Abstract
Developing accurate methods to map vegetation structure in tropical forests is essential to protect their biodiversity and improve their carbon stock estimation. We integrated LIDAR (Light Detection and Ranging), multispectral and SAR (Synthetic Aperture Radar) data to improve the prediction and mapping of canopy height (CH) at high spatial resolution (30 m) in tropical forests in South America. We modeled and mapped CH estimated from aircraft LiDAR surveys as a ground reference, using annual metrics derived from multispectral and SAR satellite imagery in a dry forest, a moist forest, and a rainforest of tropical South America. We examined the effect of the three forest types, five regression algorithms, and three predictor groups on the modelling and mapping of CH. Our CH models reached errors ranging from 1.2–3.4 m in the dry forest and 5.1–7.4 m in the rainforest and explained variances from 94–60% in the dry forest and 58–12% in the rainforest. Our best models show higher accuracies than previous works in tropical forests. The average accuracy of the five regression algorithms decreased from dry forests (2.6 m +/− 0.7) to moist (5.7 m +/− 0.4) and rainforests (6.6 m +/− 0.7). Random Forest regressions produced the most accurate models in the three forest types (1.2 m +/− 0.05 in the dry, 4.9 m +/− 0.14 in the moist, and 5.5 m +/− 0.3 the rainforest). Model performance varied considerably across the three predictor groups. Our results are useful for CH spatial prediction when GEDI (Global Ecosystem Dynamics Investigation lidar) data become available.
Highlights
As part of the response to mitigate the current environmental and biodiversity crises, several measures to monitor global biodiversity change, termed essential biodiversity variables (EBVs), have been proposed [1,2]
By forest type, we found that the five regression models in each of the three methods of predictor selection resulted in the lowest RMSEs in Mato Grosso dry forest (MAT) (2.6 m +/− 0.7 in average), intermedia RMSEs in Tapajós-Xingu moist forest (TAP) (5.7 m +/− 0.4 in average), and the highest RMSEs in CHOCO (6.6 m +/− 0.7 in average), showing that the prediction of canopy height (CH) are more accurate in the dry forest (MAT) (Figure 4a)
The improvement of the spatial estimation of CH is a result of the integration of two main factors: (1) The use of bands of different regions of the electromagnetic spectrum increased sensitivity to CH, likely due to forest characteristics such as leaf phenology, leaf density, moisture, and temperature; (2) The use of annual metrics reduced the errors produced when data of specific dates are used as we showed in our analysis of annual curves (Figure 3), where lower and upper CHs had similar band and vegetation indices values during some parts of the year while X-means were different
Summary
As part of the response to mitigate the current environmental and biodiversity crises, several measures to monitor global biodiversity change, termed essential biodiversity variables (EBVs), have been proposed [1,2]. Leaf phenology (patterns of leaf expansion, senescence, and abscission) in the upper and lower strata may be asynchronous within and across years, resulting in high and persistent greenness from the perspective of the multispectral satellite imagery. This greenness maximization is the outcome of seasonal cycles operating at different canopy levels; leaf senescence and abscission increase in the upper canopy during the dry season due to water stress while leaf expansion increases in the lower canopy due to its lower susceptibility to water stress and the higher light produced by the leaf abscission in the upper canopy. Degraded and early successional forests do not have a complex vertical structure [15] and, display differences in their greenness and microclimates relative to later successional forests
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