Land Use Cover Classification Research Articles

Assessing SAR C-band data to effectively distinguish modified land uses in a heavily disturbed Amazon forest

The Amazon is the largest expanse of tropical rainforest globally and deforestation resulting from land use changes poses a major concern for sustainable resource management. Synthetic aperture radar (SAR) data have all-weather and all-day capability, and thus are well-suited for mapping land use land cover (LULC) in tropical regions, which are seasonally influenced by cloud cover. Understanding modified land uses and drivers of deforestation is fundamental for the development of policies and measures to reduce emissions and for developing forest reference levels. Sentinel-1 C-band SAR data present unprecedented potential since the observations are free and openly available, providing for the first regular and standardize SAR data. This study analyzes the applicability of Sentinel-1 data for LULC classification as an effort to differentiate modified land uses, which is a current need for early-warning deforestation systems. The study area covers a deforestation frontier in the Peruvian Amazon where the landscape is characterized by a mosaic of LULC types. Collect Earth Online is used for reference LULC data collection, and seven classes are defined for this study: forest, secondary vegetation, agriculture, pasture, urban, mining, water. Amplitude γo time-series spanning 2017–2019 are analyzed along with statistical metrics for each class, and a classification decision tree is developed in Google Earth Engine. Overall accuracy obtained is considered low (52%). Results show high user's accuracy for forest and water classification, a lot of confusion between agriculture, secondary vegetation, and forest, and the use of the polarization ratio VV/VH is suggested to be useful for pasture classification. The orientation of streets in a urban environment is confirmed to have high influence on backscattering response. This study provides information for future research on LULC and the identification of drivers in deforestation monitoring systems that could result in additional actionable information for decision-making.

Scenario-based estimation of catchment carbon storage: linking multi-objective land allocation with InVEST model in a mixed agriculture-forest landscape

This study performed a scenario-based land allocation in a mixed agriculture-forest landscape in northern Iran to investigate how different land use policies contribute to changes in carbon storage. In pursuit of this goal, a temporal profile of the trade-off between the region’s land use land cover (LULC) classes was produced using Landsat image of the year 2016. The weighted linear combination procedure was also used to map the suitability of land for agriculture, forest, urban, and rangeland based on ecological and socio-economic criteria. The suitability maps were analyzed through the Multi-Objective Land Allocation procedure under five scenarios with differing areas devoted to each LULC to generate different patterns of LULC distribution in the region. In addition, the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model was used to estimate the potential of LULC classes in carbon storage. The amount of carbon storage differed significantly between the scenarios, ranging from 1.29 tons/ha/year when the majority of the land was devoted to agriculture (76% of the area) to 5.40 tons/ha/year when the landscape was dominated by forest (77% of the area). The extreme conditions presented in this research may not be as likely to occur, but opens a dialog between different stakeholders and informs of a probable trend of ecosystem service loss due to agricultural land expansion.

TEXTURE ANALYSIS FOR LAND USE LAND COVER (LULC) CLASSIFICATION IN PARTS OF AHMEDABAD, GUJARAT

Abstract. The present study addresses the potential of RADARSAT-2 data for Land Use Land Cover (LULC) Classification in parts of Ahmedabad, Gujarat, India. Texture measures of the original SAR data were obtained by the Gray Level Co-occurrence Matrix (GLCM). Results suggested False Colour Composite (FCC) of Mean, Homogeneity and Entropy showed a good discrimination of different land cover classes. Further, Principal Component Analysis (PCA) was also applied to the eight texture measures and FCC of Principal components is generated. Unsupervised classification is carried out for the above generated FCCs and accuracy assessment is carried out. The result of classification shows that the PCA generated from GLCM texture measures could obtain higher accuracy than using only the classification carried out by texture measures. Overall results of the study suggested possible use of single polarization and single date Radarsat-2 data for LULC classification with better accuracy using PCA generated image.

