The main purpose of thesis is creation and evaluation of models for change detection of arable land to grassland by Hybrid-based Change Detection method, which combined approaches based on the Vegetation Indices, Image Differencing and Principal Component Analysis. Six locations with different seasonal configuration of images with high resolution and one locality covered by image with very high resolution were used. The areas were spread across the foothill areas of the Czech Republic. The selection of predictors and the most suitable model was supported by statistical calculation. Application selected models were carried out using a multi-temporal object classification and their accuracy were verified using reference data. The benefit of this thesis is finding generally applicable model useful to investigate the land cover change and evaluation of the potentially most appropriate seasonal configuration of images. Valuable is also methodology in this thesis which focus on selection of predictors and calculation the order of the most appropriate models, which is unique in the available literature. The thesis provides useful findings fitting to insufficiently explored issue of Change Detection arable land to grassland. Powered by TCPDF (www.tcpdf.org)

In the thesis, I focus on the relationship between visual memory and the ability to detect changes in photographs. In the theoretical part I introduce the change detection and "change blindness" phenomenon. Next, this work explores visual memory and refers to studies that focused on visual long-term memory and its role in change detection. The objective of the empirical part of this thesis is to map the relation between visual memory (scene memorability) and change detection illustrated on the ability to recognize changes in photographs of indoor and outdoor scenes. Research was conducted by means of an experiment devised in PsychoPy using flicker paradigm. The research sample comprised 42 respondents, mainly university students. Research results did not confirm the existence of a relation between visual memory and change detection. No correspondence was found between these variables, not even at the level of each category, suggesting that change detection depends on other factors than visual long-term memory. KEYWORDS: Change blindness, visual long-term memory, change detection, memorability, perception, experiment

The role and scale of human impact on the global environment is a question of special importance to the scientific community and the world as a whole. This impact has dramatically increased since the beginning of industrialisation, yet its understanding remains patchy. The sub-Arctic plays a central role in forming the global environment due to the vast territory of boreal forest and tundra. Severe climatic conditions make its ecosystems highly sensitive to any natural and human disturbances. In this context, the dynamics of boreal vegetation, and of the forest/tundra interface (the treeline), is the most representative indicator of environmental changes in the sub-Arctic. For some time now, monitoring land cover and vegetation changes using remote sensing techniques have been a powerful method for studying human impact on environment from landscape to global scales. It is particularly efficient when applied to the sub-Arctic ecosystems. Remote sensing gives access to accurate and specific information about distant and hard-to-reach areas across forest and tundra. Despite all the e orts, there is a lack of uniformity in studying human impact, a shortage of mapping of impact over large territories and a lack of understanding of the relation between human activity and environmental response. This dissertation develops a systematic approach to monitoring land cover and vegetation changes under human impact over northern Fennoscandia. The study area extends north and south of the treeline and covers around 400,000km2 reaching from Finnmark in Norway, through Norrbotten in Sweden, Lapland in Finland up to the Murmansk region in Russia. This is the most populated and industrially developed region of the whole sub-Arctic and, therefore, suffering most from human impact. This dissertation identifies industrial atmospheric pollution, reindeer herding, forest logging, forest fires and infrastructure development as the primary types of human impact close to the treeline. For each type characteristic hotspots are identified and human impact is analysed in the context of physical environment as well as cultural, economical and political development of the area. This dissertation presents an automated workflow enabling large-scale land cover mapping in northern Fennoscandia with high throughput. It starts with automated image pre-processing using image metadata and ends with automated mapping of classification results. A single classifier for multispectral Landsat data is trained on extensive field data collected across the whole region. Open source tools are used extensively to set up the processing scripts enabling rapid and reproducible analysis. Using the developed advanced remote sensing methodology land cover maps are constructed for all identified hotspots and types of human impact. Changes in vegetation are analysed using three or four historical land cover maps for each hotspot. More than 35 Landsat TM and ETM+ images covering the period from the 1980s until 2011 are processed in an automated manner. A strong correlation between the level of impact and the scale of vegetation change is confirmed and analysed. The structure and dynamics of the local treeline and the quality of environment are analysed and assessed in the context of changing levels of impact at each hotspot and regionally.

