. The land cover preference of the Eurasian Skylark, in case of some land cover category, is depending on the grain scale (circle radius distance from the observation points). In analyses arable lands has been omitted because this land cover type is the well-known habitat of the species. According to our results, the Eurasian Skylark prefers permanent crops (vineyards, fruit trees and berry plantations) inside 600m and 1200m buffer zones, and pastures inside 1200m buffer zones, while it does not prefer urban fabric areas and heterogeneous agricultural, forests, and wetlands or water bodies inside 300m and 600m, scrub and/or herbaceous vegetation associations (transitional woodland-shrub and natural grassland areas) inside the 600m and 1200m radius buffer areas. The identification of these regional (European level) land-cover categories allowed us to analyse the recent (1990–2012) and the predicted (2006–2050) characteristics of habitat changes of this bird species, associated with land cover change. Based on our results, we could estimate that the Skylark habitat will decrease by 188 560 ha between 2006–2050 in Hungary.

Monitoring of land use and land cover change using remote sensing is important to evaluate the impacts of anthropogenic activities on the environment. Digital change detection using post-classification can help to elucidate dynamics of landscape change. This study illustrates the effectiveness of object-oriented classification compared to pixel-oriented classification in generating land cover information and its temporal changes. Spatio-temporal dynamics of land cover types in Vientiane area, Lao PDR were analyzed using Landsat images in two-time series (1990 and 2015). We used the top-down approach to classify the Landsat images in iterative steps with three hierarchical scale levels. Scale levels of 25, 10 and 5 with different weighting parameters were used to map the land cover type of Vientiane in 1990 and 2015. With object-oriented classification, overall accuracy and Kappa statistic were improved by 13.44% and 0.16 for land cover classification (LCC) 1990. For LCC 2015, the improvements in overall accuracy and Kappa statistic were 28.71% and 0.25. Based on the LCC 1990 and 2015, we observed an significant growth of plantation areas over the 25 years in the study area . Instead of traditional agricultural activity, the plantation seemed to be the new driver in the rural areas of Lao PDR. The object-oriented classification approach can be applied in other areas of Lao PDR to generate accurate information on land cover changes for better land resource management.

Monitoring land cover changes from the 1970s in West Africa is important for assessing the dynamics between land cover types and understanding the anthropogenic impact during this period. Given the lack of historical land cover maps over such a large area, Landsat data is a reliable and consistent source of information on land cover dynamics from the 1970s. This study examines land cover changes occurring between 1975 and 1990 in West Africa using a systematic sample of satellite imagery. The primary data sources for the land cover classification were Landsat Multispectral Scanner (MSS) for 1975 and Landsat Thematic Mapper (TM) for the 1990 period. Dedicated selection of the appropriate image data for land cover change monitoring was performed for the year 1975. Based on this selected dataset, the land cover analysis is based on a systematic sample of 220 suitable Landsat image extracts (out of 246) of 20 km × 20 km at each one degree latitude/longitude intersection. Object-based classification, originally dedicated for Landsat TM land cover change monitoring and adapted for MSS, was used to produce land cover change information for four different land cover classes: dense tree cover, tree cover mosaic, other wooded land and other vegetation cover. Our results reveal that in 1975 about 6% of West Africa was covered by dense tree cover complemented with 12% of tree cover mosaic. Almost half of the area was covered by other wooded land and the remaining 32% was represented by other vegetation cover. Over the 1975–1990 period, the net annual change rate of dense tree cover was estimated at −0.95%, at −0.37% for the other wooded land and very low for tree cover mosaic (−0.05%). On the other side, other vegetation cover increased annually by 0.70%, most probably due to the expansion of agricultural areas. This study demonstrates the potential of Landsat MSS and TM data for large scale land cover change assessment in West Africa and highlights the importance of consistent and systematic data processing methods with targeted image acquisition procedures for long-term monitoring.

We successfully used optical remote sensing approach to test the skills of post-classification change detection technique as well as techniques of circumventing the challenges of cloud/cloud-shadow contamination and of working in a data-scarce environment in tropical humid highlands. The aim was to generate an accurate estimate of current land cover distribution map and analyze land-cover change around Ndakaini area in Kenya. Landsat imageries (TM and ETM + ) acquired between 1985 and 2011 and corresponding to the study area was selected. Employing bands 3 and 4 of respective Landsat images, thresholding techniques, Boolean and masking operations were implemented in detecting cloud/cloud-shadows and subsequent removal and filling of gaps. In absence of other historical ancillary data about land cover types, a total of 278 points across the study area were captured from Google Earth and used to evaluate the accuracy of each of the generated land cover maps. From the results, cloud/cloud-shadow gaps were reduced immensely (e.g. 90% for the 1985 image and 82% for the 2011 image). With regard to quality of classification outputs, the respective land cover/land-use maps of 2000, 2005 and 2010 anniversaries had fairly high level of overall accuracy (64%, 79% and 68% respectively) and Kappa statistic (0.47, 0.69 and 0.53 respectively) while classification outputs of 1985 and 1995 yielded slightly lower overall accuracy (60%) and Kappa statistic (0.42). Post-classification change involving three land cover classes, tea plantation, forest/woodlot and annual crop fields denoted as others were successfully determined and conclusions based on trend analysis drawn. The satisfactory results of this study imply the usefulness of post-classification change detection method in generating information about land cover dynamics in tropical humid highlands especially when coupled with robust techniques that adequately circumvent the cloud and cloud-shadow problem and scarcity of ancillary data often common in these areas. Keywords : Post-classification change detection, thresholding and Boolean techniques, landcover change, tropical humid-highlands DOI: 10.7176/JNSR/10-8-03 Publication date: April 30 th 2020