The Assessment of land use land cover and carbon sequestration in forests of Joida Taluk of Uttar Kannada district using Remote sensing technique

The study was conducted in Joida Taluk of Uttar Kannada district, Karnataka to assess the land use land cover (LULC) and carbon sequestration of the forest during the year 2019-20. The ground truth data for different LULC was collected using GPS, and data was used for classification of IRS LISS 4 data using maximum likelihood classifier in ERDAS imagine software. The sample plots of 23.2 m X 23.2 m were laid out randomly in forests and growth parameters (tree height and diameter) were recorded, and biomass was estimated using the standard formula. There are eight LULC classes’ viz., dense forest, moderately dense forest, open/sparse forest, scrub forest, agriculture, settlement, horticulture plantation and waterbody. The overall accuracy of the classification was 97.09%. The total biomass in Joida Taluk from four forest classes (dense forest, moderately dense forest, open/sparse forest and scrub forest) was 44.16 million m3 and carbon sequestered was 15.57 million tonnes. The NDVI values ranging from -0.23 to 0.74, indicating a higher value for dense forest. Based on this study, it is concluded that forests have potential in carbon sequestration, which in turn helps in mitigating the climate change.

Use of Remote Sensing in Comprehending the Influence of Urban Landscape’s Composition and Configuration on Land Surface Temperature at Neighbourhood Scale

The spatial composition and configuration of land use land cover (LULC) in the urban landscape impact the land surface temperature (LST). In this study, we assessed such impacts at the neighbourhood level of the City of Edmonton. In doing so, we employed Landsat-8 Operational Land Imager (OLI) and Thermal Infrared Sensors (TIRS) satellite images to derive LULC and LST maps, respectively. We used three classification methods, such as ISODATA, random forest, and indices-based, for mapping LULC classes including built-up, water, and green. We obtained the highest overall accuracy of 98.53 and 97.90% with a kappa value of 0.96 and 0.92 in the indices-based method for the 2018 and 2015 LULC maps, respectively. Besides, we estimated the LST map from the brightness temperature using a single-channel algorithm. Our analysis showed that the highest contributors to LST were the industrial (303.51 K in 2018 and 295.99 K in 2015) and residential (303.47 K in 2018 and 296.56 K in 2015) neighbourhoods, and the lowest contributor was the riverine/creek (298.77 K in 2018 and 292.89 K in 2015) during the 2018 late summer and 2015 early spring seasons. We also found that the residential neighbourhoods exhibited higher LST in comparison with the industrial with the same LULC composition. The result was also supported by our surface albedo analysis, where industrial and residential neighbourhoods were giving higher and lower albedo values, respectively. This indicated that the rooftop materials played further role in impacting the LST. In addition, our spatial autocorrelation (local Moran’s I) and proximity (near distance) analyses revealed that the structural configurations would additionally play an important role in contributing to the LST in the neighbourhoods. For example, the cluster pattern with a small gap of minimum 2.4 m between structures in the residential neighbourhoods were showing higher LST in compared with the sparse pattern, with large gaps between structures in the industrial areas. The wide passages for wind flow through the large gaps would be responsible for cooling the LST in the industrial neighbourhoods. The outcomes of this study would help planners in planning and designing urban neighbourhoods, and policymakers and stakeholders in developing strategies to balance surface energy and mitigate local warming.

ASSESSING THE ACCURACY OF LAND USE LAND COVER (LULC) MAPS USING CLASS PROPORTIONS IN THE REFERENCE DATA

Abstract. Traditionally the accuracy assessment of a hard raster-based land use land cover (LULC) map uses a reference data set that contains one LULC class per pixel, which is the class that has the largest area in each pixel. However, when mixed pixels exist in the reference data, this is a simplification of reality that has implications for both the accuracy assessment and subsequent applications of LULC maps, such as area estimation. This paper demonstrates how the use of class proportions in the reference data set can be used easily within regular accuracy assessment procedures and how the use of class proportions can affect the final accuracy assessment. Using the CORINE land cover map (CLC) and the more detailed Urban Atlas (UA), two accuracy assessments of the raster version of CLC were undertaken using UA as the reference and considering for each pixel: (i) the class proportions retained from the UA; and (ii) the class with the majority area. The results show that for the study area and the classes considered here, all accuracy indices decrease when the class proportions are considered in the reference database, achieving a maximum difference of 16% between the two approaches. This demonstrates that if the UA is considered as representing reality, then the true accuracy of CLC is lower than the value obtained when using the reference data set that assigns only one class to each pixel. Arguments for and against using class proportions in reference data sets are then provided and discussed.

