O processo da desertificação é um problema de importância mundial, pois reduz a produtividade das terras como também a função ecológica dos ecossistemas onde acontece este processo. A desertificação é o resultado dos processos de degradação ambiental nas zonas áridas, semiáridas e sub-úmidas secas produto de fatores biofísicos como a variação climática e também das atividades humanas. A região do Vale de Villa de Leyva, Boyacá, Colômbia, tem paisagens em processo de transformação por desertificação porque esta localizada num local seco, pouca precipitação e com solos frágeis. Após da conquista espanhola, esta área teve a maior transformação ambiental iniciando com a sobre exploração dos recursos naturais como as florestas, os solos e os corpos de água, acelerando ainda mais este processo de degradação. Tendo em conta esse contexto, o método de análise das dinâmicas da zona para identificar padrões e processos de desertificação a partir de séries temporais de índices de vegetação, como o NDVI e EVI, foram empregadas técnicas de análise espacial, a traves de Sistemas de Informação Geográfica SIG e ferramentas de Sensoriamento Remoto. Foi feita aquisição das imagens de Índices de vegetação NDVI e EVI do produto MOD13Q1 do sensor MODIS, entre os anos 2001 e 2016. As imagens foram filtradas como o algoritmo Savitzky-Golay para diminuir os erros da informação original, como também os vazios. As series temporais foram analisadas com o intuito de identificar as áreas e os períodos em que ocorreram as mudanças ambientais mais significativas na região com relação à camada vetorial de erosão, elaborada a uma escala 1:100.000 Posteriormente, foi feito uma análise de tendência pelo algoritmo Mann-Kendall para identificar tendências negativas dos índices de vegetação, e fazer uma sobreposição com os índices do último ano, obtendo assim as áreas com maior risco de sofrer processos de degradação ambiental e que podem gerar desertificação. Para validar esses procedimentos, foi feita uma comparação visual com o NDVI do programa Landsat 8 no mesmo período, identificando padrões de distribuição da vegetação muito semelhantes, embora a resolução espacial dos dois tipos de imagens seja muito diferente. Entre os principais resultados, foi possível identificar que as áreas em risco de sofrer processos de desertificação não estão associadas nem obedecem a um processo constante desde sua origem como foi pensado inicialmente, mas as atividades atuais de agricultura não sustentável são as que causam esses processos de degradação. Na atualidade, este processo acontece de forma mais pontual e esta associado a fatores antrópicos, principalmente pelas práticas agrícolas não sustentáveis na região, principalmente pelo incremento dos cultivos de tomate sob estufa. Também existem áreas com tendência a recuperação da vegetação por fatores naturais, por processos de crescimento de gramíneas, como também por processos artificiais, semeadura de arvores e gramado, com o fim de adequar a paisagem nas áreas de expansão urbana que limitam com o “deserto”, gerando novas dinâmicas de ocupação da região que posteriormente precisam ser estudadas. / Desertification process represents a problem of global importance, due to the reduction not only of the productivity of farmlands, but also of the ecological function of ecosystems where this process takes place. Desertification is the result of processes of environmental degradation in the arid, semi-arid and dry sub-humid areas, resulting from biophysical factors such as climatic variation and from human activities, as well. The valley of Villa de Leyva, Boyacá, Colombia, has landscapes in transformation process by desertification because it is located in a dry place, with little precipitation and with fragile soils. After the Spanish conquest, this area had the greatest environmental transformation, starting with overexploitation of natural resources such as forests, soils and bodies of water; all these factors accelerated the process of degradation. Taking into account this context, it is necessary to establish a method of analysis of the dynamics of the zone, in order to identify patterns and processes of desertification through temporal series of vegetation indexes, such as NDVI and EVI, using spatial analysis techniques, through of GIS Geographic Information Systems and Remote Sensing tools. The first step was the acquisition of the vegetation indices NDVI and EVI of the product the images of NDVI and EVI vegetation indices of the MODIS product MOD13Q1 between 2001 and 2016 were acquired. The images were filtered as the Savitzky-Golay algorithm to reduce the errors of the original information as well as the voids. The time series were analyzed in order to identify the areas and periods in which the most significant environmental changes occurred in the region in relation to the vector layer of erosion, elaborated at a scale 1:100.000 Afterwards, a trend analysis was performed by the Mann-Kendall algorithm to identify negative trends of vegetation indexes, and to overlap with the indices of the last year, thus obtaining the areas with the highest risk of environmental degradation processes that can generate desertification. To validate these procedures, a visual comparison was made with the NDVI of the Landsat 8 program in the same period, identifying vegetation distribution patterns very similar, although the spatial resolution of the two types of images is very different. Among the main results, it was possible to identify that the areas at risk of suffering desertification processes are neither associated nor obeyed a constant process since its origin as initially thought, but the current activities of non sustainable agriculture are those that cause these processes of degradation. Nowadays, this process happens in a more punctual way and is associated to anthropic factors, mainly by the unsustainable agricultural practices in the region, mainly by the increase of tomato crops under greenhouse. There are also areas where the vegetation recovers by natural factors, by processes of grass growth, as well as artificial processes, planting trees and pastures, with the purpose of adjusting the landscape in areas of urban expansion that limit with the “desert”, generating new dynamics of occupation in the region that need to be studied further.

