A large portion of Earth¿s terrestrial surface is subject to arid climatic water stress. As in these regions the hydrological cycle and the vegetation dynamics are tightly interconnected, a coupled modeling of these two systems is needed to fully reproduce the ecosystems¿ behavior over time and to predict possible future responses to climate change. In this thesis, the performance of three parsimonious dynamic vegetation models, suitable for inclusion in an operational ecohydrological model, are tested in a semi-arid Aleppo pine forest area in the south-east of Spain. The first model considered, HORAS (Quevedo & Francés, 2008), simulates growth as a function of plant transpiration (T), evaluating environmental restraints through the transpiration-reference evapotranspiration ratio. The state variable related to vegetation is R, relative foliar biomass, which is equivalent to FAO crop coefficient (Allen et al., 1998), but not fixed in time. The HORAS model was then abandoned because of its unsatisfactory results, probably due to a poor simulation of evaporation and transpiration processes. As for the other two models, WUE-model and LUE-model, the state variable is the leaf biomass (Bl, kg dry mass m-2 vegetation cover). Both models simulate gross primary production (GPP), in the first case as a function of transpiration and water use efficiency (WUE), in the second case as a function of absorbed photosynthetically active radiation (APAR) and light use efficiency (LUE). Net primary production (NPP) is then calculated taking into account respiration. The modelling is focused particularly on simulating foliar biomass, which is obtained from NPP through an allocation equation based on the maximum leaf area index (LAI) sustainable by the system, and considering turnover. An analysis of the information offered by MODIS EVI, NDVI, and LAI products was also performed, in order to investigate vegetation dynamics in the study site and to select the best indices to be used as observational verification for models. MODIS EVI is reported in literature (Huete et al., 2002) to be highly correlated with leaf biomass. In accordance with the phenological cycle timing described for the Aleppo pine in similar climates (Muñoz et al., 2003), the EVI showed maximum values in spring and minimum values in winter. Similar results were found applying the aforementioned WUE- and LUE- models to the study area. Contrasting simulated LAI with the EVI series, the correlation coefficients rWUE = 0.45 and rLUE = 0.57 were found for the WUE-model and LUE-model respectively. Concerning NDVI, its own definition links this index to the ¿greenness¿ of the target, so that it appears highly linked to chlorophyll content and vegetation condition, but only indirectly related to LAI. Photosynthetic pigment concentrations are reported to be sensitive to water stress in Aleppo pine (Baquedano and Castillo, 2006) so, to compare the models¿ results with NDVI, the simulated LAI was corrected by plant water-stress. The resulting correlation coefficients were rWUE = 0.62 and rLUE = 0.59. Lastly, MODIS LAI and ET were found to be unreliable in the study area because very low compared to field data and to values reported in literature (e.g. Molina & del Campo, 2012) for the same species in similar climatic conditions. The performance of both WUE- and LUE- models in this semi-arid region is found to be reasonable. However, the LUE-model presents the advantages of a better performance, the possibility to be used in a wider range of climates and to have been extensively tested in literature. / Pasquato, M. (2013). Comparison of parsimonious dynamic vegetation modelling approaches for semiarid climates [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/34326

Uma das atividades mais relevantes para a economia brasileira é a agricultura. Entre os produtos de maior importância no cenário agrícola nacional, estão a soja e o arroz, os quais representam uma grande parcela da produção. Somente o Estado do Rio Grande do Sul é responsável por aproximadamente 67% da produção nacional de arroz e 10% de soja (IBGE, 2012). Portanto, informações confiáveis sobre a produção agrícola são relevantes para o desenvolvimento do setor e o desenvolvimento de metodologias capazes de auxiliar no monitoramento das áreas agrícolas torna-se peça importante na geração de dados confiáveis e com maior rapidez de obtenção. Desta forma, o objetivo deste trabalho foi desenvolver uma metodologia de baixo custo para a execução do mapeamento da área cultivada de arroz