Land Use Cover Classification Research Articles

Hydrological Responses to Land Use Land Cover Changes in the Fincha’a Watershed, Ethiopia

Land use land cover (LULC) changes are highly pronounced in African countries, as they are characterized by an agriculture-based economy and a rapidly growing population. Understanding how land use/cover changes (LULCC) influence watershed hydrology will enable local governments and policymakers to formulate and implement effective and appropriate response strategies to minimize the undesirable effects of future land use/cover change or modification and sustain the local socio-economic situation. The hydrological response of the Ethiopia Fincha’a watershed to LULCC that happened during 25 years was investigated, comparing the situation in three reference years: 1994, 2004, and 2018. The information was derived from Landsat sensors, respectively Landsat 5 TM, Landsat 7 ETM, and Landsat 8 OLI/TIRS. The various LULC classes were derived via ArcGIS using a supervised classification system, and the accuracy assessment was done using confusion matrixes. For all the years investigated, the overall accuracies and the kappa coefficients were higher than 80%, with 2018 as the more accurate year. The analysis of LULCC revealed that forest decreased by 20.0% between the years 1994–2004, and it decreased by 11.8% in the following period 2004–2018. Such decline in areas covered by forest is correlated to an expansion of cultivated land by 16.4% and 10.81%, respectively. After having evaluated the LULCC at the basin scale, the watershed was divided into 18 sub-watersheds, which contained 176 hydrologic response units (HRUs), having a specific LULC. Accounting for such a detailed subdivision of the Fincha’a watershed, the SWAT model was firstly calibrated and validated on past data, and then applied to infer information on the hydrological response of each HRU on LULCC. The modelling results pointed out a general increase of average water flow, both during dry and wet periods, as a consequence of a shift of land coverage from forest and grass towards settlements and build-up areas. The present analysis pointed out the need of accounting for past and future LULCC in modelling the hydrological responses of rivers at the watershed scale.

Ontology-Based Probabilistic Estimation for Assessing Semantic Similarity of Land Use/Land Cover Classification Systems

To accurately and formally represent the historical trajectory and present the current situation of land use/land cover (LULC), numerous types of classification standards for LULC have been developed by different nations, institutes, organizations, etc.; however, these land cover classification systems and legends generate polysemy and ambiguity in integration and sharing. The approaches for dealing with semantic heterogeneity have been developed in terms of semantic similarity. Generally speaking, these approaches lack domain ontologies, which might be a significant barrier to implementing these approaches in terms of semantic similarity assessment. In this paper, we propose an ontological approach to assess the similarity of the domain of LULC classification systems and standards. We develop domain ontologies to explicitly define the descriptions and codes of different LULC classification systems and standards as semantic information, and formally organize this semantic information as rules for logical reasoning. Then, we utilize a Bayes algorithm to create a conditional probabilistic model for computing the semantic similarity of terms in two separate LULC land cover classification systems. The experiment shows that semantic similarity can be effectively measured by integrating a probabilistic model based on the content of ontology.

Hydrological Responses of Watershed to Historical and Future Land Use Land Cover Change Dynamics of Nashe Watershed, Ethiopia

Land use land cover (LULC) change is the crucial driving force that affects the hydrological processes of a watershed. The changes of LULC have an important influence and are the main factor for monitoring the water balances. The assessment of LULC change is indispensable for sustainable development of land and water resources. Understanding the watershed responses to environmental changes and impacts of LULC classes on hydrological components is vigorous for planning water resources, land resource utilization, and hydrological balance sustaining. In this study, LULC effects on hydrological parameters of the Nashe watershed, Blue Nile River Basin are investigated. For this, historical and future LULC change scenarios in the Nashe watershed are implemented into a calibrated Soil and Water Assessment Tool (SWAT) model. Five LULC scenarios have been developed that represent baseline, current, and future periods corresponding to the map of 1990, 2005, 2019, 2035, and 2050. The predicted increase of agricultural and urban land by decreasing mainly forest land will lead till 2035 to an increase of 2.33% in surface runoff and a decline in ground water flow, lateral flow, and evapotranspiration. Between 2035 and 2050, a gradual increase of grass land and range land could mitigate the undesired tendency. The applied combination of LULC prognosis with process-based hydrologic modeling provide valuable data about the current and future understanding of variation in hydrological parameters and assist concerned bodies to improve land and water management in formulating approaches to minimize the conceivable increment of surface runoff.

