Possibilities and limits of prospective GIS land cover modelling--a compared case study: Garrotxes (France) and Alta Alpujarra Granadina (Spain)

International audienceThis study focuses on the possibilities and the limits of a prospective GIS land cover modelling applied to two case studies (France and Spain). The methodology, based on available GIS tools, consists of using earlier land cover maps and relevant environmental factors (calibration data) to model actual, known land cover to validate the model. The model aggregates Markov chain analysis for time prediction and multi-critera evaluation, multi-objective evaluation and cellular automata to perform spatial contiguity of modelled land cover scores. The first results give an accurate, pixel by pixel prediction rate of approximately 75%. An important issue of this study consists of analysing prediction residues to improve the model

Case study greater Cairo Region Egypt

The rapid growth of big cities has been noticed since 1950s when the majority of world population turned to live in urban areas rather than villages, seeking better job opportunities and higher quality of services and lifestyle circumstances. This demographic transition from rural to urban is expected to have a continuous increase. Governments, especially in less developed countries, are going to face more challenges in different sectors, raising the essence of understanding the spatial pattern of the growth for an effective urban planning. The study aimed to detect, analyse and model the urban growth in Greater Cairo Region (GCR) as one of the fast growing mega cities in the world using remote sensing data. Knowing the current and estimated urbanization situation in GCR will help decision makers in Egypt to adjust their plans and develop new ones. These plans should focus on resources reallocation to overcome the problems arising in the future and to achieve a sustainable development of urban areas, especially after the high percentage of illegal settlements which took place in the last decades. The study focused on a period of 30 years; from 1984 to 2014, and the major transitions to urban were modelled to predict the future scenarios in 2025. Three satellite images of different time stamps (1984, 2003 and 2014) were classified using Support Vector Machines (SVM) classifier, then the land cover changes were detected by applying a high level mapping technique. Later the results were analyzed for higher accurate estimations of the urban growth in the future in 2025 using Land Change Modeler (LCM) embedded in IDRISI software. Moreover, the spatial and temporal urban growth patterns were analyzed using statistical metrics developed in FRAGSTATS software. The study resulted in an overall classification accuracy of 96%, 97.3% and 96.3% for 1984, 2003 and 2014’s map, respectively. Between 1984 and 2003, 19 179 hectares of vegetation and 21 417 hectares of desert changed to urban, while from 2003 to 2014, the transitions to urban from both land cover classes were found to be 16 486 and 31 045 hectares, respectively. The model results indicated that 14% of the vegetation and 4% of the desert in 2014 will turn into urban in 2025, representing 16 512 and 24 687 hectares, respectively

An assessment of land cover changes using GIS and remote sensing : a case study of the uMhlathuze Municipality, KwaZulu-Natal, South Africa.

Thesis (M.Env.Dev.)-University of KwaZulu-Natal, Pietermaritzburg, 2005.Rapid growth of cities is a global phenomenon exerting much pressure on land resources and causing associated environmental and social problems. Sustainability of land resources has become a central issue since the Earth Summit in Rio de Janeiro in 1992. A better understanding of the processes and patterns of land cover change will aid urban planners and decision makers in guiding more environmentally conscious development. The objective of this study was firstly, to determine the location and extent of land use and land cover changes in the uMhlathuze municipality, KwaZulu-Natal, South Africa between 1992 and 2002, and secondly, to predict the likely expansion of urban areas for the year 2012. The uMhlathuze municipality has experienced rapid urban growth since 1976 when the South African Ports and Railways Administration built a deep water harbour at Richards Bay, a town within the municipality. Three Landsat satellite images were obtained for the years, 1992, 1997 and 2002. These images were classified into six classes representing the dominant land covers in the area. A post classification change detection technique was used to determine the extent and location of the changes taking place during the study period. Following this, a GIS-based land cover change suitability model, GEOMOD2, was used to determine the likely distribution of urban land cover in the year 2012. The model was validated using the 2002 image. Sugarcane was found to expand by 129% between 1992 and 1997. Urban land covers increased by an average of 24%, while forestry and woodlands decreased by 29% between 1992 and 1997. Variation in rainfall on the study years and diversity in sugarcane growth states had an impact on the classification accuracy. Overall accuracy in the study was 74% and the techniques gave a good indication of the location and extent of changes taking place in the study site, and show much promise in becoming a useful tool for regional planners and policy makers

Comparing the structural uncertainty and uncertainty management in four common Land Use Cover Change (LUCC) model software packages

