Land Use And Land Cover Categories Research Articles

Characterizing land use/land cover change dynamics by an enhanced random forest machine learning model: a Google Earth Engine implementation

Land use and land cover (LULC) analysis is crucial for understanding societal development and assessing changes during the Anthropocene era. Conventional LULC mapping faces challenges in capturing changes under cloud cover and limited ground truth data. To enhance the accuracy and comprehensiveness of the descriptions of LULC changes, this investigation employed a combination of advanced techniques. Specifically, multitemporal 30 m resolution Landsat-8 satellite imagery was utilized, in addition to the cloud computing capabilities of the Google Earth Engine (GEE) platform. Additionally, the study incorporated the random forest (RF) algorithm. This study aimed to generate continuous LULC maps for 2014 and 2020 for the Shrirampur area of Maharashtra, India. A novel multiple composite RF approach based on LULC classification was utilized to generate the final LULC classification maps utilizing the RF-50 and RF-100 tree models. Both RF models utilized seven input bands (B1 to B7) as the dataset for LULC classification. By incorporating these bands, the models were able to influence the spectral information captured by each band to classify the LULC categories accurately. The inclusion of multiple bands enhanced the discrimination capabilities of the classifiers, increasing the comprehensiveness of the assessment of the LULC classes. The analysis indicated that RF-100 exhibited higher training and validation/testing accuracy for 2014 and 2020 (0.99 and 0.79/0.80, respectively). The study further revealed that agricultural land, built-up land, and water bodies have changed adequately and have undergone substantial variation among the LULC classes in the study area. Overall, this research provides novel insights into the application of machine learning (ML) models for LULC mapping and emphasizes the importance of selecting the optimal tree combination for enhancing the accuracy and reliability of LULC maps based on the GEE and different RF tree models. The present investigation further enabled the interpretation of pixel-level LULC interactions while improving image classification accuracy and suggested the best models for the classification of LULC maps through the identification of changes in LULC classes.

An integrated hierarchical classification and machine learning approach for mapping land use and land cover in complex social-ecological systems

Mapping land use and land cover (LULC) using remote sensing is fundamental to environmental monitoring, spatial planning and characterising drivers of change in landscapes. We develop a new, general and versatile approach for mapping LULC in landscapes with relatively gradual transition between LULC categories such as African savannas. The approach integrates a well-tested hierarchical classification system with the computationally efficient random forest (RF) classifier and produces detailed, accurate and consistent classification of structural vegetation heterogeneity and density and anthropogenic land use. We use Landsat 8 OLI imagery to illustrate this approach for the Extended Greater Masai Mara Ecosystem (EGMME) in southwestern Kenya. We stratified the landscape into eight relatively homogeneous zones, systematically inspected the imagery and randomly allocated 1,697 training sites, 556 of which were ground-truthed, proportionately to the area of each zone. We directly assessed the accuracy of the visually classified image. Accuracy was high and averaged 88.1% (80.5%–91.7%) across all the zones and 89.1% (50%–100%) across all the classes. We applied the RF classifier to randomly selected samples from the original training dataset, separately for each zone and the EGMME. We evaluated the overall and class-specific accuracy and computational efficiency using the Out-of-Bag (OOB) error. Overall accuracy (79.3%–97.4%) varied across zones but was higher whereas the class-specific accuracy (25.4%–98.1%) was lower than that for the EGMME (80.2%). The hierarchical classifier identified 35 LULC classes which we aggregated into 18 intermediate mosaics and further into five more general categories. The open grassed shrubland (21.8%), sparse shrubbed grassland (10.4%) and small-scale cultivation (13.3%) dominated at the detailed level, grassed shrubland (31.9%) and shrubbed grassland (28.9%) at the intermediate level, and grassland (35.7%), shrubland (35.3%) and woodland (12.5%) at the general level. Our granular LULC map for the EGMME is sufficiently accurate for important practical purposes such as land use spatial planning, habitat suitability assessment and temporal change detection. The extensive ground-truthing data, sample site photos and classified maps can contribute to wider validation efforts at regional to global scales.