Assessment of Dynamic Land System in Nilgiri Biosphere Reserve Using MODIS Derived Temporal Data Sets during 2001 to 2018

The Nilgiri Biosphere Reserve (NBR) is one of the largest protected ecologically sensitive areas in India. This study examined the land use/land cover (LULC) changes in NBR for past 18 years from 2001 to 2018 to figure out the LULC changed within a protected area using datasets in 2001, 2010, and 2018 with the help of pertinent geospatial techniques. MODIS Land Cover Type Product (MCD12Q1) accuracy was quantitatively analyzed based on ground truth data and Google Earth imagery. Validation of data were assessed using and overall 635 locations for its accuracy assessment. The obtained kappa coefficient of 0.75, denotes the classification has moderate accuracy. The results showed that in the past 18 years, woody savannas and grasslands were reduced by 299.47 sq.km and 155.32 sq.km respectively. The areas of croplands and cropland/natural vegetation mosaics were also increased by 34.84 sq.km and 54.41 sq.km, respectively. These results showed anthropogenic influences through agricultural practices within the NBR buffer zones. The mixed forests were increased by 266.01 sq.km. One of the significant changes was seen in closed shrublands, which were absent in 2018, that covered 1.50 sq.km in 2001. In addition, A gradual decrease in the area were noticed in woody savannas. From the outcomes, it is recommended that the LULC classes that cover minimal area may be unstable, so measures should be taken for their conservation. The study proved the usefulness of MODIS land cover type data in monitoring large areas periodically for quick decision-making.

A machine learning approach to monitoring and forecasting spatio-temporal dynamics of land cover in Cox's Bazar district, Bangladesh from 2001 to 2019

This study uses machine learning techniques to monitor the areal extent of land use land cover (LULC) classes in Cox's Bazar district, a well-known tourist destination in Bangladesh from 2001 to 2019. The main objective of this study is to quantify the changes in LULC classes during this period and forecasting the areal extents classes for 2019 and 2025. Satellite imagery of 2001, 2005, and 2010 from Landsat 4-5 TM and, 2015, and 2019 from Landsat 8 OLI/TIRS were used to classify the study area into four LULC classes (water bodies, urban areas, vegetation, and bare land) using Random Forest (RF) classifier. These classes were used to forecast areal extents of land cover using Cellular Automata Simulation (CAS) for 2025. The eleven LULC classes from MODIS land cover type products (IGBP scheme) were also forecasted using RF regressor to compare prediction accuracy. This study observed an increase in vegetation cover and urban settlements by 26.65% and 2.52%, respectively, with decreased water bodies and bare lands by 17.25% and 11.91%. The overall accuracy of the classification is between 89.04% to 95.24%; CAS forecast accuracy is 93.2% and RF regressor forecast accuracy ranges from 87% to 99.8%. Grid search with cross-validations was performed to boost the accuracy of both models. The results are consistent with overall global trends of LULC change.

Ecosystem service variation and its importance to the wellbeing of smallholder farmers in contrasting agro‐ecological zones of East African Rift

AbstractSmallholder farmers lead their lives using multiple ecosystem services (ESs). Understanding the contribution of these services to smallholder farmers' wellbeing is essential for addressing ecosystem‐related problems. While the economic valuation of ESs has received major emphasis, smallholder farmer perceptions of ESs, ecosystem disservices (EDs), and the importance of their wellbeing are repeatedly overlooked. We analyzed variations in ES values (ESVs) in response to land use land cover (LULC) change, perceptions of ES/ED and the importance of these ecosystems to wellbeing in contrasting agro‐ecological zones (AEZs). To obtain datasets, we used geospatial technology, focus group discussion, and face‐to‐face cross‐sectional surveys in a highly populated area of the Southeastern escarpment of the Ethiopian Rift Valley. The research was carried out between October 1 and December 30, 2018. The results revealed the following. (1) Total area of 75,246.98 ha (34%) was changed to various LULC classes across AEZs. Woodland decreased at annual rates of 5.28% while agroforestry increased at annual rates of 1.03%, respectively. (2) ESV was estimated to be approximately $164 million and declined by approximately $24 million between 1988 and 2018, owing to the expansion of cultivated land. (3) More than 55% of smallholder farmers consistently identified benefits from food‐cereal and vegetables, food‐meat, and erosion control, problems associated with human disease vectors, as of very high importance for their wellbeing. ESs are the positive benefits while ecosystem functions that are perceived as negative for human wellbeing are termed as EDs. ESs were perceived as positively contributing to wellbeing, while EDs detracted from health and material wellbeing. The assigned values varied significantly with AEZs and socioeconomic groups, highlighting the need for careful consideration of site‐specific ecosystem management strategies that improve smallholder farmers' wellbeing and sustainable development. Moreover, the cost incurred due to ESV loss may not be covered by economic gains resulting from the expansion of cultivated land and thus, implementing appropriate land‐use policies at the local level would recuperate ES values.

