Land Use And Land Cover Classes Research Articles

Optimizing Land Use and Land Cover Allocation for Flood Mitigation Using Land Use Change and Hydrological Models with Goal Programming, Chaiyaphum, Thailand

Floods represent one of the most severe natural disasters threatening the development of human society worldwide, including in Thailand. In recent decades, Chaiyaphum province has experienced a problem with flooding almost every year. In particular, the flood in 2010 caused property damage of 495 million Baht, more than 322,000 persons were affected, and approximately 1046.4 km2 of productive agricultural area was affected. Therefore, this study examined how to optimize land use and land cover allocation for flood mitigation using land use change and hydrological models with optimization methods. This research aimed to allocate land use and land cover (LULC) to minimize the surface for flood mitigation in Mueang Chaiyaphum district, Chaiyaphum province, Thailand. The research methodology consisted of six stages: data collection and preparation, LULC classification, LULC prediction, surface runoff estimation, the optimization of LULC allocation for flood mitigation and mapping, and economic and ecosystem service value evaluation and change. According to the results of the optimization and mapping of suitable LULC allocation to minimize surface runoff for flood mitigation in dry, normal, and wet years using goal programming and the CLUE-S model, the suitable LULC allocation for flood mitigation in 2049 under a normal year could provide the highest future economic value and gain. In the meantime, the suitable LULC allocation for flood mitigation in 2049 under a drought year could provide the highest ecosystem service value and gain. Nevertheless, considering future economic and ecosystem service values and changes with surface runoff reduction, the most suitable LULC allocation for flood mitigation is a normal year. Consequently, it can be concluded that the derived results of this study can be used as primary information for flood mitigation project implementation. Additionally, the presented conceptual framework and research workflows can be used as a guideline for government agencies to examine other flood-prone areas for flood mitigation in Thailand.

IMPACT OF LAND USE AND LAND COVER CHANGES ON RUNOFF PREDICTION IN OGBESE RIVER WATERSHED

Land use and land cover (LULC) changes in Ogbese watershed due to urbanization implies increased areas of low infiltration. This results to higher flow rates downstream the watershed. This study estimates the changes in peak flow rates at the watershed’s outlet for present and future LULC. Rainfall-runoff simulation was achieved with Hydrologic Engineering Centre-Hydrologic Modeling System (HEC-HMS) version 4.2 while future LULC was projected with Markov Chain model. Rainfall inputs to the hydrologic model were obtained from intensity-duration-frequency curves for Ondo state. Landsat 7, Enhanced Thematic mapper plus (ETM+) image and Landsat 8 operational land imager (OLI) with path 190 and row 2 were used to generate LULC images for the years 2002, 2015 and 2019. Six LULC classes were extracted as follows: built up area, bare surface, vegetation, wetland, rock outcrop and waterbody. Future LULC in year 2025 and 2029 were projected with Markov Chain model. The model prediction was verified with Nash Sutcliffe Efficiency index (NSE). NSE value of 0.79 was calculated indicating LULC changes in the watershed was Markovian. Results show that built up area cover in 2019 is projected to increase by 26.1% in 2024 and 39.9% in 2029 and wetland is projected to decreased by 1.2% in 2024 and 2.3% by 2029. Runoff peaks for these LULC projections indicate increase by 0.24% in 2024 and 1.19% in 2029 at the watershed’s outlets for 100-year return period rainfall.

Impact of Multitemporal Land Use and Land Cover Change on Land Surface Temperature Due to Urbanization in Hefei City, China

Land surface temperature (LST) is an essential parameter in the climate system whose dynamics indicate climate change. This study aimed to assess the impact of multitemporal land use and land cover (LULC) change on LST due to urbanization in Hefei City, Anhui Province, China. The research methodology consisted of four main components: Landsat data collection and preparation; multitemporal LULC classification; time-series LST dataset reconstruction; and impact of multitemporal LULC change on LST. The results revealed that urban and built-up land continuously increased from 2.05% in 2001 to 13.25% in 2020. Regarding the impact of LULC change on LST, the spatial analysis demonstrated that the LST difference between urban and non-urban areas had been 1.52 K, 3.38 K, 2.88 K and 3.57 K in 2001, 2006, 2014 and 2020, respectively. Meanwhile, according to decomposition analysis, regarding the influence of LULC change on LST, the urban and built-up land had an intra-annual amplitude of 20.42 K higher than other types. Thus, it can be reconfirmed that land use and land cover changes due to urbanization in Hefei City impact the land surface temperature.

