Abstract
Accurately mapping forest effective leaf area index (LAIe) at the landscape level is a crucial step to better simulate various ecological and physiological processes such as photosynthesis, respiration, transpiration, and precipitation interception. The LAIe products obtained from two-dimensional (2-D) remotely sensed optical imageries are usually biased due to their inability to identify the vertical forest structure and eliminate the effects of forest background (i.e., shrubs, grass, snow, and bare earth). In this study, we first stratified the forest overstory and background layers and generated a forest background mask layer based on the structural information implicitly contained within the aerial laser scanning (ALS) data. We improved the retrieval accuracy of LAIe by combining light detection and ranging (Lidar)-based three dimensional (3-D) structural and 2-D spectral information. Then, we obtained the improved final LAIe estimation result by masking the forest background pixels from the optical remotely sensed imageries. Our results showed that: (1) Removing forest background information could effectively (R2 increase from 20% to 30%) improve the estimation accuracy of optical-based forest LAIe depending on forest structure characteristics. (2) The forest background in the forest stands with low canopy cover showed more apparent effects on LAIe estimation compared with the forest stands with a high canopy cover. (3) The combination of ALS and optical remotely sensed data could produce the best LAIe retrieval result effectively by removing the forest background information.
Highlights
Leaf area index (LAI), defined as one half of total green leaf surface area per unit horizontal ground surface area [1], is a crucial biophysical structural parameter for global climate change research [2,3]
With the increase in forest plots densities, the number of falsely segmented trees and crown radius root mean squared error (RMSE) increased in both the Washington Park Arboretum (WPA) and Panther Creek (PC) sites
We found that the R2 values of normalized difference vegetation index (NDVI)-leaf area index (LAIe) were lower than the corresponding R2 values of SR-LAIe in both the WPA and PC sites
Summary
Leaf area index (LAI), defined as one half of total green leaf surface area per unit horizontal ground surface area [1], is a crucial biophysical structural parameter for global climate change research [2,3]. The accurate estimation of forest canopy LAI at the landscape level is the premise and basis for better understanding the matter and energy cycles of terrestrial ecosystems and the spatiotemporal variations in solar radiations within or under a forest canopy [4,5,6,7]. LAIe is usually used as a proxy of LAI because it is easier to obtain from the field-based instruments and remotely sensed data [8]. LAIe can be converted to LAI by the methods from previous studies [9,10,11]. Forest background refers to all the materials below the forest canopy, including understory, leaf litter, grass, lichen, moss, rock, soil, snow, or their mixtures [13]
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