Land Use/Land Cover Changes in the Tlemcen Region (Algeria) and Classification of Fragile Areas

The Tlemcen region is characterized by very diverse and steep areas exposed to gravity hazards, especially in high and medium mountain areas. Tlemcen National Park was chosen for this study, the main objective of which is to map fragile areas in close relation to reduced vegetation cover due to land-use changes and forest fires. Multi-source data were used to monitor land use/land cover (LULC)patterns in the study area between 1987 and 2017. The methodology is based on an object-oriented classification of the Landsat images, using the K nearest neighbor method for mapping the major LULC classes at the national park level. The results show that LULC is constantly changing in the study area. In 1987, the landscape was made up of (16.5%) oak forests (holm oak, cork oak, zean oak) and Aleppo pine, which then deteriorated following repeated fires in the nineties to barely represent 7.22% of the surface in 1995, followed by a fast forest reclamation, with the forest area doubling in 10 years (13.46% of the area in 2005), and a near stabilization of the forest cover in 2017 with 14.68% of the area. These mutations are mainly due to fluctuations in anthropogenic action. Despite past declines and disturbances, the current forested area in the Tlemcen area represents significant forest capital classified as a national park to be protected and developed.

An Alternative Method for the Generation of Consistent Mapping to Monitoring Land Cover Change: A Case Study of Guerrero State in Mexico

Land cover is crucial for ecosystems and human activities. Therefore, monitoring land cover changes has become relevant in recent years. This study proposes an alternative method based on conventional change detection techniques combined with maximum likelihood (MaxLike) supervised classification of satellite images to generate consistent Land Use/Land Cover (LULC) maps. The novelty of this method is that the supervised classification is applied in an earlier stage of change detection exclusively to identified dynamics zones. The LULC categories of the stable zones are acquired from an initial date’s previously elaborated base map. The methodology comprised the use of Landsat images from 2011 and 2016, applying the Sun Canopy Sensor (SCS + C) topographic correction model enhanced through the classification of slopes, using derived topographic corrected images with NDVI, and employing Tasseled Cap (TC) Brightness-Greenness-Wetness indices and Principal Components (PCs). The study incorporated a comparative analysis of the consistency of the LULC mapping, which is generated based on control areas. The results show that the proposed method, although slightly laborious, is viable and fully automatable. The generated LULC map is accurate and robust and achieves a Kappa concordance index of 87.53. Furthermore, the boundary consistency was visually superior to the conventional classified map.

Temporal dynamics change of land use/land cover in Jhansi district of Uttar Pradesh over past 20 years using LANDSAT TM, ETM+ and OLI sensors

Mapping and monitoring land use land cover (LULC) dynamics is vital for sustainable land development, planning and management. Using advanced techniques of remote sensing (RS) and geographic information system (GIS), the present study is an effort to monitor the changes in LULC patterns of Jhansi District of Uttar Pradesh for the periods 2000–2010 and 2010–2020. Satellite images from Landsat-5 Thematic Mapper (TM) for 2001; Landsat-7 Enhanced Thematic Mapper (ETM+) for 2010 and Landsat 8 (OLI) for 2020 were used to extract Land use/Land cover classes. Supervised classification using Maximum Likelihood Classifier (MLC) was applied to monitor LULC of the study area. Six major LULC classes viz; cropland, fallow/barren land, built-up area, water bodies and forest have been identified and showed that major land use in the district is crop land. The accuracy assessment of the classified map was done using 256 random points distributed all over the image. Results indicated that LULC in Jhansi district has undergone a series of changes over the past two decades. Results showed that crop land, built-up area and water bodies have increased by 27.16% (1367.26 Km2), 0.58% (29.4 Km2) and 0.3% (15.26 Km2), respectively while fallow/barren land and forest have decreased by27.55% (1386.58 Km2), and 0.5% (25.34 Km2), respectively. The research revealed that substantial shift from fallow/barren land (27.55%) into crop land. The analysis and findings of this study facilitate researchers, policy makers, land use planners and managers to implement the appropriate land-use management plan for Jhansi district.