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.

Evaluation of Land Use and Land Cover Change and Its Drivers in Battambang Province, Cambodia from 1998 to 2018

The main objective of this research was to evaluate land use and land cover (LULC) change in Battambang province of Cambodia over the last two decades. The LULC maps for 1998, 2003, 2008, 2013 and 2018 were produced from Landsat satellite imagery using the supervised classification technique with the maximum likelihood algorithm. Each map consisted of seven LULC classes: built-up area, water feature, grassland, shrubland, agricultural land, barren land and forest cover. The overall accuracies of the LULC maps were 93%, 82%, 94%, 93% and 83% for 1998, 2003, 2008, 2013 and 2018, respectively. The LULC change results showed a significant increase in agricultural land, and a large decrease in forest cover. Most of the changes in both LULC types occurred during 2003–2008. Overall, agricultural land, shrubland, water features, built-up areas and barren land increased by 287,600 hectares, 58,600 hectares, 8300 hectares, 4600 hectares and 1300 hectares, respectively, while forest cover and grassland decreased by 284,500 hectares and 76,000 hectares respectively. The rate of LULC changes in the upland areas were higher than those in the lowland areas of the province. The main drivers of LULC change identified over the period of study were policy, legal framework and projects to improve economy, population growth, infrastructure development, economic growth, rising land prices, and climate and environmental change. Landmine clearance projects and land concessions resulted in a transition from forest cover and shrubland to agricultural land. Population and economic growth not only resulted in an increase of built-up area, but also led to increasing demand for agricultural land and rising land prices, which triggered the changes of other LULC types. This research provides a long-term and detailed analysis of LULC change together with its drivers, which is useful for decision-makers to make and implement better policies for sustainable land management.

Spatio-temporal dynamics of land use changes of an intense anthropized basin in the Brazilian semi-arid region

The temporal dynamics of land use and land cover (LULC) changes affect natural resources and biodiversity on a local and global scale. The negative impacts of LULC changes can be enhanced in arid and semi-arid areas, where vegetations are more sensitive to changes due to limited water availability. The present study aimed to explore the detailed spatio-temporal LULC changes in the Jaguaribe River basin, Ceará state, Brazil. Specifically, we aimed to identify the dynamics of LULC changes from 1985 to 2018, including the main LULC conversations and the sub-basins that presented the most intense dynamics. For this, we used annual LULC maps generated by the national Mapbiomas project. The LULC conversions were determined through cross-tabulation analysis and pixel comparison analysis. Our results indicate that in the last three decades there was a significant conversion of the natural vegetation Caatinga (Arboreal and Herbaceous) to agricultural and livestock areas, showing that the anthropic activities are the main factors behind the historical landscape changes in the Jaguaribe River basin. The Middle Jaguaribe and the Lower Jaguaribe sub-basins were the most affected by LULC changes during the study period. While the LULC changes in Middle Jaguaribe sub-basin reveal that this area has been impacted by anthropic interference with similar areas of pasture and the caatinga class since 1985, the Lower Jaguaribe sub-basin was continuously impacted mainly by the expansion of the annual and perennial culture and reduction of natural vegetation areas. The Lower Jaguaribe sub-basin had a higher percentage of altered areas, LULC classes per pixel and LULC changes during the study period. The spatio-temporal analysis presented can orient planning actions to conserve natural resources and preserve the biodiversity in this semi-arid region of Brazil.

