Une tendance à l’augmentation des dépérissements forestiers est observée et risque de s’accentuer dans le contexte actuel de changement climatique. La télédétection peut proposer des méthodes innovantes pour l’évaluation de l’état et du devenir des écosystèmes forestiers. Ce travail de thèse vise à proposer, valider et interpréter des mesures de baisse d’activité des résineux du Sud du Massif-Central et de la sapinière pyrénéenne. Le premier objectif est, par l’utilisation de séries temporelles d’images à moyenne résolution spatiale (images NDVI-MODIS), d’identifier les méthodes permettant de mesurer des baisses d’activité, et de vérifier qu’elles correspondent à des baisses de vitalité, dans des peuplements où se manifestent des phénomènes de dépérissement. La détection de changement d’activité, que l’on peut assimiler à des perturbations, repose sur deux approches : la première mesure des écarts ou des tendances de paramètres de phénologie de surface et la deuxième utilise une procédure de décomposition de la série temporelle. Les mesures de changement ont été réalisées sur la période 2000-2011. La détection des ruptures négatives et de forte amplitude dans la réponse de NDVI de 2003 à 2011 confirme l’influence de la sècheresse de 2003, qui se traduit à la fois par les baisses d’activité liées à l’état des arbres mais également par des coupes de dépérissement qui se sont succédées les années suivantes. Un travail préliminaire à l’étape de validation des baisses de vitalité détectées, a consisté à proposer et appliquer un modèle de détection des coupes afin d’éliminer ces situations des zones d’observation. Une procédure de validation des baisses de vitalité a été mise en place dans le cas de la sapinière des Pyrénées. Pour cela, deux approches ont été utilisées : (1) la confrontation à des données indirectes de l’état des peuplements mais spatialement exhaustives, à travers les inventaires des coupes de dépérissement sur la période 2000-2012 et une cartographie du dépérissement datant de 2001 et (2) la confrontation à des données d’observations directes de l’état des Sapins dans le Pays de Sault (Est des Pyrénées), en utilisant une méthode de diagnostic basée sur l’architecture des arbres (méthode ARCHI), avec un échantillonnage adapté à l’échelle des pixels MODIS (Lambert et al. 2013). Des relations ont été mises en évidence, permettant de valider les méthodes utilisées, mais aussi d’en ressortir des limites d’interprétation. Enfin, pour donner des éléments d’interprétation des phénomènes observés, les variations d’activité observées par télédétection ont été confrontées à des données climatiques et édaphiques spatialisées, adaptées à l’étude des milieux forestiers. Les résultats montrent que les baisses de vitalité constatées dans les peuplements de Sapins du Pays de Sault sont significativement corrélées au facteur climatique température et dans une moindre mesure, aux précipitations. Dans les Pyrénées Centrales, où les facteurs de causalité semblent être multiples, l’influence des conditions de sècheresse hydrique et édaphique n’a pas pu être démontrée. / An increasing trend of forest decline is observed and is likely to increase in the current context of climate change. Remote sensing can provide innovative methods for the forest ecosystems status assessment. This thesis aims at proposing, validating and interpreting activity measurements of some Southern Massif Central and Pyrenees mountains coniferous stands. The first objective is, using of time series of medium spatial resolution (MODIS-NDVI) images, to identify methods to measure decreases of activity, and to verify if they correspond to vitality decreases in stands in which has been observed forest decline. Change detection of activity, which can be considered as disturbances, is based on two approaches: the first allows to measure differences or trends of phenology surface parameters, and the second uses a method based on the time series decomposition. Changes that occur during the 2000-2011 times-period were measured. The detection of high magnitude negative breakpoints in NDVI time series from 2003 to 2011 confirms the influence of the 2003 summer drought, which both led to decreases in activity related to trees heath status and also to clear-cuts during the following years. Before the validation process, a clear-cut detection method was proposed in order to eliminate these situations in the study areas. A validation procedure was implemented on Pyrenean fir stands. For this step, two approaches were implemented: (1) the use of spatially extensive state stands proxies, through cuts inventory inventories during the 2000-2012 times-period and a 2001 forest decline map, and (2) the use of data from direct tree heath’s observations in the fir stands of Pays de Sault region (Eastern Pyrenees) using a diagnostic method based on the observation of tree architecture (ARCHI method). For this second approach, an appropriate sampling was assessed to deal with the MODIS pixels scale (Lambert et al. 2013). Relationships have been identified, allowing to validate the used methods, but also to highlight theirs interpretation’s limits. Finally, to provide an interpretation of the observed phenomena, the remote sensing activity variations were compared to climatic and soil spatial data which are adapted to the study of forest environments. The results show that vitality declines in Pays de Sault fir stands are significantly correlated with climatic factors, temperature and to a lesser degree to precipitations. In the Central Pyrenees, where the causal factors appear to be numerous, the influence of water and soil drought conditions has not been demonstrated.

