Forest canopy height variation in relation to topography and forest types in central Japan with LiDAR

2022 ◽  
Vol 503 ◽  
pp. 119792
Author(s):  
Md. Farhadur Rahman ◽  
Yusuke Onoda ◽  
Kaoru Kitajima
Keyword(s):  
Forest Canopy ◽  
Canopy Height ◽  
Forest Types ◽  
Height Variation ◽  
Central Japan

scholarly journals Forest Canopy Height Mapping by Synergizing ICESat-2, Sentinel-1, Sentinel-2 and Topographic Information Based on Machine Learning Methods

2022 ◽  
Vol 14 (2) ◽  
pp. 364
Author(s):  
Zhilong Xi ◽  
Huadong Xu ◽  
Yanqiu Xing ◽  
Weishu Gong ◽  
Guizhen Chen ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Forest Canopy ◽  
Remote Sensing Data ◽  
Canopy Height ◽  
Gradient Boosting ◽  
Forest Types ◽  
Height Estimation ◽  
Sensing Data ◽  
Continuous Forest ◽  
Estimation Models

Spaceborne LiDAR has been widely used to obtain forest canopy heights over large areas, but it is still a challenge to obtain spatio-continuous forest canopy heights with this technology. In order to make up for this deficiency and take advantage of the complementary for multi-source remote sensing data in forest canopy height mapping, a new method to estimate forest canopy height was proposed by synergizing the spaceborne LiDAR (ICESat-2) data, Synthetic Aperture Radar (SAR) data, multi-spectral images, and topographic data considering forest types. In this study, National Geographical Condition Monitoring (NGCM) data was used to extract the distributions of coniferous forest (CF), broadleaf forest (BF), and mixed forest (MF) in Hua’ nan forest area in Heilongjiang Province, China. Accordingly, the forest canopy height estimation models for whole forest (all forests together without distinguishing types, WF), CF, BF, and MF were established, respectively, by Radom Forest (RF) and Gradient Boosting Decision Tree (GBDT). The accuracy for established models and the forest canopy height obtained based on estimation models were validated consequently. The results showed that the forest canopy height estimation models considering forest types had better performance than the model grouping all types of forest together. Compared with GBDT, RF with optimal variables had better performance in forest canopy height estimation with Pearson’s correlation coefficient (R) and the root-mean-squared error (RMSE) values for CF, BF, and MF of 0.72, 0.59, 0.62, and 3.15, 3.37, 3.26 m, respectively. It has been validated that a synergy of ICESat-2 with other remote sensing data can make a crucial contribution to spatio-continuous forest canopy height mapping, especially for areas covered by different types of forest.

scholarly journals Using GEDI Waveforms for Improved TanDEM-X Forest Height Mapping: A Combined SINC + Legendre Approach

2021 ◽  
Vol 13 (15) ◽  
pp. 2882
Author(s):  
Hao Chen ◽  
Shane R. Cloude ◽  
Joanne C. White
Keyword(s):  
High Resolution ◽  
Forest Canopy ◽  
Test Site ◽  
Convergent Series ◽  
Canopy Height ◽  
Ecosystem Dynamics ◽  
Sinc Function ◽  
Vertical Profiles ◽  
Legendre Series ◽  
Vertical Profiling

In this paper, we consider a new method for forest canopy height estimation using TanDEM-X single-pass radar interferometry. We exploit available information from sample-based, space-borne LiDAR systems, such as the Global Ecosystem Dynamics Investigation (GEDI) sensor, which offers high-resolution vertical profiling of forest canopies. To respond to this, we have developed a new extended Fourier-Legendre series approach for fusing high-resolution (but sparsely spatially sampled) GEDI LiDAR waveforms with TanDEM-X radar interferometric data to improve wide-area and wall-to-wall estimation of forest canopy height. Our key methodological development is a fusion of the standard uniform assumption for the vertical structure function (the SINC function) with LiDAR vertical profiles using a Fourier-Legendre approach, which produces a convergent series of approximations of the LiDAR profiles matched to the interferometric baseline. Our results showed that in our test site, the Petawawa Research Forest, the SINC function is more accurate in areas with shorter canopy heights (<~27 m). In taller forests, the SINC approach underestimates forest canopy height, whereas the Legendre approach avails upon simulated GEDI forest structural vertical profiles to overcome SINC underestimation issues. Overall, the SINC + Legendre approach improved canopy height estimates (RMSE = 1.29 m) compared to the SINC approach (RMSE = 4.1 m).

