High-Precision Stand Age Data Facilitate the Estimation of Rubber Plantation Biomass: A Case Study of Hainan Island, China
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data and Processing
2.2.1. Field Inventory Data
2.2.2. Satellite Imagery
2.2.3. Terrain Data
2.3. Rubber Plantation Biomass Estimation
2.3.1. Map** the Stand Age of Rubber Plantations
2.3.2. Regression Biomass Model
2.3.3. Random Forest (RF) Biomass Model
2.4. Accuracy Assessment
3. Results
3.1. Variable Sensibility to the Biomass of Rubber Plantations
3.2. Biomass Estimated by the Regression Model
3.3. Biomass Estimated by RF Models
3.4. The Spatial Distribution of Rubber Plantations and Biomass
4. Discussion
4.1. Biomass Saturation with Different LS2-Based Variables
4.2. Biomass Estimation Using Stand Age and Remote Sensing Parameters
4.4.3. Tree Density
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
- Suratman, M.N.; Bull, G.Q.; Leckie, D.G.; Lemay, V.M.; Marshall, P.L.; Mispan, M.R. Prediction models for estimating the area, volume, and age of rubber (Hevea brasiliensis) plantations in Malaysia using Landsat TM data. Int. For. Rev. 2004, 6, 1–12. [Google Scholar] [CrossRef]
- Yasen, K.; Koedsin, W. Estimating Aboveground Biomass of Rubber Tree Using Remote Sensing in Phuket Province, Thailand. J. Med. Bioeng. 2015, 4, 451–456. [Google Scholar] [CrossRef]
- Chen, B.; **. J. Remote Sens. 2006, 10, 932–940. [Google Scholar] [CrossRef]
- Su, Y.; Guo, Q.; Xue, B.; Hu, T.; Alvarez, O.; Tao, S.; Fang, J. Spatial distribution of forest aboveground biomass in China: Estimation through combination of spaceborne lidar, optical imagery, and forest inventory data. Remote Sens. Environ. 2016, 173, 187–199. [Google Scholar] [CrossRef]
- Phua, M.-H.; Johari, S.A.; Wong, O.C.; Ioki, K.; Mahali, M.; Nilus, R.; Coomes, D.A.; Maycock, C.R.; Hashim, M. Synergistic use of Landsat 8 OLI image and airborne LiDAR data for above-ground biomass estimation in tropical lowland rainforests. For. Ecol. Manag. 2017, 406, 163–171. [Google Scholar] [CrossRef]
- Quegan, S.; Le Toan, T.; Chave, J.; Dall, J.; Exbrayat, J.-F.; Minh, D.H.T.; Lomas, M.; D’Alessandro, M.M.; Paillou, P.; Papathanassiou, K.; et al. The European Space Agency BIOMASS mission: Measuring forest above-ground biomass from space. Remote Sens. Environ. 2019, 227, 44–60. [Google Scholar] [CrossRef]
- Lu, D.; Chen, Q.; Wang, G.; Liu, L.; Li, G.; Moran, E. A survey of remote sensing-based aboveground biomass estimation methods in forest ecosystems. Int. J. Digit. Earth 2014, 9, 63–105. [Google Scholar] [CrossRef]
- Xu, W.; Ma, Y.; Li, H.; Liu, W. Estimating biomass for rubber plantations in ** tropical forests and deciduous rubber plantations in Hainan Island, China by integrating PALSAR 25-m and multi-temporal Landsat images. Int. J. Appl. Earth Obs. 2016, 50, 117–130. [Google Scholar] [CrossRef]
- Teluguntla, P.; Thenkabail, P.S.; Oliphant, A.; ** cropland extent of Southeast and Northeast Asia using multi-year time-series Landsat 30-m data using a random forest classifier on the Google Earth Engine Cloud. Int. J. Appl. Earth Obs. 2019, 81, 110–124. [Google Scholar] [CrossRef]
- Dash, J.P.; Watt, M.S.; Pearse, G.D.; Heaphy, M.; Dungey, H.S. Assessing very high resolution UAV imagery for monitoring forest health during a simulated disease outbreak. ISPRS J. Photogramm. 2017, 131, 1–14. [Google Scholar] [CrossRef]