O processo da desertificação é um problema de importância mundial, pois reduz a produtividade das terras como também a função ecológica dos ecossistemas onde acontece este processo. A desertificação é o resultado dos processos de degradação ambiental nas zonas áridas, semiáridas e sub-úmidas secas produto de fatores biofísicos como a variação climática e também das atividades humanas. A região do Vale de Villa de Leyva, Boyacá, Colômbia, tem paisagens em processo de transformação por desertificação porque esta localizada num local seco, pouca precipitação e com solos frágeis. Após da conquista espanhola, esta área teve a maior transformação ambiental iniciando com a sobre exploração dos recursos naturais como as florestas, os solos e os corpos de água, acelerando ainda mais este processo de degradação. Tendo em conta esse contexto, o método de análise das dinâmicas da zona para identificar padrões e processos de desertificação a partir de séries temporais de índices de vegetação, como o NDVI e EVI, foram empregadas técnicas de análise espacial, a traves de Sistemas de Informação Geográfica SIG e ferramentas de Sensoriamento Remoto. Foi feita aquisição das imagens de Índices de vegetação NDVI e EVI do produto MOD13Q1 do sensor MODIS, entre os anos 2001 e 2016. As imagens foram filtradas como o algoritmo Savitzky-Golay para diminuir os erros da informação original, como também os vazios. As series temporais foram analisadas com o intuito de identificar as áreas e os períodos em que ocorreram as mudanças ambientais mais significativas na região com relação à camada vetorial de erosão, elaborada a uma escala 1:100.000 Posteriormente, foi feito uma análise de tendência pelo algoritmo Mann-Kendall para identificar tendências negativas dos índices de vegetação, e fazer uma sobreposição com os índices do último ano, obtendo assim as áreas com maior risco de sofrer processos de degradação ambiental e que podem gerar desertificação. Para validar esses procedimentos, foi feita uma comparação visual com o NDVI do programa Landsat 8 no mesmo período, identificando padrões de distribuição da vegetação muito semelhantes, embora a resolução espacial dos dois tipos de imagens seja muito diferente. Entre os principais resultados, foi possível identificar que as áreas em risco de sofrer processos de desertificação não estão associadas nem obedecem a um processo constante desde sua origem como foi pensado inicialmente, mas as atividades atuais de agricultura não sustentável são as que causam esses processos de degradação. Na atualidade, este processo acontece de forma mais pontual e esta associado a fatores antrópicos, principalmente pelas práticas agrícolas não sustentáveis na região, principalmente pelo incremento dos cultivos de tomate sob estufa. Também existem áreas com tendência a recuperação da vegetação por fatores naturais, por processos de crescimento de gramíneas, como também por processos artificiais, semeadura de arvores e gramado, com o fim de adequar a paisagem nas áreas de expansão urbana que limitam com o “deserto”, gerando novas dinâmicas de ocupação da região que posteriormente precisam ser estudadas. / Desertification process represents a problem of global importance, due to the reduction not only of the productivity of farmlands, but also of the ecological function of ecosystems where this process takes place. Desertification is the result of processes of environmental degradation in the arid, semi-arid and dry sub-humid areas, resulting from biophysical factors such as climatic variation and from human activities, as well. The valley of Villa de Leyva, Boyacá, Colombia, has landscapes in transformation process by desertification because it is located in a dry place, with little precipitation and with fragile soils. After the Spanish conquest, this area had the greatest environmental transformation, starting with overexploitation of natural resources such as forests, soils and bodies of water; all these factors accelerated the process of degradation. Taking into account this context, it is necessary to establish a method of analysis of the dynamics of the zone, in order to identify patterns and processes of desertification through temporal series of vegetation indexes, such as NDVI and EVI, using spatial analysis techniques, through of GIS Geographic Information Systems and Remote Sensing tools. The