Research on the uncertainty of Land Use Cover Change (LUCC) models is still limited. Through this paper, we aim to globally characterize the structural uncertainty of four common software packages (CA_Markov, Dinamica EGO, Land Change Modeler, Metronamica) and analyse the options that they offer for uncertainty management. The models have been compared qualitatively, based on their structures and tools, and quantitatively, through a study case for the city of Cape Town. Results proved how each model conceptualised the modelled system in a different way, which led to different outputs. Statistical or automatic approaches did not provide higher repeatability or validation scores than user-driven approaches. The available options for uncertainty management vary depending on the model. Communication of uncertainties is poor across all models.Spanish GovernmentEuropean Commission INCERTIMAPS PGC2018-100770-B-100Spanish Ministry of Economy and Competitiveness and the European Social Fund [Ayudas para contratos predoctorales para la formacion de doctores 2014]University of Granada [Contratos Puente 2018]Spanish Ministry of Science and Innovation [Ayudas para contratos Juan de la Cierva-for-macion] 2019-FJC2019-040043University of Cape Town (Centre for Transport Studies

Tropical deforestation modelling : a comparative analysis of different predictive approaches. The case study of Peten, Guatemala.

The frequent use of predictive models for analysing of complex, natural or artificial, phenomena is changing the traditional approaches to environmental and hazard problems. The continuous improvement of computer performances allows more detailed numerical methods, based on space-time discretisation, to be developed and run for a predictive modeling of complex real systems, reproducing the way their spatial patterns evolve and pointing out the degree of simulation accuracy. In this contribution we present an application of several models (Geomatics, Neural Networks, Land Cover Modeler and Dinamica EGO) in a tropical training area of Peten, Guatemala. During the last decades this region, included into the Biosphere Maya reserve, has known a fast demographic raise and a subsequent uncontrolled pressure on its own geo-resources; the test area can be divided into several sub-regions characterized by different land use dynamics. Understand and quantify these differences permits a better approximation of real system; moreover we have to consider all the physic, socio-economic parameters which will be of use for represent the complex and sometime at random, human impact. Because of the absence of detailed data for our test area, nearly all information were derived from the image processing of 41 ETM+, TM and SPOT scenes; we pointed out the past environmental dynamics and we built the Input layers for the predictive models. The data from 1998 and 2000 were used during the calibration to simulate the Land Cover changes in 2003, selected as reference date for the validation. The basic statistics permit to highlight the qualities or the weaknesses for each model on the different sub-regions

Spatio-Temporal data modeling in response to deforestation monitoring (a case study of small region in Riau Province, Indonesia)

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.Indonesia with large amount of area covered by tropical forest faces a critical problem of deforestation. A lot of forested areas were converted into other coverage influenced by human activities. Therefore, deforestation monitoring and forest prediction have to be done in order to manage the sustainability of forest. To monitor deforestation, this research has analyzed the trend of forest cover in the study area by combining NDVI differencing and image classification to describe the forest cover change. In order to do that, Landsat images acquired in different time (1996, 2000, and 2005) have been chosen as input. NDVI differencing has been conducted by doing normalization of one image to another image initially. Subsequently, thresholds to identify the change and no change have been carried out separately for decrease and increase part. Apart from that, image classification was applied using supervised classification. Eventually, land cover change detection has been performed by combining NDVI differencing and image classification. It has been proved by the research that forest in study area has decreased by 6% during 1996-2005. In order to forecast future forest cover, three models were chosen to get the best model for prediction. These models are Stochastic Markov Modal, Cellular Automata Markov (CA_Markov) Model, and GEOMOD. To measure the best model among them, Kappa index was employed to validate the simulation. As the result, GEOMOD performed the highest Kappa. Therefore, GEOMOD was implemented to model forest cover in 2015. The result of GEOMOD implementation revealed that forest cover will be decreased by 12% during 2005-2015

Implementation of the local land-use and land-cover change model CLUE-s for Central Benin by using socio-economic and remote sensing data