CBERS data cubes for land use and land cover mapping in the Brazilian Cerrado agricultural belt

ABSTRACT The agricultural frontier expansion in the Cerrado biome made Brazil a leader in commodity exports and is changing its landscape. Hence, efforts to accurate land use and land cover (LULC) monitoring in this region are strategic, due to its role in Brazil’s food, environmental, and economic security policy. Thinking on planning and technical sovereignty in the spatial sector, the China–Brazil Earth Resources Satellite (CBERS) Program was launched to provide useful data for decision-makers to manage the Brazilian territory independently of external policies. Their data, especially from CBERS-4 Wide-Field Imager (CBERS-4/WFI), are largely applied in deforestation monitoring by remote sensing specialists but less applied than data from other image providers for machine learning-based LULC mapping due to the small number of spectral bands and limitations related to clouds and shadows detection. However, with advances in orbital data analysis, data cubes enabled storing and accessing large spatio-temporal analysis-ready data. Within this scope, the Brazil Data Cube Project (BDC) creates multidimensional data cubes from orbital sensors’ data for all Brazilian territory. We applied BDC CBERS-4/WFI data cubes to generate LULC classifications for the Extremo Oeste Baiano agricultural belt correspondent to the 2017/2018 and 2019/2020 harvest periods, at two levels of detail: broad and crop type, incorporating ground truth samples, crop calendar knowledge, and vegetation indices to a dense time series analysis approach. Overall Accuracies were equal to 0.87 and 0.89 for broad, and 0.91 and 0.94 for crop type classifications. The results indicate CBERS-4/WFI data cubes as a useful tool for improving crop monitoring in the Cerrado biome based on machine learning.

Detecting and Mapping the Influences of Vegetation Cover on Runoff and Torrents in Khartoum State, Sudan

The study was carried out in Omdurman and Sharg El-Neel to map and detect how the status of vegetation cover impacts on runoff and torrents. Satellite imageries of Landsat Thematic Mapper (TM) of 1994-2009 and Operational Land Imager/Thermal Infrared Sensor (OLI/TIRS) of 2018 were processed to derive accurate land use and land cover (LULC), using hybrid classification and normalized differences vegetation index (NDVI) of the study area. The results revealed changes in LULC classes during the study period (1994, 2009 and 2018). Omdurman area showed that the vegetation cover for the years 1994, 2009 and 2018 was 3.2%, 3.1% and 4.2% respectively. The respective sandy soil ratio was 18.2%, 34.9% and 27.6%. The water bodies were found to cover only 0.5%, 0.5% and 0.7% respectively. These results indicated that the area of vegetation cover was less than other LULC. Contrary, results from Sharg El-Neel revealed that the percentage of vegetation cover for the years 1994, 2009 and 2018 accounted for 31.4%, 41.3% and 24.7% respectively. The sandy soil ratios for these years were 26.3%, 14.8% and 38.3% respectively. The water bodies covered 0.5%, 0.6% and 1.3% respectively. The research concluded that changes in status of vegetation cover plays a role on water runoff and torrents events in the study areas. Thestudy recommends further studies to help produce robust plans that consist with LULC status, to minimize the water related risks and hazards.

Selecting features for LULC simultaneous classification of ambiguous classes by artificial neural network