Spatiotemporal dynamics of land use and cropping systems in the young meghna estuarine floodplain

To explore long-term changes in land use and land cover (LULC), cropping systems, and driving forces of LULC change, a study was carried out in Subarnachar upazila under the Young Meghna Estuarine Floodplain in Bangladesh. For studying LULC change and cropping systems, images from Landsat (TM) of 1989, Landsat (ETM+) of 2000 and 2010, and Landsat (Oli) of 2019 were used. A focus group discussion (FGD) was conducted to gather information on actors driving LULC changes. In the study area, five LULC categories—cropland, accreted land, settlements, mangrove forests, and water bodies were found. It reveals that cropland increased by 0.20% annually, while water bodies, accreted land, and mangrove forest decreased by 0.29, 0.17, and 0.23%, respectively. Settlements grew relatively at a faster rate (0.49%). The major land conversions between 1989 and 2019 were cropland to settlement (10.2%), mangrove forest to settlement area (3.2%), and mangrove forest to cropland area (4.0%). Other transformations included the shifting of accreted land to cropland and water bodies to settlements. From 1989 to 2019, double cropland increased by 0.71% annually due to the conversion of single to double cropland. Increased population pressure, especially for those displaced by the threat of river erosion, was a major factor in the conversion of cropland and mangroves into settlements. Due to the high demand for food in the study area, mangrove forests were converted to cropland and single cropland to double cropland more rapidly. These findings will assist farmers, stakeholders, and planners in developing and implementing optimal land use planning and sustainable agricultural production strategies in the region.
 Ann. Bangladesh Agric. (2023) 27 (1) : 1-15

Spatiotemporal Land Use and Land Cover Changes and Associated Runoff Impact in Itaperuna, Brazil

The urban growth intricately linked to the hydrological cycle outlines a crucial dynamic in the environmental transformations of cities. Utilizing the InVEST (Integrated Valuation of Ecosystem Services and Tradeoffs) Urban Flood Risk Mitigation model, we conducted hydrological modeling to assess the impact of urbanization on land use and land cover (LULC) changes and their subsequent effects on runoff generation in Itaperuna, Brazil, spanning the years 2015 to 2020. The analysis, performed across 17 urban sub-basins, highlights rapid urban expansion, notably in sub-basins 3 and 7, reflecting the city’s spatial dynamics and growth. Significantly, sub-basin 3 exhibited a 7.42% increase in runoff production capacity. The study meticulously documents changes in six LULC categories—water bodies, urban area, exposed soil, forest, natural pasture, and grassland vegetation—revealing that urban growth has directly amplified surface runoff in specific sub-basins, thereby impacting water resource management and flood prevention. Emphasizing the urgency of environmental conservation, especially in deforested basins, the findings hold substantial importance for urban planners and local authorities, offering relevant insights for flood risk mitigation and water security. Future research directions may explore additional facets, including water quality, advanced hydrological models, impacts on biodiversity and society, socioeconomic assessments of preventative measures, public policy considerations, and monitoring systems.

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

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

Assessing the status and spatial-temporal dynamics of the Bamenda Mountains (BM), North West region of Cameroon.

Change in land use and land cover (LULC) contributes in worsening ecological issues. Studying the trends of change in land use is highly significant to deal with global climate change and sustainable development. The aim of this paper is to evaluate the spatial-temporal dynamics of LULC of the Bamenda Mountains (BM) in the North West region of Cameroon, over a period of 34 years (1988-2022) and predict 34 years (2022-2056) future land use scenario of this site using time series satellite imagery (MSS, TM, ETM+, and OLI-TIRS) and ancillary data and to comprehend the driving forces of land use/land cover change (LULCC). The trends of LULCC were quantified; LULC maps were derived by classifying time series satellite images. Six LULC categories were identified during the study period (1988-2022). The research revealed a significant LULCC of the BM which can be justified by increase in the human population observed in the study area and the desire to extend agricultural lands to sustain the growing population. Overall, cultivated area 5684 ha (10.47%), 10680 ha (19.57 %), and 15163 ha (27.78%) and built-up area 449 ha (0.83%), 996 ha (1.83%), and 3242 ha (5.94%) for the study years 1988, 2003, and 2022, respectively, were all on the increase throughout the study period at the expense of other land cover types. The predicted figures of 2056 showed a continuous reduction of montane forest and savanna: 2401.92 ha (4.40%) and 25,862.67 ha (47.39%), respectively. Bare area is expected to drop in 2056 (2905.92 ha (5.32%)). The above decrease, when compared to 2022 figures, represents a loss of 3.97%, 4.53%, and 0.57%, respectively. The losses observed are gained by built-up and cultivated land (5.72% and 3.39%, respectively), covering surfaces areas of 6364.89 ha (11.66%) and 17,008.56 ha (31.17%), respectively. The above findings suggest that population growth is likely the major menace to the natural environment. It is thus safe to say that substantial LULCC was observed throughout the study period and will undoubtedly continue if nothing is done. This necessitates urgent measures such as reforestation and afforestation, encouraging off-farm activities and even improving technologies to combat the rate of forest degradation of the BM. Additionally, rebuilding trust between the French and English Cameroons through dialogue is premodial, to end the curent conflictual civil war and lessen the landscape configuration in Bamenda.