The terrestrial carbon cycle is a part of the global carbon cycle, where one important component is the terrestrial vegetation. Terrestrial vegetation largely controls the land surface carbon exchanges and leverage the atmospheric greenhouse gas concentrations, significantly affecting the trajectory of global warming. It is therefore important to improve the understanding of vegetation response to different climatic factors, in particular for those linked to large-scale climate variability, which is still less studied so far. Vegetation greenness is suggested to be a useful tool in order to understand vegetation response. Looking at Europe, the climate factors that affect vegetation the most are linked to the large-scale atmospheric circulation over the North Atlantic, like the jet stream, which varies in speed and latitude, and the North Atlantic Oscillation (NAO). Here, I compute monthly indices representing the variability of these atmospheric features, and correlate them with monthly vegetation greenness data (NDVI) anomalies over a period of five years. This is done both for regionally-averaged NDVI and the months April-July and as a geographical point-by-point analysis for the month of May. The results show a significant correlation between Scandinavian NDVI and the NAO as well as jet speed at multiple time lags, up until 2 months. The jet latitude, instead, showed significant correlation for three regions in mid/southwestern Europe at longer time lags of 3-4 months. This means that the position of the jet in winter can affect the spring vegetation growth in this area. The jet speed and NAO, however, works mostly at shorter timespans. / Den jordbunda kolcykeln, som är en del av den globala kolcykeln, består av olika komponenter där en viktig del är vegetation. Växtlighet på land kontrollerar till stor del utbytet av kol vid jordytan och har därigenom inflytande på atmosfäriska växthusgaskoncentrationer, vilket medför stor påverkan på global uppvärmning. Det är därför viktigt att förbättra förståelsen för hur vegetation reagerar på olika klimatologiska faktorer, särskilt de som är kopplade till storskalig klimatvariabilitet då dessa kopplingar har studerats i mindre utsträckling hittils. Ett bra sätt att mäta den jordbunda kolcyklen på är med grönhet av vegatation. Om vi beaktar Europa så är det främst storskaliga atmosfäriska cirkulatoiner över norra Atlanten av de klimatologiska faktorerna som påverkar vegetation. En av dessa faktorer är jetströmmen, vilken varierar i fart och latitud, samt Nordatlantiska Oscillationen(NAO). I detta arbete beräknar jag index som representerar variationen i dessa i form av månadsgenomsnitt och korrelerar dem med månatlig data över avvikelser i vegetationsgrönhet (NDVI) över en femårsperiod. Det här gjordes för både regionala medelvärden och månaderna april-juli samt en geografisk punkt till punkt analys utförd för maj. Resultatet visar att det finns en signifikant korrelation mellan NDVI i Skandinavien och NAO samt jetfarten vid flera tidsfördröjningar, upp till 2 månader. Jetlatituden visade däremot signifikant korrelation för tre regioner i centrala/sydvästa Europa vid längre tidsfördröjningar på 3-4 månader. Detta innebär att positionen på jetströmmen under vintern kan påverka vegetationstillväxten under våren i detta område. Jetfarten och NAO påverkar däremot mest vid kortare tidsspan.

The construction of Baku-Tbilisi-Ceyhan (BTC) Oil and South Caucasus Gas (SCP) pipelines was completed in 2005. The Azerbaijan section of BTC Oil and SCP Gas pipelines is 442 km long and 44 m wide corridor named as the Right-of-Way. BTC and SCP pipelines are aligned parallel to each other within the same 44m corridor. The construction process of the pipelines significantly disturbed vegetation and soil cover along Right-of-Way of pipelines. The revegetation and erosion control measures were conducted after the completion of construction to restore the disturbed footprints of construction activities. The general goals of the present studies, dedicated to the environmental monitoring of revegetation and planning of erosion control measures were: to evaluate the status of the revegetation in 2007 since the completion of the construction activities in 2005, to determine the climate and ground factors controlling the vegetation regrowth and to predict erosion-prone areas along Right-of-Way of pipelines. Regression and root mean square error analysis between the Normalized Difference Vegetation Index (NDVI) of IKONOS images acquired in 2007 and in-situ estimations of vegetation cover percentage revealed R2 equal to 0.80 and RMSE equal to 6% which were optimal for the normalization of NDVI to vegetation cover. The total area of restored vegetation cover between 2005 and 2007 was 8.9 million sq. m. An area of 10.7 million sq. m. of ground vegetation needed restoration in order to comply with the environmental acceptance criteria. Based on the Global Spatial Regression Model, precipitation, land surface temperature and evapotranspiration were determined as the main climate factors controlling NDVI of grasslands along Right-of-Way of pipelines. In case of croplands, precipitation, evapotranspiration and annual minimum temperature were determined as the main factors controlling NDVI of croplands. The regression models predicting NDVI for grasslands and croplands were also formulated. The Geographically Weighted Regression analyses in comparison with the global regression models results clearly revealed that the relationship between NDVI of grasslands and croplands and the predictor variables was spatially non-stationary along the corridor of pipelines. Even though the observed R2 value between elevation and