Land Use Cover Information Research Articles

Environmental Monitoring of Land Use/ Land Cover by Integrating Remote Sensing and Machine Learning Algorithms

Evaluation of the land use/ land cover (LULC) case over large regions is very important in a variety of domains, including natural resources such as soil, water, etc., and climate change risks and LULC change has emerged as a high anxiety for the environment. Therefore, we tested and compared the performance of three classification algorithms: Support Vector Machines (SVM), Random Trees (RT), and Maximum Likelihood (MaxL) to derive and extract LULC information for the district of Sarayönü/ Konya across five distinct classes: water, plantation, grassland, built-up, and bare land. Two remote sensing indices, the normalized difference vegetation index (NDVI) and the normalized difference water index (NDWI), were used as supplementary inputs for the classification of LULC. To evaluate the performance of the algorithms, a confusion matrix was employed. The average overall accuracy of support vector machines, random trees, and maximum likelihood algorithms was found 85.60%, 79.20%, and 74.80%, respectively, and 82.00%, 74.00%, and 68.50% for the Kappa coefficient. These results indicate that the support vector machines algorithm outperforms other algorithms in terms of accuracy. As a result of the research, it was determined that classification algorithms integrated with remote sensing in LULC change monitoring/determination could produce accurate classification maps that can be used as base data. This is due to the ability of machine learning algorithms to learn complex patterns, adapt to diverse data, and continuously improve, making them achieve higher accuracy compared to traditional classifiers. Therefore, their use was recommended for decision-makers.

Evaluating Machine-Learning Algorithms for Mapping LULC of the uMngeni Catchment Area, KwaZulu-Natal

Analysis of land use/land cover (LULC) in catchment areas is the first action toward safeguarding freshwater resources. LULC information in the watershed has gained popularity in the natural science field as it helps water resource managers and environmental health specialists develop natural resource conservation strategies based on available quantitative information. Thus, remote sensing is the cornerstone in addressing environmental-related issues at the catchment level. In this study, the performance of four machine learning algorithms (MLAs), namely Random Forests (RFs), Support Vector Machines (SVMs), Artificial Neural Networks (ANNs), and Naïve Bayes (NB), were investigated to classify the catchment into nine relevant classes of the undulating watershed landscape using Landsat 8 Operational Land Imager (L8-OLI) imagery. The assessment of the MLAs was based on a visual inspection of the analyst and commonly used assessment metrics, such as user’s accuracy (UA), producers’ accuracy (PA), overall accuracy (OA), and the kappa coefficient. The MLAs produced good results, where RF (OA = 97.02%, Kappa = 0.96), SVM (OA = 89.74%, Kappa = 0.88), ANN (OA = 87%, Kappa = 0.86), and NB (OA = 68.64%, Kappa = 0.58). The results show the outstanding performance of the RF model over SVM and ANN with a significant margin. While NB yielded satisfactory results, its sensitivity to limited training samples could primarily influence these results. In contrast, the robust performance of RF could be due to an ability to classify high-dimensional data with limited training data.

A comprehensive systematic review: Impact of Land Use/ Land Cover (LULC) on Land Surface Temperatures (LST) and outdoor thermal comfort

Globally, urbanization-driven changes in Land Use and Land Cover (LULC) jeopardize Land Surface Temperatures (LSTs) and the outdoor thermal comfort of urban residents. While urban heat stress's impact on outdoor thermal comfort garners ample scholarly attention, research on the parallel influence of LULC remains limited. This paper comprehensively reviews prevalent tools for collecting LULC data, criteria for evaluating LST and outdoor thermal comfort, correlation measures and mitigation strategies. Focused on peer-reviewed publications from the last 25 years, our data is drawn from Scopus, Web of Science, Google Scholar and Science Direct.Our findings reveal Landsat and geographic information systems (GIS) as predominant datasets and processing platforms for LULC and LST information delineation. Common outdoor thermal comfort indices include the Universal Thermal Climate Index (UTCI), Physiologically Equivalent Temperature (PET) and Surface Urban Heat Island Intensity (SUHII). Validation employs Normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI) and Pearson's correlation. Geographically, Asia and Africa feature prominently in studies, with Europe comparatively underrepresented. We also highlight key LULC mitigation strategies, including augmenting vegetation in moderate climates, effective albedo management in warmer climates, and optimizing urban layouts for enhanced outdoor thermal comfort.

