Genetic Programming Guided Mapping of Forest Canopy Height by Combining LiDAR Satellites with Sentinel-1/2, Terrain, and Climate Data
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- @Article{wu:2024:RS,
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author = "Zhenjiang Wu and Fengmei Yao and Jiahua Zhang and
Enhua Ma and Liping Yao and Zhaowei Dong",
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title = "Genetic Programming Guided Mapping of Forest Canopy
Height by Combining {LiDAR} Satellites with
Sentinel-1/2, Terrain, and Climate Data",
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journal = "Remote Sensing",
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year = "2024",
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volume = "16",
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number = "1",
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pages = "Article No. 110",
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keywords = "genetic algorithms, genetic programming",
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ISSN = "2072-4292",
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URL = "https://www.mdpi.com/2072-4292/16/1/110",
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DOI = "doi:10.3390/rs16010110",
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abstract = "Accurately mapping the forest canopy height is vital
for conserving forest ecosystems. Employing the forest
height measured by satellite light detection and
ranging (LiDAR) systems as ground samples to establish
forest canopy height extrapolation (FCHE) models
presents promising opportunities for mapping
large-scale wall-to-wall forest canopy height. However,
despite the potential to provide more samples and
alleviate the stripe effect by synergistically using
the data from two existing LiDAR datasets, Global
Ecosystem Dynamics Investigation (GEDI) and Ice, Cloud,
and land Elevation Satellite-2 (ICESat-2), the
fundamental differences in their operating principles
create measurement biases, and thus, there are few
studies combining them for research. Furthermore,
previous studies have typically employed existing
regression algorithms as FCHE models to predict forest
canopy height, without customizing a model that
achieves optimal performance based on the current
samples. These shortcomings constrain the accuracy of
predicting forest canopy height using satellite LiDAR
data. To surmount these difficulties, we proposed a
genetic programming (GP) guided method for mapping
forest canopy height by combining the GEDI and ICESat-2
LiDAR data with Sentinel-1/2, terrain, and climate
data. In this method, GP autonomously constructs the
fusion model of the GEDI and ICESat-2 datasets
(hereafter GIF model) and the optimal FCHE model based
on the explanatory variables for the specific study
area. The outcomes demonstrate that the fusion of the
GEDI and ICESat-2 data shows high consistency (R2 =
0.85, RMSE = 2.2m, pRMSE = 11.24percent). The
synergistic use of the GEDI and ICESat-2 data, coupled
with the optimisation of the FCHE model, substantially
improves the precision of forest canopy height
predictions, and finally achieves R2, RMSE, and pRMSE
of 0.64, 3.38m, and 16.08percent, respectively. In
summary, our research presents a reliable approach to
accurately estimate forest canopy height using remote
sensing data by addressing measurement biases between
the GEDI and ICESat-2 data and overcoming the
limitations of traditional FCHE models.",
-
notes = "also known as \cite{rs16010110}",
- }
Genetic Programming entries for
Zhenjiang Wu
Fengmei Yao
Jiahua Zhang
Enhua Ma
Liping Yao
Zhaowei Dong
Citations