irrigado e soja, em escala municipal e estadual, baseado na análise do comportamento espectro-temporal de índices de vegetação de imagens de satélite de alta resolução temporal. O estudo foi realizado no Estado do Rio Grande do Sul, abrangendo os 497 municípios no ano safra 2011/2012. Para realizar o estudo, foram utilizadas imagens multitemporais do sensor MODIS, índices de vegetação EVI e NDVI. Foi aplicado o modelo HAND para gerar as áreas de inundação, as quais foram utilizadas para discriminar a cultura do arroz irrigado de outras culturas, especialmente a soja. Para avaliar os resultados foram utilizados como dados de referência, os dados coletados a campo, dados de área cultivada do IBGE e dados do mapeamento gerados a partir de imagens do satélite RapidEye. Os resultados mostraram que a metodologia proposta foi satisfatória, com valores médios do índice Kappa de 0,90 para a cultura de arroz irrigado e de 0,84 para a soja. Não houve diferença significativa entre as estimativas de área cultivada utilizando os dados EVI e NDVI para ambas as culturas. A utilização do Modelo HAND para discriminar o arroz irrigado de outros cultivos, mostrou-se muito eficiente, separando as áreas de várzea, que são mais aptas para o cultivo de arroz irrigado. Apesar dos resultados terem sido considerados como satisfatórios alguns municípios apresentaram problemas de subestimação ou superestimação quando foram comparados com os dados oficiais do IBGE. Esses problemas podem estar relacionados ao caráter subjetivo de aquisição de dados por parte do IBGE e também o fato de ter sido utilizada para a validação dos dados da safra 2011/2012 a média das últimas três safras, podendo desta maneira ter fragilizado ou comprometido os resultados para alguns municípios. Portanto, técnicas de sensoriamento remoto e geoprocessamento podem ser úteis no auxilio dos atuais métodos de monitoramento e mapeamento de culturas agrícolas, melhorando as estatísticas oficiais do arroz irrigado e soja. / One of the most relevant activities for the Brazilian economy is agriculture. Among the products of greatest importance in the national agricultural, are soybeans and rice, which represent a large portion of the production. Only the State of Rio Grande do Sul is responsible for approximately 67% of the national rice production and 10% of soybean (IBGE, 2012). Therefore, reliable information on agricultural production are relevant to the development of the sector and the development of methodologies capable of assist in the monitoring of agricultural areas becomes important part in the generation of reliable data and faster of obtaining. Thus, the objective of this work was to develop a methodology of low cost to implement the mapping of acreage irrigated rice and soybeans, at the municipal and state levels, based on the analysis of the spectral-temporal behavior of vegetation indices from satellite images high temporal resolution. The study was conducted in the state of Rio Grande do Sul, covering 497 municipalities in crop year 2011/2012. To conduct the study, images were used multitemporal MODIS vegetation indices EVI and NDVI. HAND model was applied to generate the inundation areas, which were used to discriminate the rice culture of other crops, especially soybeans. To evaluate the results were used as reference data, data collected in the field, the cultivated area data from the IBGE and mapping data generated from satellite images RapidEye. The results show that the proposed method was satisfactory, with mean values of Kappa 0.90 for irrigated rice and 0.84 for soybeans. There was no significant difference between the estimates of acreage using EVI and NDVI data for both crops. The use of the HAND model to discriminate irrigated rice from other crops, was very efficient, separating the lowland areas, which are more suitable for the cultivation of irrigated rice. Although the results were considered satisfactory as some municipalities had problems underestimation or overestimation when they were compared with the official data. These problems may be related to the subjective nature of data acquisition by the IBGE and the fact of having been used for the validation of data from 2011/2012 season the average of the last three years, and may in this way be weakened or compromised results for some municipalities. Therefore, techniques of remote sensing and GIS can be useful in the aid of the current methods of monitoring and mapping of agricultural crops, improving the official statistics of irrigated rice and soybeans.