Canadian remote areas are characterized by a minimal human footprint, restricted accessibility, ubiquitous lichen/snow cover (e.g. Arctic) or continuous forest with water bodies (e.g. Sub-Arctic). Effective mapping of earth surface cover and land cover changes using free medium-resolution Landsat images in remote environments is a challenge due to the presence of spectrally mixed pixels, restricted field sampling and ground truthing, and the often relatively homogenous cover in some areas. This thesis investigates how remote sensing methods can be applied to improve the capability of Landsat images for mapping earth surface features and land cover changes in Canadian remote areas. The investigation is conducted from the following four perspectives: 1) determining the continuity of Landsat-8 images for mapping surficial materials, 2) selecting classification algorithms that best address challenges involving mixed pixels, 3) applying advanced image fusion algorithms to improve Landsat spatial resolution while maintaining spectral fidelity and reducing the effects of mixed pixels on image classification and change detection, and, 4) examining different change detection techniques, including post-classification comparisons and threshold-based methods employing PCA(Principal Components Analysis)-fused multi-temporal Landsat images to detect changes in Canadian remote areas. Three typical landscapes in Canadian remote areas are chosen in this research. The first is located in the Canadian Arctic and is characterized by ubiquitous lichen and snow cover. The second is located in the Canadian sub-Arctic and is characterized by well-defined land features such as highlands, ponds, and wetlands. The last is located in a forested highlands region with minimal built-environment features. The thesis research demonstrates that the newly available Landsat-8 images can be a major data source for mapping Canadian geological information in Arctic areas when Landsat-7 is decommissioned. In addition, advanced classification techniques such as a Support-Vector-Machine (SVM) can generate satisfactory classification results in the context of mixed training data and minimal field sampling and truthing. This thesis research provides a systematic investigation on how geostatistical image fusion can be used to improve the performance of Landsat images in identifying surface features. Finally, SVM-based post-classified multi-temporal, and threshold-based PCA-fused bi-temporal Landsat images are shown to be effective in detecting different aspects of vegetation change in a remote forested region in Ontario. This research provides a comprehensive methodology to employ free Landsat images for image classification and change detection in Canadian remote regions.

As Unidades de Conservação (UC) são espaços territoriais com características naturais relevantes, que têm a função de assegurar a representatividade de amostras significativas e ecologicamente viáveis das diferentes populações, habitats e ecossistemas. A Legislação Brasileira instituiu no ano de 2000 o Sistema Nacional de Unidades de Conservação da Natureza (SNUC). Dentre os diversos ambientes encontrados em território nacional, o Pampa tem uma representatividade de apenas 0,4% de sua área protegida, conforme consta no SNUC. O Refúgio da Vida Silvestre Banhado dos Pachecos (RVSBP) é uma UC de proteção integral estadual, localizada no Rio Grande do Sul e no Bioma Pampa, com uma área de 2.560 ha e representa cerca de 3,5% de todas as UC’s de proteção integral desse bioma. O RVSBP criada no ano de 2002, e ainda não possui plano de manejo, bem como carece de maiores investimentos e atenção. O uso de imagens de satélites como subsídio aos estudos ambientais já está consolidado e a interpretação destas imagens, a partir de diversos métodos, para classificar o uso e cobertura da terra, tem se tornado uma constante, munindo os pesquisadores de informações dos diversos processos que possam estar ocorrendo em uma determinada área de estudo, inclusive monitorando as mudanças ao longo do tempo. Os objetivos desta dissertação são os de verificar as mudanças no uso e cobertura do solo ocorridas entre 2001 e 2017 no RVSBP e em seu entorno direto de 10km, baseando-se na análise de imagens de satélite. Para tanto serão mapeadas as classes de uso e cobertura do solo, a partir de imagens dos satélites LANDSAT 5 – Sensor TM, LANDSAT 7 – Sensor ETM+ e LANDSAT 8 – Sensor OLI, para os anos de 2001, 2009 e 2017. O método de detecção das mudanças no uso e cobertura do solo aplicada foi a técnica de comparação pós-classificação para uma melhor compreensão das interações entre os