The anthropic activities carried out in the vicinity of the water body trigger various harmful effects on flora, fauna, and human health. The release of effluents and nutrients renders the aquatic environment increasingly eutrophic, favoring the proliferation of macrophytes, leading to loss of biodiversity, in addition to degradation of water quality. Since land use and land cover (LULC) around water bodies strongly influence macrophyte proliferation, it is necessary to simultaneously monitor the water body and its surroundings to manage and control the overabundance of aquatic vegetation. Due to the spectral similarity between aquatic and terrestrial vegetation, it is a challenge to simultaneously monitor these targets, especially when macrophyte mapping is placed within the context of LULC mapping. The inclusion of auxiliary data that promotes an increase the aquatic vegetation discrimination can even improve the accuracy of a LULC mapping. However, an excessive number of features can increase the complexity and reduce the mapping accuracy. We assume that it is necessary to select a set of suitable features, including spectral and textural data extracted from multispectral images, in addition to a DEM, allows the macrophytes discrimination in a water body and increases the classification accuracy in a simultaneous mapping of LULC classes, but only with the addition of a DEM, there is better discrimination of aquatic macrophytes in a simultaneous mapping of LULC classes. To achieve this, four feature selection methods (Correlation, Gain Ratio, Relief, and out-of-bag Random Forest) were applied to define the best subsets for a LULC mapping that integrates the water body with the watershed landscape. The various subsets of selected features were classified using a supervised Artificial Neural Network (ANN) algorithm. The results show that features derived from OLI/Landsat 8 combined with DEM data provided an effective contribution to increase the accuracy classification and reduce confusion between aquatic and terrestrial vegetation. The best kappa coefficient was obtained from the Correlation features set, achieving a 10% improvement after the inclusion of DEM. Thus, the results of this study showed that the DEM inclusion can improve the aquatic vegetation discrimination in LULC classifications, even when different feature selection methods do not indicate that variable as relevant to machine learning-based classification.

Semantic Boosting: Enhancing Deep Learning Based LULC Classification

The classification of land use and land cover (LULC) is a well-studied task within the domain of remote sensing and geographic information science. It traditionally relies on remotely sensed imagery and therefore models land cover classes with respect to their electromagnetic reflectances, aggregated in pixels. This paper introduces a methodology which enables the inclusion of geographical object semantics (from vector data) into the LULC classification procedure. As such, information on the types of geographic objects (e.g., Shop, Church, Peak, etc.) can improve LULC classification accuracy. In this paper, we demonstrate how semantics can be fused with imagery to classify LULC. Three experiments were performed to explore and highlight the impact and potential of semantics for this task. In each experiment CORINE LULC data was used as a ground truth and predicted using imagery from Sentinel-2 and semantics from LinkedGeoData using deep learning. Our results reveal that LULC can be classified from semantics only and that fusing semantics with imagery—Semantic Boosting—improved the classification with significantly higher LULC accuracies. The results show that some LULC classes are better predicted using only semantics, others with just imagery, and importantly much of the improvement was due to the ability to separate similar land use classes. A number of key considerations are discussed.

Land Use and Land Cover Change Detection for Water Yield Estimation using Remote Sensing Data in Batu Pahat, Johor

Globally, the accelerating urbanization led by industrialization and population growth causes severe environmental degradation. The urban expansion particularly to the conversion of land activities affects the ecosystem services critically. This study helps to fill in the gap of determining water yield in the urban area to reduce water stress due to the spatial land use change. The research objectives are to quantify the spatial land use change in Batu Pahat, Johor in the year 1999, 2010, and 2018. Second, to identify the water yield of Batu Pahat in the years 1999, 2010, and 2018. Third, to determine the relationship between water yield, vegetation, and urban expansion. The methods used are landscape change, water yield simulation, and statistical analysis by using the software included ENVI, ArcGIS, FRAGSTAT, Annual Water Yield InVEST Model, and Microsoft Excel. Raw satellite images were extracted for the year 1999, 2010, and 2018. The supervised classification of LULC (Land Use and Land Cover) was done based on the region created which are built-up area, cleared land, vegetation, and water bodies. This study generates results for the changes in percentage area for each LULC class. The highest percentage of area in Batu Pahat is vegetation while the cleared land ranked lowest. In conclusion, this study will aid in understanding and provided empirical data result for the urban expansion and water yield in Batu Pahat, Johor by using GIS and remote sensing applications to produce land use and water yield map as final output.