Monitoring of Changes in Land Use/Land Cover in Syria from 2010 to 2018 Using Multitemporal Landsat Imagery and GIS

Understanding the effects of socio-ecological shocks on land use/land cover (LULC) change is essential for developing land management strategies and for reducing adverse environmental pressures. Our study examines the impacts of the armed conflict in Syria, which began in mid-2011, and the related social and economic crisis on LULC between 2010 and 2018. We used remote sensing for change detection by applying a supervised maximum likelihood classification to Landsat images of the three target years 2010, 2014, and 2018. Based on the computed extent of our LULC classes and accuracy assessment, we calculated area-adjusted estimates and 95% confidence intervals. Our classification achieved an overall accuracy of 86.4%. Compared to 2010, we found an increase in spatial extent for bare areas (40,011 km2), forests (2576 km2), and urban and peri-urban areas (3560 km2), whereas rangelands (37,005 km2) and cultivated areas (9425 km2) decreased by 2018. It is not possible to determine whether the changes in LULC in Syria will be permanent or temporary. Natural conditions such as climate fluctuations had an impact on the uses of the natural environment and cultivated areas during the study period, especially in regions suffering from water stress. Although seasonal precipitation patterns and temperature affect LULC change, however, we could not identify a prevailing climate trend towards more drought-prone conditions. Our analysis focuses on (potential) direct and indirect implications of the Syrian conflict on LULC change, which most notably occurred between 2014 and 2018. Conflict-related main drivers were human activities and demographic changes, which are mainly attributable to large-scale population displacement, military operations, concomitant socio-economic status, and control of local resources. As the study provides quantitative and qualitative information on the dynamics of LULC changes in Syria, it may serve as a framework for further relevant conflict-related research and support planning, management practices, and sustainable development.

Land use/land cover changes and bare soil surface temperature monitoring in southeast Brazil

The land surface temperature (LST) provides important information about energy exchange processes, which are influenced by land use/land cover (LULC). Thus, our objective was to evaluate LST patterns driven by LULC changes, detected over a time series of Landsat images. The study area of 2990 km2 is located in the Piracicaba region, state of São Paulo, Brazil. We acquired Landsat images from 1985 to 2019, in dry and moist seasons. Six LULC classes (agriculture, bare soil, straw, forest, water, and pasture) were identified by maximum-likelihood supervised classification every five years and then LST was estimated using the inversion of Planck’s function in the thermal band. Spectral indices representing vegetation, water, bare soil, and straw were calculated and correlated to LST in specific years. Bare soil images and their respective LST in both seasons were used annually to approach the influence of bare soil areas on the LST, considering soil class, time and rainfall. LULC alterations over 1985–2015 were an important factor on the LST change, which varied on average from 21.46 °C to 41.31 °C in the moist season and 17.05 °C to 31.67 °C in the dry one. Water bodies and vegetation had the lowest LST values, whereas bare soil and straw had the highest ones. The correlation between LST and spectral indices somewhat agreed with such patterns. Arenosols presented the highest LST mean values in both seasons and differed from Acrisols in the dry season, which is probably related to their texture and mineralogical composition. In the moist season, LST was negatively correlated to rainfall, suggesting the influence of soil moisture content on its surface temperature. In the dry season, the LST of bare soil areas increased by an average of 0.13 °C per year, indicating a warming trend. In general, LST increased in the studied period, probably due to the increase of anthropic activity, such as the expansion of agricultural areas. These findings can assist future studies on the influence of soils and land use on climate alterations.

Analyses of land use land cover (LULC) change and built-up expansion in the suburb of a metropolitan city: Spatio-temporal analysis of Delhi NCR using landsat datasets

During past four decades, in post economic reforms period, Delhi and its surrounding regions has attracted a large number of populations which led to the rapid transformation of its LULC pattern. Therefore, this study is aimed to analyze the LULC changes during 1990–2018 as well as the growth and pattern of built-up surfaces in relation to the population growth and migration in the suburbs of Delhi metropolitan city which is also known as the National Capital Region (NCR). The Landsat 5 (TM) and Landsat 8 (OLI/TIRS) data has been used for the LU/LC classification of Delhi NCR. The K means clustering technique was applied on the Landsat data for the LULC classification and then the change detection technique was used to quantify the LULC change. The result shows that the considerable changes in LULC have occurred with continuous increase in built-up area and open/fallow land and decrease in agriculture land and vegetation over the study time period. Built-up area increased by about 326 percent and open/fallow land by 44 percent while the agricultural land and vegetation cover have decreased by 12 percent and 34 percent of the total area of study respectively during the study period. Built-up area has mostly increased at the expense of agricultural land and vegetation cover while vegetation cover has been transformed into Built-up area, Ridge and Agriculture. The statistical analysis shows that the association between built-up expansion and the population and migrants varies from weak to high but the coefficient of determination was always positive.