Nowadays it's still very difficult to find accurate information on land-cover areas and types, which mainly results from the confusion between land use types and land cover types (e.g., many researchers equated land cover with land use and land use types were often employed for the replacements of land cover types) and the absence of a standard land cover classification system with an unambiguous, repeatable definition of land cover and quantificational classification criteria so as to the classification result comparable. In this study, a new logic for land cover classification at regional scale has been introduced. The critical features of this classification are that: it's indeed distinguished from land use classification system and driven by remote sensing so that repeatable and efficient re-classifications of existing land cover will be possible; spectrum and primary attributes of plant-canopy structure (i.e. permanence of aboveground live biomass, leaf longevity and leaf type) are adopted as the primary criterions of land cover classification; based on the phonological difference among broadly defined vegetation, some typical land cover is easily distinguished by using the characteristics of seasonal dynamic; mixed land cover is differentiated by its constituent characteristics and influence on land surface processes. Taking the areas between Yangtze River Basin and Weihe River Basin in China as a case and using time-series MODIS 250 m data (i.e. NDVI and reflectance), a two-level hierarchical land cover classification scheme was produced for the areas. At the initial stage, the entire study area was mapped into seven classes, i.e. evergreen cover (woody), seasonal green cover (woody), seasonal green cover (herbaceous), seasonal green cover (crops), seasonal green cover (mixed), grey cover (non-vegetated and terrestrial) and blue cover (aquatic or regularly flooded). The sub-classes includes Coniferous evergreen forest, Broadleaf deciduous forest, Single cropping in one year, Continuous double cropping in one year, grassland, Wetland, Urban or Built-up land, Barren or Sparsely land, River, Lake, Mixed Cover of Crop and tree, etc.

<p>The spatio-temporal heterogeneity in surface characteristics is considered to play a key role in terrestrial surface processes. Its characterization is essential for adaptation strategies. Here, we conducted regional climate simulations with COSMO-CLM (v5.0_clm16) with different land cover input data driven by ERA5 reanalysis over Germany at convection-permitting horizontal resolution of 3 km from 2000 to 2011. The difference between the land cover data of GLC2000, CCI_LC and ECOCLIMAP and the operational used GLOBCOVER2009 dataset on temperature and its extremes is investigated. The results reveal that the differences in turbulent fluxes and temperature are related to land cover classes. Even though the land cover class fractional differences are small among the land cover maps, some land cover types, such as croplands and urban areas, have greatly changed over the years. These distribution changes can be seen in the temperature differences. Simulations based on the CCI_LC retrieved in 2000 and 2015 revealed no accreditable difference in the climate variables as the land cover changes that occurred between these years are marginal, and thus, the influence is small over Germany. Increasing the land cover types as in ECOCLIMAP leads to higher temperature variability. The largest differences among the simulations occur in maximum temperature and from spring to autumn, which is the main vegetation period. The temperature differences seen among the simulations relate to changes in the leaf area index, plant coverage, roughness length, latent and sensible heat fluxes due to differences in land cover types. The vegetation fraction was the main parameter affecting the seasonal evolution of the latent heat fluxes based on linear regression analysis, followed by roughness length and leaf area index. If the same natural vegetation (e.g. forest) or pasture grid cells changed into urban types in another land cover map, daily maximum temperatures increased accordingly. Similarly, differences in climate extreme indices (e.g., SU or TR) are strongest for any land cover type change to urban areas. The uncertainties in regional temperature due to different land cover datasets were overall lower than the uncertainties associated with climate projections. Although the impact and their implications are different on different spatial and temporal scales as shown for urban area differences in the land cover maps. Thus, to realistically simulate land use/cover change effects on regional and local climate and draw conclusions for management strategies, numerical models would benefit from land surface characteristics, which are as accurate as possible in retrieval year, number of land cover classes, their distribution and fractions and have a high spatial resolution.</p>