100 Years of Land-Use and Land-Cover Data: What Has Been the Effect of Spatial Planning in Coastal Land-Use and Land-Cover Change?

The Sustainable Development Goals require us to rethink spatial planning policies’ effectiveness. This article proposes a reproducible method for assessing the effect of past planning practices and simulating future land-use and land-cover (LULC) changes with a Cellular Automata model. The originality of our approach is to systematically compare observed changes in LULC with the planning rules in force over almost a century of evolution. A quasi-exhaustive database was constructed at a very fine spatial resolution for the municipality of Portimão (Southern Portugal), including the location and changes of LULC categories, and the planning rules of the corresponding time period on nine dates between 1947 and 2018. The quantified measurement of the actual effect of planning rules enables us to identify other determinants of the evolution. Findings reveal that the policies established by the local government—which aimed to foster well-planned comprehensive urban areas—were not as effective as intended. The quantified discrepancies between planning recommendations and observed evolution help to simulate which LULC scenarios could be designed to reach the expected result in future planning policies. Our assessment method could be applied in other urban and tourist regions where land artificialization exerts strong pressure on the environment.

Improving Land Use/Cover Classification Accuracy from Random Forest Feature Importance Selection Based on Synergistic Use of Sentinel Data and Digital Elevation Model in Agriculturally Dominated Landscape

Land use and land cover (LULC) mapping can be of great help in changing land use decisions, but accurate mapping of LULC categories is challenging, especially in semi-arid areas with extensive farming systems and seasonal vegetation phenology. Machine learning algorithms are now widely used for LULC mapping because they provide analytical capabilities for LULC classification. However, the use of machine learning algorithms to improve classification performance is still being explored. The objective of this study is to investigate how to improve the performance of LULC models to reduce prediction errors. To address this question, the study applied a Random Forest (RF) based feature selection approach using Sentinel-1, -2, and Shuttle Radar Topographic Mission (SRTM) data. Results from RF show that the Sentinel-2 data only achieved an out-of-bag overall accuracy of 84.2%, while the Sentinel-1 and SRTM data achieved 83% and 76.44%, respectively. Classification accuracy improved to 89.1% when Sentinel-2, Sentinel-1 backscatter, and SRTM data were combined. This represents a 4.9% improvement in overall accuracy compared to Sentinel-2 alone and a 6.1% and 12.66% improvement compared to Sentinel-1 and SRTM data, respectively. Further independent validation, based on equally sized stratified random samples, consistently found a 5.3% difference between the Sentinel-2 and the combined datasets. This study demonstrates the importance of the synergy between optical, radar, and elevation data in improving the accuracy of LULC maps. In principle, the LULC maps produced in this study could help decision-makers in a wide range of spatial planning applications.