NDVI of grasslands was low (R2= 0.14), the accumulation of the largest NDVI patterns was observed higher than 150m elevation. This revealed that elevation has non-direct control of NDVI of grasslands through its control of precipitation and temperature along the grasslands of Right-of-Way. The spatial distribution percentage of NDVI classes within slope aspect categories was decreasing in the southern directions of slope faces. Land surface temperature was decreasing with elevation but no particular patterns of land surface temperature in the relationship with NDVI accumulation within the aspect categories were observed. Aspect categories have non-direct control of NDVI and there are some other factors apart from land surface temperature which require further investigations. Precipitation was determined to be controlling the formation of topsoil depth and the topsoil obviously controls the VC growth of grasslands as one of the main ground factors. The regression analysis between NDVI of grasslands and croplands with groundwater depth showed very low correlation. But the clustered patterns of vegetation cover were observed in the relationship with groundwater depth and soil moisture for both grasslands and croplands. The modeling of groundwater depth relative to soil moisture and MODIS NDVI of grasslands determined that the threshold of groundwater depth for vegetation growth is in the range of 1-5 m. MODIS NDVI and soil moisture did not reveal a significant correlation. Soil moisture revealed R2 equal to 0.34 with elevation, R2 equal to 0.23 with evapotranspiration, R2 equal to 0.57 with groundwater depth and R2 equal to 0.02 with precipitation. This allowed to suspect that precipitation is not the main factor controlling soil moisture whereas elevation, evapotranspiration and groundwater depth have non-direct control of soil moisture. Therefore, soil moisture has also non-direct control of vegetation cover growth along the corridor of pipelines. The variations of soil moisture in the 1-3 m soil depth range may have the threshold of depth controlling vegetation cover regrowth and this requires more detailed soil moisture data for further investigations. The reliability of the Global Spatial Regression Model and Geographically Weighted Regression predictions is limited by the MODIS images spatial resolution equal to 250 m and spectral characteristics. The Morgan-Morgan-Finney (MMF) and Universal Soil Loss Equation (USLE) predictions revealed non-similarity in the spatial distribution of soil loss rates along Right-of-Way. MMF model revealed more clustered patterns of predicted critical erosion classes with soil loss more than 10 ton/ha/year in particular ranges of pipelines rather than Universal Soil Loss Equation model with the widespread spatial distribution. Paired-Samples T-Test with p-value less than 0.05 and Bivariate correlation with the Pearson\'s correlation coefficient equal to 0.23 showed that the predictions of these two models were significantly different. Verification of USLE- and MMF- predicted erosion classes against in-situ 316 collected erosion occurrences collected in the period of 2005-2012 revealed that USLE performed better than MMF model along pipeline by identifying of 192 erosion occurrences out of 316, whereas MMF identified 117 erosion sites. USLE revealed higher ratio of frequencies of erosion occurrences within the critical erosion classes (Soil Loss > 10 t/ha), what also showed higher reliability of soil loss predictions by USLE. The validation of quantitative soil loss predictions using the measurements from 48 field erosion plots revealed higher R2 equal to 0.67 by USLE model than by MMF. This proved that USLE-predicted soil loss rates were more reliable than MMF not only in terms of spatial distributions of critical erosion classes but also in the quantitative terms of soil loss rates. The total number of erosion-prone pipeline segments with the identified erosion occurrences was 316 out of 38376. The number of erosion-prone pipeline segments realistically predicted by USLE model e.g. soil loss more than 10 t/ha was 97 whereas MMF predicted only 70 erosion-prone pipeline segments. The regression analysis between 354 USLE and MMF erosion-prone segments revealed R2 equal to 0.36 what means that the predictions by USLE and MMF erosion models are significantly different on the level of pipeline segments. The average coefficients of variation of predicted soil loss rates by USLE and MMF models and the number of accurately predicted erosion occurrences within the geomorphometric elements of terrain, vegetation cover and landuse categories were larger in the USLE model. This supported the hypothesis that larger spatial variations of erosion prediction models can contribute to the better soil loss prediction performance and reliability of erosion prediction models. This also supported the hypothesis that better understanding of spatial variations within geomorphometric elements of terrain, land-use and vegetation cover percentage classes can support in the selection of the appropriate erosion models with better performance in the particular areas of pipelines. Qualitative multi-criteria assessment for the determination of erosion-prone areas revealed stronger relations with the USLE predictions rather than with MMF. Multi-criteria assessment identified 35 of erosion occurrences but revealed more reliable predictions on the level of terrain units. Predicted erosion-prone areas by USLE revealed higher correlation coefficient with erosion occurrences than MMF model within terrain units what proved higher reliability of the USLE predictions and its stronger relation with the multi-criteria assessment.