English

It is believed that since the advent of agriculture, changes in land use and land cover (LULC) have happened. However, rates, extents and intensities of LULC changes have become more aggravated at all levels. The current study aims to examine the dynamics and perceptions of LULC change in the last four decades (1973-2013) in Aykoleba, Ethiopia using a combination of remote sensing data and the ground truth data. Focus group discussions were employed to obtain data on the status of land degradation. Remote sensing data were obtained using Landsat imageries of MSS (1973), Landsat TM, 1986 and 2000, and Landsat ETM+ (2013) with 30 m spatial resolution. ArcGIS10.2 and ERDAS Imagine13.1 were used to generate LULC classes. Accordingly, four LULC classes were identified, of which forest and bare LULC have been augmented by 8.8 and 54.9%, respectively. The escalation in forest cover is associated with plantation of eucalyptus near the home gardens, farmlands, and degraded areas. Nevertheless, open bush and grassland, and cultivated and settlement land cover classes were lessened by 27.4 and 37.8%, respectively although increase in bare land is related to abandonment of the cultivated land in hilly and sloppy areas, and overgrazing, among other factors. The local community perceived that population pressure is a top driver of LULC change in the study area. Overgrazing and lack of appropriate land use policy are also significant causes of change. Thus, the establishment of land use plan and appropriate population policy is recommended in Ethiopia.   Key words: Analysis, dynamics, Ethiopia, land cover, land use.

Land-Use and Land-Cover Changes in Dong Trieu District, Vietnam, during Past Two Decades and Their Driving Forces

Land-use and land-cover (LULC) change analyses are useful in understanding the changes in our living environments and their driving factors. Modeling changes of LULC in the future, together with the driving factors derived through analyzing the trends of past LULC changes, bring the opportunity to assess and orientate the current and future land-use policies. As the entryway of Quang Ninh province, Vietnam, Dong Trieu locale has experienced significant LULC changes during the past two decades. In this study, the spatial distribution of six Level I LULC classes, forest, cropland, orchards, waterbody, built-up, and barren land, in Dong Trieu district at 2000, 2010, and 2019 were obtained from Landsat imageries by maximum likelihood technique. The most significant changes observed over the past twenty years are a decrease of barren land (9.1%) and increases of built-up (8.1%) and orchards (6.8%). Driving factor analysis indicated that the changes of cropland and built-up were dependent on distance from road (DFR), distance from main road (DFMR), distance from urban (DFU), distance from water (DFW), elevation, slope, and population density. The changes of forest were dependent on all the driving forces listed above, except DFMR. The orchards mainly appeared near the high-population-density area. The transformation of the waterbody was affected by geography (elevation and slope) and population density. The higher the population density, the less barren the land would appear.

Bias in Deep Neural Networks in Land Use Characterization for International Development

Understanding the biases in Deep Neural Networks (DNN) based algorithms is gaining paramount importance due to its increased applications on many real-world problems. A known problem of DNN penalizing the underrepresented population could undermine the efficacy of development projects dependent on data produced using DNN-based models. In spite of this, the problems of biases in DNN for Land Use and Land Cover Classification (LULCC) have not been a subject of many studies. In this study, we explore ways to quantify biases in DNN for land use with an example of identifying school buildings in Colombia from satellite imagery. We implement a DNN-based model by fine-tuning an existing, pre-trained model for school building identification. The model achieved overall 84% accuracy. Then, we used socioeconomic covariates to analyze possible biases in the learned representation. The retrained deep neural network was used to extract visual features (embeddings) from satellite image tiles. The embeddings were clustered into four subtypes of schools, and the accuracy of the neural network model was assessed for each cluster. The distributions of various socioeconomic covariates by clusters were analyzed to identify the links between the model accuracy and the aforementioned covariates. Our results indicate that the model accuracy is lowest (57%) where the characteristics of the landscape are predominantly related to poverty and remoteness, which confirms our original assumption on the heterogeneous performances of Artificial Intelligence (AI) algorithms and their biases. Based on our findings, we identify possible sources of bias and present suggestions on how to prepare a balanced training dataset that would result in less biased AI algorithms. The framework used in our study to better understand biases in DNN models would be useful when Machine Learning (ML) techniques are adopted in lieu of ground-based data collection for international development programs. Because such programs aim to solve issues of social inequality, MLs are only applicable when they are transparent and accountable.