Valuation of Land-Use/Land-Cover-Based Ecosystem Services in Afghanistan—An Assessment of the Past and Future

Being one of the weakest economies in the world, livelihoods in Afghanistan remain highly dependent on local ecosystem services. However, the risk of ecosystem services degradation in Afghanistan over the past two decades has significantly increased, mainly due to rapid changes in land-use and land-cover (LULC). As such, policy makers must be able to estimate the impact of LULC changes on various ecosystem services. By utilizing GlobeLand30 land cover products for 2000, 2010 and 2020, and by adopting the value transfer method, this study assessed the ecosystem services value (ESV) changes in response to the changes of LULC in Afghanistan. Additionally, the dynamics of the land system (DLS) model was innovatively coupled with linear programming to predict likely scenarios of ESV changes by 2030. The predicted results were also validated against actual land cover and achieved a Kappa value of 0.78. The results showed that over the 20-year period, ecologically important LULC categories such as forest, water bodies and grassland were severely unstable and rapidly decreasing in scope. These LULC types were being threatened by agricultural, built-up and unused lands. During this period, we estimated a decrease in the total ESV from 161 billion USD in 2000 to 152.27 billion USD in 2020. About 92% of this decrease was shared by supporting and provisioning services. The simulated scenarios also showed that ESV will likely further decrease under Business-As-Usual (BAU), and Rapid Economic Development (RED) scenarios. Positively, an Environmental Protection (ENP) scenario is predicted, with a 4.5% increase in ESV by 2030. However, achieving this scenario requires the enforcement of strict environmental protection measures.

Impact of land-use land-cover datasets and urban parameterization on weather simulation over the Jakarta Metropolitan Area

Human-caused changes in land-use and land-cover (LULC) are most visible in metropolitan areas, when the majority of the land has been converted to urban land or built up. This study presents a modeling approach for simulating the spatiotemporal distribution of urban microclimate with the Weather Research and Forecasting (WRF) model using four urban parameterization schemes, namely a bulk, urban canopy model (UCM), building effect parameterization (BEP), and building energy model (BEM). The WRF model is set-up at 1 km spatial resolutions over the Jakarta Metropolitan Area to study the model’s sensitivity to the usage of alternative LULC datasets, the default MODIS and its modification 2017. The results show that the UCM and BEM schemes appear to be reliable in mapping urban weather conditions for all meteorological parameters examined. Given that the LULC categories in urban areas remained unchanged, changing the LULC in the model did not result in a large difference in error. The LULC dataset, on the other hand, can be used as information related to suburban areas that continue to grow in concurrently with urbanization. LULC updates can provide insight into how much temperature rise is occurring in urban areas and how it affects climate change.

Linking Pattern to Process: Intensity Analysis of Land-Change Dynamics in Ghana as Correlated to Past Socioeconomic and Policy Contexts

Spatio-temporal analysis of transitions in land cover is critical to understanding many ecological challenges, especially in environmentally vulnerable regions. For instance, in Sub-Saharan Africa, large-scale cropland expansion is expected due to the increasing demand for fuel, food, and fibre. Clearing land for cropland expansion is a driving factor in the degradation of natural ecosystems. We present a spatio-temporal analysis of land-cover change in Ghana’s Northern, Upper East, and Upper West provinces using Intensity Analysis on the periods from 1992 to 2003 and 2003 to 2015. The objectives of this study were to determine whether the intensity of land-use and land-cover (LULC) change is consistent between the two periods and to investigate the direction and extent of change for different LULC categories in northern Ghana. The methodology measures land-cover changes at the interval, category, and transition levels. The results suggest that the annual rate of land change was higher between 1992 and 2003 compared to that between 2003 and 2015. Furthermore, the category-level analysis reveals that the gains in the arable land and tree/forest-cover classes during both time intervals were higher than the uniform intensity. The transition-level analysis results indicate that most of the gains in arable land and tree/forest-cover came at the cost of semi-arid shrublands during both periods. There is also evidence of local increases in forest-cover, likely linked to afforestation policies established by the Ghanian government; however, overall, there has been a loss of natural habitat. The study provides data to improve our understanding of the magnitude and direction of land-cover change, essential for the development of policies designed to mitigate the impact of land-cover change on the livelihoods of local people and the environment at the national and sub-national levels.

Intensity Characteristics and Multi-Scenario Projection of Land Use and Land Cover Change in Hengyang, China.

