Validating and Linking the GIMMS Leaf Area Index (LAI3g) with Environmental Controls in Tropical Africa

Abstract

Abstract:The recent Global Inventory Modeling and Mapping Studies (GIMMS) LAI3g product provides a 30-year global times-series of remotely sensed leaf area index (LAI), an essential variable in models of ecosystem process and productivity. In this study, we use a new dataset of field-based LAI True to indirectly validate the GIMMS LAI3g product, LAI avhrr , in East Africa, comparing the distribution properties of LAI avhrr across biomes and environmental gradients with those properties derived for LAI True 1974 driver. Socio-economic variables such as governance (protected areas) and population affect both LAI responses, although cause and effect are not always obvious: a positive relationship with human population pressure was detected, but shown to be an artefact of both LAI and human settlement covarying with precipitation. Despite these complexities, targeted field measurements, stratified according to both environmental and socio-economic gradients, could provide crucial data for improving satellite-derived LAI estimates, especially in the human-modified landscapes of tropical Africa.

Vegetation dynamics and responses to climate anomalies in East Africa

“…As described in Zhu et al (2013), the dataset was produced by the fusion of GIMMS NDVI3g and an improved version of the Moderate Resolution Imaging Spectroradiometer (MODIS) LAI using a Feed Forward Neural Network (FFNN) algorithm. The GIMMS LAI3g data has been validated using ground based observations measured across East Africa (Pfeifer et al, 2014) and has also been used to study vegetation dynamics at a global scale (Cook and Pau, 2013). 10…”

“…Particularly, LAI, which is defined in broadleaf canopies as the one-sided green leaf area per unit vegetated ground area, and in coniferous canopies as one-half the total needle surface area per unit vegetated ground area, characterizes the physiologically functioning surface area 10 for energy, mass and momentum exchange between the vegetated land surface and the planetary boundary layer. Hence, it is widely used by the global change research community to assess and quantify vegetation dynamics and their effects (Bobée et al, 2012;Cook and Pau, 2013;Pfeifer et al, 2014). This dataset is also a pertinent input or state variable in land surface process-based models for simulating land-atmosphere dynamics.…”

Spatio-temporal vegetation dynamics and relationship with climate over East Africa

“…This dataset has a spatial resolution of about 8 km and 15-day temporal resolution. As described in Zhu et al (2013), the dataset was produced from the fusion of GIMMS NDVI3g (Pinzon and Tucker, 2014) (Pfeifer et al, 2014) and has also been used to study vegetation dynamics at a global scale (Cook and Pau, 2013). 30 Hydrol.…”

“…Particularly, Leaf Area Index (LAI), which is defined, in broadleaf canopies, as the one-sided green leaf area per unit vegetated ground area, and in coniferous canopies, as one-half the total needle surface area per unit vegetated ground area. It defines the physiologically functioning surface area for energy, mass and momentum exchange between the vegetated land surface and the planetary boundary layer hence widely used by the global change research community to assess and quantify vegetation dynamics and 15 their effects (Bobée et al, 2012;Cook and Pau, 2013;Pfeifer et al, 2014). This dataset is also a pertinent input or state variable in land surface process-based models for simulating land-atmosphere dynamics.…”

Índice de Área Foliar em Caatinga Preservada

“…Ambos os trabalhos subestimam o valor do IAF para o bioma Caatinga. O desconhecimento ou a estimativa de forma errôneas do IAF pode levar a sérios erros na previsão de modelos ecoló-gicos, hidrológicos e fisiológicos (GOWER; KUCHA-RIK; NORMAN, 1999;PFEIFER et al, 2014). Mais especificamente o IAF pode ser utilizado como parâme-tro para: modelar a fotossíntese, respiração e acúmulo de matéria seca em seringueiras, podendo ser importante na gestão da produção (XIE et al, 2010), avaliar a absorção de nitrogênio na cultura do arroz (ATA-UL-KARIM et al, 2014), simular escoamento superficial (TESEMMA et al, 2015), simular evapotranspiração e produtividade primária bruta da vegetação (CAO et al, 2015), avaliar a distribuição de água no sistema soloplanta-atmosfera para uma área de Caatinga preservada (PINHEIRO et al, 2016).…”