Twenty years of change: Land and water resources in the Chindwin catchment, Myanmar between 1999 and 2019

Since 2011, Myanmar has undergone a more rapid socio-economic development, which may substantially have affected land use and land cover (LULC) and water resources. This study investigates the changes in land and water resources of the Chindwin River catchment (114,686.9 km2) in Myanmar over a twenty-year timespan from 1999 to 2019. The main aim of this study is to assess LULC change and evaluate its effects on the water balance and the people in the region. To this end, interviews were conducted, LULC classifications based on multi-temporal multi-spectral satellite data and in-situ ground truth data were created, and a hydrologic model was built. The hydrologic model shows a reasonable performance for daily discharge simulation at the catchment outlet (percent bias between -2 and 13.2, Kling-Gupta Efficiency between 0.75 and 0.76, Nash-Sutcliffe Efficiency between 0.57 and 0.61, RMSE-observations standard deviation between 0.63 and 0.66). The LULC changes detected include a decrease in forest area of about 2%, an increase in shrubland area indicating increased degradation of the forest, an increase in mining areas of 0.38%, an overall decrease in agricultural area (2.1%), but also the presence of new agricultural land pointing toward relocation of agricultural areas and an indication of an increase in settlement areas (1.5%). With the help of the hydrologic model, the most significant hydrologic impacts detected were a decrease in evapotranspiration and an increase in water yield which is correlated with the decrease of forest at the sub-catchment scale (R2 = 0.72 and 0.46, respectively). Moreover, an increase of mining areas contributed to the increase in water yield (R2 = 0.62). Interviews confirm that the identified LULC changes deforestation and increased mining activities contribute to major issues, e.g., water pollution, sedimentation, and changes in the river course.

Land Use and Land Cover Changes at Hova Farm in Bindura District, Zimbabwe

Land use and land cover (LULC) change analyses are critical for the sustainable planning and management of natural resources in the face of rapid population growth across the globe. It is believed that LULC changes cause severe environmental challenges such as climate change, biodiversity loss, pollution, alteration to the physical and chemical properties of the soil as well as the destruction of the ozone layer. The main objective of the study was to assess the LULC changes at Hova Farm from 1992 to 2011 using geospatial technologies. Three Landsat images for 1992, 2001, and EMT+ for 2011 were used. The Landsat images had a resolution of 30m by 30m. Five LULC classes of woodland, wooded grassland, cultivated land, bushland and water body were created using the supervised classification maximum likelihood in ENVI 5.0. Field observation and measurements were also used to validate remotely sensed data. The accuracy assessment for the classified maps for 1992, 2001 and 2011 was 88.74%, 86, 67% and 87% respectively. The results indicated that the greatest LULC changes occurred between 1992 and 2001 and was attributed to the fast-track land reform programme and illegal mining activities on the farm. The study recommends the creation of a LULC database for the periodic monitoring and sustainable management of natural resources at both local and national levels in Zimbabwean.

Use of multispectral satellite data to assess impacts of land management practices on wetlands in the Limpopo Transfrontier River Basin, South Africa

ABSTRACT The study sought to assess the impacts of land use and land cover (LULC) changes on two wetland systems (Makuleke and Nylsvley Nature Reserve) in the Limpopo Transfrontier River Basin (LTRB) in South Africa between 2014 and 2018. To fulfil this objective, multi-date Landsat images were used. Furthermore, the maximum likelihood classification algorithm was used to identify various LULC classes within delineated wetlands. The LULC changes were mapped from the two wetlands, with high overall accuracies, ranging from 80% to 89% for both study areas. The spatial extent of the Makuleke wetland declined by 2% between 2014 and 2018, whereas the Nylsvley wetland decreased by 3%. Built-up areas have increased slightly over the 2014 and 2018 period because of population growth and infrastructure development, which occupy a portion of the wetland. In Nylsvley wetland, it was evident that during the 5-year monitoring period, croplands increased steadily in the Nylsvley catchment. Overall, the results demonstrated a steady decline in natural vegetation cover in both wetlands. This information can aid in enforcing wetland legislations and LULC management practices that can help protect them from further encroachment and degradation.