Tracking conservation effectiveness in the Vhembe Biosphere Reserve in South Africa using Landsat imagery.

Biosphere reserves (BRs) seek to reconcile a sustainable relationship between human welfare and environmental integrity by adopting a landscape model that distinguishes between three interdependent management zones (core, buffer, transition). Considering the increasing human influence on landscapes in BRs, the tracking land use-land cover (LULC) dynamics is crucial for the development and planning of efficient management strategies for specific management zone. This study aimed at (i) assessing biodiversity protection around the core zones to highlight the threats facing the core zones and (ii) tracking the effect of the proclamation of the Vhembe Biosphere Reserve (VBR) on the LULC dynamics in the management zones through spatio-temporal analysis using Landsat imagery acquired from1999 to 2018. Six LULC categories (water body, forest/bush, shrubs/grass, agricultural land, bare soil, and built-up/mines) were identified and mapped using the support vector machine (SVM) classification to address both objectives. Assessment of threats around the core zones using artificial buffers (0-5, 10-15, and 15-20km radius) created around them showed agricultural activities in the most immediate buffers (0-5km radius). The LULC dynamics showed vegetation increase in all the management zones evidenced by the reduction of bare soil as well shrub/grass lands, and by the corresponding increase in foliage-richer forest/bush lands since the proclamation of the reserve in 2009. The findings might signify a positive outcome of vegetation increase as a consequence of the proclamation of the VBR. However, firmer conservation measures must be adopted and priority must be given to the arrangement of the management zones to strengthen biodiversity protection in the core zone.

Modeling the artificial lake-surface area change in arid agro-ecosystem: A case study in the newly reclaimed area, Egypt

Land reclamation is critically required to overcome the environmental and anthropogenic challenges in arid lands. The Western Nile Delta region, Egypt, is experiencing rapid reclamation processes for agriculture expansion. West Nubaria (781.92 km2) is one of the newly reclaimed areas in the Western Nile Delta. Due to extensive agricultural practices and poor management, an artificial saline lake formed in this area. Two primary goals of this research; 1) monitoring the annual change in the lake surface area between 2013 and 2017. 2) Predicting the areal extent of the lake surface in 2020, 2030, and 2040 based on two management scenarios. The maximum likelihood classifier (MLC) was applied to distinguish the LULC classes in 2017. Additionally, the annual modified normalized difference water index (MNDWI) calculated between 2013 and 2017. Then, the land change modeler (LCM) was utilized to predict the 2017 free water area based on the resulted MNDWI maps of 2013 and 2016 using two scenarios. With the high agreement between the actual and predicting free water area of 2017 (Kappa index = 0.93), the LCM was applied to predict the future surface water expansion in 2020, 2030, and 2040. Three land use/land cover (LULC) distinguished in 2017; agricultural land, uncultivated land, and free water class based on MLC. The MNDWI results reveal that there was an increase in the surface water area from 593 to 883 ha between 2013 and 2017, respectively. The LCM results indicate that expected increases in the surface water areas of 1068, 1711, and 2267 ha in 2020, 2030 and 2040, respectively (scenario 1) and 1065, 1726, and 2343 ha in the respective dates (scenario 2). These extend will exist over the agricultural and uncultivated lands surrounding the lake causing land degradation. Two solutions were suggested to combat the waterlogging and land degradation in this area by evacuating the artificial saline lake.

Investigating the Effects of Land Use and Land Cover on the Relationship between Moisture and Reflectance Using Landsat Time Series

To better understand the Earth system, it is important to investigate the interactions between precipitation, land use/land cover (LULC), and the land surface, especially vegetation. An improved understanding of these land-atmosphere interactions can aid understanding of the climate system and modeling of time series satellite data. Here, we investigate the effect of precipitation and LULC on the reflectance of the land surface in the northern U.S. Great Plains. We utilize time series satellite data from the 45 year Landsat archive. The length of the Landsat record allows for analysis of multiple periods of drought and wet conditions (reflecting climate, as well as weather), such that the precipitation-reflectance relationship can be investigated robustly for every individual pixel in the study area. The high spatial resolution of Landsat (30 m) allows for investigation of spatial patterns in weather (i.e., precipitation extremes) interactions with land surface reflectance at the scale of individual fields. Weather history is represented by a drought index that describes effective moisture availability, the Standardized Precipitation and Evaporation Index (SPEI). We find that effective moisture has a robust and consistent effect on reflectance over many types of land cover, with ∼90% of all pixels having significantly ( p < 0.01 ) higher visible reflectance during dry periods than during wet, occurring in nearly all regional, temporal, and LULC categories investigated. In grassland, the relationship is especially strong; there is an average reflectance increase of more than a third between very wet and very dry conditions (red band), and ∼99% of pixels have a significant relationship. In cropland, the effective moisture-reflectance relationship is more variable, suggesting that management decisions are an important factor in cropland-reflectance relationships.