Climate variability and the carbon cycle (C-cycle) are tied together in complex feedback loops and due to these complexities there are still knowledge-gaps of this coupling. However, to make accurate predictions of future climate, profound understanding of the C-cycle and climate variability is essential. To gain more knowledge of climate variability, the study aims to identify recurring spatial patterns of the variability of precipitation anomalies over Scandinavia during spring and summer respectively between 1981 to 2014. These patterns will be related to the C-cycle through changes in summer vegetation greenness, measured as normalized difference vegetation index (NDVI). Finally, the correlation between the patterns of precipitation variability in summer and the teleconnection patterns over the North Atlantic will be investigated. The precipitation data was obtained from ERA5 from the European Centre for Medium-Range Weather Forecasts and the patterns of variability were found through empirical orthogonal function (EOF) analysis. The first three EOFs of the spring and the summer precipitation anomalies together explained 73.5 % and 65.5 % of the variance respectively. The patterns of precipitation variability bore apparent similarities when comparing the spring and summer patterns and the Scandes were identified to be important for the precipitation variability in Scandinavia during both seasons. Anomalous events of the spring EOFs indicated that spring precipitation variability had little impact on anomalies of summer NDVI. Contradictory, summer precipitation variability seemed to impact anomalies of summer NDVI in central- and northeastern Scandinavia, thus indicating that summer precipitation variability modulates some of the terrestrial C-cycle in these regions. Correlations were found between a large part of the summer precipitation variability and the Summer North Atlantic Oscillation and the East Atlantic pattern. Hence, there is a possibility these teleconnections have some impact, through the summer precipitation variability, on the terrestrial C-cycle. / Förändringar och variation i klimatet är sammankopplade med kolcykeln genom komplexa återkopplingsmekanismer. På grund av denna komplexitet är kunskapen om kopplingen mellan klimatvariation och kolcykeln fortfarande bristande, men för att möjliggöra precisa prognoser om framtida klimat är det viktigt att ha kunskap om denna koppling. För att få mer kunskap om klimatvariation syftar därför denna studie till att identifiera återkommande strukturer av nederbördsvariation över Skandinavien under vår respektive sommar från 1981 till 2014. Dessa relateras till förändringar i sommarväxtlighetens grönhet, uppmätt som skillnaden i normaliserat vegetationsindex (NDVI). Även korrelationen mellan sommarstrukturerna av nederbördsvariationen och storskaliga atmosfäriska svängningar, s.k. "teleconnections", över Nordatlanten undersöks. Nederbördsdatan erhölls från ERA5 analysdata från Europacentret för Medellånga Väderprognoser och strukturer av nederbördsvariationen identifierades genom empirisk ortogonal funktionsanalys (EOF) av nederbördsavvikelser. De tre första EOF av vår- respektive sommarnederbördsavvikelser förklarade tillsammans 73,5 % respektive 65,5 % av nederbördsvariationen. Strukturerna av nederbördsvariation under vår respektive sommar uppvisade tydliga likheter sinsemellan. Dessutom identifierades Skanderna vara av stor vikt för nederbördsvariationen i Skandinavien under båda årstider. Avvikande år av nederbördsvariation under våren indikerade att sagda nederbördsvariation haft liten påverkan på NDVI-avvikelser under sommaren. Emellertid verkade nederbördsvariationen under sommaren påverkat NDVI-avvikelser under sommaren i centrala och nordöstra Skandinavien. Detta indikerar att nederbördsvariationen under sommaren till viss del styr den terrestra kolcykeln i dessa regioner. För nederbördsvariationen under sommaren fanns korrelation mellan både Nordatlantiska sommaroscillationen och Östatlantiska svängningen. Det finns således en möjlighet att dessa "teleconnections" har en viss påverkan på den terrestra kolcykeln genom nederbördsvariationen under sommaren.

An investigation of temporal dynamics of El Niño–Southern Oscillation (ENSO) and spatial patterns of dryness/wetness period over arid and semi-arid zones of Central Asia and their relationship with Normalized Difference Vegetation Index (NDVI) values (1982-2011) have explored in this article. For identifying periodical oscillations and their relationship with NDVI values have selected El Nino 3.4 index and thirty years of new generation bi-weekly NDVI 3g acquired by the Advanced Very High Resolution Radiometer (AVHRR) satellites time-series data. Based on identification ONI (Oceanic Nino Index) is a very strong El Nino (warm) anomalies observed during 1982-1983, 1997-1998 and very strong La Nino (cool) period events have observed 1988-1989 years. For correlation these two factors and seeking positive and negative trends it has extracted from NDVI time series data as “low productivity period” following years: 1982-1983, 1997 -1998; and as “high productivity period” following years: 1988 -1989. Linear regression observed warm events as moderate phase period selected between moderate El Nino (ME) and NDVI with following eriods:1986-1987; 1987-1988; 1991-1992; 2002-2003; 2009-2010; and moderate La Niña (ML) periods and NDVI (1998-1999; 1999-2000; 2007-2008) which has investigated a spatial patterns of wetness conditions. The results indicated that an inverse relationship between very strong El Nino and NDVI, decreased vegetation response with larger positive ONI value; and direct relationship between very strong La Niña and NDVI, increased vegetation response with smaller negative ONI value. Results assumed that significant impact of these anomalies influenced on vegetation productivity. These results will be a beneficial for efficient rangeland/grassland management and to propose drought periods for assessment and reducing quantity of flocks’ due to a lack of fodder biomass for surviving livestock flocks on upcoming years in rangelands. Also results demonstrate that a non-anthropogenic drivers of variability effected to land surface vegetation signals, nderstanding of which will be beneficial for efficient rangeland and agriculture management and establish ecosystem services in precipitation-driven drylands of Central Asia.

