ABOVEGROUND BIOMASS ESTIMATION USING RECONSTRUCTED FEATURE OF AIRBORNE DISCRETE-RETURN LIDAR BY AUTO-ENCODER NEURAL NETWORK

Aboveground biomass (AGB) estimation is critical for quantifying carbon stocks and essential for evaluating carbon cycle. In recent years, airborne LiDAR shows its great ability for highly-precision AGB estimation. Most of the researches estimate AGB by the feature metrics extracted from the canopy height distribution of the point cloud which calculated based on precise digital terrain model (DTM). However, if forest canopy density is high, the probability of the LiDAR signal penetrating the canopy is lower, resulting in ground points is not enough to establish DTM. Then the distribution of forest canopy height is imprecise and some critical feature metrics which have a strong correlation with biomass such as percentiles, maximums, means and standard deviations of canopy point cloud can hardly be extracted correctly. In order to address this issue, we propose a strategy of first reconstructing LiDAR feature metrics through Auto-Encoder neural network and then using the reconstructed feature metrics to estimate AGB. To assess the prediction ability of the reconstructed feature metrics, both original and reconstructed feature metrics were regressed against field-observed AGB using the multiple stepwise regression (MS) and the partial least squares regression (PLS) respectively. The results showed that the estimation model using reconstructed feature metrics improved R<sup>2</sup> by 5.44&thinsp;%, 18.09&thinsp;%, decreased RMSE value by 10.06&thinsp;%, 22.13&thinsp;% and reduced RMSE<sub>cv</sub> by 10.00&thinsp;%, 21.70&thinsp;% for AGB, respectively. Therefore, reconstructing LiDAR point feature metrics has potential for addressing AGB estimation challenge in dense canopy area.

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Synthesis of Leaf-on and Leaf-off Unmanned Aerial Vehicle (UAV) Stereo Imagery for the Inventory of Aboveground Biomass of Deciduous Forests

Remote Sensing ◽

10.3390/rs11070889 ◽

2019 ◽

Vol 11 (7) ◽

pp. 889 ◽

Cited By ~ 5

Author(s):

Wenjian Ni ◽

Jiachen Dong ◽

Guoqing Sun ◽

Zhiyu Zhang ◽

Yong Pang ◽

...

Keyword(s):

Aboveground Biomass ◽

Point Cloud ◽

Forest Canopy ◽

Deciduous Forest ◽

Ground Surface ◽

Digital Terrain Model ◽

Canopy Height ◽

Surface Model ◽

Lidar Data ◽

Stereo Imagery

Applications of stereo imagery acquired by cameras onboard unmanned aerial vehicles (UAVs) as practical forest inventory tools are hindered by the unavailability of ground surface elevation. It is still a challenging issue to remove the elevation of ground surface in leaf-on stereo imagery to extract forest canopy height without the help of lidar data. This study proposed a method for the extraction of forest canopy height through the synthesis of UAV stereo imagery of leaf-on and leaf-off, and further demonstrated that the extracted forest canopy height could be used for the inventory of deciduous forest aboveground biomass (AGB). The points cloud of the leaf-on and leaf-off stereo imagery was firstly extracted by an algorithm of structure from motion (SFM) using the same ground control points (GCP). The digital surface model (DSM) was produced by rasterizing the point cloud of UAV leaf-on. The point cloud of UAV leaf-off was processed by iterative median filtering to remove vegetation points, and the digital terrain model (DTM) was generated by the rasterization of the filtered point cloud. The mean canopy height model (MCHM) was derived from the DSM subtracted by the DTM (i.e., DSM-DTM). Forest AGB maps were generated using models developed based on the MCHM and sampling plots of forest AGB and were evaluated by those of lidar. Results showed that forest AGB maps from UAV stereo imagery were highly correlated with those from lidar data with R2 higher than 0.94 and RMSE lower than 10.0 Mg/ha (i.e., relative RMSE 18.8%). These results demonstrated that UAV stereo imagery could be used as a practical inventory tool for deciduous forest AGB.

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Comparison of forest canopy height profiles in a mountainous region of Taiwan derived from airborne lidar and unmanned aerial vehicle imagery

GIScience & Remote Sensing ◽

10.1080/15481603.2019.1627044 ◽

2019 ◽

Vol 56 (8) ◽

pp. 1289-1304 ◽

Cited By ~ 8

Author(s):

Chih-Hsin Chung ◽

Chao-Huan Wang ◽

Han-Ching Hsieh ◽

Cho-Ying Huang

Keyword(s):

Unmanned Aerial Vehicle ◽

Forest Canopy ◽

Airborne Lidar ◽

Canopy Height ◽

Mountainous Region ◽

Aerial Vehicle

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Democratic Republic of the Congo Tropical Forest Canopy Height and Aboveground Biomass Estimation with Landsat-8 Operational Land Imager (OLI) and Airborne LiDAR Data: The Effect of Seasonal Landsat Image Selection