Intensity Analysis has generally been applied as a top-bottom hierarchical accounting method to understand regional dynamic characteristics of land use and land cover (LULC) change. Given the inconvenience of transition level in the detailed and overall presentation of various category transitions at multiple intervals, a novel transition pattern is proposed to represent the transition’s size and intensity and to intuitively identify the stationary mode of transition, which helps the transition level to connect to the mode with the process. Intensity Analysis was conducted to communicate the transition between LULC categories in Hengyang from 1980 to 2015. The patch-generating land use simulation (PLUS) model was employed for multi-scenario projection from 2015 to 2045. From 1980 to 2015, 2005 was a significant turning point in the speed of LULC change in Hengyang, and the change rate after this time point was three times that before the time point. The gain of built-up and bare, and the loss of cultivated was always active. The reason for the large loss of forest is that forest comprises the largest proportion of Hengyang. The loss of cultivated and the loss of forest contributing to the built-up’s gain is much larger, but the mechanism behind the transition differed. A stationary targeting transition mode from cultivated to built-up in Hengyang was detected. The PLUS model confirmed that the area of forest, cultivated and grass will reduce, and the rate of decrease will slow down in the future, while water areas will slightly increase. Our work enriches the methodology of Intensity Analysis and provides a scientific reference for the sustainable development and management of land resources in Hengyang.

Spatio-temporal variation in land use/land cover pattern and channel migration in Majuli River Island, India.

Precise land use and land cover (LULC) change information of a land surface is significant for sustainable development programs as the earth's surface is undergoing rapid changes. Majuli Island is located in the upper reach of the Brahmaputra River in India. It is continuously changing its shape by the action of erosion of the Brahmaputra River, incurring both tangible and intangible losses. This study aims to find out the changes that occurred in the island by analyzing the land use/land cover along with channel migration in the Brahmaputra River that occurred in that area over the period 1973 to 2019. This paper assesses the changes and present status of Majuli River Island from 1973 to 2019 using Landsat MSS (1973), TM (1985, 1995), ETM + (2009), and OLI (2019) satellite imageries. Here, the maximum likelihood classification (MLC) technique for LULC analysis and their temporal changes and normalized difference vegetation index (NDVI) technique for the vegetation characteristics have been processed and analyzed with the help of the geospatial information system (GIS). From the results, it is found that area of vegetation has gradually decreased from 365.59 (26.85%) in 1973 to 262.79 km2 (19.29%) in 2019. In contrast, the barren land had increased from 4.82 (0.35%) in 1973 to 31.88 km2 (2.34%) in 2019. Other LULC categories like agricultural lands, built-up areas, water bodies, and sand deposition also have changed significantly. The NDVI values are also changed due to channel shifting, soil erosion, and deforestation. The accuracy assessment for the supervised classification of LULC classes for all years showed excellent results in all six classes.

Impacts of Projected Urban Expansion on Rainfall and Temperature during Rainy Season in the Middle-Eastern Region in Tanzania

Future changes of land use and land cover (LULC) due to urbanization can cause variations in the frequency and severity of extreme weather events, affecting local climate and potentially worsening impact of such events. This work examines the local climatic impacts associated with projected urban expansion through simulations of rainfall and temperature over the rapidly growing city of the middle-eastern region in Tanzania. Simulations were conducted using a mesoscale Weather Research and Forecasting (WRF) model for a period of 10 days during the rainfall season in April 2018. The Global Forecasting System data of 0.25° resolution was used to simulate the WRF model in two-way nested domains at resolutions of 12 km and 4 km correspondingly. Urban and built-up areas under the current state, low urbanization (30%), and high urbanization (99%) scenarios were taken into account as LULC categories. As the urbanized area increased, daily mean, maximum and minimum air temperatures, as well as precipitation increased. Local circulation affected the spatial irregularities of air temperature and precipitation. Results imply that urbanization can amplify the impacts of future climate changes dramatically. These results can be applicable to the city planning to minimize the adverse effect of urbanization on temperature and precipitation.