Integrating Multiresolution and Multitemporal Sentinel-2 Imagery for Land-Cover Mapping in the Xiongan New Area, China

Accurate land use/land cover (LULC) mapping over a large area is essential to environmentally sustainable development. Recently, the Chinese government established a new national economic zone called the Xiongan New Area, and along with the upcoming large-scale urban construction, this area will inevitably experience a dramatic LULC change, which will threaten the local ecological balance. In this article, we proposed a two-stage approach for LULC mapping in the Xiongan New Area ahead of the forthcoming dense urban construction. The first stage is to obtain base-class maps through a supervised imagery classification. Specifically, we designed a new object-based framework consisting of automatic image segmentation, pixel-based probabilistic estimation, and area-weighted probability statistics for Sentinel-2 multiresolution imagery classification. The second stage is an LULC map refinement process in which the temporal features of each land use category are extracted to refine the LULC classification. Through the implementation of the proposed two-stage approach, an LULC map containing permanent water, temporal water, natural vegetation, barren land, built-up land, and cropland categories can be produced. Through an accuracy assessment, the proposed multiresolution imagery classification method achieved the highest overall accuracy of 88.58% and an average accuracy (AA) of 87.78% compared with conventional classification methods. After obtaining the refined LULC map, we find that the current Xiongan New Area is in a less developed state, that is, cropland accounts for the highest proportion of 51.59%, which is followed by natural vegetation (22.38%) and built-up land (15.69%).

A Cloud-Based Evaluation of the National Land Cover Database to Support New Mexico’s Food–Energy–Water Systems

Accurate estimation of land use/land cover (LULC) areas is critical, especially over the semi-arid environments of the southwestern United States where water shortage and loss of rangelands and croplands are affecting the food production systems. This study was conducted within the context of providing an improved understanding of New Mexico’s (NM’s) Food–Energy–Water Systems (FEWS) at the county level. The main goal of this analysis was to evaluate the most important LULC classes for NM’s FEWS by implementing standardized protocols of accuracy assessment and providing bias-corrected area estimates of these classes. The LULC data used in the study was based on National Land Cover Database (NLCD) legacy maps of 1992, 2001, 2006, 2011, and 2016. The analysis was conducted using the cloud-based geospatial processing and modeling tools available from System for Earth Observation Data Access, Processing, and Analysis for Land Monitoring (SEPAL) of the Food and Agricultural Organization. Accuracy assessment, uncertainty analysis, and bias-adjusted area estimates were evaluated by collecting a total of 11,428 reference samples using the Open Foris Collect Earth tool that provided access to high spatial and temporal resolution images available in Google Earth. The reference samples were allocated using a stratified random sampling approach. The results showed an overall accuracy that ranged from 71%–100% in all six study counties. The user’s and producer’s accuracy of most LULC classes were about or above 80%. The obtained bias-adjusted area estimates were higher than those based on pixel counting. The bias-adjusted area estimates simultaneously showed decreasing and increasing trends in grassland and shrubland, respectively in four counties that include Curry, Roosevelt, Lea, and Eddy during the 1992–2016 period. Doña Ana county experienced increasing and decreasing trends in grassland and shrubland areas, respectively. San Juan county experienced decreasing trends in both grassland and shrubland areas. Cultivated cropland areas showed decreasing trends in three counties in southeast NM that rely on groundwater resources including Curry, Roosevelt, and Lea. Similarly, cultivated cropland areas showed increasing trends in the other three counties that rely on surface water or conjunctive use of surface and groundwater resources including San Juan, Doña Ana, and Eddy. The use of SEPAL allowed for efficient assessment and production of more accurate bias-adjusted area estimates compared to using pixel counting. Providing such information can help in understanding the behavior of NM’s food production systems including rangelands and croplands, better monitoring and characterizing NM’s FEWS, and evaluating their behavior under changing environmental and climatic conditions. More effort is needed to evaluate the ability of the NLCD data and other similar products to provide more accurate LULC area estimates at local scales.

