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Arevalo-Ramirez, T. A., Castillo, A. H. F., Cabello, P. S. R., & Cheein, F. A. A. (2021). Single bands leaf reflectance prediction based on fuel moisture content for forestry applications. Biosyst. Eng., 202, 79–95.
Abstract: Vegetation indices can be used to perform quantitative and qualitative assessment of vegetation cover. These indices exploit the reflectance features of leaves to predict their biophysical properties. In general, there are different vegetation indices capable of describing the same biophysical parameter. For instance, vegetation water content can be inferred from at least sixteen vegetation indices, where each one uses the reflectance of leaves in different spectral bands. Therefore, if the leaf moisture content, a vegetation index and the reflectance at the wavelengths to compute the vegetation index are known, then the reflectance in other spectral bands can be computed with a bounded error. The current work proposes a method to predict, by a machine learning regressor, the leaf reflectance (spectral signature) at specific spectral bands using the information of leaf moisture content and a single vegetation index of two tree species (Pinus radiata, and Eucalyptus globulus), which constitute 97.5% of the Valparai ' so forests in Chile. Results suggest that the most suitable vegetation index to predict the spectral signature is the Leaf Water Index, which using a Kernel Ridge Regressor achieved the best prediction results, with an RMSE lower than 0.022, and an average R2 greater than 0.95 for Pinus radiata and 0.81 for Eucalyptus globulus, respectively. (c) 2020 IAgrE. Published by Elsevier Ltd. All rights reserved.
Keywords: Leaf water index; Machine learning; Remote sensing; Wildfire; Wildland fuels
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Gimeno, F., Galleguillos, M., Manuschevich, D., & Zambrano-Bigiarini, M. (2022). A coupled modeling approach to assess the effect of forest policies in water provision: A biophysical evaluation of a drought-prone rural catchment in south-central Chile. Sci. Total Environ., 830, 154608.
Abstract: The effect of different forest conservation policies on water provision has been poorly investigated due to a lack of an integrative methodological framework that enables its quantification. We developed a method for assessing the effects of forest conservation policies on water provision for rural inhabitants, based on a land-use model coupled with an ecohydrological model. We used as a case study the Lumaco catchment, Chile, a territory dominated by native forests (NF) and non-native tree farms, with an extended dry period where nearly 12,600 people of rural communities get drinking water through water trucks. We analyzed three land-use policy scenarios: i) a baseline scenario based on historical land-cover maps; ii) a NF Recovery and Protection (NFRP) scenario, based on an earlier implementation of the first NF Recovery and Forestry Development bill; and iii) a Pristine (PR) scenario, based on potential vegetation belts; the latter two based on Dyna CLUE, and simulated between 1990 and 2015. Impacts on water provision from each scenario were computed with SWAT. The NFRP scenario resulted in an increase of 6974 ha of NF regarding the baseline situation, and the PR scenario showed an increase of 26,939 ha of NF. Despite large differences in NF areas, slight increases in inflows (Q) were found between the NFRP and the PR scenarios, with relative differences with respect to the baseline of 0.3% and 2.5% for NFRP and PR, respectively. Notwithstanding, these small differences in the NFRP scenario, they become larger if we analyze the cumulative values during the dry season only (December, January, and February), where they reach 1.1% in a normal year and 3.1% in a dry year. Flows increases were transformed into water truck costs resulting in up to 441,876 USD (monthly) of fiscal spending that could be avoided during a dry period.
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Lopatin. (2023). Interannual Variability of Remotely Sensed Phenology Relates to Plant Communities. IEEE Geosci. Remote. Sens. Lett., 20, 2502405.
Abstract: Vegetation phenology is considered an essential biological indicator in understanding the behavior of ecosystems and how they respond to environmental cues. However, the potential of interannual variations of remotely sensed phenology signals to differentiate plant types remains poorly understood, especially in understudied systems with highly heterogeneous landscapes such as wetlands. This study presents a case study in a San Francisco Bay area marsh that investigates the usefulness of interannual variation, defined as the root-mean-square error of enhanced vegetation index (EVI) measurements against a fitted phenology curve, at the beginning, middle, and end of the growing season as indicators of plant types. The study found that altitude above sea level and certain land surface phenology metrics, such as the day-of-the-year of the end of the season, the mid-autumn day, and the greening rate before the summer peak, were significantly related to these interannual variation trends. These results indicate that a detailed time-series analysis at the beginning and end of growing seasons may enhance large-scale wetland characterization. Overall, the findings of this study contribute to our understanding of vegetation phenology and provide a framework for more accurate wetland classification in future studies.
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