Remote sensing of leaf area index : enhanced retrieval from close-range and remotely sensed optical observations

A wide range of models used in agriculture, ecology, carbon cycling, climate and other related studies require information on the amount of leaf material present in a given environment to correctly represent radiation, heat, momentum, water, and various gas exchanges with the overlying atmosphere or the underlying soil. Leaf area index (LAI) thus often features as a critical land surface variable in parameterisations of global and regional climate models, e.g., radiation uptake, precipitation interception, energy conversion, gas exchange and momentum, as all areas are substantially determined by the vegetation surface. Optical wavelengths of remote sensing are the common electromagnetic regions used for LAI estimations and generally for vegetation studies. The main purpose of this dissertation was to enhance the determination of LAI using close-range remote sensing (hemispherical photography), airborne remote sensing (high resolution colour and colour infrared imagery), and satellite remote sensing (high resolution SPOT 5 HRG imagery) optical observations. The commonly used light extinction models are applied at all levels of optical observations. For the sake of comparative analysis, LAI was further determined using statistical relationships between spectral vegetation index (SVI) and ground based LAI. The study areas of this dissertation focus on two regions, one located in Taita Hills, South-East Kenya characterised by tropical cloud forest and exotic plantations, and the other in Gatineau Park, Southern Quebec, Canada dominated by temperate hardwood forest. The sampling procedure of sky map of gap fraction and size from hemispherical photographs was proven to be one of the most crucial steps in the accurate determination of LAI. LAI and clumping index estimates were significantly affected by the variation of the size of sky segments for given zenith angle ranges. On sloping ground, gap fraction and size distributions present strong upslope/downslope asymmetry of foliage elements, and thus the correction and the sensitivity analysis for both LAI and clumping index computations were demonstrated. Several SVIs can be used for LAI mapping using empirical regression analysis provided that the sensitivities of SVIs at varying ranges of LAI are large enough. Large scale LAI inversion algorithms were demonstrated and were proven to be a considerably efficient alternative approach for LAI mapping. LAI can be estimated nonparametrically from the information contained solely in the remotely sensed dataset given that the upper-end (saturated SVI) value is accurately determined. However, further study is still required to devise a methodology as well as instrumentation to retrieve on-ground green leaf area index . Subsequently, the large scale LAI inversion algorithms presented in this work can be precisely validated. Finally, based on literature review and this dissertation, potential future research prospects and directions were recommended.Ei saatavill

Changing Sensitivity of Diverse Tropical Biomes to Precipitation Consistent with the Expected Carbon Dioxide Fertilization Effect

Publisher Copyright: © 2022 Tenaw Geremew et al., published by Sciendo.Global environmental changes have implications for the terrestrial ecosystem functioning, but disentangling individual effects remains elusive. The impact of vegetation responses to increasing atmospheric CO2 concentrations is particularly poorly understood. As the atmospheric CO2 concentration increases, the CO2 acts as a fertilizer for plant growth. An increase in atmospheric CO2 reduces the amount of water needed to produce an equivalent amount of biomass due to closing or a narrowing of the stomata that reduces the amount of water that is transpired by plants. To study the impacts of climate change and CO2 fertilization on plant growth, we analyzed the growing season sensitivity of plant growth to climatic forcing from alpine to semi-desert eco-climatic zones of Ethiopia for various plant functional types over the period of 1982-2011. Growing season 3rd generation Normalized Difference Vegetation Index of Global Inventory Modeling and Mapping Studies (NDVI) was used as a proxy of plant growth, while mean growing season precipitation (prec), temperature (temp), and solar radiation (sr) as the climate forcing. The sensitivities of plant growth are calculated as a partial correlation, and a derivative of NDVI with respect to prec, temp and sr for earliest and recent 15-year periods of the satellite records, and using a moving window of 15-year. Our results show increasing trends of plant growth that are not explained by any climate variables. We also find that an equivalent increase in prec leads to a larger increase in NDVI since the 1980s. This result implies a given amount of prec has sustained greater amounts of plant foliage materials over time due to decreasing transpiration with increasing CO2 concentration as expected from the CO2 fertilization effect on water use efficiency and plant growth. Increasing trends of growth in shallow-rooted vegetation tend to be associated with woody vegetation encroachment.Peer reviewe

The sensitivity based estimation of leaf area index from spectral vegetation indices

Peer reviewe

Detangling the role of climate in vegetation productivity with an explainable convolutional neural network

Forests of the Earth are a vital carbon sink while providing an essential habitat for biodiversity. Vegetation productivity (VP) is a critical indicator of carbon uptake in the atmosphere. The leaf area index is a crucial vegetation index used in VP estimation. This work proposes to predict the leaf area index (LAI) using climate variables to better understand future productivity dynamics; our approach leverages the capacities of the V-Net architecture for spatiotemporal LAI prediction. Preliminary results are well-aligned with established quality standards of LAI products estimated from Earth observation data. We hope that this work serves as a robust foundation for subsequent research endeavours, particularly for the incorporation of prediction attribution methodologies, which hold promise for elucidating the underlying climate change drivers of global vegetation productivity.Comment: 7 pages, 2 figures, submitted to Tackling Climate Change with Machine Learning at NeurIPS 202