Submitted by Automa??o e Estat?stica (sst@bczm.ufrn.br) on 2016-05-10T23:18:28Z No. of bitstreams: 1 KionaraSarabellaTuribioESilva_DISSERT.pdf: 1524779 bytes, checksum: a034505b357fab3aef4e1f83a7bbc3d6 (MD5) / Approved for entry into archive by Arlan Eloi Leite Silva (eloihistoriador@yahoo.com.br) on 2016-05-18T19:54:55Z (GMT) No. of bitstreams: 1 KionaraSarabellaTuribioESilva_DISSERT.pdf: 1524779 bytes, checksum: a034505b357fab3aef4e1f83a7bbc3d6 (MD5) / Made available in DSpace on 2016-05-18T19:54:55Z (GMT). No. of bitstreams: 1 KionaraSarabellaTuribioESilva_DISSERT.pdf: 1524779 bytes, checksum: a034505b357fab3aef4e1f83a7bbc3d6 (MD5) Previous issue date: 2015-06-15 / O bioma Caatinga, ecossistema de clima semi?rido localizado no nordeste do Brasil, apresenta forte sazonalidade com baixo regime de precipita??o. Esta regi?o possui as proje??es de mudan?as clim?ticas mais alarmantes do pa?s, com o aumento temperatura do ar e a redu??o da pluviosidade com tend?ncias mais fortes que as previs?es m?dias globais. As mudan?as clim?ticas no bioma podem acarretar na diminui??o da cobertura vegetal e altera??o de sua distribui??o, bem como do funcionamento ecossist?mico. Desta forma, influencia fortemente na diversidade de esp?cies. Nesse contexto, a proposta desse estudo ? modelar a din?mica da vegeta??o em fun??o das condi??es ambientais (pluviosidade e temperatura), bem como predizer as consequ?ncias das mudan?as clim?ticas, baseadas nesses determinantes ambientais para o verdor da vegeta??o. O ?ndice de vegeta??o EVI (Enhanced Vegetation Index) foi usado para estimar o verdor da vegeta??o no bioma. A fim de considerar a forte autocorrrela??o temporal e espacial, bem como a heterogeneidade presente nos dados, v?rios modelos GLS (Generalized Least Squares) foram desenvolvidos e comparados para se obter o melhor modelo. Tal modelo refletiu a influ?ncias das condi??es ambientais na previs?o do verdor da vegeta??o no futuro. Diante da aplica??o dos novos cen?rios de mudan?as clim?ticas no modelo, a modifica??o dos determinantes ambientais, pluviosidade e temperatura, influenciou negativamente no verdor da vegeta??o no bioma Caatinga. Esse modelo foi usado para criar imagens de vegeta??o potencial nos per?odos presente e futuro do bioma Caatinga considerando que at? 2040 haver? uma diminui??o de 20% da precipita??o e um aumento de 1?C na temperatura; at? 2070 haver? uma diminui??o de 35% na precipita??o e um aumento de 2,5?C na temperatura; e at? o final do s?culo o percentual de diminui??o das chuvas alcan?ar? 50% e a temperatura ter? um aumento de at? 4,5?C. Os resultados desse estudo sugerem que o funcionamento do ecossistema ser? afetado com a diminui??o de 5,9% do verdor da vegeta??o at? 2040, 14,2% at? 2070 e 24,3 % at? o final do s?culo. A vegeta??o da Caatinga em ?reas de baixa altitude (maior parte do bioma) ser? mais afetada pelas mudan?as clim?ticas. / The Caatinga biome, a semi-arid climate ecosystem found in northeast Brazil, presents low rainfall regime and strong seasonality. It has the most alarming climate change projections within the country, with air temperature rising and rainfall reduction with stronger trends than the global average predictions. Climate change can present detrimental results in this biome, reducing vegetation cover and changing its distribution, as well as altering all ecosystem functioning and finally influencing species diversity. In this context, the purpose of this study is to model the environmental conditions (rainfall and temperature) that influence the Caatinga biome productivity and to predict the consequences of environmental conditions in the vegetation dynamics under future climate change scenarios. Enhanced Vegetation Index (EVI) was used to estimate vegetation greenness (presence and density) in the area. Considering the strong spatial and temporal autocorrelation as well as the heterogeneity of the data, various GLS models were developed and compared to obtain the best model that would reflect rainfall and temperature influence on vegetation greenness. Applying new climate change scenarios in the model, environmental determinants modification, rainfall and temperature, negatively influenced vegetation greenness in the Caatinga biome. This model was used to create potential vegetation maps for current and future of Caatinga cover considering 20% decrease in precipitation and 1 ?C increase in temperature until 2040, 35% decrease in precipitation and 2.5 ?C increase in temperature in the period 2041-2070 and 50% decrease in precipitation and 4.5 ?C increase in temperature in the period 2071-2100. The results suggest that the ecosystem functioning will be affected on the future scenario of climate change with a decrease of 5.9% of the vegetation greenness until 2040, 14.2% until 2070 and 24.3% by the end of the century. The Caatinga vegetation in lower altitude areas (most of the biome) will be more affected by climatic changes.

