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.

O conceito de ‘ecossistema’ emergiu da necessidade de compreender o caráter extremamente dinâmico da vegetação, interpretado a partir daí como o resultado da interação recíproca entre um dado complexo de organismos e seu conjunto amplo de fatores do ambiente físico. Um ramo das ciências ecológicas desenvolveu-se desse conceito, visando examinar o resultado de tais interações em termos de fluxos de energia, matéria e informação. Desenvolvimentos conceituais recentes apontam para uma concepção do ecossistema sob a ótica de um novo paradigma, para o qual aninhamento, hierarquia, decomposabilidade relativa, probabilidade e dependência de escala são critérios chave. Outro desenvolvimento importante, a análise de trajetórias, abriu a possibilidade de tratar a dinâmica e o funcionamento do ecossistema como fenômenos em múltiplas escalas. Incertezas metodológicas e ecológicas decorrem numa visão pouco nítida de como o funcionamento e a estrutura do ecossistema interagem sob a influência de um determinado conjunto de fatores de uso e do ambiente físico. A situação demanda uma abordagem analítica na qual classificações funcionais e estruturais sejam implementadas independentemente, com o fim de estabelecer ‘a posteriori’ quanto e como as classificações estão interconectadas. A tarefa é ainda mais desafiante, em termos de método e interpretação, quando consideramos o contexto hierárquico e complexo em que a análise deve ser feita e a dependência de definição dos resultados. Esta tese refere-se ao desenvolvimento de ferramentas conceituais e metodológicas para analisar a heterogeneidade funcional dos ecossistemas no espaço, em relação a fatores significativos de uso e do ambiente, e aos diferentes tipos de vegetação presentes numa determinada região. Com esse objetivo, adotamos o conceito de ‘Tipos Funcionais de Ecossistemas’ (TFEs), os quais reúnem unidades espaciais com padrão de funcionamento similar, sem considerar seus atributos estruturais, e avançamos num esquema classificatório de TFEs que permite capturar as respostas funcionais de curto prazo dos ecossistemas em cenários de mudanças ambientais e de uso altamente dinâmicas. Também examinamos a sensibilidade dos tipos funcionais de ecossistemas a diferentes definições de funcionamento e parâmetros de escala espacial. Os TFEs provaram ser sensíveis a estas variáveis analíticas, oferecendo assim a possibilidade de indagar a natureza multidimensional e multi-escala dos fenômenos do ecossistema. Os TFEs capturam eficientemente os aspectos mais relevantes da resposta sazonal da vegetação aos fatores do ambiente biofísico, provendo assim uma ferramenta útil para descrever a heterogeneidade espacial do funcionamento dos ecossistemas em domínios temporais e geográficos específicos. Nesta tese avançamos no reconhecimento e descrição dos principais tipos de paisagem no planalto basáltico do Rio Grande do Sul, e propomos mecanismos e controles responsáveis desses padrões característicos. Da associação espacial entre feições do terreno, solos, tipos de uso e vegetação, identificamos três tipos básicos de paisagens e definimos preliminarmente seu domínio espacial. Os resultados descrevem um forte relacionamento entre a distribuição dos grandes tipos fisionômicos de vegetação, os solos e os processos formadores de relevo. Assim sendo, os campos dominam onde relevo e solos indicam a ocorrência de remanescentes de uma antiga superfície de pediplanação, em quanto as florestas prevalecem onde os agentes geomorfológicos têm rejuvenescido a paisagem. Porém, com o objetivo de compreender os processos responsáveis destes padrões, é essencial fazer ‘downscaling’ desde a escala regional na qual os processos formadores de relevo e de solos dominam a diferenciação espacial de variáveis ecológicas, até a escala local na qual fatores biológicos e relacionados com o regime de distúrbio adquirem maior importância na produção de padrões de heterogeneidade espacial. Identificamos que a abordagem ecossistêmica funcional é a maneira mais promissora de relacionar processos de natureza tão divergente. / The ‘ecosystem’ concept emerged from the need for understanding the highly dynamic nature of the vegetation, interpreted from thereon as the reciprocal interaction among the organism-complex and a wide array of factors of the physical environment. A full branch of the ecological sciences developed from this concept, aimed to assessing the outcome of such interactions as flows of energy, matter and information. Recent conceptual developments points to a conception of ecosystem as an entity evolving under the influence of a novel paradigm, for which nestedness, hierarchy, relative decomposability, probability and scale-dependency are central. Another important development, trajectory analysis, opens the possibility to treat ecosystem dynamics and ecosystem functioning as multi-scale phenomena. Methodological and ecological uncertainties determine a rather fuzzy picture of how ecosystem function and structure interplay under the influence of some set of drivers of the physical environment and land use. The whole situation waits for an analytical path to be designed in which functional and structural classifications are carried out independently, in order to establish a posteriori whether they are connected and how they are connected. The task is even more defiant, both in terms of methods and interpretation, if we consider the already complex hierarchical context in which the analysis should be set and the definition-dependency of the outcome. This thesis is about the development of conceptual and analytical tools for analyzing the functional heterogeneity of the ecosystems in the space, in relation to meaningful environmental and land-use factors and to the different types of vegetation present over a given region. To that aim, we adopt the concept of Ecosystem Functional Types (EFTs), which enclose spatial units with similar functional patterns, no attention paid to their structure, and advance on an EFT classificatory scheme that allows capturing the short-term functional response of the ecosystems to environmental and land-use changes. Furthermore, we examine the effect of using different surrogates of ecosystem functioning on the resulting picture of functional patchiness. The effect of changing parameters of spatial scale is also tested. The Ecosystem Functional Types proved to be heavily definition-dependent and sensitive to spatial scale, which allows exploring the multi-dimensional and multi-scale nature of ecosystem phenomena. The EFTs efficiently capture the most relevant features of the seasonal response of the vegetation to the drivers of the biophysical environment, providing so a useful tool for depicting the spatial heterogeneity of ecosystem functioning in a given geographic and temporal domain. In this report we also accomplished the recognition and description of main landscape types in the basaltic tablelands of Rio Grande do Sul, and proposed mechanisms and controls responsible for their characteristic patterns. From the spatial association of terrain features, soils, land-use and vegetation, we identified three basic landscape types and broadly defined their spatial domain. The picture described tells of a rather close relationship among the distribution of the major physiognomic types of the vegetation, soils, land-use and land-forming processes. In this picture, the grasslands prevail where terrain and soil features suggest there are the remnants of an old pediplanation surface, while forests seems to dominate wherever geomorphic agents have rejuvenated the landscape. However, in order to understand the processes responsible of these patterns it is then essential to downscale from the regional realm where terrain and soil-forming phenomena dominate spatial differentiation, to the fine-scale processes at which biological and disturbance-related factors are most influential in the production of patterns of spatial heterogeneity. We identify the functional approach to the ecosystems as the most promising way to correlate processes of such a different nature.