GIS-based landform and LULC classifications in the Sub-Himalayan Kaljani Basin: Special reference to 2016 Flood

Progress in GIS technology has facilitated the topographic position index (TPI)-based auto-classification of landform units. Multispectral satellite images, when used along with terrain analysis output, provide valuable information on structure, soil, land use land cover (LULC), hazards etc. The present work makes an endeavour to classify landforms using TPI and assess its distribution pattern across different LULC segments in the Kaljani River Basin of the Sub-Himalayan region. An attempt is also made to analyse the inundation pattern during the most recent major flood (2016) across the landform and LULC units.The Kaljani Basin is occupied by upper slopes, mountain tops and gorges in its upper (northern) part, while its lower (southern) part is characterised by wide valleys and plains. The areas occupied by gorges and midslope drainages in the hills, and wide valleys in the foothills, are comparatively more exposed to fluvial erosion, as found from the topographic wetness index (TWI). LULC classification reveals that the northern and central parts of the basin have plenty of vegetation cover. Land use units like fallows, farmlands and built-up areas are more common in the southern part of the basin. During the flood of 2016, three-fourth of the total inundation occurred in the wide valleys and plains. More than 90% of the inundated areas are found in the human-modified terrain, indicating its susceptibility to the flood hazard.

Predicting changes in land use/land cover and seasonal land surface temperature using multi-temporal landsat images in the northwest region of Bangladesh

Land use/land cover (LULC) variations are accelerated by rapid urbanization and significantly impacted global Land Surface Temperature (LST). The dynamic increase in LST results in the Urban Heat Island (UHI) effect. In this study, future LULC change scenarios, seasonal (summer & winter) LST variations, and LST distribution over different LULC classes were predicted using Landsat satellite images for 1999, 2009, and 2019 in Rajshahi District, Bangladesh. Cellular Automata (CA) and Artificial Neural Network (ANN) procedures were used to predict the LULC changes and seasonal LST variations for 2029 and 2039. In addition, Focus Group Discussions (FGDs) and Key Informants Interviews (KIIs) were conducted to identify the possible impacts of LULC change, LST shifts, and climate change on agricultural productivity and developed a sustainable land use management plan for the study area. Validation of the CA model demonstrated an excellent accuracy with a kappa value of 0.82. Similarly, the ANN model's validation using Mean Square Error (0.523 and 0.796 for summer) and Correlation coefficient (0.6023 and 0.831 for winter) values demonstrated a good prediction accuracy. The LULC prediction result indicated that the built-up area will be expanded by 58.03 km2 and 79.90 km2, respectively, from 2019 to 2029 and 2039. The predicted seasonal LST indicated that in 2029 and 2039, more than 23.30 % and 50.46 % of the summer and 3.02 % and 13.02 % of the winter seasons will likely be experienced LSTs greater than 35 °C. The results of public participation exposed that changes in LULC classes, variations in LST, and climate change significantly impact the regional biodiversity (loss of farmland and water bodies), reduce agricultural productivity, and increase extreme weather events (flood, heavy rainfall, and cold/warm temperature). This study provides the useful guidelines for agricultural officers, urban planners, and environmental engineers to understand the spatial configurations of built-up area enlargement and provide effective policy measures to conserve farming lands to ensure environmental sustainability.