Spatial–Temporal Changes in Land Use and Their Driving Forces in the Circum-Bohai Coastal Zone of China from 2000 to 2020

Over the past two decades, the location and morphology of the coastline, as well as the land use/land cover (LULC) in the Circum-Bohai region in China, have undergone significant changes due to rapid industrialization and urbanization. Analyzing the temporal and spatial variation in coastal lines and LULC can provide a meaningful basis for the rational allocation of land resources. Using Landsat TM/OLI series dates from the Google Earth Engine (GEE) platform, this study applied the Linear Superposition Water Index (LSWI) and the Otsu threshold method (OTSU) algorithm to extract and analyze the coastline of the Circum-Bohai region. Additionally, the Random Forests (RF) method was employed to extract LULC information in the coastal zone. Using the geographical detector, we further explored the influence of social and economic factors, as well as natural factors, on spatial differentiation mechanisms of LULC change in the Circum-Bohai. Our results show that between 2000 and 2020, the Circum-Bohai coastline generally expanded towards the ocean by a total of 1062.99 km. The highest rate of change occurred during 2010 to 2015, and human activities were the primary cause of most of the changes, with the exception of the Yellow River Delta, where natural factors were dominant. The main types of LULC in the study area from 2000 to 2020 were farmland and construction land. The area of farmland proportion decreased by 1.75%, while the area of construction land proportion increased from 16.73% to 29.54%. Our findings indicate that the degree of land use in the Circum-Bohai is deepening. Based on our factor detection analysis, the added value of the secondary industry was the most critical influencing factor on LULC. Furthermore, the combined effect of the added value of the secondary industry and gross domestic product (GDP) has a significant driving impact on LULC. These findings can provide reference and data support for the sustainable development and comprehensive management of land resources. The relevant departments can use these results to prompt corresponding policies for the rational allocation of land resources.

Wildlife habitat mapping using Sentinel-2 imagery of Mehao Wildlife Sanctuary, Arunachal Pradesh, India

Mehao Wildlife Sanctuary, situated in the state of Arunachal Pradesh, is part of an important biodiversity hotspot in the north-eastern part of India in the Himalayas. The current study deals with the identification of important wildlife habitats in the sanctuary. We used a supervised classification technique to delineate these habitats in the sanctuary, which are used by several mammals and bird species encountered during camera trap and sign surveys conducted between November 2017 and May 2020. Satellite images from Sentinel – 2A were used to classify the land use land cover (LULC) of the sanctuary. The LULC information was generated by using a maximum likelihood classifier. We classified a total of thirteen LULC classes, i.e., water, built-up, agriculture, orchard, grassland, bamboo forest, bamboo-mixed forest, riverbed, barren land, snow, wild banana, riverine forest and mixed forest. LULC classification reveals a high percentage of mixed forest, about 69.9%, followed by wild bananas at 7.2%. The commission and omission error rates, however, are high for riverbed and agriculture (0.5) and bamboo forest (0.5), respectively. The accuracy assessment showed an overall classification accuracy of 88.5% with a Kappa coefficient of 0.87. The abundance of mammals was high in the mixed forest, but Ivlev's electivity index shows that species generally avoided this habitat and preferred specialized forest habitats, such as bamboo forest, bamboo-mixed forest, grassland, riverbed and riverine forest. Our LULC map will provide a baseline for potential planning and monitoring changes of wildlife habitats in Mehao WLS.