Uma das atividades mais relevantes para a economia brasileira é a agricultura. Entre os produtos de maior importância no cenário agrícola nacional, estão a soja e o arroz, os quais representam uma grande parcela da produção. Somente o Estado do Rio Grande do Sul é responsável por aproximadamente 67% da produção nacional de arroz e 10% de soja (IBGE, 2012). Portanto, informações confiáveis sobre a produção agrícola são relevantes para o desenvolvimento do setor e o desenvolvimento de metodologias capazes de auxiliar no monitoramento das áreas agrícolas torna-se peça importante na geração de dados confiáveis e com maior rapidez de obtenção. Desta forma, o objetivo deste trabalho foi desenvolver uma metodologia de baixo custo para a execução do mapeamento da área cultivada de arroz irrigado e soja, em escala municipal e estadual, baseado na análise do comportamento espectro-temporal de índices de vegetação de imagens de satélite de alta resolução temporal. O estudo foi realizado no Estado do Rio Grande do Sul, abrangendo os 497 municípios no ano safra 2011/2012. Para realizar o estudo, foram utilizadas imagens multitemporais do sensor MODIS, índices de vegetação EVI e NDVI. Foi aplicado o modelo HAND para gerar as áreas de inundação, as quais foram utilizadas para discriminar a cultura do arroz irrigado de outras culturas, especialmente a soja. Para avaliar os resultados foram utilizados como dados de referência, os dados coletados a campo, dados de área cultivada do IBGE e dados do mapeamento gerados a partir de imagens do satélite RapidEye. Os resultados mostraram que a metodologia proposta foi satisfatória, com valores médios do índice Kappa de 0,90 para a cultura de arroz irrigado e de 0,84 para a soja. Não houve diferença significativa entre as estimativas de área cultivada utilizando os dados EVI e NDVI para ambas as culturas. A utilização do Modelo HAND para discriminar o arroz irrigado de outros cultivos, mostrou-se muito eficiente, separando as áreas de várzea, que são mais aptas para o cultivo de arroz irrigado. Apesar dos resultados terem sido considerados como satisfatórios alguns municípios apresentaram problemas de subestimação ou superestimação quando foram comparados com os dados oficiais do IBGE. Esses problemas podem estar relacionados ao caráter subjetivo de aquisição de dados por parte do IBGE e também o fato de ter sido utilizada para a validação dos dados da safra 2011/2012 a média das últimas três safras, podendo desta maneira ter fragilizado ou comprometido os resultados para alguns municípios. Portanto, técnicas de sensoriamento remoto e geoprocessamento podem ser úteis no auxilio dos atuais métodos de monitoramento e mapeamento de culturas agrícolas, melhorando as estatísticas oficiais do arroz irrigado e soja. / One of the most relevant activities for the Brazilian economy is agriculture. Among the products of greatest importance in the national agricultural, are soybeans and rice, which represent a large portion of the production. Only the State of Rio Grande do Sul is responsible for approximately 67% of the national rice production and 10% of soybean (IBGE, 2012). Therefore, reliable information on agricultural production are relevant to the development of the sector and the development of methodologies capable of assist in the monitoring of agricultural areas becomes important part in the generation of reliable data and faster of obtaining. Thus, the objective of this work was to develop a methodology of low cost to implement the mapping of acreage irrigated rice and soybeans, at the municipal and state levels, based on the analysis of the spectral-temporal behavior of vegetation indices from satellite images high temporal resolution. The study was conducted in the state of Rio Grande do Sul, covering 497 municipalities in crop year 2011/2012. To conduct the study, images were used multitemporal MODIS vegetation indices EVI and NDVI. HAND model was applied to generate the inundation areas, which were used to discriminate the rice culture of other crops, especially soybeans. To evaluate the results were used as reference data, data collected in the field, the cultivated area data from the IBGE and mapping data generated from satellite images RapidEye. The results show that the proposed method was satisfactory, with mean values of Kappa 0.90 for irrigated rice and 0.84 for soybeans. There was no significant difference between the estimates of acreage using EVI and NDVI data for both crops. The use of the HAND model to discriminate irrigated rice from other crops, was very efficient, separating the lowland areas, which are more suitable for the cultivation of irrigated rice. Although the results were considered satisfactory as some municipalities had problems underestimation or overestimation when they were compared with the official data. These problems may be related to the subjective nature of data acquisition by the IBGE and the fact of having been used for the validation of data from 2011/2012 season the average of the last three years, and may in this way be weakened or compromised results for some municipalities. Therefore, techniques of remote sensing and GIS can be useful in the aid of the current methods of monitoring and mapping of agricultural crops, improving the official statistics of irrigated rice and soybeans.

