Urban Above-Ground Biomass Estimation Using GEDI Laser Data and Optical Remote Sensing Images
Abstract
:1. Introduction
2. Study Area and Materials
2.1. Study Area
2.2. Laser Altimetry Data
2.3. Optical Remote Sensing Imagery
2.4. Digital Elevation Model (DEM) Data
2.5. Measurements from Fieldwork
3. Methods
3.1. Canopy Height Extraction from GEDI Waveform Data
3.2. Feature Extraction from Optical Images in Vegetated Areas
3.3. Canopy Height Estimation Model through Random Forest
3.3.1. Canopy Height Estimation Model Using GEDI Joint Optical Images
3.3.2. Canopy Height Estimation Model Only Using Optical Images
3.4. Estimation of AGB with Canopy Height of Vegetation
4. Results
4.1. Vegetation Canopy Heights Mappings
4.2. Vegetation AGB Mappings
4.3. Comparison and Validation
4.3.1. Canopy Height Accuracy
4.3.2. AGB Accuracy
5. Discussion
5.1. Importance of Multi-Features for Canopy Height and AGB Estimation
5.2. The Impact of Vegetation Distribution on AGB
5.3. Seasonal Factor for the Estimation of Canopy Height and AGB
5.4. Influence of Field Measurements on AGB Estimation
6. Conclusions
- (1)
- The method of combining GEDI and optical images can improve the accuracy of vegetation canopy height estimation, and thus effectively improve the accuracy of AGB estimation. The estimation accuracy (R2 = 0.58) is higher than that of using only optical images (R2 = 0.45).
- (2)
- Three typical vegetated areas including the coniferous forest, broadleaf forest, and the shorter vegetation region are extracted to estimate the AGB. The total AGB in the shorter vegetation region is higher than the other two in the broadleaf forest and the coniferous forest, but the AGB per unit area is the lowest in the shorter vegetation area at 33.60 Mg/ha, and the coniferous forests have a wider distribution range of AGB per unit area at 46.60 Mg/ha.
- (3)
- Seasonal variations in vegetation also have a significant impact on the estimation of AGB. However, the highest average AGB occurs in October–December at 59.55 Mg/ha in Xuzhou using GEDI joint optical images, which is different from the normal pattern and the results using only optical images. A more accurate estimation of AGB calls for more laser observations or more field measurements.
- (4)
- The importance of multi-features is different for canopy height and AGB estimation. The near-infrared band and texture features have a greater influence on the estimation. However, the spectral similarity of trees and shrubs in the optical images leads higher estimation of canopy height in the shorter vegetation region and lower estimation in forest areas while using GEDI joint optical images. More detailed structures and precise features of vegetation are needed for AGB estimation in the future.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Image | Count | Time | Resolution | Use Bands |
---|---|---|---|---|
7 | March 2021 | 30 m | Coastal; blue; green; red; NIR; SWIR1; SWIR2 | |
2 | April 2021 | |||
3 | June 2021 | |||
Landsat-8 | 4 | August 2021 | ||
2 | October 2021 | |||
4 | November 2021 | |||
4 | December 2021 | |||
5 | January 2021 | Prioritize the use of 10 m resolution images, followed by 20 m resolution images | Coastal; blue; green; red; VRE1; VRE2; VRE3; NIR; Narrow NIR; SWIR1; SWIR2 | |
1 | February 2021 | |||
8 | March 2021 | |||
1 | April 2021 | |||
Sentinel-2 | 9 | May 2021 | ||
2 | June 2021 | |||
3 | July 2021 | |||
6 | September 2021 | |||
5 | November 2021 | |||
19 | December 2021 |
Type | Parameters | Description & Equation | Reference |
---|---|---|---|
Vegetation indices | NDVI | (NIR − Red)/(NIR + Red) | [50] |
GNDVI | (NIR − Green)/(NIR + Green) | [51] | |
EVI | 2.5 × (NIR − Red)/(NIR + 6 × Red − 7.5 × Blue + 1) | [51] | |
NBR | (NIR − SWIR)/(NIR + SWIR) | [52] | |
RVI | NIR/Red | [51] | |
DVI | NIR − Red | [53] | |
ARVI | (NIR − 2 × Red + Blue)/(NIR + 2 × Red − Blue) | [51] | |
Spectral bands | Blue | Blue band | / |
Green | Green band | / | |
Red | Red band | / | |
NIR | NIR band | / | |
SWIR | SWIR band | / | |
Coastal | Coastal band | / | |
VRE | Vegetation red edge band | / | |
Texture feature (gray level co-occurrence matrix window size: 7 × 7 pixels; gray level: 64; moving step length: 1; direction: 45°) | Mean | [54] | |
Variance | [54] | ||
Homogeneity | [54] | ||
Dissimilarity | [54] | ||
Entropy | [54] | ||
Second moment | [54] | ||
Correlation | [54] | ||
Contrast | [54] | ||
Topography | Slope | Slope in degrees | / |
Elevation | Elevation above sea level in meters | / |
Tree/Shrub Species | AGB Model (Mg) | Reference |
---|---|---|
Platycladus orientalis (L.) Franco | [56] | |
Populus L. | [57] | |
Cinnamomum camphora (L.) Presl. | [58] | |
Ginkgo biloba L. | [59] | |
Pinoideae Pilger | [60] | |
Ligustrum lucidum Ait. | [61] | |
Koelreuteria paniculata Laxm. | [61] | |
Firmiana platanifolia | [62] | |
Metasequoia glyptostroboides | [63] | |
Other hard broadlesf trees | [63] | |
Other soft broadlesf trees | [63] | |
Other broadlesf trees | [58] | |
Other coniferous trees | [58] | |
Photinia serrulata | [64] | |
Hibiscus syriacus | [64] | |
Tree-like shrub | [64] | |
Other shrubs | [64] | |
Grass | / |
Shorter Vegetation | Broadleaf Forest | Coniferous Forest | |
---|---|---|---|
Total AGB (Mg) | 25,342,735.52 | 5,866,387.54 | 904,178.41 |
Area (ha) | 754,389 | 168,768 | 19,404 |
Average AGB (Mg/ha) | 33.60 | 34.76 | 46.60 |
January–March | April–June | July–September | October–December | |
---|---|---|---|---|
Shorter vegetation (Mg/ha) | 34.56 | 33.59 | 31.01 | 59.70 |
Broadleaf forest (Mg/ha) | 40.98 | 34.76 | 29.48 | 57.42 |
Coniferous forest (Mg/ha) | 39.44 | 46.60 | 32.07 | 53.74 |
Average AGB (Mg/ha) | 36.04 | 34.29 | 30.95 | 59.55 |
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Zhao, X.; Hu, W.; Han, J.; Wei, W.; Xu, J. Urban Above-Ground Biomass Estimation Using GEDI Laser Data and Optical Remote Sensing Images. Remote Sens. 2024, 16, 1229. https://doi.org/10.3390/rs16071229
Zhao X, Hu W, Han J, Wei W, Xu J. Urban Above-Ground Biomass Estimation Using GEDI Laser Data and Optical Remote Sensing Images. Remote Sensing. 2024; 16(7):1229. https://doi.org/10.3390/rs16071229
Chicago/Turabian StyleZhao, Xuedi, Wenmin Hu, Jiang Han, Wei Wei, and Jiaxing Xu. 2024. "Urban Above-Ground Biomass Estimation Using GEDI Laser Data and Optical Remote Sensing Images" Remote Sensing 16, no. 7: 1229. https://doi.org/10.3390/rs16071229