Classification of land use/land cover of Aniocha north local government area, Delta state using satellite imagery

Remote Sensing (RS) and Geographic Information System (GIS) have been established as indispensable tools in the assessment of Land use / Land cover (LULC) change. RS and GIS are important for the monitoring, modelling and mapping of land use and land cover changes across a range of spatial and temporal scales, in order to assess the extent, direction, causes, and effects of the changes. Change detection has provided suitable and wide-ranging information to various decision support systems for natural resource management and sustainable development. The main objective of the study is to assess and evaluate the extent and direction of changes in LULC of Aniocha North Local Government Area (LGA), Delta State, Nigeria to explain the changes and identify some of their effects on both the livelihoods of the local people and the local environment, and also to explore some of the conservation measures designed to overcome problems associated with land use and land cover changes. Landsat 7 Enhanced Thematic Mapper (ETM+) of 2002 with 30 meters resolution and landsat 7 Enhanced Thematic Mapper (ETM) 2014satellite images as well as GIS techniques were used to monitor the changes and to generate maps of the LULC of the area in these periods. Supervised Land Use/Land Cover classification algorithm (Maximum likelihood with null class) was used in the analysis of classification. The classification result of LandSat ETM+ (2002) revealed that farmland accounted for 36.34% of the total LULC class, followed by savannah which accounted for 24.15%. Forest built up area, and waterbody constituted 20.42%, 16.46% and 2.62% respectively. Also, the result of LandSat ETM (2014) shows that forest accounted for 38.59% followed by farmland with 30.93%. Built up area covers 25.55% while savannah and river cover 2.86% and 2.08% respectively. The classification shows 83.26 % average accuracy and 79.16 % overall accuracy for 2002 while the 2014 accuracy assessment showed 95.06% average accuracy and 94.99% overall accuracy. Growing population pressure and its associated problems, such as the increasing demand for land and trees, poor institutional and socio-economic settings, and also unfavorable government policies, such as lack of land tenure security and poor infrastructure development, have been the major driving forces behind the LULC changes.

Modeling the relationship between land use/land cover and land surface temperature in Dhaka, Bangladesh using CA-ANN algorithm

Rises in land surface temperature (LST) significantly impacted by land use/land cover (LULC) changes. The increase in LST massively impacted the urban biodiversity, ecosystem and population health. This study aims to estimate the changes in LULC classes and identify their impacts on LST in Dhaka city, Bangladesh using Landsat satellite images from 2000 to 2020. Based on the past estimated change maps of LULC and LST, the study finally predicted the future LULC and LST scenario for the year 2030. The support vector machine algorithm was applied to perform the LULC classification. Artificial neural network and cellular automata algorithms were used to predict the LST and LULC changes for 2030. Results suggested a significant reduction in vegetation cover (5%) and an increase in built-up area (14%) from 2000 to 2020. Due to this massive increase in built-up areas, the LST increment took place by 7.24 °C in the last two decades. The maximum temperature was recorded in built-up areas (34 °C), and water bodies (19 °C) exhibited minimum temperature. A strong positive correlation was found between LST and Normalized Difference Built-up Index (NDBI), where negative relation estimated between LST and Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI). The predicted results for 2030 also exhibit significant loss of urban green cover areas by 13% and rises in built-up areas by 21%. The maximum LST will likely be increased by 9.29 °C in the predicted year. For ensuring sustainable urban development and minimizing the urban heat island effects, this study will play a significant role by providing effective guidelines for urban planners, policymakers and respective authorities of Dhaka city.

Land use/land cover dynamics and perception of the local communities in Bita district, south western Ethiopia

ABSTRACT Human settlements and swift expansion of agriculture have simplified the natural ecosystems and causing the loss of copious biological diversity. The current study was conducted in Bita district, southwestern Ethiopia to examine the pattern of land use/land cover (LULC) dynamics using multispectral satellite data for the years 1973, 1986, 2000 and 2018. The remote-sensed data were processed using ERDAS imagine 2014, ArcGIS map 10.5, and ENVI software. A supervised maximum likelihood classification algorithm was employed along with the visual interpretation of satellite images to classify each Landsat image into LULC categories. Semi-structured interviews and Focus Group Discussions were employed to identify major driving forces, impacts, and periodic LULC changes. The result revealed an alarming decrease in forestland but with an increase in the cropland. During the study periods, cropland, bareland and settlement areas have augmented by over 450% each. Conversely, grasslands and forestlands have reduced by over 45% each. The local communities perceived the population growth, and the expansion of coffee plantations and other croplands as the dominant drivers of LULC changes. The respondents also observed that the decline in forest cover triggered the loss of biodiversity, soil fertility and water availability. Decision-making on issues related to natural resource conservation, utilization and participatory resource management has to involve the engagement of the local community and other stakeholders. The deterioration in ecosystem services such as lack of benefits from medicinal plants from the forest, increase land degradation, and the decrease in water availability found to be the impacts of LULC change. This calls for a proper natural resource management plan and improvement in the livelihood of the community in the study area.