Within the last decades wide areas in West Africa are subjected to serious land-use and land-cover changes (LUCC). The detection of the changes, the understanding of the underlying processes as well as modeling of scenarios for future development is a precondition for the set up of sustainable land-use planning schemes. In this thesis the implementation of the local LUCC model CLUE-s is demonstrated for a savanna environment in central Benin. The study was performed in the framework of the Integrated approach to the efficient management of scarce water resources in West Africa (IMPETUS) project. The study area has a size of around 900 km². The population density is quite low (11 persons/km²) but it is subjected to migration and the population growth is very high (up to 13 % for some villages). Land-use is mainly slash-and-burn agriculture. Uncontrolled forest logging and practice of vegetation fires are frequent. The degree of LUCC was derived from multitemporal LANDSAT images. Between 1991 and 2000 deforestation of 8 % was observed; 20% of Woodland savanna and 5 % of Shrub savanna had been transformed respectively into Shrub savanna and farmland. In order to explain and to model present and future LUCC, the underlying processes have been analysed with geostatistics and through the integration of socio-economic factors. Due to the insufficient availability of official data, I undertook an own survey, and 188 households had been questioned. It turned out that the socalled drivers to describe the relevant land-use changes can be divided in two broad categories: proximate causes (e.g. accessibility, agriculture expansion) and underlying causes (demographic factors and socio-economic conditions). To implement the spatial explicit statistic-dynamic CLUE-s model, different input parameters were used: the results from the socio-economic analyses as well as datasets describing the geographical situation like land-use and land-cover and distances (e.g. distance to settlements). The calibration of the model was performed using historical data describing the land-use and land-cover patterns between 1991 and 2000. Different scenarios for future development of the boundary conditions were defined according to the findings of the IMPETUS project. The outcome of the base line scenario (“business as usual”) predicts that there will be some forests left in 2025 while the scenario (“environmental damage”) assuming an increase of 6 % a year of agricultural area results in nearly complete deforestation of the area in 2020. The resulting spatial pattern of the predicted changes shows strong changes along the main road Oubérou- Kikélé, where most of the immigrant farmers settle. This tendency will be maintained as long as the population increases. The spatial locations of areas subjected to strong deforestation are clearly indicated. The validation process based on multiple resolution technique shows the ability of the CLUE-s model to predict the land-use changes at the local level. However further results can be achieved with improved datasets (e.g. detailed crops and land-use statistics, historical land-use, sound population census) which remain the principal constraint faced in the study area. Meanwhile, the results are valuable for assessing the land-use changes at local level and useful for setting up a Decision Support System (DSS) for the purpose of sustainable land-use management.Innerhalb der letzten Jahrzehnte vollzogen sich in Westafrika tief greifende Veränderungen der Landnutzung und der Landbedeckung (LUCC). Die Erkennung dieser Veränderungen, das Verstehen der steuerndne Prozesse ebenso wie die Modellierung zukünftiger Entwicklungen über Szenarien sind für die Erstellung von Planungsgrundlagen für eine nachhaltige Landnutzung unerlässlich. In der vorliegenden Arbeit wird im Rahmen des Integrativen Management-Projekts für einen Effizienten und Tragfähigen Umgang mit Süßwasser in Westafrika (IMPETUS) ein lokales LUCCModell für ein Savannengebiet in Zentralbenin vorgestellt. Der Untersuchungsraum hat eine Größe von ca. 900 km². Die Bevölkerungsdichte ist mit 11 Einwohner/km² zwar eher gering, allerdings führen Wanderungen in das Gebiet zu einem sehr hohen Bevölkerungswachstum, welches in einigen Dörfern 13% beträgt. Die dominierende Landnutzungsform ist Landwirtschaft durch Brandrodung. Die Veränderungen der Landnutzung und Landbedeckung wurden aus multitemporalen LANDSAT-Szenen abgeleitet. Zwischen 1991 und 2000 wurden 8% Entwaldung beobachtet; 20% Baumsavanne und 5% Strauchsavanne wurden in Strauchsavanne bzw. landwirtschaftliche Nutzflächen umgewandelt. Um diesen Wandel erklären zu können, wurden die wesentlichen Prozesse mittels Geostatistik und der Integration von sozioökonomischen Faktoren analysiert. Aufgrund unzureichend verfügbarer offizieller Daten wurden dafür eigene Befragungen durchgeführt, wobei 188 Haushalte befragt wurden. Es stellte sich heraus, dass die treibenden Kräfte zur Beschreibung der Landnutzungs- und Landbedeckungsveränderungen in direkte (z.B. Zugänglichkeit, landwirtschaftliche Expansion) und indirekte sachen (demographische Faktoren und sozioökonomische Bedingungen) unterteilt werden können. Für die LUCC-Modellierung wurde das räumlich explizit arbeitende statistischdynamische CLUE-s Modell verwendet. Als Eingabeparameter wurden die Ergebnisse der sozioökonomischen Analysen sowie räumliche Daten, wie Veränderungen der Landnutzung und Landbedeckung sowie Entfernungen (Entfernungen zu Siedlungen oder Strassen) verwendet. Für die Modellkalibrierung wurden historische Daten, die Veränderungsmuster der Landnutzung und -bedeckung zwischen 1991 und 2000 beschreiben, eingesetzt. Außerdem wurden basierend auf den Projektergebnissen Rahmenbedingungen für Zukunftsszenarien