Modelagem da produtividade primária líquida utilizando dados coletados de sensores remotos : avaliação de impactos e perdas em área agrícola

A Produtividade Primária Líquida (Net Primary Productivity - NPP) é um parâmetro de importância global, devido ao papel que desempenha no ciclo do carbono. Muitos modelos têm sido desenvolvidos nos últimos anos, principalmente para integrar dados de sensores remotos e facilitar a estimativa de NPP. No entanto, alguns ajustes ainda são necessários para que estes modelos consigam representar eficientemente os dados reais na superfície. Assim, o objetivo desta pesquisa foi o teste e o desenvolvimento de metodologias capazes de quantificar e mapear a NPP através de dados coletados de sensores remotos, nas condições ambientais do Noroeste do Rio Grande do Sul. Sendo assim, foram feitas estimativas de NPP potencial, através do modelo Thornthwaite, para um período de 10 anos, incorporando dados climáticos obtidos de Estações Meteorológicas e dados de reanálise do ERA-Interim. As estimativas de NPP potencial foram similares aquelas obtidas com dados medidos na superfície, indicando que estes podem ser utilizados nas estimativas do potencial de NPP. Para a estimativa de NPP real, utilizou-se o modelo Carnegie-Ames-Stanford Approach (CASA), baseado em dados de superfície e de sensores remotos. O modelo CASA produziu estimativas acuradas da NPPreal quando comparadas aos dados medidos de superfície e se mostraram adequadas para representar o perfil temporal da soja durante o ciclo de desenvolvimento da cultura. Além disso, utilizouse o Índice de Umidade de Superfície (TVDI) como alternativa ao coeficiente de estresse hídrico para compor o modelo CASA, o que produziu estimativas acuradas de NPP em relação ao modelo original. Existe vantagem no uso da abordagem que introduz o TDVI, em função dos resultados com maior detalhamento espacial, além de utilizar dados exclusivamente de sensores remotos para rodar o modelo CASA. O uso do sensoriamento remoto ajuda a capturar pequenas mudanças hídricas e seus efeitos sobre a vegetação de forma mais precisa e com maior detalhamento. As estimativas de NPP potencial e NPP real foram comparadas para verificar as mudanças causadas na produção agrícola na região de estudo. Observou-se que quando realizado apenas um cultivo agrícola anual, a apropriação poder chegar a até 28% da NPP potencial. A quantificação da HANPP permite verificar se existem perdas ou ganhos de NPP potencial e, assim, subsidiar a busca de estratégias de gerenciamento para incremento da produtividade dos cultivos e minimização da demanda de terras novas de produção agrícola.Net Primary Productivity (NPP) is a parameter of global importance, due to the role it plays in the carbon cycle. Many models have been developed in recent years, mainly to integrate data from remote sensors and facilitate the estimation of NPP. However, some adjustments are still necessary for these models to be able to efficiently represent the actual data on the surface. Thus, the objective of this research was to test and develop methodologies capable of quantifying and mapping NPP through data collected from remote sensors, in the environmental conditions of northwest Rio Grande do Sul. Therefore, estimates of potential NPP were made, through the Thornthwaite model, for a period of 10 years, incorporating climatic data obtained from Meteorological Stations and reanalysis data from ERA-Interim. The estimates of potential NPP were similar to those obtained with data measured on the surface, indicating that these can be used in estimates of the potential of NPP. For estimating real NPP, the Carnegie-Ames-Stanford Approach (CASA) model was used, based on surface data and remote sensors. The CASA model produced accurate estimates of the actual NPP when compared to the measured surface data and proved to be adequate to represent the soybean temporal profile during the crop development cycle. In addition, the Temperature-Vegetation Dryness Index (TVDI) was used as an alternative to the water stress coefficient to compose the CASA model, which produced accurate NPP estimates in relation to the original model. There is an advantage in using the approach that introduces TDVI, due to the results with greater spatial detail, in addition to using data exclusively from remote sensors to run the CASA model. The use of remote sensing helps to capture small water changes and their effects on vegetation more precisely and in greater detail. The estimates of potential NPP and actual NPP were compared to verify the changes caused in agricultural production in the study area. It was observed that when there is only one annual agricultural crop, the appropriation can reach up to 28% of the potential NPP. The quantification of the HANPP allows to verify if there are losses or gains of potential NPP and, therefore, to subsidize the search for management strategies to increase the productivity of the crops and minimize the demand for new agricultural production lands