- Vastaranta, M.; Kantola, T.; Lyytikäinen-Saarenmaa, P.; Holopainen, M.; Kankare, V.; Wulder, M.A.; Hyyppä, J.; Hyyppä, H. Area-Based Map** of Defoliation of Scots Pine Stands Using Airborne Scanning LiDAR. Remote Sens. 2013, 5, 1220–1234. [Google Scholar] [CrossRef]
- Zeinab, S.; Omid, A.; Manfred, B. A Synergetic Analysis of Sentinel-1 and -2 for Map** Historical Landslides Using Object-Oriented Random Forest in the Hyrcanian Forests. Remote Sens. 2019, 11, 2300. [Google Scholar]
- Belgiu, M.; Drăguţ, L. Random forest in remote sensing: A review of applications and future directions. ISPRS J. Photogramm. 2016, 114, 24–31. [Google Scholar] [CrossRef]
- Yang, J.; Huang, J.; Tang, J.; Pan, Q.; Han, X. Carbon sequestration in rubber tree plantations established on former arable lands in ** Rubber Tree Stand Age Using Pléiades Satellite Imagery: A Case Study in Thalang District, Phuket, Thailand. Eng. J. 2015, 19, 45–56. [Google Scholar] [CrossRef]
- Razak, J.A.B.A.; Shariff, A.R.B.M.; Ahmad, N.B.; Ibrahim Sameen, M. Map** rubber trees based on phenological analysis of Landsat time series data-sets. Geocarto Int. 2018, 33, 627–650. [Google Scholar] [CrossRef]
- Chen, B.; **e, G.; Wang, J.; Wu, Z.; Cao, J. Estimation of rubber stand age using statistical and artificial neutral network approaches with Landsat TM data. Chin. J. Trop. Crops. 2012, 33, 182–188. [Google Scholar]
- Gislason, P.O.; Benediktsson, J.A.; Sveinsson, J.R. Random Forests for land cover classification. Pattern Recognit. Lett. 2006, 27, 294–300. [Google Scholar] [CrossRef]
- Liu, S.; Zhang, J.; Cai, D.; Tian, G.; Zhang, G.; Zou, H. Spatial-temporal characteristics of rubber typhoon disaster in Hainan Island. Guangdong Agric. Sci. 2015, 42, 132–135. [Google Scholar]
Mean ± Std | Min | 25th Percentile | 50th Percentile | 75th Percentile | Max | |
---|---|---|---|---|---|---|
Stand Age | 14.49 ± 6.69 | 5.0 | 10.0 | 13.0 | 16.5 | 30.0 |
DBH (cm) | 17.83 ± 3.31 | 12.05 | 15.33 | 17.49 | 20.06 | 24.60 |
Blue | Green | Red | NIR | |
---|---|---|---|---|
Landsat 7 ETM+ | B1 (0.45–0.52 µm) | B2 (0.53–0.61 µm) | B3 (0.63–0.69 µm) | B4 (0.78–0.90 µm) |
Landsat 8 OLI | B2 (0.45–0.52 µm) | B3 (0.53–0.60 µm) | B4 (0.63–0.68 µm) | B5 (0.85–0.89 µm) |
Sentinel-2 MSI | B2 (0.44–0.54 µm) | B3 (0.54–0.58 µm) | B4 (0.65–0.69 µm) | B8 (0.77–0.91 µm) |
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Chen, B.; Yun, T.; Ma, J.; Kou, W.; Li, H.; Yang, C.; **ao, X.; Zhang, X.; Sun, R.; **e, G.; et al. High-Precision Stand Age Data Facilitate the Estimation of Rubber Plantation Biomass: A Case Study of Hainan Island, China. Remote Sens. 2020, 12, 3853. https://doi.org/10.3390/rs12233853
Chen B, Yun T, Ma J, Kou W, Li H, Yang C, **ao X, Zhang X, Sun R, **e G, et al. High-Precision Stand Age Data Facilitate the Estimation of Rubber Plantation Biomass: A Case Study of Hainan Island, China. Remote Sensing. 2020; 12(23):3853. https://doi.org/10.3390/rs12233853
Chicago/Turabian StyleChen, Bangqian, Ting Yun, Jun Ma, Weili Kou, Hailiang Li, Chuan Yang, **angming **ao, **an Zhang, Rui Sun, Guishui **e, and et al. 2020. "High-Precision Stand Age Data Facilitate the Estimation of Rubber Plantation Biomass: A Case Study of Hainan Island, China" Remote Sensing 12, no. 23: 3853. https://doi.org/10.3390/rs12233853