Estimativa da produção primária bruta por sensoriamento remoto no estado de Mato Grosso

• Autor(a) principal: Velasque, Maísa Caldas Souza
• Data de Publicação: 2016
• Tipo de documento: Tese
• Idioma: por
• Título da fonte: Repositório Institucional da UFMT
• Texto Completo: http://ri.ufmt.br/handle/1/2162
• topic: Fluxo de carbono; Índice de vegetação; Floresta tropical úmida; MODIS; TM Landsat 5; CNPQ::CIENCIAS EXATAS E DA TERRA::FISICA; Carbon flux; Vegetation index; Rainforest; MODIS; TM Landsat 5

Gross primary productivity (GPP) is defined as the overall rates of carbon sink through photosynthesis and it provides important information on the seasonal dynamics of the carbon cycle, allowing the location of the monitoring and study of climate change. The measures of GPP are generally made in micrometeorological stations using the eddy covariance technique, but this technique demands high cost of installation and maintenance of equipment. Thus, the objective of this study was to evaluate different orbital methods for estimate GPP in an Amazon-Cerrado transitional forest (SIN) and a pasture in the Cerrado (FMI) in the state os Mato Grosso by remote sensing. Through data derived from the Moderate Sensor Resolution Imaging Spectroradiometer (MODIS) and TM Landsat 5 satellite, four orbital models to estimated GPP were tested: the vegetation photosynthesis model (VPM); temperature and greenness model (TG); vegetation index model (VI) and MOD17A2 product. The models were compared with the GPP measured by eddy covariance (EC) in two sperimental sites. The most reliable model to estimated GPP was VPM model estimated with MODIS data in SIN when we taken into consideration the variability of eficiency of use of light. The second best performance in SIN was the VPM model with data TM Landsat 5, followed by MOD17A2 product. All the tested models showed satisfatory results in the FMI. The validation and comparison models will be useful in developing future models estimate GPP , still needed to evaluate these models in different vegetation cover.