Modeling of seasonal leaf area index values in a tropical dry forest using high resolution satellite imagery
DOI:
https://doi.org/10.21829/myb.2018.2431666Keywords:
spectral data, dry season, vegetation indices, rain, texture metrics, kriging regressionAbstract
The leaf area index (LAI) provides information about the amount of photosynthetic area in relation to the total surface of an ecosystem and it is related to vital processes such as photosynthesis, respiration, and productivity. Thus, it is important to have information about the spatial distribution of LAI at the landscape level. One of the most used methods for estimating LAI from satellite images is to associate it with spectral characteristics of the image and vegetation indices. However, these indices have a strong limitation due to saturation problems, which reduces the possibility of generating accurate LAI maps, particularly in forests with high levels of biomass. Here, we obtained regression models to map LAI in a tropical dry forest of Yucatan, during the rainy and dry seasons from high resolution satellite imagery. We used regression analysis combined with kriging, as this procedure integrates the relationship between LAI and both spectral and texture information of the imagery, as well as the spatial dependence of the observations. LAI values were obtained in the field using hemispheric photographs. The results show that LAI values differ significantly between seasons, with mean values of 3.37 in the rainy season and 2.49 in the dry season. The R2adj values of the regression analysis were 0.58 and 0.63 for the rainy and dry season respectively. Overall, our results demonstrate that by using texture measures, we are able to obtain accurate estimations of LAI in tropical dry forests with high levels of biomass.Downloads
References
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