EFFECTS OF GEOSPATIAL DATA SOURCES ON THE IDENTIFICATION AND CHARACTERIZATION OF BURNT AREAS IN PORTUGAL

Abstract. The work presented in this paper compares the burnt areas in continental Portugal in 2017 and 2018 mapped by three initiatives, namely the Portuguese Institute of Nature and Forests Conservation (ICNF), the Corine Land Cover (CLC) inventory of the Copernicus programme and the European Forest Fire Information System (EFFIS). Then, the Land Use Land Cover (LULC) classes affected by the 2017 burnt areas mapped by ICNF are analysed considering CLC 2018 and the 2018 LULC map produced by the Portuguese National Mapping Agency (Direção Geral do Território) – “Carta de Ocupação do Solo” (COS 2018). To enable a comparison between the classes of both LULC products, a nomenclature was selected and both CLC 2018 and COS 2018 were mapped into the chosen classes. The comparison of the burnt area’s extent showed that there are large differences in both area and levels of detail between the analysed data sources. The results regarding the LULC classes affected by the 2017 fires mapped by ICNF show large differences in terms of burnt area in each class as well as the proportion of the burnt areas associated to the classes. This analysis shows that very different results may be reached if different products are used, and therefore a large level of uncertainty is associated with the conclusions achieved with these products.

The Use of Land Cover Indices for Rapid Surface Urban Heat Island Detection from Multi-Temporal Landsat Imageries

The aims of this study were to determine surface urban heat island (SUHI) effects and to analyze the land use/land cover (LULC) and land surface temperature (LST) changes for 11 time periods from the years 2002 to 2020 using Landsat time series images. Bursa, which is the fourth largest metropolitan city in Turkey, was selected as the study area, and Landsat multi-temporal images of the summer season were used. Firstly, the normalized difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI), modified normalized difference water index (MNDWI) and index-based built-up index (IBI) were created using the bands of Landsat images, and LULC classes were determined by applying automatic thresholding. The LST values were calculated using thermal images and SUHI effects were determined. The results show that NDVI, SAVI, MNDWI and IBI indices can be used effectively for the determination of the urban, vegetation and water LULC classes for SUHI studies, with overall classification accuracies between 89.60% and 95.90% for the used images. According to the obtained results, generally the LST values increased for almost all land cover areas between the years 2002 and 2020. The SUHI magnitudes were computed by using two methods, and it was found that there was an important increase in the 18-year time period.

A Review on Land Use and Land Cover Change in Ethiopian Basins

Land Use Land Cover (LULC) changes analysis is one of the most useful methodologies to understand how the land was used in the past years, what types of detections are to be expected in the future, as well as the driving forces and processes behind these changes. In Ethiopia, Africa, the rapid variations of LULC observed in the last decades are mainly due to population pressure, resettlement programs, climate change, and other human- and nature-induced driving forces. Anthropogenic activities are the most significant factors adversely changing the natural status of the landscape and resources, which exerts unfavourable and adverse impacts on the environment and livelihood. The main goal of the present work is to review previous studies, discussing the spatiotemporal LULC changes in Ethiopian basins, to find out common points and gaps that exist in the current literature, to be eventually addressed in the future. A total of 25 articles, published from 2011 to 2020, were selected and reviewed, focusing on LULC classification using ArcGIS and ERDAS imagine software by unsupervised and maximum likelihood supervised classification methods. Key informant interview, focal group discussions, and collection of ground truth information using ground positioning systems for data validation were the major approaches applied in most of the studies. All the analysed research showed that, during the last decades, Ethiopian lands changed from natural to agricultural land use, waterbody, commercial farmland, and built-up/settlement. Some parts of forest land, grazing land, swamp/wetland, shrubland, rangeland, and bare/ rock out cropland cover class changed to other LULC class types, mainly as a consequence of the increasing anthropogenic pressure. In summary, these articles confirmed that LULC changes are a direct result of both natural and human influences, with anthropogenic pressure due to globalisation as the main driver. However, most of the studies provided details of LULC for the past decades within a specific spatial location, while they did not address the challenge of forecasting future LULC changes at the watershed scale, therefore reducing the opportunity to develop adequate basin-wide management strategies for the next years.