Land use/land cover mapping using deep neural network and sentinel image dataset based on google earth engine in a heavily urbanized area, China

Accurate and detailed identification of land cover types is beneficial to the ecological environment and sustainable urban development, yet the extraction of urban land use/land cover (LULC) information with high accuracy is challenged by the high degree of landscape fragmentation. Therefore, this study is based on the Google Earth Engine (GEE) cloud platform and uses the U-Net model combined with spectral image data and SAR data to conduct a LULC classification study of highly heterogeneous urban areas in central China. The results indicated that the overall accuracy of classification result by using the U-Net model and the optimal combination of image features was 95.58%, which was 1.37%, 4.84%, and 7.85% higher than that of random forest (RF), support vector machine (SVM) and k- Nearest Neighbor (kNN), respectively. It showed that the U-Net model can effectively extract LULC information and obtain better classification results in urban areas than the machine learning algorithms. The results of this study could provide technical support to improve the accuracy of information extraction in urban areas with fragmented features.

Prediction of land use land cover changes of a river basin using the CA-Markov model

Simulation and prediction of land use land cover (LULC) has been presented using Hybrid CA-Markov model for the Mahi River basin. LULC information for the year 2000, 2010, and 2020 have been obtained from classification of Landsat TM, IRS -LISS III, and Sentinel 2 sensors, respectively. Fuzzy membership function and Analytical Hierarchical Process (AHP), a Multi-Criteria Evaluation (MCE) have been used for LULC change suitability map preparation. Population density, distance from road, settlement, streams, and reservoir/lakes, slope, and DEM are used as biophysical and socio-economic LULC change drivers. Model results have been validated for year 2020 and accuracy has been found as satisfactory 2020. Results revealed a significant change in forest, agriculture, barren and built-up LULC classes during different years. Agriculture and the built-up area may increase by 641 and 21 sq.km, respectively in 2030. The predicted LULC information provides necessary data to investigate the future hydrological and climatological scenario.

Land use Land Cover Classification of Burhner River Watershed Using Remote Sensing and GIS Technique

Information on Land Use Land Cover (LULC) pattern is very important for developing management strategies for land use planning. Remotely sensed satellite data has proved to be an unmatched source of information that can deliver LULC information with good accuracy especially in regions where acquiring LULC information through intensive ground surveys seems to be impractical. The present study aims at performing LULC classification of Burhner river watershed situated in Mandla, Balaghat and Dindori districts of Madhya Pradesh, India by adopting unsupervised classification. It also discusses merits and demerits of using unsupervised classification for a high resolution satellite image along with key factors responsible for using it. Sentinel-2B satellite imagery with spatial resolution of 10 m was used in the present investigation. A total number of 6 LULC classes were identified in the study area namely agricultural land, fallow/open land, forest, habitation, wasteland and waterbodies using ERDAS IMAGINE® 2011. Accuracy assessment of the LULC classified image from reference (ground truth) data using error matrix revealed an overall accuracy of 95.72% with kappa coefficient of 0.94. Furthermore, the error matrix also aided in computing classified image producer’s and user’s accuracy which were under acceptable limits. The LULC statistics of study area indicated that highest area is covered by forest (53.01%), followed by fallow/open land (24%), agricultural land (19.44%) and least area is covered by waterbodies (1.38%) and habitation (0.19%). A major portion of study area under fallow/open land category pointed that underutilized land resource potential exist in watershed. Such land can be further utilized for crop production and plantation purposes in order to maximize output from the available natural resources in a sustainable manner.

Four decadal urban land degradation in Pakistan a case study of capital city islamabad during 1979–2019

The study of Land Use Land Cover (LULC) changes and their patterns enhance our understanding about various man caused phenomenon on earth surface. The Capital City of Pakistan, Islamabad has expereinced high rate of urbanization and population increase in the last 3-4 decades and as a result, the LULC changes have occurred in an unsustainable way. The accurate and reliable data of LULC changes for the study period is not available. The main aim of this study was to provide compatible data on LULC change during the last 40 years for the functional city Islamabad, Pakistan. LULC statistics were extracted from Landsat Multi-Spectral Scanner (MSS), Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+) and Operational Land Imager (OLI) images. For each of the target years (1979, 1989, 1999, 2009 and 2019) LULC information was obtained through a supervised classification algorithm. The study area was classified into five major landscape categories, namely built-up, agriculture, forest, water bodies and barren land. The results have indicated that the urban expansion has been increasing due to mass migration and uncontrolled urbanization in the city. The urban sprawl analysis revealed that built-up area and agricultural land has expanded by 41.7% and 5.20% respectively, during 1979–2019. On the other hand, the forest and water bodies declined by 9.03% and 1.21% respectively. The land use changes, have forced the planners to revise the original master plan of the city and including the agricultural sector in the housing area during 2019.