Analysis of Land Use and Land Cover Change in Gummidipoondi Using Remote Sensing and GIS Tools

The aim of the study was to evaluate the changes in land use and land cover (LULC) in Gummidipoondi and the surrounding areas in Thiruvallur district, Tamilnadu India.Spatio-temporal variation in the land use and land cover were analysed on a decadal basis for the period from 1990 to 2019 using remote sensing and GIS based tools. The Landsat 5 (TM) and Resource-Sat 2 (LISS-III) data was used for the LULC classificationin the study area. During the study period from 1990 to 2019, built-up area including industrial, urban and rural land use increased by about 147%. Predominant change was also noticed in the mudflat category where more than 95% of it was lost to various other land uses such as agriculture and marsh area. This observation calls for planning and conservation of sensitive ecosystems in the study area that may be lost due to anthropogenic pressures via pollution and undesirable conversion of LULC. The study revealed no significant changes in the extent of other LULC classes such as agriculture, forests, plantations, land with or without scrub, rivers and waterbodies in the study area

Assessment of land surface temperature and land cover variability during winter: A spatio-temporal analysis of Pabna municipality in Bangladesh

Monitoring the change of land use and land cover (LULC) and land surface temperature (LST) at different spatio-temporal scales is vital for evaluating landscape dynamics and thermal environment. This study investigates the decadal change of LULC and winter LST on Pabna municipality over the period between 1990 and 2020 using Landsat images (TM, ETM+ and OLI). The study further explores LST distribution for different LULC classes and the explanatory power of various land surface indicators for change in LST. A supervised maximum likelihood classification (MLC) technique was used for LULC mapping of the study area. The results showed that built-up areas were increasing rapidly while water bodies, bare lands and vegetation decreased. The built-up area expanded by 358% between 1990 and 2020, with the occupied area rising from 1.44 km2 to 6.60 km2. To obtain reliable LST results, average values of LST obtained from multiple Landsat images for each year were used. The mean LST in the winter season has risen by 0.63 °C over the last 30 years. The variation in LST between separate days of the same year increased significantly, although the change in mean LST was small. Statistical analysis of land surface indicators revealed that NDVI, NDBI and NDBaI have significant explanatory power to describe LST scenarios. The explanatory power of NDBI and NDBaI to explain the rise of LST is increasing over time while the cooling capacity of NDVI is declining. LST had a moderate positive correlation with NDBI and NDBI and a weak negative correlation with NDVI.

Monitoring of Land Use and Land Cover Changes in Mt. Pulag National Park Using Landsat and Sentinel Imageries

The degree of land use and land cover (LULC) conversion of Mt. Pulag National Park (MPNP), Benguet Province, Philippines, from 1990–2020 was assessed to provide valuable information for land resource management strategies. The increasing demand for vegetable farming threatens the park with further degradation. The magnitude of changes in the LULC of MPNP was determined using Landsat 5 for 1990–2010 and Sentinel-2 for 2020. The FLAASH (Fast Line-ofsight Atmospheric Analysis of Hypercubes) algorithm was performed to enhance the reflectance of the satellite images before data analysis. The maximum likelihood classification (MLC) was used to classify the MPNP into five LULC changes: agricultural lands, barren lands, built-up areas, forest lands, and grasslands with areal percentage cover of 15.88, 0.26, 1.34, 80.51, and 2.01%, respectively, in 2020, and overall accuracy of 90.58%. The LULC percentage of change from 1990–2020 showed that agricultural areas increased by 8.96%, barren lands decreased by 2.14%, built-up areas increased by 1.02%, forest lands decreased by 8.35%, and grasslands increased by 0.51%. The results showed that the increasing area of human-induced LULC class such as the agriculture lands and built-up areas is the primary cause of forest land loss. However, the barren lands undergoing natural regeneration contribute to the gain of the forest area. Therefore, the LULC conversion pattern serves as a warning that the park will continue to lose a significant portion of the forest due to land conversion if no intervention is done.