fenômenos naturais e as atividades humanas. Essa técnica foi aplicada para os períodos de 2001 a 2009, de 2009 a 2017 e de 2001 a 2017. Para o período de 2001 a 2009 as mudanças ocorreram em 17,5% da área de estudo e em 19,9% do RVSBP. Para o período de 2009 a 2017 as mudanças ocorreram em 22,8% da área de estudo e em 23,9% do RVSBP. Para o período de 2001 a 2017 as mudanças ocorreram em 24% da área de estudo e em 32% do RVSBP. Dentre esses 32% a classe que apresentou os maiores acréscimos de área foram as classes de Agricultura – Arroz e de Associação de Sítio e produtores rurais, que respectivamente compreendem áreas de 410 hectares e de 135 hectares. As classes que foram mais impactadas com perda de área foram as classes Banhado e Vegetação Arbórea, que respectivamente compreendem áreas de 435 hectares e de 173 hectares. A análise de detecção de mudanças se mostrou efetiva como uma forma de monitoramento sistemático do uso e cobertura do solo do RVSBP e entorno, trazendo elementos importantes para a gestão da UC. / Conservation Units (UC) are territorial spaces with relevant natural characteristics, which have a role of ensuring the representativeness of significant and ecologically viable samples of different populations, habitats and ecosystems. Brazilian legistlation established in 2000 the National System of Nature Conservation Units (SNUC). Among the several environments found in the national territory, the Pampa has a representation of only 0,4% of its own protected area, according to SNUC. The Wildlife Refuge Banhado dos Pachecos (RVSBP) is a state UC of integral protection, located in Rio Grande do Sul and Bioma Pampa, with 2.560 ha and comprises about 3,5% of all integral protection UC of this biome. RVSBP was created in 2002, still does not have a management plan, and lacks greater investments and attention. The use of satellite images to suppott environmental studies is already consolidate and the interpretation of these images, using different methods, to classify land and use cover, has become a constant, providing researchers with information on the various processes that may be occurring in a particular study area, including monitoring changes over time. The objective of this dissertation is to verify the changes in the land use and cover occurred between 2001 and 2017 in RVSBP and in its surrounds of 10km, based on the analysis of satellite images. Therefore, the land and use coverage classes were mapped using images from the LANDSAT 5 - Sensor TM, LANDSAT 7 - ETM + and LANDSAT 8 - OLI Sensor, for the years 2001, 2009 and 2017. The method of detecting changes in land use and cover was the post-classification comparison technique for a better understanding of the interactions between natural phenomena and human activities. This technique was applied for the periods from 2001 to 2009, from 2009 to 2017, and from 2001 to 2017. For the period 2001 to 2009 the changes occurred in 17,5% of the whole study area and in 19,9% of RVSBP. For the period from 2009 to 2017, changes occurred in 22,8% of the whole study area and 23,9% of RVSBP. For the period from 2001 to 2017, changes occurred in 24% of the whole study area and 32% of RVSBP. Among these 32%, the class with the greatest increases in area were Agriculture – Rice crops and Site Association of Rural Producers, which respectively comprises areas of 410 hectares and 135 hectares. The classes that were most impacted with loss of area were the class Weands and Arboreal Vegetation, which respectively comprise areas of 435 and 173 hectares. The change detection analysis was effective as a way of systematically monitoring the land use and coverage of RVSBP and surroundings, bringing important elements to the management of the UC.

Le bruit de chatoiement (speckle) lié aux systèmes d'imagerie cohérente a des conséquences sur l'analyse et l'interprétation des images radar à synthèse d'ouverture (RSO). Pour corriger ce défaut, nous profitons de séries temporelles d'images RSO bien recalées. Nous améliorons le filtre adaptatif temporel non-local à l'aide de méthodes performantes de débruitage adaptatif et proposons un filtrage temporel adaptatif basé sur les patchs. Pour réduire le biais du débruitage, nous proposons une méthode originale, rapide et efficace de débruitage multitemporel. L'idée principale de l'approche proposée est d'utiliser l'image dite "de ratio", donnée par le rapport entre l'image et la moyenne temporelle de la pile. Cette image de ratio est plus facile à débruiter qu'une image isolée en raison de sa meilleure stationnarité. Par ailleurs, les structures fines stables dans le temps sont bien préservées