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.

The remote sensing technology is increasingly being used to study land use and vegetation cover changes and identify changes that has occur through different land use activities which may have negative impact on the sustainability of the environment, biodiversity protection and conservation. With increase in population growth rate in Sudan, there has been an increase for food crop production with agriculture playing a prominent role in livelihood security for the increasing population.   The increase use of irrigation and mechanisation has brought about an increase in demand for agricultural land use in Sudan with the conversion of other land use types and vegetation for agricultural land use. This does have effect and impact on the vegetation and environment with the country highly exposed to the incidence of environmental and social hazards and disasters including drought and desertification, deforestations, floods, loss of biodiversity, ethnic conflicts and poverty.   The research study work focused on agricultural land use changes in the country with the aim of investigating the agricultural land use changes that has occurred in the country from 1986 to 2002 using the remote sensing technique. This is important for agricultural land use planning and sustainability monitoring to reduce the negative impact of agricultural land use for crop production and increase long term resource use and environmental sustainability. Two remote sensing methods were used for the classification analysis to identify the land use changes namely the NDVI and the parallelepiped classification techniques. The NDVI method was used to identify the changes in the agricultural land use vegetation cover classes and determine the magnitude of changes in land area use that has occurred from 1986 to 2002 when the former and latter remote sensing images were acquired. The parallelepiped classification technique was however used to identify the aggregate agricultural land use changes in the area of study and conversion to and from other categories of land use. A qualitative analytic technique was also used to identify the possible causes of the changes that have occurred in Sudan in the study period using empirical materials.   The research study result gives information on the role the remote sensing technology can play in analyzing land use cover changes for agricultural land use sustainability monitoring.