Hydrodynamic modelling and vulnerability analysis to assess flood risk in a dense Indian city using geospatial techniques

Urban flooding and waterlogging are causing menace in many cities around the world from the perspective of day-to-day functioning, health and hygiene, communication, and the consequent damages they cause to urban environment. The present study is an attempt to understand the urban flood risks in parts of Bhubaneswar City, India, based on its hydrodynamic set-up and level of urbanisation. The Storm Water Management Model is used for peak flow analysis, and the flooding extent has been assessed while taking into consideration the elevation, slope, land use/land cover (LULC) and design Storm Water Drain (SWD) infrastructure of the city. The micro-watersheds for each SWD are delineated using digital surface model derived from airborne Light Detection and Ranging (LiDAR) data (1 m), and the LULC information is obtained from high-resolution optical remote sensing data. After the model simulation, it is estimated that peak runoff is relatively higher, i.e. 0.1–0.5 cumecs for a large number of micro-watersheds, even rising to more than 1.5 cumecs for some, indicating the severity of urban floods in the city. After integrating the simulated flooding pattern with the vulnerability associated with socio-economic characteristics of urban dwellers, the flood risk has been assessed. The study suggests that capacity of design SWD systems needs augmentation according to present and predicted flooding conditions for the city.

Annual 30-m land use/land cover maps of China for 1980–2015 from the integration of AVHRR, MODIS and Landsat data using the BFAST algorithm

Annual land use land cover (LULC) change information at medium spatial resolution (i.e. at 30 m) is required in numerous subjects, such as biophysical modelling, land management and global change studies. Annual LULC information, however, is usually not available at continental or national scale due to reasons such as insufficient remote sensing data coverage or lack of computational capabilities. Here we integrate high temporal resolution and coarse spatial resolution satellite images (i.e., Moderate Resolution Imaging Spectroradiometer (MODIS) and Global Inventory Modelling and Mapping Studies (GIMMS) normalized difference vegetation index (NDVI)) with high spatial resolution datasets (China’s Land-Use/cover Datasets (CLUDs) derived from 30-meter Landsat TM/ETM+/OLI) to generate reliable annual nominal 30 m LULC maps for the whole of China between 1980 and 2015. We also test the performance of a statistical based change detection algorithm (Breaks for Additive Seasonal and Trend), originally designed for tracking forest change, in classifying all-type LULC change. As a result, a nominal 30 m annual land use/land cover datasets (CLUD-A) from 1980 to 2015 was developed for the whole China. The mapping results were assessed with a change sample dataset, a regional annual validation sample set and a three-year China sample set. Of the detected change years, 75.61% matched the exact time of conversion within ±1 year. Annual mapping results provided a detail process of urbanization, deforestation, afforestation, water and cropland dynamics over the past 36 years. The consistent characterization of land change dynamics for China can be further used in scientific research and to support land management for policy-makers.

Spatio‐temporal prediction of land surface temperature using semantic kriging

AbstractSpatio‐temporal prediction and forecasting of land surface temperature (LST) are relevant. However, several factors limit their usage, such as missing pixels, line drops, and cloud cover in satellite images. Being measured close to the Earth's surface, LST is mainly influenced by the land use/land cover (LULC) distribution of the terrain. This article presents a spatio‐temporal interpolation method which semantically models LULC information for the analysis of LST. The proposed spatio‐temporal semantic kriging (ST‐SemK) approach is presented in two variants: non‐separable ST‐SemK (ST‐SemKNSep) and separable ST‐SemK (ST‐SemKSep). Empirical studies have been carried out with derived Landsat 7 ETM+ satellite images of LST for two spatial regions: Kolkata, India and Dallas, Texas, U.S. It has been observed that semantically enhanced spatio‐temporal modeling by ST‐SemK yields more accurate prediction results than spatio‐temporal ordinary kriging and other existing methods.