Environmental heterogeneity in human health studies. A compositional methodology for Land Use and Land cover data

The use of Land use and Land cover (LULC) data is gradually becoming more widely spread in studies relating the environment to human health. However, little research has acknowledged the compositional nature of these data. The goal of the present study is to explore, for the first time, the independent effect of eight LULC categories (agricultural land, bare land, coniferous forest, broad-leaved forest, sclerophyll forest, grassland and shrubs, urban areas, and waterbodies) on three selected common health conditions: type 2 diabetes mellitus (T2DM), asthma and anxiety, using a compositional methodological approach and leveraging observational health data of Catalonia (Spain) at area level.We fixed the risk exposure scenario using three covariates (socioeconomic status, age group, and sex). Then, we assessed the independent effect of the eight LULC categories on each health condition. Our results show that each LULC category has a distinctive effect on the three health conditions and that the three covariates clearly modify this effect.This compositional approach has yielded plausible results supported by the existing literature, highlighting the relevance of environmental heterogeneity in health studies. In this sense, we argue that different types of environment possess exclusive biotic and abiotic elements affecting distinctively on human health.We believe our contribution might help researchers approach the environment in a more multidimensional manner integrating environmental heterogeneity in the analysis.

Accuracy assessment and performance analysis of raster to vector conversions on LULC data – India

Purpose The purpose of this paper is to convert real-world raster data into vector format and evaluate loss of accuracy in the conversion process. Open-source Geographic Information System (GIS) is used in this process and system resource utilizations were measured for conversion and accuracy analysis methods. Shape complexity attributes were analyzed in co-relation to the observed conversion errors. Design/methodology/approach The paper empirically evaluated the challenges and overheads involved in the format conversion algorithms available in open-source GIS with real-world land use and land cover (LULC) map data of India. Across the different LULC categories, geometric errors of varying density were observed in Quantum GIS (QGIS) algorithm. Area extents of original raster data were compared to the vector forms and the shape attributes such as average number of vertices and shape irregularity were evaluated to explore the possible correlation. Findings The results indicate that Geographic Resources Analysis Support System provides near error-free conversion algorithm. At the same time, the overall time taken for the conversion and the system resource utilizations were optimum as compared to the QGIS algorithm. Higher vector file sizes were generalized and accuracy loss was tested. Research limitations/implications Complete shape complexity analysis could not be achieved, as the weight factor for the irregularity of the shapes is to be varied based on the demography as well as on the LULC category. Practical implications Because of the higher system resource requirements of topological checker tool, positional accuracy checks for the converted objects could not be completed. Originality/value This paper addresses the need of accuracy analysis of real-world spatial data conversions from raster to vector format along with experimental setups challenges and impact of shape complexity.

Impact of population growth and land use and land cover (LULC) changes on water quality in tourism-dependent economies using a geographically weighted regression approach.

This paper aims to assess the influence of land use and land cover (LULC) indicators and population density on water quality parameters during dry and rainy seasons in a tourism area in Indonesia. This study applies least squares regression (OLS) and Pearson correlation analysis to see the relationship among factors, and all LULC and population density were significantly correlated with most of water quality parameter with P values of 0.01 and 0.05. For example, DO shows high correlation with population density, farm, and built-up in dry season; however, each observation point has different percentages of LULC and population density. The concentration value should be different over space since watershed characteristics and pollutions sources are not the same in the diverse locations. The geographically weighted regression (GWR) analyze the spatially varying relationships among population density, LULC categories (i.e., built-up areas, rice fields, farms, and forests), and 11 water quality indicators across three selected rivers (Ayung, Badung, and Mati) with different levels of tourism urbanization in Bali Province, Indonesia. The results explore that compared with OLS estimates, GWR performed well in terms of their R2 values and the Akaike information criterion (AIC) in all the parameters and seasons. Further, the findings exhibit population density as a critical indicator having a highly significant association with BOD and E. Coli parameters. Moreover, the built-up area has correlated positively to the water quality parameters (Ni, Pb, KMnO4 and TSS). The parameter DO is associated negatively with the built-up area, which indicates increasing built-up area tends to deteriorate the water quality. Hence, our findings can be used as input to provide a reference to the local governments and stakeholders for issuing policy on water and LULC for achieving a sustainable water environment in this region.