grâce au moyennage multitemporel. Disposant d'images débruitées, nous proposons ensuite d'utiliser la méthode du rapport de vraisemblance généralisé simplifié pour détecter les zones de changement ainsi que l'amplitude des changements et les instants de changements intéressants dans de longues séries d'images correctement recalées. En utilisant le partitionnement spectral, on applique le rapport de vraisemblance généralisé simplifié pour caractériser les changements des séries temporelles. Nous visualisons les résultats de détection en utilisant l'échelle de couleur 'jet' et une colorisation HSV. Ces méthodes ont été appliquées avec succès pour étudier des zones cultivées, des zones urbaines, des régions portuaires et des changements dus à des inondations. / The inherent speckle which is attached to any coherent imaging system affects the analysis and interpretation of synthetic aperture radar (SAR) images. To take advantage of well-registered multi-temporal SAR images, we improve the adaptive nonlocal temporal filter with state-of-the-art adaptive denoising methods and propose a patch based adaptive temporal filter. To address the bias problem of the denoising results, we propose a fast and efficient multitemporal despeckling method. The key idea of the proposed approach is the use of the ratio image, provided by the ratio between an image and the temporal mean of the stack. This ratio image is easier to denoise than a single image thanks to its improved stationarity. Besides, temporally stable thin structures are well-preserved thanks to the multi-temporal mean. Without reference image, we propose to use a patch-based auto-covariance residual evaluation method to examine the residual image and look for possible remaining structural contents. With speckle reduction images, we propose to use simplified generalized likelihood ratio method to detect the change area, change magnitude and change times in long series of well-registered images. Based on spectral clustering, we apply the simplified generalized likelihood ratio to detect the time series change types. Then, jet colormap and HSV colorization may be used to vividly visualize the detection results. These methods have been successfully applied to monitor farmland area, urban area, harbor region, and flooding area changes.

<div><div><div><p>Face redaction is needed by law enforcement and mass media outlets to guarantee privacy. In this thesis, a performance analysis of several face redaction/obscuration methods, such as blurring and pixelation is presented. The analysis is based on various threat models and obscuration attackers to achieve a comprehensive evaluation. We show that the traditional blurring and pixelation methods cannot guarantee privacy. To provide a more secured privacy protection, we propose two novel obscuration methods that are based on the generative adversarial networks. The proposed methods not only remove the identifiable information, but also preserve the non-identifiable facial information (as known as the utility information), such as expression, age, skin tone and gender.</p><p>We also propose methods for change detection in satellite imagery. In this thesis, we consider two types of building changes: 2D appearance change and 3D height change. We first present a model with an attention mechanism to detect the building appearance changes that are caused by natural disasters. Furthermore, to detect the changes of building height, we present a height estimation model that is based on building shadows and solar angles without relying on height annotation. Both change detection methods require good building segmentation performance, which might be hard to achieve for the low-quality images, such as off-nadir images. To solve this issue, we use uncertainty modeling and satellite imagery metadata to achieve accurate building segmentation for the noisy images that are taken from large off-nadir angles.</p></div></div></div>

The content of this thesis is the issue of monitoring and analysis of web page changes. Work offers an overview of tools and applications that are used today to monitoring and deals with their functionality and implementation. The most important part is devoted to creating an information system for detecting and monitoring changes. Provides a theoretical introduction, design and implementation of an application, that aims to detect changes based on user defined filters. So application gives users only the information, which really interested him. Results are presented in the form of RSS channels. The paper describes a complete design and implementation, including possible future expansion.