definiert und berechnet. Das Ergebnis des Basisszenarios („business as usual“) prognostiziert für 2025 ein Bestehen der Wälder wohingegen das Szenario („environmental damage“), basierend auf einer jährliche Zuwachsrate landwirtschaftlicher Nutzflächen von 6%, eine fast komplette Vernichtung der Wälder schon für 2020 vorhersagt. Die räumliche Analyse zeigt, dass sich die Veränderungen vor allem endlang der Hauptstrasse zwischen den Dörfern Ouberou und Kikele, die durch Ansiedlung eingewanderter Bauern gekennzeichnet ist, vollziehen werden. Diese Tendenz wird, solange wie die Bevölkerung weiter wächst, bestehen bleiben. Gebiete, die durch starke Entwaldung gekennzeichnet sind, können räumlich klar abgebildet werden. Die Validierung durch eine „multiple resolution technique“ belegt die Eignung des CLUE-s Modells, Landnutzungsveränderungen auf lokaler Ebene vorauszusagen. Allerdings stellte die bestehende Datengrundlage für das Untersuchungsgebiet die wesentliche Einschränkung für diese Arbeit dar, so dass verbesserte Datensätze (z.B. detaillierte Statistiken zur Anbaufrüchten und Landnutzung, historische Daten zur Landnutzung, solide Bevölkerungszahlen) die Aussagefähigkeit erweitern würde. Nichtsdestotrotz konnten die Veränderungen auf lokaler Ebene mit den verwendeten Daten und Methoden gut abgebildet werden, so dass die Ergebnisse für den Aufbau eines Entscheidungsunterstützungssystem (DSS) mit dem Ziel eines nachhaltigen Landnutzungsmanagements verwendet werden können.Implémentation du modèle local CLUE-s aux transformations spatiales dans le Centre Bénin aux moyens de données socio-économiques et de télédétection De vastes superficies ont subi de profondes transformations spatiales au cours de ces dernières décennies en Afrique de l’Ouest. La détection de ces dynamiques spatiales, la compréhension du processus de changement de même que la modélisation des scénarii sont autant de conditions requises pour la mise en place d’un plan d’aménagement aux fins d’une utilisation durable des ressources naturelles. Cette thèse présente l’application du modèle CLUE-s à la dynamique de l’occupation du sol et de l’utilisation des terres à l’échelle locale en région de savane dans le Centre Bénin. L’étude est réalisée dans le cadre du projet “Approche intégrée pour la gestion efficiente des ressources hydriques limitées en Afrique de l’Ouest et au Maroc” (IMPETUS). Le secteur d’étude couvre une superficie d’environ 900 km². Bien que la densité de population y soit faible (11 hab./km²), le secteur connaît une forte immigration et un fort taux de croissance de population (parfois supérieur à 13 % pour certains villages). L’agriculture itinérante surbrûlis est le principal système agricole pratiqué dans le secteur. L’exploitation incontrôlée des forêts et la pratique de feux de végétation y sont courantes. La dynamique de l’occupation du sol dérive de l’exploitation des images multitemporelles LANDSAT. Entre 1991 et 2000, 8 % des forêts ont été dévastées; 20 % de savane boisée et 5 % de savane arbustive ont été converties respectivement en savane arbustive et en champs. L’explication du processus qui sous-tend ce changement est faite aux moyens d’analyses géostatistiques et des facteurs socio-économiques explicatifs. Par manque de données socio-économiques officielles, une enquête socio-économique, niveau ménage a été effectuée; elle a porté sur 188 ménages agricoles. Les facteurs explicatifs de la dynamique de l’occupation du sol sont classés en deux catégories: les causes endogènes liées aux activités humaines (ex : accessibilité, expansion de l’agriculture) et les causes exogènes (facteurs démographiques et les conditions socio-économiques). Pour modéliser la dynamique de l’occupation et de l’utilisation du sol, le modèle statistique, spatial et explicite, CLUE-s a été implémenté. Les résultats des analyses socio-économiques ainsi que les données géographiques telles que l’occupation du sol et les distances (distance aux habitations, voies) ont été les principaux paramètres d’entrée. Le calibrage du modèle a été mis en oeuvre par l’utilisation de données historiques décrivant l’occupation du sol entre 1991 et 2000. Les scénarii de développement futur définis se sont inspirés des résultats obtenus par le projet IMPETUS. Le premier scénario (“business as usual”) présage encore de l’existence de couverts forestiers d’ici à 2025 alors que le scénario 2 (“environmental damage”) qui suppose un accroissement annuel de 6 % des terres agricoles présage d’une déforestation complète du secteur d’étude à l’horizon 2020. Le résultat de cette modélisation montre que les changements spatiaux s’opèrent davantage le long de la voie principale Oubérou-Kikélé où la majorité des migrants s’installent d’année en année. A terme, cette tendance sera maintenue tant que la population croîtra. La localisation spatiale des aires affectées par la déforestation est aussi clairement indiquée par le modèle. La validation du modèle s’est inspirée de la récente technique de résolution multiple. Cette technique a démontré l’habileté du modèle CLUE-s à prédire la dynamique spatiale au niveau local. Cependant les résultats obtenus ici peuvent être améliorés par la mise à disposition de données statistiques affinées (ex : statistiques sur les cultures, occupation et utilisation du sol dans les villages, recensement exhaustif des populations locales) dont l’absence constitue la principale contrainte de cette étude. Néanmoins, les résultats obtenus constituent une référence pour l’évaluation de la dynamique spatiale de l’occupation et de l’utilisation du sol au niveau local. Ils peuvent par ailleurs être valorisés dans la mise en place de système d’aide à la décision en vue d’une gestion durable des plans d’aménagement