Uma das atividades mais relevantes para a economia brasileira é a agricultura. Entre os produtos de maior importância no cenário agrícola nacional, estão a soja e o arroz, os quais representam uma grande parcela da produção. Somente o Estado do Rio Grande do Sul é responsável por aproximadamente 67% da produção nacional de arroz e 10% de soja (IBGE, 2012). Portanto, informações confiáveis sobre a produção agrícola são relevantes para o desenvolvimento do setor e o desenvolvimento de metodologias capazes de auxiliar no monitoramento das áreas agrícolas torna-se peça importante na geração de dados confiáveis e com maior rapidez de obtenção. Desta forma, o objetivo deste trabalho foi desenvolver uma metodologia de baixo custo para a execução do mapeamento da área cultivada de arroz irrigado e soja, em escala municipal e estadual, baseado na análise do comportamento espectro-temporal de índices de vegetação de imagens de satélite de alta resolução temporal. O estudo foi realizado no Estado do Rio Grande do Sul, abrangendo os 497 municípios no ano safra 2011/2012. Para realizar o estudo, foram utilizadas imagens multitemporais do sensor MODIS, índices de vegetação EVI e NDVI. Foi aplicado o modelo HAND para gerar as áreas de inundação, as quais foram utilizadas para discriminar a cultura do arroz irrigado de outras culturas, especialmente a soja. Para avaliar os resultados foram utilizados como dados de referência, os dados coletados a campo, dados de área cultivada do IBGE e dados do mapeamento gerados a partir de imagens do satélite RapidEye. Os resultados mostraram que a metodologia proposta foi satisfatória, com valores médios do índice Kappa de 0,90 para a cultura de arroz irrigado e de 0,84 para a soja. Não houve diferença significativa entre as estimativas de área cultivada utilizando os dados EVI e NDVI para ambas as culturas. A utilização do Modelo HAND para discriminar o arroz irrigado de outros cultivos, mostrou-se muito eficiente, separando as áreas de várzea, que são mais aptas para o cultivo de arroz irrigado. Apesar dos resultados terem sido considerados como satisfatórios alguns municípios apresentaram problemas de subestimação ou superestimação quando foram comparados com os dados oficiais do IBGE. Esses problemas podem estar relacionados ao caráter subjetivo de aquisição de dados por parte do IBGE e também o fato de ter sido utilizada para a validação dos dados da safra 2011/2012 a média das últimas três safras, podendo desta maneira ter fragilizado ou comprometido os resultados para alguns municípios. Portanto, técnicas de sensoriamento remoto e geoprocessamento podem ser úteis no auxilio dos atuais métodos de monitoramento e mapeamento de culturas agrícolas, melhorando as estatísticas oficiais do arroz irrigado e soja. / One of the most relevant activities for the Brazilian economy is agriculture. Among the products of greatest importance in the national agricultural, are soybeans and rice, which represent a large portion of the production. Only the State of Rio Grande do Sul is responsible for approximately 67% of the national rice production and 10% of soybean (IBGE, 2012). Therefore, reliable information on agricultural production are relevant to the development of the sector and the development of methodologies capable of assist in the monitoring of agricultural areas becomes important part in the generation of reliable data and faster of obtaining. Thus, the objective of this work was to develop a methodology of low cost to implement the mapping of acreage irrigated rice and soybeans, at the municipal and state levels, based on the analysis of the spectral-temporal behavior of vegetation indices from satellite images high temporal resolution. The study was conducted in the state of Rio Grande do Sul, covering 497 municipalities in crop year 2011/2012. To conduct