Multi-layer Land Cover Data for Remote-Sensing based Vegetation Modelling for South Korea

Land cover data is essential input for vegetation productivity models that are often driven by coarse resolution data. In this study, we analyze how well 1 km land cover data represent land cover at 30 m for South Korea. We derive multi-layer 1 km land cover classes and coverages and analyze how much of land cover heterogeneity is represented by the successive layers. Comparison to global land cover data shows varying agreement. The multilayer land cover data can be used for example for net primary productivity modelling. Especially, for models that can include more than one vegetation type per pixel, multi-layer land cover data and their corresponding coverages are a major asset

Monitoring the Sustainable Intensification of Arable Agriculture:the Potential Role of Earth Observation

Sustainable intensification (SI) has been proposed as a possible solution to the conflicting problems of meeting projected increases in food demand and preserving environmental quality. SI would provide necessary production increases while simultaneously reducing or eliminating environmental degradation, without taking land from competing demands. An important component of achieving these aims is the development of suitable methods for assessing the temporal variability of both the intensification and sustainability of agriculture. Current assessments rely on traditional data collection methods that produce data of limited spatial and temporal resolution. Earth Observation (EO) provides a readily accessible, long-term dataset with global coverage at various spatial and temporal resolutions. In this paper we demonstrate how EO could significantly contribute to SI assessments, providing opportunities to quantify agricultural intensity and environmental sustainability. We review an extensive body of research on EO-based methods to assess multiple indicators of both agricultural intensity and environmental sustainability. To date these techniques have not been combined to assess SI; here we identify the opportunities and initial steps required to achieve this. In this context, we propose the development of a set of essential sustainable intensification variables (ESIVs) that could be derived from EO data

Fire

Vegetation plays a crucial role in regulating environmental conditions, including weather and climate. The amount of water and carbon dioxide in the air and the albedo of our planet are all influenced by vegetation, which in turn influences all life on Earth. Soil properties are also strongly influenced by vegetation, through biogeochemical cycles and feedback loops (see Volume 1A—Section 4). Vegetated landscapes on Earth provide habitat and energy for a rich diversity of animal species, including humans. Vegetation is also a major component of the world economy, through the global production of food, fibre, fuel, medicine, and other plantbased resources for human consumptio

Sustainable intensification of arable agriculture:The role of Earth Observation in quantifying the agricultural landscape

By 2050, global food production must increase by 70% to meet the demands of a growing population with shifting food consumption patterns. Sustainable intensification has been suggested as a possible mechanism to meet this demand without significant detrimental impact to the environment. Appropriate monitoring techniques are required to ensure that attempts to sustainably intensify arable agriculture are successful. Current assessments rely on datasets with limited spatial and temporal resolution and coverage such as field data and farm surveys. Earth Observation (EO) data overcome limitations of resolution and coverage, and have the potential to make a significant contribution to sustainable intensification assessments. Despite the variety of established EO-based methods to assess multiple indicators of agricultural intensity (e.g. yield) and environmental quality (e.g. vegetation and ecosystem health), to date no one has attempted to combine these methods to provide an assessment of sustainable intensification. The aim of this thesis, therefore, is to demonstrate the feasibility of using EO to assess the sustainability of agricultural intensification. This is achieved by constructing two novel EO-based indicators of agricultural intensity and environmental quality, namely wheat yield and farmland bird richness. By combining these indicators, a novel performance feature space is created that can be used to assess the relative performance of arable areas. This thesis demonstrates that integrating EO data with in situ data allows assessments of agricultural performance to be made across broad spatial scales unobtainable with field data alone. This feature space can provide an assessment of the relative performance of individual arable areas, providing valuable information to identify best management practices in different areas and inform future management and policy decisions. The demonstration of this agricultural performance assessment method represents an important first step in the creation of an operational EO-based monitoring system to assess sustainable intensification, ensuring we are able to meet future food demands in an environmentally sustainable way