Multi-temporal image analysis for LULC classification and change detection

ABSTRACT Land use/Land cover (LULC) plays a vital role in planning and supervising the utilization of the natural resources based on the gradual increase in the human demands in the current ecosystem. This study mainly focuses on the usage of the geographic information system (GIS) and land usage to identify the changes in Ananthapuramu, which is located in the Ananthapur district of Andhra Pradesh (AP) state, South India. This paper mainly focuses on the classification and identification of the changes in LULC in the period of 1999–2019 based on a survey of India’s topographic map and temporal images gathered from the satellite. The experimental results indicate that over the specified period of study, the rate of increment and decrement in the built-up area, rate of underwater bodies, forest bodies that may demonstrate a significant impact on the environmental ecosystem.

Multi-temporal LULC Classification using Hybrid Approach and Monitoring Built-up Growth with Shannon’s Entropy for a Semi-arid Region of Rajasthan, India

Abstract Land use land cover (LULC) classification of Churu district of Rajasthan state in India was done through hybrid classification technique. Landsat imageries of 1998, 2008 and 2018 were used to ascertain the rate and nature of spatio-temporal LULC changes. Major focus was given on vegetative cover change detection of the study area. On the basis of field survey and standard classification classes, the land use classes of the study area were divided into eleven classes. A detailed vegetative classification has been done while categorizing the classes. This classification method employed maximum likelihood and ISODATA clustering. The decision tree approach was used to create the multi-temporal hybrid LULC classification. The accuracy assessment results have shown excellent results at 91% overall accuracy with a kappa of 0.92. The results indicated that agriculture, crop land dominated the land use of Churu district while natural vegetation (forest areas) had the least share in land cover during the entire study period from 1998 to 2018. Shannon’s entropy index was used to determine the changes in spatial distributional pattern of built up during the period 1998 to 2018 for the study area in general and also for each of the eight sub-administrative regions (tehsils). The increase in the built up area in the study area during the period of 1998 to 2018 was quite paltry with a general dispersed type of built up. The increase varied from moderate to nominal with the entropy value decreasing from 0.84 to 0.71 for the study area in general.

Land use change mapping and its impact on storm water runoff using Remote sensing and GIS: a case study of Mumbai, India

The rapid increase in urbanization worldwide has induced a change in land use–land cover (LULC) over the years. In Mumbai, India, this has further altered the hydrological processes like infiltration, evapotranspiration, interception and erosion and also caused loss of current drainage capacity and flooding in urban areas. This paper mainly assesses the changes in land-use land-cover types and its impact on storm water discharge from 1973 to 2018 in Mumbai using ArcGIS software. The image was classified into five LULC categories after superimposing in Arch-GIS, such as forest, open land, wetland, water and built-up area. The analysis of LULC pattern for the area under study over a period of 45 years showed that there was 66% rise in the built-up area whereas the Forest, open land and Wetland and water body together had reduced by 21%, 36% and 28%, respectively with respect to overall catchment area of Mumbai. Also, the analysis results gave a rise in peak discharge of storm water by 36% because of the changes in LULC over the given time period. On the whole, the land area affected by water logging has significantly increased over the time. The study therefore indicates that the integration of GIS and remote sensing was an effective way of approach for assessing the process of land-use land-cover change.

Land Use / Land Cover Change And Impact On Carbon Stocks In The Atacora Chain Of Mountains, A Biodiversity Hotspot In Benin (West Africa)