first step was the acquisition of the vegetation indices NDVI and EVI of the product the images of NDVI and EVI vegetation indices of the MODIS product MOD13Q1 between 2001 and 2016 were acquired. The images were filtered as the Savitzky-Golay algorithm to reduce the errors of the original information as well as the voids. The time series were analyzed in order to identify the areas and periods in which the most significant environmental changes occurred in the region in relation to the vector layer of erosion, elaborated at a scale 1:100.000 Afterwards, a trend analysis was performed by the Mann-Kendall algorithm to identify negative trends of vegetation indexes, and to overlap with the indices of the last year, thus obtaining the areas with the highest risk of environmental degradation processes that can generate desertification. To validate these procedures, a visual comparison was made with the NDVI of the Landsat 8 program in the same period, identifying vegetation distribution patterns very similar, although the spatial resolution of the two types of images is very different. Among the main results, it was possible to identify that the areas at risk of suffering desertification processes are neither associated nor obeyed a constant process since its origin as initially thought, but the current activities of non sustainable agriculture are those that cause these processes of degradation. Nowadays, this process happens in a more punctual way and is associated to anthropic factors, mainly by the unsustainable agricultural practices in the region, mainly by the increase of tomato crops under greenhouse. There are also areas where the vegetation recovers by natural factors, by processes of grass growth, as well as artificial processes, planting trees and pastures, with the purpose of adjusting the landscape in areas of urban expansion that limit with the “desert”, generating new dynamics of occupation in the region that need to be studied further.

Retention forestry is a key measure in Swedish forestry policy to combine a clearcutting regime and sustainability goals regarding forest biodiversity. The aims of increasing industrial production from forestry and to protect the environment is equally prioritized in the Swedish forestry legislation. Freedom for landowners is a cornerstone in Swedish forestry policy. This position is heavily debated among activists and scholars and is politically questioned, especially from the European Commission which challenges the Swedish position. This study investigates how Swedish landowners interpret and practice retention in forestry, discussed through a theoretical framework of political ecology. Logged areas from 2020 in Trosa municipality in Sweden was investigated through field work and remote sensing through NDVI-analysis. Retention was practiced in all the logged areas, but the measures and level of retention varied greatly. The method can be used for different purposes at different seasons. If production and environment is equally prioritized as the legislation requires is questioned.

This master thesis in geography investigates how remote sens- ing can be used in Transnational Corporations (TNC) global Corporate Social Responsibility (CSR) initiatives. The study aims to delineate an accurate method in remote sensing to be used to monitor deforestation in global value chains. Research questions asked are 1) What are the current monitoring practises used by TNCs to monitor global value chains? 2) Which is the most user-friendly and accurate remote sensing technique to map deforestation? 3) How can remote sensing successfully be implemented in TNCs CSR-initiatives? The study is approached from two perspectives, building on theories of value chains, and qualitative methods to answer the first research question. While the second question is a method study, investigating how well a spectral approach versus a contextual approach can map deforest- ation in Landsat scenes. The results are compared with Global Forest Watch (GFW), and the highest accuracy were acquired from the WICS (Window Indipendent Context Segmentation) technique. Conclusions includes that remote sensing can be used in CSR initiatives, to establish a baseline level or as a fifth dimen- sion in a score sheet approach. However, inconclusive mapping of value chains are a big hinder today.