Assessment And Detection Of Land Cover Changes In The Southern Fringe Of Kolkata Using Remotely Sensed Data

Continual, historical, and precise information about the land use and land cover (LULC) changes of the Earth’s surface is extremely important for any kind of sustainable development program, in which LULC serves as one of the major input criteria. In this study, a supervised classification was applied to five types of Landsat images collected over time (1980, 1990, 2000, 2010 and 2015) that provided recent and historical LULC conditions for the area. Four LULC categories were identified and mapped. Post-classification comparisons of the classified images indicated that the major change consisted of barren land changing into agricultural land. This analysis revealed that substantial growth of built-up areas in the south eastern part of Kolkata over the study period resulted in significant decrease in the area of water bodies, cultivated land, vegetation and wetlands. Urban land transformation has been largely driven by large number of population and high population growth rate with rapid economic and infrastructural development like the extension of metro railway, flyovers and hence huge real estate development.

Comparison of ESA climate change initiative land cover to CORINE land cover over Eastern Europe and the Baltic States from a regional climate modeling perspective

High-quality land use and land cover (LULC) information is of crucial importance for the performance of regional climate models (RCMs), in particular at high spatial resolutions down to convection permitting scales below 4 km. Several satellite-based high-resolution products are currently available for implementation into RCMs. One of the most recent products is the European Space Agency Climate Change Initiative Land Cover (ESA CCI LC) dataset. While the ESA CCI LC has been assessed globally, an evaluation against regional, independent LULC datasets is necessary to identify LULC inaccuracies in the respective region of interest and to give regional climate modelers estimates for the uncertainty in the land use forcing. In the present work the ESA CCI LC dataset is compared to the COoRdination and INformation on the Environment (CORINE) Land Cover (CLC). Agreement between the datasets is assessed by proportional area comparison (PAC). The resulting agreement measures are compared to the results of a majority approach (MA) to explore possible differences between the methods. Three timesteps of ESA CCI LC matching the timesteps of CLC are assessed to take a change in agreement over time into account. In addition to the quantification of agreement, spatial patterns of possible issues with ESA CCI LC are identified through utilization of geospatial information systems (GIS). Using the PAC, the agreement of ESA CCI LC with CLC is found to be ∼76 % for the research area (RA). Although the agreement decreases slightly using the PAC, no substantial differences in agreement measures were found compared to the results of the MA. Dominant LULC categories agriculture and forest show an agreement of over 80 % with CLC. A few major issues were found for grassland, wetlands, settlements, and water bodies in the RA of which some might influence RCM performance if the dataset is implemented without adjustment. We highly recommend to apply the PAC to other regions in Europe and further globally to investigate if the found issues are also found elsewhere. The use of more independent regional and specified datasets for validation but also for possible improvement of the ESA CCI LC dataset is suggested.

Land-use and land-cover change in Lake Ziway watershed of the Ethiopian Central Rift Valley Region and its environmental impacts

Assessing land use and land cover (LULC) change in Lake Ziway watershed is important to evaluate the degradation of ecosystems and their environmental processes caused by the ongoing increase in human pressures. The present study analyzed the long-term LULC dynamics in the Lake Ziway watershed, which covers 7300 km2 in the Ethiopian Central Rift Valley region. Three Landsat Satellite Image Series - Landsat MSS (1973), Landsat TM (1989) and Landsat Enhanced ETM+ (2018) - were the main input data from which three LULC maps were produced by employing Remote Sensing Techniques and Geographical Information Systems. The satellite data were supported by Google Earth and information gathered from informal discussions from local elderly people who are knowledgeable aboutthe area. The results over the last 45 years show that the major LULC changes in the study watershed have been the expansion of cultivated, agroforestry and settlement areas and the corresponding reduction in woodlands. Cultivation, agroforestry, and settlement LULC categories increased by 45%, 10.9%, and 141.4%, respectively. These changes are attributable to a combination of the ever increasing human population and the subsequent demands on environmental resources like agricultural lands, commercial and domestic fuelwood and charcoal. Other factors include poorly defined ownership arrangements and weak enforcement strategies on the existing land use policy. This has created open access mentalities among communities and intensified LULC changes in the watershed. Awareness raising and provision of technical training about conservation interventions should be provided to communities in the watershed.This study provides information for corrective measures to protect further degradation and irreversible losses that might happen to the biotic and abiotic resources in Lake Ziway watershed.