the study, images were used multitemporal MODIS vegetation indices EVI and NDVI. HAND model was applied to generate the inundation areas, which were used to discriminate the rice culture of other crops, especially soybeans. To evaluate the results were used as reference data, data collected in the field, the cultivated area data from the IBGE and mapping data generated from satellite images RapidEye. The results show that the proposed method was satisfactory, with mean values of Kappa 0.90 for irrigated rice and 0.84 for soybeans. There was no significant difference between the estimates of acreage using EVI and NDVI data for both crops. The use of the HAND model to discriminate irrigated rice from other crops, was very efficient, separating the lowland areas, which are more suitable for the cultivation of irrigated rice. Although the results were considered satisfactory as some municipalities had problems underestimation or overestimation when they were compared with the official data. These problems may be related to the subjective nature of data acquisition by the IBGE and the fact of having been used for the validation of data from 2011/2012 season the average of the last three years, and may in this way be weakened or compromised results for some municipalities. Therefore, techniques of remote sensing and GIS can be useful in the aid of the current methods of monitoring and mapping of agricultural crops, improving the official statistics of irrigated rice and soybeans.

Agriculture and food security are interlinked. New technologies and instruments are making the agricultural system easy to operate and increasing the food production. Remote sensing technology is widely used as a non-destructive method for crop growth monitoring, climate analysis, and forecasting crop yield. The objectives of this study are to (1) monitor crop growth remotely, (2) identify climate impacts on crop yield, and (3) forecasting crop yield. This study proposed methods to improve crop growth monitoring and yield predictions by using remote sensing technology. In this study, we developed crop vegetative growth metrics (VGM) from the MODIS (Moderate Resolution Imaging Spectroradiometer) 250m NDVI (Normalized Difference Vegetation Index) and EVI (Enhanced Vegetation Index) data. We developed 19 NDVI and EVI based VGM metrics for soybean crop from a time series of 2000 to 2018, but the methods are applicable to other crops as well. We found VGMmax, VGM70, VGM85, VGM98T are about 95% crop yield predictable. However, these metrics are independent of climatic events. We modelled the climatic impacts on soybean crop from the time series data from1980-2019 collected from NOAA's National Climatic Data Center (NCDC). Therefore, we estimated the impacts of increase and decrease of temperature (maximum, mean, and minimum) and precipitation (average) pattern on crop yields which will be helpful to monitor climate change impacts on crop production. Lastly, we made crop yield forecasting statistical model across different climatic regions in USA using Google Earth Engine. We used remotely sensed MODIS Terra surface reflectance 8-day global 250m data to calculate VGM metrics (e.g. VGM70, VGM85, VGM98T, VGM120, VGMmean, and VGMmax), MODIS Terra land surface temperature and Emissivity 8-Day data for average day-time and night-time temperature and CHIRPS (Climate Hazards Group Infra-red Precipitation with station data) data for precipitation, from a time series data of 2000-2019. Our predicted models showed a NMPE (Normalized Mean Prediction error) with in a range of -0.002 to 0.007. These models will be helpful to get an overall estimate of crop production and aid in national agricultural strategic planning. Overall, this study will benefit farmers, researchers, and management system of U.S. Department of Agriculture (USDA).