Land degradation, a phenomenon referring to (drought) in arid, semi-arid and dry sub-humid regions as a result of climatic variations and anthropogenic activities most especially in the semi-arid lands of Sudan, where vast majority of the rural population depend solely on agriculture and pasture for their daily livelihood, the ecological pattern had been greatly influenced thereby leading to loss of vegetation cover coupled with climatic variability and replacement of the natural tree composition with invasive mesquite species. The principal aim of this study is to quantitatively examine the vigour of vegetation in Sudan through different vegetation indices. The assessment was done based on indicators such as soil adjusted vegetation index (SAVI). Cloud free multi-spectral remotely sensed data from LANDSAT imagery for the dry season periods of 1984 and 2009 were used in this study. Results of this study shows conversion of vegetation to other land use type. In general, an increase in area covered by vegetation was observed from the NDVI results of 2009 which is a contrast of that of 1984. The results of the vegetation indices for NDVI in 1984 (vegetated area) showed that about 21% was covered by vegetation while 49% of the area were covered with vegetation in 2009. Similar increase in vegetated area were observed from the result of SAVI. The decrease in vegetation observed in 1984 is as a result of extensive drought period which affects vegetation productivity thereby accelerating expansion of bare surfaces and sand accumulation. Although, increase in vegetated area were observed from the result of this study, this increase has a negative impact as the natural vegetation are degraded due to human induced activities which gradually led to the replacement of the natural vegetation with invasive tree species. The results of the study shows that NDVI perform better than by SAVI.

Runtom i Sverige och Europa skapar igenvuxna sjöar och vattendrag allt fler problem, vilket bland annat beror på klimatförändringar och mänsklig påverkan. En av de främsta anledningarna till igenväxning av sjöar och vattendrag är övergödning. Studiens syfte är att utvärdera om det är möjligt att på ett automatiskt sätt identifiera utbredning av vattenvegetation i sjöar och vattendrag med hjälp av fjärranalys och GIS. En analys av vegetationsförändringar i sjön Sottern i Uppland genomförs, där utbredd igenväxning skapar problem och där röjningsmaskiner används för att hantera vegetationen, som främst består av bladvass, näckrosor och annan flytande vatten-vegetation. Genom tillämpning av olika klassificeringsalgoritmer, bandkombinationer och vegetationsindex undersöks förändringar i sjöns tillstånd genom att klassa Sottern i två huvudklasser, vatten och vattenvegetation. Studien baseras på fjärranalysdata från den optiska satellitkonstellationen Sentinel-2 och en högupplöst referensbild från Google Earth Pro. Data samlades in under växtsäsongen, maj till oktober, för åren 2021 och 2022 för att analysera om och hur vattenvegetationen förändras över tid. Resultaten visar att Maximum Likelihood Classification (MLC) framträder som den mest effektiva algoritmen för att studera vegetationsförändringar, särskilt om den appliceras på en "False color" bandkombination bestående av banden 8 (NIR), 4 (rött) och 3 (grönt). MLC visar högre (94%) noggrannhet jämfört med Random Trees (RT) och Support Vector Machine (SVM). Genom att tillämpa vegetationsindexet NDVI (Normalized Difference Vegetation Index) ger studien en fördjupad förståelse för hur vegetationen förändras över tid. Genom att kombinera resultaten från dessa metoder går det att dra slutsatser om hur vattenvegetationen breder ut sig över tid i sjön Sottern, där en tydlig ökning av vattenvegetation sker mellan mitten av maj till mitten av juni, medan minskningen av vattenvegetationen inte är lika konsekvent. / In Europe, overgrown lakes and watercourses are creating increasing problems, which are partly due to climate change and human impact. One of the main reasons for the overgrowth of lakes and watercourses is eutrophication. The aim of the study is to evaluate the possibility of automatically identifying overgrown lakes and watercourses using remote sensing and GIS. An analysis of vegetation changes in Lake Sottern in Uppland county, Sweden is conducted, where overgrowth creates problems and where clearing machines are used to manage the vegetation, primarily consisting of reeds, water lilies, and other aquatic vegetation. By applying various classification algorithms, band combinations and vegetation indices, changes in the lake's condition are investigated by classifying Sottern into two main classes: water and aquatic vegetation. The study is based on remote sensing data from the optical satellite constellation Sentinel-2 and a high-resolution reference image from Google Earth Pro. Data were collected during the growing season, from May to October, for the years 2021 and 2022 to analyze if and how aquatic vegetation changes over time. The results show that Maximum Likelihood Classification (MLC) emerges as the most effective algorithm for identifying aquatic vegetation, especially when combined with a "False color" band combination consisting of bands 8 (NIR), 4 (red), and 3 (green). MLC shows higher accuracy compared to Random Trees (RT) and Support Vector Machine (SVM). By applying the Normalized Difference Vegetation Index (NDVI), the study provides a deeper understanding of how vegetation changes over time. By combining the results from these methods, it is possible to draw conclusions about how aquatic vegetation changes over time in lakes like Sottern, where a clear increase in aquatic vegetation occurs between May and June, while the decrease in aquatic vegetation is not as consistent.