Like many other Nigerian capital cities of newly created states in the 1990s, Gombe the capital city of Gombe State in the North-eastern geopolitical zone has experienced tremendous change in the land cover which is primarily driven by urbanisation. However, little is known on the extent of this urban expansion, how it has affected other land cover types or the dynamics of demographic change as a function of urban growth in the area. This spatial information is highly needed for effective planning and development. This paper, therefore, attempts to answer these questions by firstly, processing LandSat5 images of 1998 and Landsat 8 images of 2016 both of November of Gombe Local Government Area where the state capital is situated. Image processing is then carried out using the semi-automated classification plugin in QGIS 2.18. A supervised classification scheme was used to classify the 1998 and 2016 Landsat Image scenes into four land cover classes (water bodies, built-up area, bare-surface and vegetation) using the spectral angle mapping algorithm. Secondly, the paper analysed population data of Gombe Local Government Area for the same period to understand the dynamics of population change in the area. Based on the findings, urban land cover type increased from 13.02 Km2 (25.14% of the total land cover) in November 1998 to 25.98 km2 (50.17% of the entire land cover) in November 2016. As a result of this change in land cover, all other land cover types decreased in areal coverage. A Kappa index of 0.82 and 0.81 suggest that that the error margins during the supervised classification process of the 1998 and 2016 Landsat images are relatively small. The implications of rapid changes in land cover and population change in the area over a short period of 18 years on planning and management are also discussed.

Land use and land cover (LULC) changes have been one of the most important and persistent factors recently causing changes in the Earth’s land. The present study aimed to detect land use and land cover (LULC) changes in Baluchistan, in Southwestern Asia, which is shared by the three countries of Iran, Pakistan, and Afghanistan, using satellite remote-sensing products. To this end, the global land cover classification provided for a period of 13 years from 2001 to 2013 by the MODIS Land Cover Type product (MCD12Q1) was used. The changes and dynamics of different land cover classes were investigated using net change analysis and cross-tabulating matrix analysis methods. The net change analysis showed that the most area of Baluchistan is covered by the barren or sparsely vegetated land cover (about 82%) and the shrubland (about 16%) classes. The dynamics analysis of different land cover classes also indicated that there were almost mutually inverse relationships between the different land cover classes in Baluchistan. Such mutual relationships were most common between the following pair classes: shrublands—bare and non-vegetated lands; grasslands—bare and non-vegetated land classes; croplands—bare and non-vegetated lands classes; and shrublands—grasslands. The most unstable land cover classes in this territory were forests, Savannas, and grassland classes. Also, the analysis of land cover changes in the period 2001–2013 provided no clear and accurate evidence of desertification and land degradation at this spatial scale in Baluchistan.

Problem statement: It is an important task to detect land cover chang es from remotely sensed data for environmental monitoring. Although there are some applications of visual textures to the land use, they are limited to a few land cover categories with the application of one texture measure. Since land cover types are complex and oft en the integration of various objects, applying one texture measure to characterize land cover types is not possible. Approach: This study presented two types of texture measures for land cover types and applies them to detect possible land cover changes by discriminant analysis. The texture information o f land cover types were modeled by different texture extraction methods, Laws Masks and Gabor filters. Laws Masks were designed to characterize the features in local neighborhoods. Moreover infor mation in multi-channel of the spatial frequency domain was modeled by the Gabor filters with differ ent orientations and spatial periods. The performance of these texture measures to detect lan d cover changes were evaluated by the discriminant analysis. Based on the transition matrix of land co ver, the detection of land cover changes becomes to separate the land cover pair which is possible to d erive conversion between them. The discriminant analysis was designed on a statistical test, which determines the contribution of the features attendi ng the discrimination. Results: The experiments showed that this approach is capab le of detecting changes and different measures are suitable to dete ct different changes. Conclusion: The experiment presented a textural guide for the change detection .

Analysis of time series imagery from satellite and aircraft platforms is useful for detecting land cover change at plot to regional scales. In this study, we created multi-temporal high spatial resolution land cover maps for seven locations in the Beringian Arctic and assessed the change in land cover over time. Land cover classifications were site specific and mostly aligned with a soil moisture gradient. Time series varied between 60 and 21 years. Four of the five landscapes studied in Alaska underwent an expansion of drier land cover classes while the two landscapes studies in Chukotka, Russia showed an expansion of wetter land cover types. While a range of land cover types was present across the landscapes studied, the extent of shrubs (in Chukotka) and open water (in Alaska) increased in all landscapes where these land cover types were present. The results support trends documented for regional change in NDVI (a measure of vegetation greenness and productivity) as well as a host of other long term, experimental and modeling studies. Using historic change trends for each land cover type at each landscape, we use a simple probabilistic vegetation model to establish hypotheses of future change trajectories for different land cover types at each of the landscapes investigated. This study is a contribution to the International Polar Year Back to the Future project (IPY-BTF).