A combination of Alos-2/Palsar-2 and Landsat-8 satellite images for wood volume estimation of natural evergreen broadleaf forest in Dak Nong province
Keywords:
Alos-2/Palsar-2, Landsat-8, NFIS, Dak Nong province, natural evergreen broadleaf forest, wood volumeAbstract
Forest wood volume map is an important tool for managing forest resources and implementing forest policies. This study has developed models for wood volume estimation of natural evergreen broadleaf forest in Dak Nong Province based on a combination of ALOS-2/PALSAR-2 satellite images, Landsat-8 satellite images, ASTER DEM (GDEM), existing maps and 214 sample plots. The optimal prediction model has been selected. The input variables for the optimal model are mean values of HV backscatter of ALOS-2/PALSAR-2 image and the first Principal Component (PC1) from Landsat-8 image with the window size 13 × 13 pixels. The errors in wood volume estimation using the optimal model are as following: RMSE = 31.8 m3/ha, absolute error (MAE) = 25.2 m3/ha, relative error (MAE%) = 29.0%, relative RMSE% = 48.0%.
In current, ALOS-2/PALSAR-2 satellite image is always available for whole Vietnam. Landsat-8 images are observed frequently and provided in free of charge. The forest type boundaries have been defined both in the field and on the map according to the National forest inventory and statistics program that is as the basis for applying the model for wood volume estimation in other regions with similar conditions.
References
1. Attarchi, S. Richard Gloaguen, 2014. Improving the Estimation of Above Ground Biomass Using Dual Polarimetric PALSAR and ETM+ Data in the Hyrcanian Mountain Forest (Iran). Remote Sensing, No 6, pp 3693 - 3715.
2. Basuki, T.M. Andrew K. Skidmore, Yousif A. Hussin & Iris Van Duren, 2013. Estimating tropical forest biomass more accurately by integrating ALOS PALSAR and Landsat - 7 ETM+ data. International Journal of RemoteSensing, No 13, pp 4871 - 4888.
3. Dobson, M. C. F. T. Ulaby, L. E. Pierce, T. L. Sharik, K. M. Bergen, J. Kellndorfer, J. R. Kendra, 1995. Estimation of Forest Biomass Characteristics in Northern Michigan with SIR - C/XSAR Data. IEEE Transactions on Geoscience and Remote Sensing, No 33, pp 877-894.
4. Du, L., T. Zhou, Z. Zou, X. Zhao, K. Huang, and H. Wu, 2014. Mapping Forest Biomass Using Remote Sensing and National Forest Inventory Data. Forests5: 1267-1283.
5. Fuchs, H., P. Magdon, C. Kleinn, and H. Flessa, 2009. Estimating Aboveground Carbon in a Catchment of the Siberian Forest Tundra: Combining Satellite Imagery and Field Inventory. Remote Sensing of Environment113: 518-531.
6. Hamdan, O. MR Khairunnisa, AA Ammar, I Mohd Hasmadi, H Khali Aziz1, 2013. Mangrove carbon stock assessment by optical satellite imagery. Journal of Tropical Forest Science, No 25, pp 554-565.
7. Hamdan, O. I Mohd Hasmadi, H Khali Aziz1, 2014. Combination of SPOT-5 and Alos Palsar images in estimating aboveground biomass of lowland Dipterocarp forest. Earth and Environmenttal, No 18, pp 1 - 6.
8. Huang, Y. P. J. S. Chen, 2013. Advances in the Estimation of Forest Biomass Based on SAR Data. Remote Sensing for Land and Resources, No 25, pp 7-13.
9. Jonathan, Y.G. Jukka Miettinen, Aik Song Chia, Ping Ting Chew, Soo Chin Liew, 2013. Biomass Estimation in Humid Tropical Forest Using a Combination of ALOS PALSAR and Spot 5 Satellite Imagery. Asian Journal of
Geoinformatics, No 4.
10. Kasischke, E.S. Melack, J.M. and Dobson, M.C, 1997. The use of imaging radars for ecological applications - a review. Remote Sensing of Environment, No 59, pp 141 - 156.
11. Lu, D., 2006. The potential and challenge of remote sensing - based biomass estimation. International Journal of Remote Sensing, No 27:7, pp 1297 - 1328.
12. Lu, D., Q. Chen, G. Wang, E. Moran, M. Batistella, M. Zhang, G. Vaglio Laurin, and D. Saah, 2012. Aboveground Forest Biomass Estimation with Landsat and LiDAR Data and Uncertainty Analysis of the
Estimates. International Journal of Forestry Research 2012: 16.
13. Luther, J. E., R. A. Fournier, D. E. Piercey, L. Guindon, and R. J. Hall, 2006. Biomass Mapping Using Forest Type and Structure Derived from Landsat TM Imagery. International Journal of Applied Earth Observation and Geoinformation8: 173-187.
14. Song, C, 2013. Optical Remote Sensing of Forest Leaf Area Index and Biomass. Progress in Physical Geography, Vol 37, No 1, pp 98-113.
15. Songqiu, D. Masato Katoh, Qingwei Guan, Na Yin, Mingyang Li, 2014. Estimating Forest Aboveground Biomass by Combining ALOS PALSAR and WorldView - 2 Data: A Case Study at Purple Mountain National
Park, Nanjing, China. Remote Sens, No 6, pp 7878 - 7910.
16. UBND tỉnh Đắk Nông, 2017. Báo cáo kết quả rà soát, điều chỉnh quy hoạch ba loại rừng tỉnh Đắk Nông.
17. UBND tỉnh Đắk Nông, 2015. Quyết định số 67/QĐ-UBND ngày 14/01/2015 của UBND tỉnh Đắk Nông về việc phê duyệt, công bố kết quả kiểm kê rừng tỉnh Đắk Nông.
18. Viện Điều tra Quy hoạch rừng, 1995. Sổ tay điều tra quy hoạch rừng. Nhà xuất bản Nông nghiệp, Hà Nội.
