Performance of non-parametric algorithms for spatial mapping of tropical forest structure
Liang Xu1, Sassan S Saatchi2, Yan Yang3
1Institute of the Environment and Sustainability, University of California, Los Angeles, CA 90095 USA.
Carbon Balance and Management
|September 13, 2016
Summary
Accurate tropical forest mapping relies on quality data and algorithms. This study shows that using more remote sensing layers and advanced methods like maximum entropy and random forest improves canopy height mapping accuracy.
Area of Science:
- Remote Sensing
- Forestry
- Ecology
Background:
- Accurate tropical forest structure mapping is crucial for estimating land use emissions and removals.
- Advancements in remote sensing offer finer resolution vegetation characteristic data.
- Mapping accuracy depends on input data quality, algorithms, and calibration/validation sample size.
Purpose of the Study:
- To evaluate the effectiveness of maximum entropy (ME) and random forest (RF) algorithms for mapping tropical forest canopy height.
- To assess the impact of input data layers and sample size on mapping accuracy.
- To improve the estimation of forest structure parameters using airborne lidar data.
Main Methods:
- Utilized airborne lidar data as ground truth for canopy height.
- Applied maximum entropy (ME) and random forest (RF) non-parametric mapping techniques.
- Tested the influence of varying numbers of input remote sensing layers on mapping accuracy.
Main Results:
- Both ME and RF algorithms showed improved accuracy in mapping mean canopy height (MCH) at 100m pixels when using more input layers.
- Bias-corrected spatial models enhanced estimates for extreme tree heights (small and large trees).
- Increased sample size can compensate for the rise in overall mean squared error.
Conclusions:
- Optimizing inventory sample size and selecting appropriate remote sensing layers and algorithms significantly improves tropical forest mapping.
- Current mapping efforts may exhibit bias towards the mean, underestimating/overestimating extreme height distributions without improved satellite sensitivity to forest biomass.
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