Detailed Urban Land Use Land Cover Classification at the Metropolitan Scale Using a Three-Layer Classification Scheme

Urban Land Use/Land Cover (LULC) information is essential for urban and environmental management. It is, however, very difficult to automatically extract detailed urban LULC information from remote sensing imagery, especially for a large urban area. Medium resolution imagery, such as Landsat Thematic Mapper (TM) data, cannot uncover detailed LULC information. Further, very high resolution (VHR) satellite imagery, such as IKONOS and QuickBird data, can only be applied to a small area, largely due to the data unavailability and high computation cost. As a result, little research has been conducted to extract detailed urban LULC information for a large urban area. This study, therefore, developed a three-layer classification scheme for deriving detailedurban LULC information by integrating newly launched Chinese GF-1 (medium resolution) and GF-2 (very high resolution) satellite imagery and synthetically incorporating geometry, texture, and spectral information through multi-resolution image segmentation and object-based image classification (OBIA). Homogeneous urban LULC types such as water bodies or large areas of vegetation could be derived from GF-1 imagery with 16 m and 8 m spatial resolutions, while heterogeneous urban LULC types such as industrial buildings, residential buildings, and roads could be extracted from GF-2 imagery with 3.2 m and 0.8 m spatial resolutions. The multi-resolution segmentation method and a random forest algorithm were employed to perform image segmentation and object-based image classification, respectively. An analysis of the results suggests an overall accuracy of 0.89 and 0.87 were achieved for the second and third level urban LULC classification maps, respectively. Therefore, the three-layer classification scheme has the potential to derive high accuracy urban LULC information through integrating medium and high-resolution remote sensing imagery.

Relationships of groundwater quality and associated health risks with land use/land cover patterns: A case study in a loess area, Northwest China

This study investigated the relationships of groundwater quality and associated health risks with land use/land cover (LULC) patterns. Twenty-nine groundwater samples were collected from a loess area in northwest China, and analyzed for twelve water quality parameters. Water quality was assessed using entropy water quality index (EWQI) and the non-carcinogenic risks caused by NO3− and Cr6+, and the carcinogenic risks caused by Cr6+ through drinking water exposure pathway were considered. The LULC information was extracted from remote sensing image data and classified using a neural net classification method. A curved streamline searchlight shaped model (CS-SLM) was proposed and applied to determine the domain around a well where LULC influences well water quality. Kendall’s tau (τ) was calculated to determine the relationship of water quality and associated health risks with LULC. As suggested by EWQI, 51.72% of the groundwater samples were unacceptable for drinking proposes. The main LULC types in the study area are loess, forest, grassland, agricultural land and urban land, and the groundwater quality is influenced by forest, grassland and loess. Non-carcinogenic risks caused by NO3− and Cr6+ in the groundwater are related to grassland, loess and urban land, and carcinogenic risks caused by Cr6+ are associated with loess and urban land.

Prediction of spatio-temporal land use/land cover dynamics in rapidly developing Varanasi district of Uttar Pradesh, India, using geospatial approach: a comparison of hybrid models

Land use/land cover changes (LULCC) are one of the foremost aspects of environmental changes caused by human-induced activities mainly in rapidly developing areas. This study endeavors to evaluate and compare three hybrid models: stochastic Markov chain (ST-MC), cellular automata-Markov chain (CA-MC), and multi-layer perceptron-Markov chain (MLP-MC) to predict future land use/land cover (LULC) scenario in Varanasi district. LULC information extracted for years 1988 and 2001 was first employed to predict LULC scenario for 2015 using three hybrid models. The predicted results were compared with the observed LULC information for the year 2015 to appraise the validity of models through kappa index statistics. The MLP-MC model yielded reliable and best results. Finally, based on this consequence, the prediction of future LULC scenarios for years 2030 and 2050 was performed. The findings of this study exhibited the constant but overall increase of built up area and a considerable reduction in agricultural land. The results also demonstrate the potentiality of MLP-MC hybrid model for better understanding of spatio-temporal dynamics and predicting future landsacpe scenario in Varanasi district of Uttar Pradesh, India.