Modelling land-use and climate change impacts on hydrology: the Upper Ganges river basin

This thesis explores the effects that large-scale land-use/cover change (LUCC) and climate change pose to the terrestrial water cycle, by developing a case study in the Upper Ganges (UG) river basin, in India. In an area experiencing rapid rates of LUCC and changes in irrigation practices, historic land-use maps are developed, based on satellite images, to investigate historical trends of LUCC. Future projection scenarios of LUCC for years up to 2035 are derived from Markov chain analysis. To explore the impacts of those changes in hydrology, the generated maps are used to force the Land Surface Model (LSM) JULES. JULES is found to be reasonably skilful in terms of its ability to reproduce observed streamflow. However, the results indicate that there is much room left for improved estimates of evapotranspiration (ET) fluxes, which JULES is found to over-predict. By dynamically coupling JULES with the crop model InfoCrop, the simulated ET fluxes are improved, compared to the original JULES model. The difference in mean annual ET between the two models (coupled and original) is approximately 150 mm/yr and indicates the potential error in ET flux estimations of an LSM without dynamic vegetation. The impact of LUCC and climate change on the hydrological response of the UG basin is quantified, by calculating variations in hydrological components (streamflow, ET and soil moisture) during the period 2000–2035. Severe increases in the high extremes of flows (+40% in the multi-model mean) are being projected for the nearby future (2030–2035). The changes in all examined hydrological components are greater in the combined land-use and climate change scenario, whilst climate change is the main driver of those changes. These results provide the necessary evidence-base to support regional land-use planning, advanced irrigation practices and develop future-proof water resource management strategies under a water-limited environment.Open Acces

Data-driven Analysis of Potential Impacts of Land-use/cover Change on Water Resources in Coastal Watersheds: Perspectives from Non-stationarity and Nonlinearity

Water resource conditions are highly influenced by human activities. As one of the most important indicators that reflects the intensity of human activities, LUCC has drawn much attention in recent decades. Thus, it is necessary to understand the LUCC patterns in watersheds and identify their impacts on the local water resources. We also analyzed the impacts of the human activities on the streamflow regime as well as the regional climate changes. Furthermore, the nonlinear relationship between land use and water quality was identified in this study. The major findings of this study are as follows: (1) Spatial variation in land use was highly related to the driving factors, and population and local economic development may be the major factors influencing urbanization processes in the coastal watersheds. (2) Streamflow extremes are highly impacted by the human activities and climate variability, and the human activities may be the major factor controlling streamflow extremes at short time scales. (3) The coupled effects of climate variability and human activities were identified by analyzing the relationship between urbanization and climate patterns in the studied watersheds, and the patterns of precipitation and temperature may be modified in highly urbanized areas. (4) A nonlinear relationship between land use and water quality has been widely observed, especially in highly polluted watersheds

Land Use Cover Datasets and Validation Tools

This open access book represents a comprehensive review of available land-use cover data and techniques to validate and analyze this type of spatial information. The book provides the basic theory needed to understand the progress of LUCC mapping/modeling validation practice. It makes accessible to any interested user most of the research community's methods and techniques to validate LUC maps and models. Besides, this book is enriched with practical exercises to be applied with QGIS. The book includes a description of relevant global and supra-national LUC datasets currently available. Finally, the book provides the user with all the information required to manage and download these datasets