Drought is one of the most widespread and least understood natural phenomena. Many indices using multiple data types have been created, and their success at identifying periods of extreme wetness and dryness has been well documented. In recent years, researchers have begun to assess the potential of total water storage (TWS) anomalies in drought monitoring method- ologies. The Gravity Recovery and Climate Experiment (GRACE) provides temporally and spatially consistent TWS measurements across the globe, and studies have shown GRACE TWS anomalies are suited to identify drought. GRACE TWS is used with MODIS-derived normalized difference veg- etation index (NDVI) and NOAA/NWS precipitation data to create a new drought index, the Merged-dataset Drought Index (MDI). Each dataset corre- lates with a different type of drought, giving robustness to MDI. MDI is based on dataset deviations from a monthly climatology and is objective and easy to calculate. MDI is studied across Texas, which is broken into five dataset- defined sub-regions. Multiple drought events are identified from 2002 - 2014, with the most severe beginning in October 2010. A new drought severity clas- sification scheme is proposed based on MDI, and it is organized to match the current US Drought Monitor Classification Scheme. MDI shows strong correlation with existing drought indices, notably the Palmer Drought Severity Index (PDSI). MDI consistently identifies droughts in different sub-regions of Texas, but shows better performance in regions with large GRACE TWS signals. MDI performance is enhanced through a weighting scheme that relies more on GRACE TWS. Even with this scheme, MDI and PDSI exhibit occasional behavioral differences. Drought analysis using MDI shows for the first time that GRACE data provides information on a sub-regional scale in Texas, an area with low signal amplitudes. Past studies have shown TWS capable of identifying drought, but MDI is the first index to quantitatively use GRACE TWS in a manner consistent with current practices of identifying drought. MDI also establishes a framework for a future, completely remote-sensing based index that can enable temporally and spatially consistent drought identification across the globe. This study is useful as well for establishing a baseline for the necessary spatial resolution required from future geodetic space missions for use in drought identification at smaller scales. / text

Resumo: A utilização de novas técnicas para monitoramento de nutrição de plantas baseadas em características espectrais vem ganhando espaço no cenário agrícola mundial por serem promissoras para atender às necessidades de manejo de adubação nitrogenada. Objetivou-se neste trabalho avaliar a resposta de aveiabranca à adubação nitrogenada e aferir o desempenho dos índices Normalized Diference Vegetation Index (NDVI) e Índice de Clorofila (ICF) no diagnóstico nutricional da cultura, visando à recomendação de aplicação do nutriente em taxa variável. O experimento foi conduzido na região nordeste do estado de São Paulo, constituído de cinco tratamentos correspondentes às estratégias de manejo de adubação nitrogenada: T1 - 160 kg ha-1 de N, T2 - 90 kg ha-1de N, T3 - 60 kg ha-1de N, T4 - NDVI (60 kg ha-1 de N quando NDVI < 90% do tratamento T1) e T5 - ICF (60 kg ha-1 de N quando ICF < 90% do tratamento T1). A cultivar de aveia foi a IAC 7, irrigada por aspersão e a fonte de nitrogênio foi ureia. A aveia-branca não apresentou incrementos de produtividade de biomassa e grãos. Os índices NDVI e ICF foram eficientes para indicar que as plantas apresentavam nutrição adequada em Latossolo vermelho eutrófico de textura argilosa, apontando que, para área em questão, não era necessária a adubação nitrogenada. Portanto, esses índices são promissores para o manejo da adubação nitrogenada em aveia-branca. / Abstract: The use of remote sensing techniques for plant nutrition has been considered a novel technology in worldwide agriculture. Efficiency in fertilizer management is a goal to be achieved and the use of modern tools based on remote sensing is promising to meet these needs. The objective of this work is to evaluate the response of the oat crop to nitrogen fertilization and also the performance of the NDVI (Normalized Differential Vegetation Index) and FCI (Foliar Chlorophyll Index Index) in the identification of the response of this crop to doses of nitrogen and in the recommendation