Land use / land cover (LULC) change assessment is getting more consideration by global environmental change studies as land use change is exposing dryland environments for transitions and higher rates of resource depletion. The semiarid regions of northwestern Ethiopia are not different as land use transition is the major problem of the region. However, there is no satisfactory study to quantify the change process of the region up to now. Hence, spatiotemporal change analysis is vital for understanding and identification of major threats and solicit solutions for sustainable management of the ecosystem. LULC change studies focus on understanding the patterns, processes and dynamics of land use transitions and driving forces of change. The change processes in dryland ecosystems can be either seasonal, gradual or abrupt changes of random or systematic change processes that result in a pattern or permanent transition in land use. Identification of these processes of change and their type supports adoption of monitoring options and indicate possible measures to be taken to safeguard this dynamic ecosystem. This study examines the spatiotemporal patterns of LULC change, temporal trends in climate variables and the insights of the communities on change patterns of ecosystems. Landsat imagery, MODIS NDVI, CRU temperature, TAMSAT rainfall and socio-ecological field data were used in order to identify change processes. LULC transformation was monitored using support vector machine (SVM) algorithm. A cross-tabulation matrix assessment was implemented in order to assess the total change of land use categories based on net change and swap change. In addition, the pattern of change was identified based on expected gain and loss under a random process of gain and loss, respectively. Breaks For Additive Seasonal and Trend (BFAST) analysis was employed for determining the time, direction and magnitude of seasonal, abrupt and trend changes within the time series datasets. In addition, Man Kendall test statistic and Sen’s slope estimator were used for assessing long term trends on detrended time series data components. Distributed lag (DL) model was also adopted in order to determine the time lag response of vegetation to the current and past rainfall distribution. Over the study period of 1972- 2014, there is a significant change in LULC as evidenced by a significant increase in size of cropland of about 53% and a net loss of over 61% of woodland area. The period 2000-2014 has shown a sharp increase of cropland and a sharp decline of woodland areas. Proximate causes include agricultural expansion and excessive wood harvesting; and underlying causes of demographic factor, economic factors and policy contributed the most to an overuse of existing natural resources. In both the observed and expected proportion of random process of change and of systematic changes, woodland has shown the highest loss compared to other land use types. The observed transition and expected transition under random process of gain of woodland to cropland is 1.7%, implies that cropland systematically gains to replace woodland. The comparison of the difference between observed and expected loss under random process of loss also showed that when woodland loses cropland systematically replaces it. The assessment of magnitude and time of breakpoints on climate data and NDVI showed different results. Accordingly, NDVI analysis demonstrated the existence of breakpoints that are statistically significant on the seasonal and long term trends. There is a positive trend, but no breakpoints on the long term precipitation data during the study period. The maximum temperature also showed a positive trend with two breakpoints which are not statistically significant. On the other hand, there is no seasonal and trend breakpoints in minimum temperature, though there is an overall positive trend along the study period. The Man-Kendall test statistic for long term average Tmin and Tmax showed significant variation where as there is no significant trend within the long term rainfall distribution. The lag regression between NDVI and precipitation indicated a lag of up to forty days. This proves that the vegetation growth in this area is not primarily determined by the current precipitation rather with the previous forty days rainfall. The combined analysis showed declining vegetation productivity and a loss of vegetation cover that contributed for an easy movement of dust clouds during the dry period of the year. This affects the land condition of the region, resulting in long term degradation of the environment

Pour prévoir la production, les systèmes de surveillance de la sécurité alimentaire doivent être renseignés par des données sur les surfaces cultivées et sur le rendement. Ces données peuvent être estimées par les systèmes d'observations satellitaires à moyenne résolution spatiale, qui, par leur vision synoptique, constituent une source d'information particulièrement adéquate. En Afrique de l'Ouest, l'estimation des surfaces cultivées par télédétection reste cependant problématique en raison d'un domaine cultivé fragmenté, d'une grande hétérogénéité spatiale due aux conditions environnementales et aux pratiques culturales, et de la synchronisation des phénologies des agrosystèmes et des écosystèmes liée au régime des précipitations. Dans ce contexte, cette thèse présente, en trois volets, des développements méthodologiques originaux pour la caractérisation