Water scarcity is both natural and man-made phenomenon. Water control and uneven distribution of upstream TeestaRiver water makes artificial scarcity in downstream areas which can be minimized at least to the water stress level by balancing distribution and sustainable water use. Tasseled Cap transformation and NDVI methods were used in this study in order to find the magnitude of water scarcity in the downstream areas. NDVI and Tasseled Cap Greenness methods were applied to get proxy for soil moisture values in the form of biomass content and Tasseled Cap Wetness method were used to detect change in soil moisture content from Landsat TM and ETM+ data (1989-2010). System dynamic analysis method was applied to identify temporal and spatial differences between supply and demand of water in the TeestaRiver catchments area in the northwestern part of Bangladesh. It was found that, the vegetation cover and soil moisture content changed and shifted over time. Overall vegetation declined between 1989 and 2010 and soil moisture content also turned down. Moreover, TeestaRiver water is playing an important role for maintaining the balance between water supply and water scarcity in this region. There is a correlation between water scarcity in the downstream and availability of water in the TeestaRiver during dry seasons. / Master's Thesis

Magister Artium - MA / Climate has been changing significantly around the globe; hence climate variability is of great interest to researchers. The changes in climate have caused variances in rainfall and temperature, both elements of paramount importance in farming, whether commercial or communal farming. As these fluctuations in temperature and rainfall occur, they cause direct impacts on different livelihoods, fauna and flora. The aim of this thesis is to investigate the human management responses of farmers in two different contexts of communal farming (Makolokwe) and commercial farming (Touws River valley), with a focus investigation on the adaptation and coping strategies of the farmers, as well as spatial analysis of the vegetation and rainfall variability. Farmers were asked to discuss climate and adaptation based on the rainfall data available as well as far as they could remember the occurrence of changes. Rainfall data was available between 1988 and 2017 for Touws River, while the data utilised for Makolokwe was available between 1928 and 2016. The link between the local knowledge of the farmers and scientific knowledge is an important aspect of this research. The Normalised Difference Vegetation Index (NDVI) was used to analyse the vegetation changes on a temporal and spatial scale in the context of Makolokwe and Touws River valley respectively. The differing variations in climate variability and change experienced by the two farming communities are placed alongside an exploration of the adaptation and coping measures which are put in place by farmers as a response to the changes evident in climate, as it allows for better and thorough understanding of the occurring changes in the two communities. The study found that perceptions about climate variability vary in the two communities although there are some common factors. Farmers’ perceptions about climate variability are drawn from their own observations at a local level as well as knowledge from the media regarding terms such as El Niño and drought. Farmers in both communities indicated that they experienced insufficient rain in the winter months which had an impact on the grazing areas and the management of the livestock. These months also threatened livelihoods, especially for farmers who depend on their livestock for their livelihood, in particular communal farmers. Perceptions of factors such as decreasing grazing and vegetation in their environments have led to the adoption of adaptation and coping strategies on the part of farmers. Commercial farmers have more choices in this regard than communal farmers, such as converting to game farming. Common coping strategies include: (1) farmers have had to subsidise and use alternative food sources for the livestock, (2) livestock numbers have been reduced in order to adapt to climate variability, with an impact on livelihoods (3) farmers have had to rely on their hope and faith that things will get better. Planning for climate variability is challenging for land managers. Knowledge and access to resources is therefore essential in ensuring that farmers are kept on track with the changing environment.

Vegetative health was measured both before and after Hurricane Harvey using remotely sensed vegetation indices on the coastal marshland surrounding Galveston Island's West Bay. Data were recorded on a monthly basis following the hurricane from September of 2005 until September of 2019 in order to document the vegetation response to this significant disturbance event. Both initial impact and recovery were found to be dependent on a variety of factors, including elevation zone, spatial proximity to the bay, the season during which recovery took place, as well as the amount of time since the hurricane. Slope was also tested as a potential variable using a LiDAR-derived slope raster, and while unable to significantly explain variations in vegetative health immediately following the hurricane, it was able to explain some degree of variability among spatially close data points. Among environmental factors, elevation zone appeared to be the most key in determining the degree of vegetation impact, suggesting that the different plant assemblages that make up different portions of the marsh react differently to the severe flooding that took place during Harvey.

No description available.