A three-pronged approach for identifying source and extent of nitrate contamination in groundwater

A method to delineate the nitrate (NO3−) contamination of a study area by sequential and statistical use of dual isotope data, principal component analysis (PCA), and land use/land cover (LULC) data was demonstrated using data from Eumseong, Korea. First, a dual isotope approach was applied to identify the possible NO3− sources and quantify their contribution to NO3− contamination using Bayesian statistics. Second, a PCA was performed to discriminate and evaluate the impact of NO3− contamination on chemical evolution in the aquifer. Lastly, we incorporated the LULC data into a regression analysis to identify the contribution of various land uses to NO3− recharge. Some samples had NO3− and iron (Fe) concentrations above the local drinking water quality standard, and the distributions of potassium (K+), sulfate (SO42−), Fe, and manganese (Mn) were skewed significantly. Trends from the dual isotope analysis suggested three major sources of NO3− contamination. Among the three sources, Bayesian statistics indicated that the NO3− contamination was largely attributable to influx from manure/sewage and soil nitrogen (N). Based on the PCA, following screening for skewed data, a contamination indicator was extracted. The indicator exhibited positive correlations with NO3−, chlorine (Cl−), strontium (Sr2+), calcium (Ca2+), sodium (Na+), magnesium (Mg2+), K+, and SO42−, suggesting a single strong source. The regression analysis, using the LULC information, determined that agriculture activities in nonpaddy areas were responsible for the NO3− recharge. This study identified the benefits of combining dual isotope analysis, PCA, and LULC data for discriminating and evaluating sources of NO3− contamination when diverse contaminants are involved in geochemical evolution.

TESTING THE POTENTIAL OF VEGETATION INDICES FOR LAND USE/COVER CLASSIFICATION USING HIGH RESOLUTION DATA

Abstract. Accurate and reliable land use/land cover (LULC) information obtained by remote sensing technology is necessary in many applications such as environmental monitoring, agricultural management, urban planning, hydrological applications, soil management, vegetation condition study and suitability analysis. But this information still remains a challenge especially in heterogeneous landscapes covering urban and rural areas due to spectrally similar LULC features. In parallel with technological developments, supplementary data such as satellite-derived spectral indices have begun to be used as additional bands in classification to produce data with high accuracy. The aim of this research is to test the potential of spectral vegetation indices combination with supervised classification methods and to extract reliable LULC information from SPOT 7 multispectral imagery. The Normalized Difference Vegetation Index (NDVI), the Ratio Vegetation Index (RATIO), the Soil Adjusted Vegetation Index (SAVI) were the three vegetation indices used in this study. The classical maximum likelihood classifier (MLC) and support vector machine (SVM) algorithm were applied to classify SPOT 7 image. Catalca is selected region located in the north west of the Istanbul in Turkey, which has complex landscape covering artificial surface, forest and natural area, agricultural field, quarry/mining area, pasture/scrubland and water body. Accuracy assessment of all classified images was performed through overall accuracy and kappa coefficient. The results indicated that the incorporation of these three different vegetation indices decrease the classification accuracy for the MLC and SVM classification. In addition, the maximum likelihood classification slightly outperformed the support vector machine classification approach in both overall accuracy and kappa statistics.