des systèmes agricoles d'Afrique de l'Ouest par télédétection. Les méthodes ont été développées à partir de séries temporelles MODIS (250 m à 500 m de résolutionspatiale) acquises sur le Mali. (i) La cartographie des surfaces cultivées a été réalisée à partir d'indices spectraux, spatiaux, texturaux et temporels dérivés des images. Deux approches ont été appliquées : une approche de type ISODATA consécutive à une segmentation du territoire basée sur les images MODIS et une approche de fouille de données basée sur des « motifs séquentiels ». Les produits cartographiques obtenus présentent une meilleure précision que les produits globaux « occupation du sol » existants (70% vs 50% en moyenne). Cependant, une part importante des erreurs d'omission et de commission (de 20% à 40%) reste incompressible en raison de la fragmentation du domaine cultivé. (ii) La cartographie des types de systèmes agricoles a nécessité un premier travail de typologie effectué à partir d'une BD d'enquêtes de terrain de l'Institut d' Economie Rurale de Bamako sur 100 villages. Trois types de systèmes agricoles ont été déterminés à l'échelle du village : céréales dominantes (mil, sorgho), cultures intensives dominantes (maïs, coton) et mélange de sorgho et de coton. La classification des systèmes agricoles à partir des indicateurs de télédétection précédemment cités a été produite par un algorithme de type Random Forest avec une précision globale de 60%. Les résultats mettent en évidence une combinaison optimale d'indicateurs comprenant le NDVI ainsi que la texture pour la caractérisation des systèmes agricoles. (iii) Enfin, pour le suivi des cultures, le produit phénologique MODIS a été testé et évalué à partir de variables phénologiques obtenues par simulations agro-météorologiques du modèle de plante SARRA-H. Les résultats montrent que ce produit comporte des incohérences dues au fort ennuagement de début de saison des pluies. Après suppression des données aberrantes, on montre que les dates de transition phénologique des surfaces cultivées issues de MODIS sont plus précoces de 20 jours comparées aux sorties du modèle de culture, en raison notamment de la nature mixte « agro-écosystème » des surfaces à l'échelle du pixel MODIS. Les résultats de cette thèse permettent de dégager de nouvelles pistes de couplage entre télédétection, données de terrain et modélisation agro-météorologique en apportant une information continue dans le temps et dans l'espace sur la caractérisation du domaine cultivé au « Sahel ». / For food security systems, data on cultivated surfaces and yields are a prerequisite for agricultural production forecast. Moderate resolution satellite remote-sensing systems offer a synoptic vision that makes them a particularly appropriate information source for the estimation of such data. However, the estimation of cultivated surfaces is still challenging in West Africa, because of highly fragmented farmland, specific weather conditions resulting in high regional variability in terms of agricultural systems and practices, and synchronized phenology of crops and natural vegetation due to the rainfall regime. In this context, this thesis presents three original methodological approaches for the characterization of agricultural systems in West Africa by remote sensing. These methods were developed using MODIS time series (from 250 to 500 m spatial resolution) acquired for Mali. (i) The mapping of cultivated areas was carried out with spectral, spatial and textural indices derived from the images. Two approaches were chosen: one of ISODATA type following a segmentation of the territory based on MODIS imagery, and the other of data mining type based on ‘sequential patterns'. The crop map obtained showed a better precision than that of the existing land cover global products (70% vs 50% in average). Furthermore, it was shown that a significant part of user and producer errors (20 to 40%) could not be compressed due to farmland fragmentation. (ii) The mapping of agricultural system types first required the definition of a typology derived from an IER (Institute of Rural Economy in Mali) field survey data base on 100 villages. Three types of agricultural systems were determined at the village scale: mainly cereals (millet, sorghum), mainly intensive crops (maize, cotton) and a mixture of sorghum and cotton. The classification of agricultural systems using the aforementioned remote sensing indicators was carried out by a Random Forest type algorithm with an overall accuracy of 62%. Results bring to light the important part played by temporal NDVI and texture in agricultural system characterization. (iii) Finally, for crop monitoring, the MODIS phenological product was tested and assessed using phenological variables obtained from agro-meteorological simulations made by the SARRA-H plant model. Results show that this product contains inconsistencies due to the significant cloud cover linked with the start of the raining season. After the suppression of incongruous data, the phenological transition dates for crop land derived from MODIS were shown to be earlier by 20 days than the SARRA-H-simulated transition dates, due mainly to the ‘agro-ecosystem' mixed nature of surfaces at MODIS pixels scale. The results of this thesis highlight new possibilities for the combinination of remote sensing, field data and agro-meteorological modelling, delivering nonstop information in time and space on the characterization of “Sahel” farmland.