La télédétection par Radar à Synthèse d’Ouverture (RSO) offre une opportunité unique d’enregistrer, d’analyser et de prédire l’évolution de la surface de la Terre. La dernière décennie a permis l’avènement de nombreuses missions spatiales équipées de capteurs RSO (Sentinel-1, UAVSAR, TerraSAR X, etc.), ce qui a engendré une rapide amélioration des capacités d’acquisition d’images de la surface de la Terre. Le nombre croissant d’observations permet maintenant de construire des bases de données caractérisant l’évolution temporelle d’images, augmentant considérablement l’intérêt de l’analyse de séries temporelles pour caractériser des changements qui ont lieu à une échelle globale. Cependant, le développement de nouveaux algorithmes pour traiter ces données très volumineuses est un défi qui reste à relever. Dans ce contexte, l’objectif de cette thèse consiste ainsi à proposer et à développer des méthodologies relatives à la détection de changements dans les séries d’images ROS à très haute résolution spatiale.Le traitement de ces séries pose deux problèmes notables. En premier lieu, les méthodes d’analyse statistique performantes se basent souvent sur des données multivariées caractérisant, dans le cas des images RSO, une diversité polarimétrique, interférométrique, par exemple. Lorsque cette diversité n’est pas disponible et que les images RSO sont monocanal, de nouvelles méthodologies basées sur la décomposition en ondelettes ont été développées. Celles-ci permettent d’ajouter une diversité supplémentaire spectrale et angulaire représentant le comportement physique de rétrodiffusion des diffuseurs présents la scène de l’image. Dans un second temps, l’amélioration de la résolution spatiale sur les dernières générations de capteurs engendre une augmentation de l’hétérogénéité des données obtenues. Dans ce cas, l’hypothèse gaussienne, traditionnellement considérée pour développer les méthodologies standards de détection de changements, n’est plus valide. En conséquence, des méthodologies d’estimation robuste basée sur la famille des distributions elliptiques, mieux adaptée aux données, ont été développées.L’association de ces deux aspects a montré des résultats prometteurs pour la détection de changements.Le traitement de ces séries pose deux problèmes notables. En premier lieu, les méthodes d’analyse statistique performantes se basent souvent sur des données multivariées caractérisant, dans le cas des images RSO, une diversité polarimétrique ou interférométrique, par exemple. Lorsque cette diversité n’est pas disponible et que les images RSO sont monocanal, de nouvelles méthodologies basées sur la décomposition en ondelettes ont été développées. Celles-ci permettent d’ajouter une diversité spectrale et angulaire supplémentaire représentant le comportement physique de rétrodiffusion des diffuseurs présents la scène de l’image. Dans un second temps, l’amélioration de la résolution spatiale sur les dernières générations de capteurs engendre une augmentation de l’hétérogénéité des données obtenues. Dans ce cas, l’hypothèse gaussienne, traditionnellement considérée pour développer les méthodologies standards de détection de changements, n’est plus valide. En conséquence, des méthodologies d’estimation robuste basée sur la famille des distributions elliptiques, mieux adaptée aux données, ont été développées.L’association de ces deux aspects a montré des résultats prometteurs pour la détection de changements. / Remote sensing data from Synthetic Aperture Radar (SAR) sensors offer a unique opportunity to record, to analyze, and to predict the evolution of the Earth. In the last decade, numerous satellite remote sensing missions have been launched (Sentinel-1, UAVSAR, TerraSAR X, etc.). This resulted in a dramatic improvement in the Earth image acquisition capability and accessibility. The growing number of observation systems allows now to build high temporal/spatial-resolution Earth surface images data-sets. This new scenario significantly raises the interest in time-series processing to monitor changes occurring over large areas. However, developing new algorithms to process such a huge volume of data represents a current challenge. In this context, the present thesis aims at developing methodologies for change detection in high-resolution SAR image time series.These series raise two notable challenges that have to be overcome:On the one hand, standard statistical methods rely on multivariate data to infer a result which is often superior to a monovariate approach. Such multivariate data is however not always available when it concerns SAR images. To tackle this issue, new methodologies based on wavelet decomposition theory have been developed to fetch information based on the physical behavior of the scatterers present in the scene.On the other hand, the improvement in resolution obtained from the latest generation of sensors comes with an increased heterogeneity of the data obtained. For this setup, the standard Gaussian assumption used to develop classic change detection methodologies is no longer valid. As a consequence, new robust methodologies have been developed considering the family of elliptical distributions which have been shown to better fit the observed data.The association of both aspects has shown promising results in change detection applications.