of nitrogen application at variable rates. A field experiment was conducted from in the Northeast region of Sao Paulo state, Brazil, with treatments corresponding to five strategies of nitrogen fertilization management: T1 - 160 kg ha-1 de N, T2 - 90 kg ha-1 de N, T3 - 60 kg ha-1 de N, T4 - NDVI (60 kg ha-1 de N when NDVI < 90% of the treatment T1); T5 - ICF (60 kg ha-1 de N when ICF < 90% of the treatment T1). The oat cultivar was IAC 7, irrigated by sprinkling and the nitrogen source was urea. The NDVI and ICF indexes were efficient to indicate that the plants presented adequate nutrition in a red clayey eutrophic Red Latosol, indicating that nitrogen fertilization is not necessary, and they are promising for the management of nitrogen fertilization in white oats. / Mestre

Magister Artium - MA / Changes in climate patterns have raised concerns for environmentalists globally and across southern Africa. The changes greatly affect the growth dynamics of vegetation to such an extent that climate elements such as rainfall have become the most important determinant of vegetation growth. In arid and semi-arid environments, vegetation relies on near-surface groundwater as the main source of water. Changes in the environment due to climate can be examined by using remotely sensed data. This approach offers an affordable and easy means of monitoring the impact of climate variability on vegetation growth. This study examined the response of vegetation to rainfall and temperature, and assessed the dependence thereof on groundwater in a climatically variable region of the semi-arid Karoo. The methodology used included sampling plant species in the riparian and non-riparian areas over two plant communities in seven vegetation plots. The Normalised Difference Vegetation Index (NDVI) derived from the Landsat OLI and TM was used to measure vegetation productivity. This was compared with rainfall totals derived from the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) and the mean monthly temperature totals. A drought index, (Standardised Precipitation Index – SPI) was an additional analysis to investigate rainfall variability. Object-based Image Analysis (OBIA) and Maximum Likelihood supervised classification approaches together with indicators of groundwater discharge areas (Topographic Wetness Index – TWI, and profile curvature) were used to map vegetation and surface water that depend on groundwater.

Student Number : 0216831W - MSc research report - School of Environmental Sciences - Faculty of Science / This report deals with the relationship between the NDVI obtained from the NOAA AVHRR sensor and that obtained from the MODIS sensor. The relationship is quantitatively assessed for distinct polygons over various land-cover types in the northeastern Kwa-Zulu Natal Province of South Africa. Spatial and temporal variations in the relationships are addressed and discussed with reference to spectral response, sunsensor- target geometries and atmospheric factors. Specifically, various methods are investigated to estimate a MODIS-equivalent NDVI from the AVHRR NDVI and in so doing create the potential to develop a self-consistent NDVI between the historically available AVHRR NDVI dataset and the relatively new MODIS NDVI dataset. NOAA-16 AVHRR NDVI data and AQUA MODIS NDVI data for the two-year period from January 2002 to December 2003 are used to develop the method. A linear relationship exists between the AVHRR and MODIS NDVI. However, spatial variations in the relationship in terms of land-cover and mean NDVI are pointed out. The potential of atmospheric corrections applied to AVHRR data through a radiative transfer atmospheric correction model to improve the relationship between the two NDVI datasets is also investigated. The importance of geo-location accuracy of the AVHRR NDVI dataset is assessed in the light of the accuracy obtainable with the proposed method to estimate a MODIS-equivalent NDVI from the AVHRR NDVI. A method to estimate the MODIS NDVI from the AVHRR NDVI that takes the mean AVHRR NDVI value into account, as opposed to a constant linear relationship over all the points, is proposed. Atmospheric correction is shown not to improve the accuracy of the method in a statistically significant way. The root-mean-square error of the proposed method is in the order of 0.05 NDVI units and varies between 0.5 and 2 standard deviations of the MODIS NDVI over an entire season.

