canopy height
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2022 ◽  
Vol 269 ◽  
pp. 112844
Author(s):  
Xiaoqiang Liu ◽  
Yanjun Su ◽  
Tianyu Hu ◽  
Qiuli Yang ◽  
Bingbing Liu ◽  
...  
Keyword(s):  

2022 ◽  
Vol 14 (2) ◽  
pp. 364
Author(s):  
Zhilong Xi ◽  
Huadong Xu ◽  
Yanqiu Xing ◽  
Weishu Gong ◽  
Guizhen Chen ◽  
...  

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.


Author(s):  
Peipei Xu ◽  
Wei Fang ◽  
Tao Zhou ◽  
Hu Li ◽  
Xiang Zhao ◽  
...  

Abstract The frequency and intensity of drought events are increasing with warming climate, which has resulted in worldwide forest mortality. Previous studies have reached a general consensus on the size-dependency of forest resistance to drought, but further understanding at a local scale remains ambiguous with conflicting evidence. In this study, we assessed the impact of canopy height on forest drought resistance in the broadleaf deciduous forest of southwestern China for the 2010 extreme drought event using linear regression and a random forest model. Drought condition was quantified with SPEI (standardized precipitation evapotranspiration index) and drought resistance was measured with the ratio of NDVI (normalized difference vegetation index) during (i.e. 2010) and before (i.e. 2009) the drought. At the regional scale we found that 1) drought resistance of taller canopies (30m and up) declined drastically more than that of canopies with lower height under extreme drought (SPEI < -2); 2). Random forest model showed that the importance of canopy height increased from 17.08% to 20.05% with the increase of drought intensities from no drought to extreme drought. Our results suggest that canopy structure plays a significant role in forest resistance to extreme drought, which has a broad range of implications in forest modeling and resource management.


2022 ◽  
Vol 14 (2) ◽  
pp. 298
Author(s):  
Kaisen Ma ◽  
Zhenxiong Chen ◽  
Liyong Fu ◽  
Wanli Tian ◽  
Fugen Jiang ◽  
...  

Using unmanned aerial vehicles (UAV) as platforms for light detection and ranging (LiDAR) sensors offers the efficient operation and advantages of active remote sensing; hence, UAV-LiDAR plays an important role in forest resource investigations. However, high-precision individual tree segmentation, in which the most appropriate individual tree segmentation method and the optimal algorithm parameter settings must be determined, remains highly challenging when applied to multiple forest types. This article compared the applicability of methods based on a canopy height model (CHM) and a normalized point cloud (NPC) obtained from UAV-LiDAR point cloud data. The watershed algorithm, local maximum method, point cloud-based cluster segmentation, and layer stacking were used to segment individual trees and extract the tree height parameters from nine plots of three forest types. The individual tree segmentation results were evaluated based on experimental field data, and the sensitivity of the parameter settings in the segmentation methods was analyzed. Among all plots, the overall accuracy F of individual tree segmentation was between 0.621 and 1, the average RMSE of tree height extraction was 1.175 m, and the RMSE% was 12.54%. The results indicated that compared with the CHM-based methods, the NPC-based methods exhibited better performance in individual tree segmentation; additionally, the type and complexity of a forest influence the accuracy of individual tree segmentation, and point cloud-based cluster segmentation is the preferred scheme for individual tree segmentation, while layer stacking should be used as a supplement in multilayer forests and extremely complex heterogeneous forests. This research provides important guidance for the use of UAV-LiDAR to accurately obtain forest structure parameters and perform forest resource investigations. In addition, the methods compared in this paper can be employed to extract vegetation indices, such as the canopy height, leaf area index, and vegetation coverage.


2022 ◽  
Vol 268 ◽  
pp. 153584
Author(s):  
Siyeon Byeon ◽  
Wookyung Song ◽  
Minjee Park ◽  
Sukyung Kim ◽  
Seohyun Kim ◽  
...  

2022 ◽  
Vol 79 (2) ◽  
Author(s):  
Igor Machado Ferreira ◽  
Bruno Grossi Costa Homem ◽  
Italo Braz Gonçalves de Lima ◽  
José Carlos Batista Dubeux Junior ◽  
Thiago Fernandes Bernardes ◽  
...  
Keyword(s):  

2022 ◽  
Vol 268 ◽  
pp. 112760
Author(s):  
Nico Lang ◽  
Nikolai Kalischek ◽  
John Armston ◽  
Konrad Schindler ◽  
Ralph Dubayah ◽  
...  

2021 ◽  
Vol 14 (1) ◽  
pp. 130
Author(s):  
Alberto Sassu ◽  
Luca Ghiani ◽  
Luca Salvati ◽  
Luca Mercenaro ◽  
Alessandro Deidda ◽  
...  

The present study illustrates an operational approach estimating individual and aggregate vineyards’ canopy volume estimation through three years Tree-Row-Volume (TRV) measurements and remotely sensed imagery acquired with unmanned aerial vehicle (UAV) Red-Green-Blue (RGB) digital camera, processed with MATLAB scripts, and validated through ArcGIS tools. The TRV methodology was applied by sampling a different number of rows and plants (per row) each year with the aim of evaluating reliability and accuracy of this technique compared with a remote approach. The empirical results indicate that the estimated tree-row-volumes derived from a UAV Canopy Height Model (CHM) are up to 50% different from those measured on the field using the routinary technique of TRV in 2019. The difference is even much higher in the two 2016 dates. These empirical findings outline the importance of data integration among techniques that mix proximal and remote sensing in routine vineyards’ agronomic practices, helping to reduce management costs and increase the environmental sustainability of traditional cultivation systems.


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