Remote Sensing ◽

10.3390/rs12091360 ◽

2020 ◽

Vol 12 (9) ◽

pp. 1360

Author(s):

Herve B. Kashongwe ◽

David P. Roy ◽

Jean Robert B. Bwangoy

Keyword(s):

Tropical Forest ◽

Aboveground Biomass ◽

Democratic Republic ◽

Tree Height ◽

Airborne Lidar ◽

Canopy Height ◽

Landsat Image ◽

Landsat 8 ◽

Republic Of The Congo ◽

The Mean

Inventories of tropical forest aboveground biomass (AGB) are often imprecise and sparse. Increasingly, airborne Light Detection And Ranging (LiDAR) and satellite optical wavelength sensor data are used to map tree height and to estimate AGB. In the tropics, cloud cover is particularly prevalent and so several years of satellite observations must be considered. This may reduce mapping accuracy because of seasonal and inter-annual changes in the forest reflectance. In this paper, the sensitivity of airborne LiDAR and Landsat-8 Operational Land Imager (OLI) based dominant canopy height and AGB 30 m mapping is assessed with respect to the season of Landsat acquisition for a ~10,000 Km2 tropical forest area in the Democratic Republic of the Congo. A random forest regression estimator is used to predict and assess the 30 m dominant canopy height using LiDAR derived test and training data. The AGB is mapped using an allometric model parameterized with the dominant canopy height and is assessed by comparison with field based 30 m AGB estimates. Experiments are undertaken independently using (i) only a wet season Landsat-8 image, (ii) only a dry season Landsat-8 image, and (iii) both Landsat-8 images. At the study area level there is little reported sensitivity to the season of Landsat image used. The mean dominant canopy height and AGB values are similar between seasons, within 0.19 m and 5 Mg ha−1, respectively. The mapping results are improved when both Landsat-8 images are used with Root Mean Square Error (RMSE) values that correspond to 18.8% of the mean study area mapped tree height (20.4 m) and to 41% of the mean study area mapped AGB (204 Mg ha−1). The mean study area mapped AGB is similar to that reported in other Congo Basin forest studies. The results of this detailed study are illustrated and the implications for tropical forest tree height and AGB mapping are discussed.

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Analysis of the Spatial Differences in Canopy Height Models from UAV LiDAR and Photogrammetry

Remote Sensing ◽

10.3390/rs12182884 ◽

2020 ◽

Vol 12 (18) ◽

pp. 2884

Author(s):

Qingwang Liu ◽

Liyong Fu ◽

Qiao Chen ◽

Guangxing Wang ◽

Peng Luo ◽

...

Keyword(s):

Point Cloud ◽

Forest Canopy ◽

Point Clouds ◽

Canopy Height ◽

Central China ◽

Data Sets ◽

Interpolation Algorithm ◽

Data Set ◽

Spatial Differences ◽

Mean Square Errors

Forest canopy height is one of the most important spatial characteristics for forest resource inventories and forest ecosystem modeling. Light detection and ranging (LiDAR) can be used to accurately detect canopy surface and terrain information from the backscattering signals of laser pulses, while photogrammetry tends to accurately depict the canopy surface envelope. The spatial differences between the canopy surfaces estimated by LiDAR and photogrammetry have not been investigated in depth. Thus, this study aims to assess LiDAR and photogrammetry point clouds and analyze the spatial differences in canopy heights. The study site is located in the Jigongshan National Nature Reserve of Henan Province, Central China. Six data sets, including one LiDAR data set and five photogrammetry data sets captured from an unmanned aerial vehicle (UAV), were used to estimate the forest canopy heights. Three spatial distribution descriptors, namely, the effective cell ratio (ECR), point cloud homogeneity (PCH) and point cloud redundancy (PCR), were developed to assess the LiDAR and photogrammetry point clouds in the grid. The ordinary neighbor (ON) and constrained neighbor (CN) interpolation algorithms were used to fill void cells in digital surface models (DSMs) and canopy height models (CHMs). The CN algorithm could be used to distinguish small and large holes in the CHMs. The optimal spatial resolution was analyzed according to the ECR changes of DSMs or CHMs resulting from the CN algorithms. Large negative and positive variations were observed between the LiDAR and photogrammetry canopy heights. The stratified mean difference in canopy heights increased gradually from negative to positive when the canopy heights were greater than 3 m, which means that photogrammetry tends to overestimate low canopy heights and underestimate high canopy heights. The CN interpolation algorithm achieved smaller relative root mean square errors than the ON interpolation algorithm. This article provides an operational method for the spatial assessment of point clouds and suggests that the variations between LiDAR and photogrammetry CHMs should be considered when modeling forest parameters.

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