FAPERJ / The importance of studying soils in order to preserve its functions includes conservation efforts whose goal is to ensure preservation of natural resources for future generations. This study works with the scientific hypothesis that changes in vegetation cover in the watershed of Palmares-Ribeir?o do Saco, Rio de Janeiro State, in the period of 2009-2015, favored the erosion reduction in this area. To verify this trend an estimate of soil losses was made with use of RUSLE (Revised Universal Soil Loss Equation) which allows generating information to build up management plans to secure agricultural production, and to preserve natural resources of the region. Thus, the objective was to develop a methodology for assessment of vegetation affected by seasonality and able to reduce erosion, based on the NDVI (Normalized Difference Vegetation Index) in order to reduce part of values overestimation associated to errors in the RUSLE model. As secondary objective, to compare estimates of erosion and natural erosion potential (NEP) in the period of 2009-2015 with those obtained in the period of 1986-2009, in order to verify environmental impacts in the watershed. The area studied was the watershed of Palmares-Ribeir?o do Saco, located between the municipalities of Paty do Alferes and Miguel Pereira in the state of Rio de Janeiro. Even without the adoption of conservation practices by some of the region farmers, the general aspect of data provided by RUSLE and the NEP in recent years has shown that soil losses are tending to decrease, mainly due to the low recorded rainfall indexes, implying at low erosivity values. / A import?ncia do estudo dos solos a fim de preservar suas fun??es compreende os esfor?os conservacionistas que tem por meta garantir a persist?ncia dos recursos naturais para as gera??es futuras. O presente estudo trabalha com a hip?tese cient?fica de que a evolu??o da cobertura vegetal na bacia hidrogr?fica de Palmares-Ribeir?o do Saco, no estado do Rio de Janeiro, ocorrida no per?odo 2009-2015, favoreceu a redu??o do processo erosivo na bacia. Para averiguar tal tend?ncia foi feita a estimativa da perda de solo com uso da RUSLE (Revised Universal Soil Loss Equation) que possibilita gerar informa??es para montar planos de manejo que visem garantir a produ??o agr?cola preservando os recursos naturais da regi?o. Sendo assim, o objetivo principal foi desenvolver uma metodologia para avalia??o da vegeta??o afetada pela sazonalidade capaz de reduzir processos erosivos, com base no NDVI (Normalized Difference Vegetation Index) a fim de reduzir parte das superestimativas associadas ao erro no modelo da RUSLE. E como objetivo secund?rio comparar as estimativas do processo erosivo e o potencial natural de eros?o (PNE) no per?odo 2009-2015 com aquelas obtidas no per?odo 1986-2009, com intuito de verificar os impactos ambientais ocorridos na bacia hidrogr?fica. A ?rea utilizada como objeto de estudo foi ? bacia hidrogr?fica de Palmares-Ribeir?o do Saco, situada entre os munic?pios de Paty do Alferes e Miguel Pereira no estado do Rio de Janeiro. Mesmo sem haver a ado??o de pr?ticas conservacionistas por partes dos produtores na regi?o, o aspecto geral dos dados fornecidos pela RUSLE e o PNE dos ?ltimos anos revelou que as perdas de solo est?o tendendo ? redu??o, sobretudo por conta dos baixos ?ndices pluviom?tricos registrados, implicando em valores baixos de erosividade.

Memoria para optar al Título Profesional de Ingeniero Forestal / Se propuso un método de gabinete para la detección de cortas no autorizadas, basado en el uso de la teledetección a partir de imágenes satelitales de libre descarga. Para este propósito se analizaron cambios producidos por cortas no autorizadas sobre bosque nativo en la Región del Maule a través de análisis multitemporales con imágenes OLI del satélite Landsat. Se crearon tres categorías de cortas no autorizadas basadas en las superficies de estas mismas, se crearon las categorías: grandes (entre 4,2 y 40,5 hectáreas), medianas (entre 0,2 y 4,1 hectáreas) y pequeñas (entre 0,01 y 0,19 hectáreas). A su vez fue calculado el índice vegetacional NDVI para los periodos antes y después de realizadas las cortas, para posteriormente determinar el ΔNDVI con el fin de crear mapas de cambio los cuales fueron comparados con los registros disponibles en las campañas de campo, a distintas escalas de análisis. La comparación se realizó a partir de una muestra de la imagen a partir de diferentes escalas, se tomaron muestras de 40,5 ha (escala 1:4.000), 81 ha (escala 1:5.000), 162 ha (escala 1:7.000) y 324 ha (1:10.000). Los mejores resultados se obtienen cuando la región de cambio fue definida como un 25% del total de la imagen, conteniendo cerca de un 30% de las cortas no autorizadas en la mayoría de las escalas de análisis utilizadas. Los mapas de cambio obtenidos fueron utilizados para la realización de la propuesta de seguimiento y detección de las cortas no autorizadas, debido que se trata de un material cartográfico es de gran utilidad en el apoyo y la planificación de campañas en terreno. La metodología propuesta puede ser mejorada como por ejemplo, con imágenes de mayor resolución que tengan celdillas de menor superficie, con la capacidad de observar las cortas no autorizadas de menor tamaño. Los valores utilizados para el cálculo de la efectividad del método fueron realizados en base a una muestra de la corta la cual fue un solo pixel, con lo cual podrían esperarse mejores resultados si se tuvieran los perímetros de corta que entreguen información de la vecindad del dato.