Leaf area index for biomes of the Eastern Arc Mountains : Landsat and SPOT observations along precipitation and altitude gradients

Peer reviewe

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

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 LAITrue to indirectly validate the GIMMS LAI3g product, LAIavhrr, in East Africa, comparing the distribution properties of LAIavhrr across biomes and environmental gradients with those properties derived for LAITrue. We show that the increase in LAI with vegetation height in natural biomes is captured by both LAIavhrr and LAITrue, but that LAIavhrr overestimates LAI for all biomes except shrubland and cropland. Non-linear responses of LAI to precipitation and moisture indices, whereby leaf area peaks at intermediate values and declines thereafter, are apparent in both LAITrue and LAIavhrr, although LAITrue reaches its maximum at lower values of the respective environmental 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.Peer reviewe

Agricultural Expansion and Its Consequences in the Taita Hills, Kenya

The indigenous cloud forests in the Taita Hills have suffered substantial degradation for several centuries due to agricultural expansion. Additionally, climate change imposes an imminent threat for local economy and environmental sustainability. In such circumstances, elaborating tools to conciliate socioeconomic growth and natural resources conservation is an enormous challenge. This chapter describes applications of remote sensing and geographic information systems for assessing land-cover changes in the Taita Hills and its surrounding lowlands. Furthermore, it provides an overall assessment on the consequences of land-cover changes to water resources, biodiversity and livelihoods. The analyses presented in this study were undertaken at multiple spatial scales, using field data, airborne digital images and satellite imagery. Furthermore, a modelling framework was designed to delineate agricultural expansion projections and evaluate the future impacts of agriculture on soil erosion and irrigation water demand.Peer reviewe

Interannual and spatial impacts of phenological transitions, growing season length, and spring and autumn temperatures on carbon sequestration: A North America flux data synthesis

Understanding feedbacks of ecosystem carbon sequestration to climate change is an urgent step in developing future ecosystem models. Using 187 site-years of flux data observed at 24 sites covering three plant functional types (i.e. evergreen forests (EF), deciduous forests (DF) and non-forest ecosystems (NF) (e.g., crop, grassland, wetland)) in North America, we present an analysis of both interannual and spatial relationships between annual net ecosystem production (NEP) and phenological indicators, including the flux-based carbon uptake period (CUP) and its transitions, degree-day-derived growing season length (GSL), and spring and autumn temperatures. Diverse responses were acquired between annul NEP and these indicators across PFTs. Forest ecosystems showed consistent patterns and sensitivities in the responses of annual NEP to CUP and its transitions both interannually and spatially. The NF ecosystems, on the contrary, exhibited different trends between interannual and spatial relationships. The impact of CUP onset on annual NEP in NF ecosystems was interannually negative but spatially positive. Generally, the GSL was observed to be a likely good indicator of annual NEP for all PFTs both interannually and spatially, although with relatively moderate correlations in NF sites. Both spring and autumn temperatures were positively correlated with annual NEP across sites while this potential was greatly reduced temporally with only negative impacts of autumn temperature on annual NEP in DF sites. Our analysis showed that DF ecosystems have the highest efficiency in accumulating NEP from warmer spring temperature and prolonged GSL, suggesting that future climate warming will favor deciduous species over evergreen species, and supporting the earlier observation that ecosystems with the greatest net carbon uptake have the longest GSL

Interannual and spatial impacts of phenological transitions, growing season length, and spring and autumn temperatures on carbon sequestration: A North America flux data synthesis

Understanding feedbacks of ecosystem carbon sequestration to climate change is an urgent step in developing future ecosystem models. Using 187 site-years of flux data observed at 24 sites covering three plant functional types (i.e. evergreen forests (EF), deciduous forests (DF) and non-forest ecosystems (NF) (e.g., crop, grassland, wetland)) in North America, we present an analysis of both interannual and spatial relationships between annual net ecosystem production (NEP) and phenological indicators, including the flux-based carbon uptake period (CUP) and its transitions, degree-day-derived growing season length (GSL), and spring and autumn temperatures. Diverse responses were acquired between annul NEP and these indicators across PFTs. Forest ecosystems showed consistent patterns and sensitivities in the responses of annual NEP to CUP and its transitions both interannually and spatially. The NF ecosystems, on the contrary, exhibited different trends between interannual and spatial relationships. The impact of CUP onset on annual NEP in NF ecosystems was interannually negative but spatially positive. Generally, the GSL was observed to be a likely good indicator of annual NEP for all PFTs both interannually and spatially, although with relatively moderate correlations in NF sites. Both spring and autumn temperatures were positively correlated with annual NEP across sites while this potential was greatly reduced temporally with only negative impacts of autumn temperature on annual NEP in DF sites. Our analysis showed that DF ecosystems have the highest efficiency in accumulating NEP from warmer spring temperature and prolonged GSL, suggesting that future climate warming will favor deciduous species over evergreen species, and supporting the earlier observation that ecosystems with the greatest net carbon uptake have the longest GSL