scholarly journals Tropical forest canopy height estimation from combined polarimetric SAR and LiDAR using machine-learning

2021 ◽  
Vol 172 ◽  
pp. 79-94
Author(s):  
Maryam Pourshamsi ◽  
Junshi Xia ◽  
Naoto Yokoya ◽  
Mariano Garcia ◽  
Marco Lavalle ◽  
...  
Keyword(s):  
Machine Learning ◽  
Tropical Forest ◽  
Forest Canopy ◽  
Canopy Height ◽  
Polarimetric Sar ◽  
Height Estimation ◽  
Tropical Forest Canopy

Forest canopy height mapping over China using GLAS and MODIS data

2014 ◽  
Vol 58 (1) ◽  
pp. 96-105 ◽  
Author(s):  
Ting Yang ◽  
Cheng Wang ◽  
GuiCai Li ◽  
SheZhou Luo ◽  
XiaoHuan Xi ◽  
...  
Keyword(s):  
Forest Canopy ◽  
Canopy Height ◽  
Modis Data

scholarly journals Weak Environmental Controls of Tropical Forest Canopy Height in the Guiana Shield

2016 ◽  
Vol 8 (9) ◽  
pp. 747 ◽  
Author(s):  
Youven Goulamoussène ◽  
Caroline Bedeau ◽  
Laurent Descroix ◽  
Vincent Deblauwe ◽  
Laurent Linguet ◽  
...  
Keyword(s):  
Tropical Forest ◽  
Forest Canopy ◽  
Canopy Height ◽  
Guiana Shield ◽  
Environmental Controls ◽  
Tropical Forest Canopy

scholarly journals Evaluation of the Simard et al. 2011 Global Canopy Height Map in Boreal Forests

2020 ◽  
Vol 12 (7) ◽  
pp. 1114
Author(s):  
Wei Yang ◽  
Akihiko Kondoh
Keyword(s):  
Boreal Forests ◽  
Forest Canopy ◽  
Spatial Separation ◽  
Canopy Height ◽  
Tree Cover ◽  
Data Sets ◽  
Data Set ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer

Light detection and ranging (LiDAR) provides a state-of-the-art technique for measuring forest canopy height. Nevertheless, it may miss some forests due to its spatial separation of individual spots. A number of efforts have been made to overcome the limitation of global LiDAR datasets to generate wall-to-wall canopy height products, among which a global satellite product produced by Simard et al. (2011) (henceforth, the Simard-map) has been the most widely applied. However, the accuracy of the Simard-map is uncertain in boreal forests, which play important roles in the terrestrial carbon cycle and are encountering more extensive climate changes than the global average. In this letter, we evaluated the Simard-map in boreal forests through a literature review of field canopy height. Our comparison shows that the Simard-map yielded a significant correlation with the field canopy height (R2 = 0.68 and p < 0.001). However, remarkable biases were observed with the root mean square error (RMSE), regression slope, and intercept of 6.88 m, 0.448, and 10.429, respectively. Interestingly, we found that the evaluation results showed an identical trend with a validation of moderate-resolution imaging spectroradiometer (MODIS) tree-cover product (MOD44B) in boreal forests, which was used as a crucial input data set for generating the Simard-map. That is, both the Simard-map and MOD44B yielded an overestimation (underestimation) in the lower (upper) tails of the scatterplots between the field and satellite data sets. This indicates that the MOD44B product is the likely source of error for the estimation biases of the Simard-map. Finally, a field calibration was performed to improve the Simard-map in boreal forests by compensating for the estimation biases and discarding non-forest areas, which provided a more reliable canopy height product for future applications.

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