Mountain areas are fragile ecosystems that play important roles in people’s livelihoods and maintenance of the global ecosystem through the provision of many ecosystem services. Land use/cover (LULC) change is considered one of the major threats to mountain areas due to its effects on ecosystem services including carbon stocks. In this study we assessed LULC change between 1987 and 2015 and its impact on aboveground carbon stocks in the Atacora Chain of Mountains (ACM) in Benin, West Africa. Supervised classification was performed to delineate LULC classes on three dates (1987, 2001 and 2015), and forest measurements carried out in the land cover classes, to estimate the aboveground biomass and the subsequent carbon stocks. Seven land cover classes were delineated: gallery forests, woodlands, savanna, water, settlements, bare lands and farm lands. LULC changes were characterized by three transitions: 1) the change of man-made land cover into savanna, 2) the change of natural vegetation into man-made land cover and 3) the degradation of gallery forests and woodland into less wooded vegetation. The aboveground carbon stock in gallery forests, woodland and tree savanna were significantly greater than in shrub savanna. During the 28 years of assessment, LULC change in gallery forests, woodland and savanna caused an estimated overall aboveground carbon release of 17.10% in the ACM. From the aboveground carbon quantity in the ACM, it appeared that this ecosystem is a potential carbon reservoir. Because the aboveground carbon stock in shrub savanna is significantly lower as compared to gallery forests, woodland and tree savanna and the rates of degradation from gallery forests and woodland to savanna are high, 53.62% and 59.99% respectively in 28 years, LULC change may undermine the ACM ability to store carbon and contribute to climate change mitigation. There is a need to investigate the drivers of this degradation for actions to preserve the natural vegetation in the ACM. Keywords: Atacora Chain of Mountains; Land use/land cover (LULC); Benin; West Africa; Carbon stock DOI: 10.7176/JEES/10-6-13 Publication date: June 30 th 2020

Evaluating Dominant Land Use/Land Cover Changes and Predicting Future Scenario in a Rural Region Using a Memoryless Stochastic Method

The present study used the official Portuguese land use/land cover (LULC) maps (Carta de Uso e Ocupação do Solo, COS) from 1995, 2007, 2010, 2015, and 2018 to quantify, visualize, and predict the spatiotemporal LULC transitions in the Beja district, a rural region in the southeast of Portugal, which is experiencing marked landscape changes. Here, we computed the conventional transition matrices for in-depth statistical analysis of the LULC changes that have occurred from 1995 to 2018, providing supplementary statistics regarding the vulnerability of inter-class transitions by focusing on the dominant signals of change. We also investigated how the LULC is going to move in the future (2040) based on matrices of current states using the Discrete-Time Markov Chain (DTMC) model. The results revealed that, between 1995 and 2018, about 28% of the Beja district landscape changed. Particularly, croplands remain the predominant LULC class in more than half of the Beja district (in 2018 about 64%). However, the behavior of the inter-class transitions was significantly different between periods, and explicitly revealed that arable land, pastures, and forest were the most dynamic LULC classes. Few dominant (systematic) signals of change during the 1995–2018 period were observed, highlighting the transition of arable land to permanent crops (5%) and to pastures (2.9%), and the transition of pastures to forest (3.5%) and to arable land (2.7%). Simulation results showed that about 25% of the territory is predicted to experience major LULC changes from arable land (−3.81%), permanent crops (+2.93%), and forests (+2.60%) by 2040.

Assessing the impact of land use land cover changes on land surface temperature over Pune city, India

The intensification of land surface temperature (LST) is a consequence of changes in land use land cover (LULC). Variation of LST and LULC can be extracted from satellite images in different spatial and temporal resolutions. Thus, in the present study, we have used remote sensing and GIS techniques to quantify the land surface temperature variation over different land use land cover classes from 1990 to 2019 using Landsat data in Pune city. Furthermore, the effect of LULC changes on seasonal land surface temperature modification over the urbanized area is also assessed in this study. The result suggests an overall increase of built-up area from 116.6 km2 in 1990 to 166.9 km2 in 2019, witnessing a 43.1% rise during the study period, while the other LULC classes show a decreasing trend, most significantly by agriculture (40.8%) followed by scrubland (37.1%), fallow land (22%), water body (11.1%) and vegetation (2.8%). The 2009–2019 decade was found to be most significant in terms of change in LULC aerial extent over the city as compared to the other two decades (1990–1999 and 1999–2009). The overall mean land surface temperature over the city shows an increasing trend during the summer season (5.8%) and decreasing in the winter season (12.4%) from 1990 to 2019. The change in mean LST over different LULC classes shows a significant increasing and positive trend during the summer season as compared to the negative and decreasing trend of the winter season. The mean LST over agriculture, shrub land, water body, and fallow land shows an increasing trend as compared to the built-up area during both summer and winter season from 1990 to 2019. Furthermore, the study also confirms a higher LST over the surrounding rural area by 1.4 °C, as compared to the city urban area, which suggests the occurrence of the urban cool island over the city. The results of the present study will be very helpful for an urban planner and policymakers to evaluate the current urbanization status and improve their decisions to make cities better and sustaining in the future.