Risks and uncertainties are unavoidable in agriculture in Sudan, due to its dependence on climatic factors and to the imperfect nature of the agricultural decisions and policies attributed to land cover and land use changes that occur. The current study was conducted in the Gash Agricultural Scheme (GAS) - Kassala State, as a semi-arid land in eastern Sudan. The scheme has been established to contribute to the rural development, to help stability of the nomadic population in eastern Sudan, particularly the local population around the Gash river areas, and to facilitate utilizing the river flood in growing cotton and other cash crops. In the last decade, the scheme production has declined, because of drought periods, which hit the region, sand invasion and the spread of invasive mesquite trees, in addition to administrative negligence. These have resulted also in poor agricultural productivity and the displacement of farmers away from the scheme area. Recently, the scheme is heavily disturbed by human intervention in many aspects. Consequently, resources of cultivated land have shrunk and declined during the period of the study, which in turn have led to dissatisfaction and increasing failure of satisfying increasing farmer’s income and demand for local consumption. Remote sensing applications and geospatial techniques have played a key role in studying different types of hazards whether they are natural or manmade. Multi-temporal satellite data combined with ancillary data were used to monitor, analyze and to assess land use and land cover (LULC) changes and the impact of land degradation on the scheme production, which provides the managers and decision makers with current and improved data for the purposes of proper administration of natural resources in the GAS. Information about patterns of LULC changes through time in the GAS is not only important for the management and planning, but also for a better understanding of human dimensions of environmental changes at regional scale. This study attempts to map and assess the impacts of LULC change and land degradation in GAS during a period of 38 years from 1972-2010. Dry season multi-temporal satellite imagery collected by different sensor systems was selected such as three cloud-free Landsat (MSS 1972, TM 1987 and ETM+ 1999) and ASTER (2010) satellite imagery. This imagery was geo-referenced and radiometrically and atmospherically calibrated using dark object subtraction (DOS). Two approaches of classification (object-oriented and pixel-based) were applied for classification and comparison of LULC. In addition, the study compares between the two approaches to determine which one is more compatible for classification of LULC of the GAS. The pixel-based approach performed slightly better than the object-oriented approach in the classification of LULC in the study area. Application of multi-temporal remote sensing data proved to be successful for the identification and mapping of LULC into five main classes as follows: woodland dominated by dense mesquite trees, grass and shrubs dominated by less dense mesquite trees, bare and cultivated land, stabilized fine sand and mobile sand. After image enhancement successful classification of imagery was achieved using pixel and object based approaches as well as subsequent change detection (image differencing and change matrix), supported by classification accuracy assessments and post-classification. Comparison of LULC changes shows that the land cover of GAS has changed dramatically during the investigated period. It has been discovered that more significant of LULC change processes occurred during the second studied period (1987 to 1999) than during the first period (1972-1987). In the second period nearly half of bare and cultivated lands was changed from 41372.74 ha (20.22 %) in 1987 to 28020.80 ha (13.60 %) in 1999, which was mainly due to the drought that hit the region during the mentioned period. However, the results revealed a drastic loss of bare and cultivated land, equivalent to more than 40% during the entire period (1972-2010). Throughout the whole period of study, drought and invasion of both mesquite trees and sand were responsible for the loss of more than 40% of the total productive lands. Change vector analysis (CVA) as a useful approach was applied for estimating change detection in both magnitude and direction of change. The promising approach of multivariate alteration detection (MAD) and subsequent maximum autocorrelation factor (MAD/MAF) transformation was used to support change detection via assessment of maximum correlation between the transformed variates and the specific original image bands related to specific land cover classes. However, both CVA and MAD/MAD strongly prove the fact that bare and cultivated land have dramatically changed and decreased continuously during the studied period. Both CVA and MAD/MAD demonstrate adequate potentials for monitoring, detecting, identifying and mapping the changes. Moreover, this research demonstrated that CVA and MAD/MAF are superior in providing qualitative details about the nature of all kinds of change. Vegetation indices (VI) such as normalized difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI), modified adjusted vegetation index (MSAVI) and grain soil index (GSI) were applied to measure the quantitative characterization of temporal and spatial vegetation cover patterns and change. All indices remain very sensitive to structure variation of LULC. The results reveal that the NDVI is more effective for detecting the amount and status of the vegetation cover in the study area than SAVI, MSAVI and GSI. Therefore, it can be stated that NDVI can be used as a response variable to identify drought disturbance and land degradation in semi-arid land such as the GAS area. Results of detecting vegetation cover observed by using SAVI were found to be more reasonable than using MSAVI, although MSAVI reduces the background of bare soil better than SAVI. GSI proves high efficiency in determining the different types of surface soils, and producing a change map of top soil grain size, which is useful in assessment of land degradation in the study area. The linkage between socio-economic data and remotely sensed data was applied to determine the relationships between the different factors derived and to analyze the reasons for change in LULC and land degradation and its effects in the study area. The results indicate a strong relationship between LULC derived from remotely sensed data and the influencing socioeconomic variables. The results obtained from analyzing socioeconomic data confirm the findings of remote sensing data analysis, which assure that the decline and degradation of agricultural land is a result of further spread of mesquite trees and of increased invasion of sand during the study period. High livestock density and overgrazing, drought, invasion of sand, spread of invasive mesquite trees, overexploitation of land, improper management, and population growth were considered as the main direct factors responsible for degradation in the study area.