In this work it is address the question of how certain climatic variables may be significant related to alterations of avian biodiversity in a semi-agricultural Natura wetland side in Northern Greece. Particularly, the current research highlights the effects of climate and land cover intensity on the Thermaikos gulf bird biodiversity and its importance for healthy ecosystem functioning. Also, the maintenance of a good state of conservation in the Thermaikos gulf has direct impacts on a larger scale since it benefits the rest of the Natura wetlands network considering the connectivity related to migratory birds. Furthermore, the methodology which is used is essential to help inform the science-based management of environments that support threatened and endangered wildlife and can be further applied to other wetlands in the Mediterranean with similar weather conditions and agricultural land use. The alteration in compositional diversity of bird abundances has been studied at the species level from 2012 to 2017 in one of the most important wetland Natura sites in Northern Greece and by using different biodiversity indices. Shannon Entropy was lower during 2012 (DH = 1.509) albeit remained in similar levels from 2013 and afterwards. The highest values of Shannon Entropy were recorded in 2014 (DH = 2.927) and 2016 (DH = 2.888) suggesting that there is a higher diversity compared to the other observation years and especially 2012. The yearly trends of the Simpson dominance index and the Gini-Simpson Index had quite similar patterns. The Berger-Parker index, DD, which represents the maximum proportion of any species estimated in the sample assemblage, had its highest values in 2012 (DD = 0.58) and 2017 (DD = 0.39) and its lowest in 2014 (DD = 0.13) and 2016 (DD = 0.15). A complete characterization of diversity was possible through the projection of Hill numbers and the Rényi entropy, parameterized by the order q in terms of an empirical curve. According to the Hills numbers pooled over the years, the mean species abundance (q = 0) was estimated at 31 species, the mean biodiversity (q = 1) was 13 species and the most dominant species (q = 2) were 8 species. The quantification of bird biodiversity in the particular research area patterns is a fundamental task to evaluate current management actions, improve conservation and design future management strategies. Moreover, the interplay between temperature, relative humidity and three different bird biodiversity indexes, including Shannon Entropy, Simpson’s dominance (evenness) index and the Berger-Parker index has been also examined. By using different modeling approaches, parametric and non- parametric multivariate models, we make effort to get a consensus on the interrelationships between climate and avian biodiversity. In particular, it is been shown that in most cases nonlinear models and surface-plot analysis methodology, are able to capture the relation of a considerable increase in the estimated biodiversity indexes with increased temperatures and rain levels. Thus, biodiversity is to a significant extent affected by the aforementioned climate factors at a proximate level involving synergies between the different climate factors. Finally, the combined effect of climate variables and remote sensing land cover indicators on bird richness has been also explored to detect any influence on bird diversity due to agricultural intensification. In particular the association between bird richness and environmental drivers, as well as remote sensed land cover indices was explored for seven successive seasons using correlation analysis and a Cox-Box transformed multivariate linear model. Three climate variables were tested: mean temperature, rain level and mean relative humidity and three land cover indices: the Normalized Difference Vegetation Index (NDVI), the Atmospheric Resistance Vegetation Index (ARVI) and an Agricultural Band Combination Index (ABCI). Among the environmental drivers explored, temperature, rain levels and ABCI were significantly correlated to bird richness in contrast to NDVI and ARVI which showed a lower correlation, while relative humidity displayed the poorest correlation. Additionally, the multivariable linear model indicates that temperature, rain levels and ABCI have a statistically significant effect (p<0.05) on bird species richness accounting for 73,02% of data variability. Based on the overall model results and the related 3D contour plot model simulations, we conclude that bird species richness increases with an increase in temperature and rain levels, as well as with a decrease in agricultural intensity (ABCI). Concluding, in most cases temperature, rain levels and agricultural intensity significantly influenced bird richness in a combined manner. Furthermore, agricultural intensification has resulted in most cases in the loss of bird richness. Understanding the factors that can affect the biodiversity is of great importance for rational land use planning and conservation management of semi-Natural areas. Agriculture is the main driving force that influences the topographic and biological diversity of Europe, shaping the natural landscape of the European countryside for thousands of years. Revealing potential interrelationship between biodiversity, climate drivers and landscape indicators, although is a complex—even though challenging—task, contributing to our understanding of the mechanisms connecting climate change with ecosystem functioning. Moreover, a better understanding of biodiversity functioning in relation to human activities in natural protected areas as well as climate is essential for biodiversity awareness and the design of effective biodiversity-related conservation management policies.

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).