Land use/land cover change assessment of Kosasthalaiyar sub basin using remote sensing and GIS

Understanding Land use land cover (LULC) changes in the Kosasthalaiyar sub basin is vital for the sustainability of the watershed. Hence mapping of these changes can help visualize the spatio-temporal dynamics in the area. Remote sensing and GIS provides valuable tools to map these changes. In this present study, Remote sensing and GIS techniques has been used to prepare LULC maps for the years 1988, 1998, 2008, and 2018. The changes that has happened from 1988 to 1998, 1998 to 2008, and 2008 to 2018 has been assessed. Images from Landsat-5 and Landsat-8 Thematic Mapper (TM) have been used. Maximum Likelihood Classifier was adopted to produce the LULC maps of the watershed. Ground truth points using GPS was used to check the accuracy of the work. Five major land use classes viz; Built up land, agriculture, forest, waste land and water bodies were identified in the watershed for the study. During the 30-year period from 1988 to 2018, major changes can be observed in a number of LULC classes. About 66.05 Km2 of crop land got converted to barren land. Out of 588.86 Km2 of crop land only 176.99 Km2 remained unchanged. About 119.94 Km2 has been converted to settlements. Settlements increased from 59.89 Km2 to 341.86 Km2. Barren land increased from 168.73Km2 to 228.59 Km2. The analysis and the results obtained can help policy makers make appropriate policies for the sustainability of the Kosasthalaiyar sub basin.

COMPARISON OF PIXEL AND OBJECT BASED CLASSIFICATION METHODS ON RAPIDEYE SATELLITE IMAGE

The aim of this study is to evaluate the classification performances of land use/land cover (LULC) classification methods by comparing the results of pixel and object-based classification approaches on RapidEye satellite image. Pixel-based classification was carried out in ERDAS Imagine 10.4 using the Maximum Likelihood-supervised approach, whilst object-based classification was performed in e-Cognition Developer 64 using the nearest neighbour-supervised classification method. A LULC map of eight classes was created in both methods. While the accuracy for thematic LULC classes varied in both methods, the overall accuracy and kappa values of LULC maps for pixel and object-based classification methods were 58.39%-0.45 and 89.58%-0.86, respectively. Accuracy assessments and comparative results showed that object-based classification gives better results for thematic LULC classes as well as the overall accuracy of LULC maps. Even though pixel-based classification method was good at mapping many thematic classes, there were misclassifications between natural/semi-natural LULC classes. These results can be attributed to parameters set by users, such as the number of control points, etc. However, the capacity of object-based classification method to include auxiliary data (e.g. DEM, NDVI) increases the accuracy of LULC maps with high-resolution satellites.

Geospatially mapping carbon stock for mountainous forest classes using InVEST model and Sentinel-2 data: a case of Bagrote valley in the Karakoram range

Geospatially assessing carbon storage is an emerging field inland science and landscape ecology, particularly in the context of the provision of ecosystem services such as global climate regulation. However, modeling the spatial distributions of carbon stocks in the heterogeneous mountainous ecosystems always remained challenging due to fragile, inaccessible, and rough terrains. This paper aims to assess the status and spatial variations in the net amount and economic value of carbon stocked by the different forest land use/land cover (LULC) classes in Bagrote Valley, Gilgit-Baltistan. To achieve this, we used the InVEST carbon and sequestration model with the LULC types identified through the object-based classification of the Sentinel-2 imagery. The results show that the carbon mainly stocked in 6 forest types out of a total of 14 LULC classes. The total carbon stock in the valley is 8114.10 tonnes in 2016, for which the dense conifer forests are the major contributor with 5409.16 tonnes of carbon. The economic value of carbon storage is approximately 74728US$/ha in the study area. The developed methodology will help to measure carbon storage in different forest types and their effects on ecosystem services at the landscape level. The research output helps policymakers to simulate the ecosystem carbon storage and its trade-offs for various environmental and economic goals.