In the southwest United States, the current prolonged warm drought is similar to the predicted future climate change scenarios for the region. This study aimed to determine patterns in vegetation response to the early 21st century drought across multiple biomes. We hypothesized that different biomes (forests, shrublands, and grasslands) would have different relative sensitivities to both climate drivers (precipitation and temperature) and legacy effects (previous-year's productivity). We tested this hypothesis at eight Ameriflux sites in various Southwest biomes using NASA Moderate-resolution Imaging Spectroradiometer Enhanced Vegetation Index (EVI) from 2001 to 2013. All sites experienced prolonged dry conditions during the study period. The impact of combined precipitation and temperature on Southwest ecosystems at both annual and sub-annual timescales was tested using Standardized Precipitation Evapotranspiration Index (SPEI). All biomes studied had critical sub-annual climate periods during which precipitation and temperature influenced production. In forests, annual peak greenness (EVImax) was best predicted by 9-month SPEI calculated in July (i.e., January-July). In shrublands and grasslands, EVImax was best predicted by SPEI in July through September, with little effect of the previous year's EVImax. Daily gross ecosystem production (GEP) derived from flux tower data yielded further insights into the complex interplay between precipitation and temperature. In forests, GEP was driven by cool-season precipitation and constrained by warm-season maximum temperature. GEP in both shrublands and grasslands was driven by summer precipitation and constrained by high daily summer maximum temperatures. In grasslands, there was a negative relationship between temperature and GEP in July, but no relationship in August and September. Consideration of sub-annual climate conditions and the inclusion of the effect of temperature on the water balance allowed us to generalize the functional responses of vegetation to predicted future climate conditions. We conclude that across biomes, drought conditions during critical sub-annual climate periods could have a strong negative impact on vegetation production in the southwestern United States.

Ce travail s’inscrit dans le cadre d’un programme de recherches appuyé par le Conseil de recherches en sciences humaines du Canada. / Les forêts de Bornéo sont inestimables. En plus d’une faune et d’une flore riche et diversifiée, ses milieux naturels constituent d’efficaces réservoirs de carbone. En outre, la matière ligneuse qui y est abondante fait l’objet d’une exploitation intensive. Par contre, c’est le potentiel agricole de l’île qui crée le plus d’enthousiasme, principalement en ce qui concerne la culture du palmier à huile. Pour tenter de mieux comprendre et surveiller le phénomène, nous avons développé des méthodes de détection de la déforestation et de la dégradation des forêts. Ces méthodes doivent tenir compte des caractéristiques propres à l’île. C’est que Bornéo est abondamment affectée par une nébulosité constante qui complexifie considérablement son observation à partir des satellites. Malgré ces contraintes, nous avons produit une série chronologique annuelle des points chauds de déforestation et de dégradation des forêts pour les années 2000 à 2009. / Borneo’s forests are priceless. Beyond the richness and diversity of its fauna and flora, its natural habitats constitute efficient carbon reservoirs. Unfortunately, the vast forests of the island are rapidly being cut down, both by the forestry industry and the rapidly expanding oil palm industry. In this context, we’ve developed methods to detect deforestation and forest degradation in order to better understand and monitor the phenomena. In doing so, the peculiarities of Borneo, such as the persistent cloud cover, had to be accounted for. Nevertheless, we succeeded in producing a time series of the yearly forest degradation and deforestations hotspots for the year 2000 through the year 2009.

Les forêts de Bornéo sont inestimables. En plus d’une faune et d’une flore riche et diversifiée, ses milieux naturels constituent d’efficaces réservoirs de carbone. En outre, la matière ligneuse qui y est abondante fait l’objet d’une exploitation intensive. Par contre, c’est le potentiel agricole de l’île qui crée le plus d’enthousiasme, principalement en ce qui concerne la culture du palmier à huile. Pour tenter de mieux comprendre et surveiller le phénomène, nous avons développé des méthodes de détection de la déforestation et de la dégradation des forêts. Ces méthodes doivent tenir compte des caractéristiques propres à l’île. C’est que Bornéo est abondamment affectée par une nébulosité constante qui complexifie considérablement son observation à partir des satellites. Malgré ces contraintes, nous avons produit une série chronologique annuelle des points chauds de déforestation et de dégradation des forêts pour les années 2000 à 2009. / Borneo’s forests are priceless. Beyond the richness and diversity of its fauna and flora, its natural habitats constitute efficient carbon reservoirs. Unfortunately, the vast forests of the island are rapidly being cut down, both by the forestry industry and the rapidly expanding oil palm industry. In this context, we’ve developed methods to detect deforestation and forest degradation in order to better understand and monitor the phenomena. In doing so, the peculiarities of Borneo, such as the persistent cloud cover, had to be accounted for. Nevertheless, we succeeded in producing a time series of the yearly forest degradation and deforestations hotspots for the year 2000 through the year 2009. / Ce travail s’inscrit dans le cadre d’un programme de recherches appuyé par le Conseil de recherches en sciences humaines du Canada.

No description available.