Transition Modeling of Land-Use Dynamics in the Pipestem Creek, North Dakota, USA

Significant land-use changes in North Dakota have been reported and are widespread over the entire state. Such changing patterns may portend localized impairment to agricultural watersheds. In this study, Land-use Land-cover (LULC) change was modeled using geostatistics. The study area was within the Pipestem Creek watershed, a part of the Missouri Watershed James Subregion of North Dakota, USA. Landsat Thematic mapper images from the years 2007, 2011 and 2015 were used as preliminary data. LULC information for these datasets was acquired from the Global Land-cover facility and Landsat Program. Data analysis, spectral classification and post classification techniques were applied on the datasets. A transition matrix was derived using a Markov chain Monte Carlo (MCMC) model. This study demonstrates that the integration of satellite remote sensing, GIS and statistics may be an effective approach for analyzing the direction, rate, and spatial pattern of land-use change.

Joint use of remote sensing data and volunteered geographic information for exposure estimation: evidence from Valparaíso, Chile

The impact of natural hazards on mankind has increased dramatically over the past decades. Global urbanization processes and increasing spatial concentrations of exposed elements induce natural hazard risk at a uniquely high level. To mitigate affiliated perils requires detailed knowledge about elements at risk. Considering a high spatiotemporal variability of elements at risk, detailed information is costly in terms of both time and economic resources and therefore often incomplete, aggregated, or outdated. To alleviate these restrictions, the availability of very-high-resolution satellite images promotes accurate and detailed analysis of exposure over various spatial scales with large-area coverage. In the past, valuable approaches were proposed; however, the design of information extraction procedures with a high level of automatization remains challenging. In this paper, we uniquely combine remote sensing data and volunteered geographic information from the OpenStreetMap project (OSM) (i.e., freely accessible geospatial information compiled by volunteers) for a highly automated estimation of crucial exposure components (i.e., number of buildings and population) with a high level of spatial detail. To this purpose, we first obtain labeled training segments from the OSM data in conjunction with the satellite imagery. This allows for learning a supervised algorithmic model (i.e., rotation forest) in order to extract relevant thematic classes of land use/land cover (LULC) from the satellite imagery. Extracted information is jointly deployed with information from the OSM data to estimate the number of buildings with regression techniques (i.e., a multi-linear model from ordinary least-square optimization and a nonlinear support vector regression model are considered). Analogously, urban LULC information is used in conjunction with OSM data to spatially disaggregate population information. Experimental results were obtained for the city of Valparaiso in Chile. Thereby, we demonstrate the relevance of the approaches by estimating number of affected buildings and population referring to a historical tsunami event.

TOWARDS A REMOTE SENSING BASED ASSESSMENT OF LAND SUSCEPTIBILITY TO DEGRADATION: EXAMINING SEASONAL VARIATION IN LAND USE-LAND COVER FOR MODELLING LAND DEGRADATION IN A SEMI-ARID CONTEXT

Land degradation (LD) is among the major environmental and anthropogenic problems driven by land use-land cover (LULC) and climate change worldwide. For example, poor LULC practises such as deforestation, livestock overstocking, overgrazing and arable land use intensification on steep slopes disturbs the soil structure leaving the land susceptible to water erosion, a type of physical land degradation. Land degradation related problems exist in Sub-Saharan African countries such as Botswana which is semi-arid in nature. LULC and LD linkage information is still missing in many semi-arid regions worldwide.Mapping seasonal LULC is therefore very important in understanding LULC and LD linkages. This study assesses the impact of seasonal LULC variation on LD utilizing Remote Sensing (RS) techniques for Palapye region in Central District, Botswana. LULC classes for the dry and rainy seasons were classified using LANDSAT 8 images at Level I according to the Food and Agriculture Organization (FAO) International Organization of Standardization (ISO) code 19144. Level I consists of 10 LULC classes. The seasonal variations in LULC are further related to LD susceptibility in the semi-arid context. The results suggest that about 985 km² (22%) of the study area is susceptible to LD by water, major LULC types affected include: cropland, paved/rocky material, bare land, built-up area, mining area, and water body. Land degradation by water susceptibility due to seasonal land use-land cover variations is highest in the east of the study area where there is high cropland to bare land conversion.