De par sa géométrie et sa situation géographique (2°N-13°N – 8°E-16°E ; ouverture sur l’océan Atlantique), le Cameroun offre l’avantage de proposer un ensemble représentatif des climats régionaux rencontrés en Afrique tropicale. La diminution des cumuls de précipitations enregistrée dans la région pendant la seconde moitié du XXe siècle, est associée à la récurrence de périodes anormalement sèches, essentiellement au cœur de la saison des pluies. Ces conditions ont amplifié la dégradation du couvert végétal au travers ses contraintes socioéconomiques et démographiques (déforestation, extension des surfaces d’activité). Les conséquences souvent dommageables de la variabilité climatique en général, et des sécheresses en particulier, sur les hommes et leurs activités suscitent l’intérêt de développer des études pour mieux comprendre comment le climat et les pressions naturelles et environnementales interagissent localement. Ainsi, l’objectif de cette thèse est de diagnostiquer la variabilité multiéchelle (saisonnière, interannuelle, intra-saisonnière, synoptique) des précipitations et les relations qu’elle entretient avec le couvert végétal au sens large qui, à ces latitudes, est associé directement ou non, à la dynamique d’occupation et d’utilisation du sol, particulièrement sur la période 1951-2002. A partir de données de précipitations observées (CRU/ponctuelles), les modes spatiaux de la variabilité ont été définis aux échelles annuelles et interannuelles, par Analyses en Composante Principale (ACP) et la Classification Ascendante Hiérarchique (CAH). Ces méthodes de classifications ont permis de discriminer cinq zones climatiques, différentes les unes des autres par l’intensité des cumuls et la saisonnalité (unimodal/bimodal). Pour chaque zone, l’attention a été portée sur les paramètres intrasaisonniers qui modulent la variabilité annuelle telle que, les séquences sèches (nombre, longueur, périodes d’occurrence) et les variations des dates de début et de fin des périodes végétatives. La répartition du couvert végétal dans l’espace et dans le temps (1982-2002) a été étudiée, en utilisant des méthodes de classification non supervisée (ISODATA) sur les données de NDVI (Normalized Difference Vegetation index) à 8km de résolution. Enfin, des méthodes statistiques et de télédétection ont permis d’évaluer l’impact des facteurs pluviométriques et anthropogéniques (croissance démographique et utilisation du sol) sur la dynamique du couvert végétal en utilisant des bases de données à plus fine résolution (NDVI/1Km ; Global Land Cover (GLC 2000/1Km)). Ces dernières investigations ont été menées dans le Nord-Cameroun (6°N-13°N – 11°E-16°E), qui est la région la plus sensible des points de vue climatique, économique et environnemental. / Due to its shape and location (2°N-13°N – 8°E-16°E; proximity of the Atlantic Ocean), Cameroon is characterized by a panel of cross-regional climate encountered widely in tropical Africa. Over the region, the decrease rainfall during the second half of the last century has been shown to be associated with stronger recurrence of drier periods, specifically in the core of the rainy season. These conditions have favored the degradation of vegetation cover, driven by socioeconomic and demographic constraints. The substantial impacts on human activities and local society highlight the need to better understand how climate and environmental dynamics do interact locally. The aim of this study is to diagnose multi-scale rainfall variability and its relationship with vegetation cover (natural and/or grown), which is directly or indirectly associated to the land-cover and land-use dynamics at these latitudes. Using observed rainfall data (Climatic Research Unit/punctual), the spatial modes of rainfall variability at annual and intraseasonal scales are defined through Principal Component Analysis (PCA) and Agglomerative Hierarchical Clustering (AHC). These regionalizations lead to the discretisation of 5 climatic zones, distinguished from each other, by both the amount of rainfall and seasonality (unimodal / bimodal). New intraseasonal dry spells statistics (number, length, period of occurrence) are produced as well as dates of onset and end of the vegetative seasons by sub-regions. Using unsupervised classification methods (such as ISODATA) in Normalized Difference Vegetation Index (NDVI) data at a 8km spatial resolution, vegetation cover spatiotemporal distribution and typology were produced. Then, based on a concomitant use of statistical and GIS approaches, higher resolutions of NDVI (SPOT-1Km) and Global Land-cover data (GLC 2000), allowed to further evaluate both the pluviometric and anthropogenic factors (demography, land use) influencing vegetation dynamics. Analysis were carried out in Northern Cameroon (6°N-13°N – 11°E-16°E), which is the most sensitive region with regards to climatic and environmental variability, that could lead to important socio-economic thread locally.