Mangroves are woody plant communities in the intertidal zone of tropical and subtropical coasts that play an important role in these zones. The infrared wave band is one of the key bands in the remote sensing identification of mangrove forest, and ALI (advanced land imagery) has a large number of infrared bands. Two angle indices were proposed based on liquid water absorption at band 5p and band 5 of EO-1 ALI, denoted as β1.25 and β1.65 respectively. A decision tree method was adopted to identify mangrove forest using remote sensing techniques for β1.25-β1.65 and NDVI (normalized difference vegetation index) for EO-1 ALI imagery acquired at Shenzhen Bay. The results showed that the reflectance of mangrove forests at band 5p and band 5 was significantly lower than that of terrestrial vegetation due to the characteristics of coastal wetlands of mangrove forests. This resulted in a greater β1.25-β1.65 value for mangrove forest than terrestrial vegetation. The decision tree method using β1.25-β1.65 and NDVI effectively identifies mangrove forest from other land cover categories. The misclassification and leakage rates were 4.29% and 5.11% respectively. ALI sensors with many infrared bands could play an important role in discriminating mangrove forest.
LIU Chunyan, GUO Hongqin, ZHANG Xuehong, CHEN Jian
. Combining Decision Trees with Angle Indices to Identify Mangrove Forest at Shenzhen Bay, China[J]. Journal of Resources and Ecology, 2017
, 8(5)
: 545
-549
.
DOI: 10.5814/j.issn.1674-764x.2017.05.012
[1] Andriamparany R, Francois F. 2010. Dynamics of mangrove forests in the Mangoky river delta, Madagascar, under the influence of natural and human factors. Forest Ecology and Management, 259(6): 1161-1169.
[2] Aschbacher J, Ofren R S, Delsol J P, et al . 1995. An integrated comparative approach to mangrove vegetation mapping using remote sensing and GIS technologies: preliminary results. Hydrobiologia, 295(1-3): 285-294.
[3] Blasco F, Aizpuru M, Gers C. 2001. Depletion of the mangroves of continental Asia. Wetlands Ecology and Management, 9(3): 245-256.
[4] Chaudhury M U. 1990. Digital analysis of remote sensing data for monitoring the ecological status of the mangrove forests of Sunderbans in Bangladesh// Proceedings of the 23rd International Symposium on Remote Sensing of the Environment, Bangkok, Thailand, 493-497.
[5] Conchedda G, Durieux L, Mayaux P. 2008. An object-based method for mapping and change analysis in mangrove ecosystems. ISPRS J. Photogramm. Remote Sens., 63(5): 578-589.
[6] Ferreira M A, Andrade F, Banderiba S O, et al . 2009. Analysis of cover change (1995-2005) of Tanzania /Mozambique transboundary mangroves using Landsat imagery. Aquatic Conservation, 19(S1): 38-45.
[7] Green E P, Clear C D, Mum P J, et al . 1998. Remote sensing techniques for mangrove mapping. International Journal of Remote Sensing, 19(5): 935-956.
[8] Giri C, Pengra B, Zhu Z., et al . 2007. Monitoring mangrove forest dynamics of the Sundarbans in Bangladesh and India using multi-temporal satellite data from 1973 to 2000. Estuarine, Coastal and Shelf Science, 73(1-2): 91-100.
[9] Jensen J R, Ramset E, Davis B A, et al . 1991.The measurement of mangrove characteristics in south-west Florida using SPOT multispectral data. Geocartography International, 6(2): 13-21.
[10] Lee T, Yeh H. 2009.Applying remote sensing techniques to monitor shifting wetland vegetation: a case study of Danshui river estuary mangrove communities, Taiwan. Ecological Engineering, 35(4): 487-496.
[11] Li C., DAI H. 2014. Extraction of mangroves spatial distribution using remotely sensed data. Wetland Science, 12(5): 580-589. (in Chinese)
[12] LI C., TAN B. 2003. Study on the method of mangrove inventory based on RS, GPS and GIS. Journal of natural resources, 18(2): 215-221. (in Chinese)
[13] Li X., Liu K., Wang S. 2006. Mangrove wetland changes in the Pear l river estuary using remote sensing. ACTA GEOGRAPHICA SINICA, 61(1): 26-34. (in Chinese)
[14] Liao B., Zhang Q. 2014. Area, distribution and species composition of mangroves in China. Wetland Science, 12(4): 435-440. (in Chinese)
[15] Lin Y., Ke L., Wang Z., et al . 2002. Seasonal changes in the caloric values of the leaves of seven mangrove species at Futian, Shenzhen. ACTA OCEANOLOGICA SINICA, 24(3): 112-118. (in Chinese)
[16] Liu K., Li X., Wang S., et al. 2005. Monitoring of the changes of mangrove wetland around the ZHUJIANG estuary in the past two decades by remote sensing. Tropical Geography, 25(2): 111-116. (in Chinese)
[17] Liu K., Li X. Shi X., et al . 2008. Monitoring mangrove forest changes using remote sensing and GIS with decision-tree learning. Wetlands, 28(2): 336-346.
[18] Long B G, Skewes T D. 1994. GIS and remote sensing improves mangrove mapping/ /Floreat W A. 7th Australasian Remote Sensing Conference, Volume 1, Melbourne: Remote Sensing and Photogrammetry Association Australia Ltd., 545-551.
[19] Middleton E M, Ungar S G, Mandl D J, et al. 2013. The earth observing one (EO-1) satellite mission: over a decade in space. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 6(2): 243-256.
[20] Myint S W, Giri C P, Wang L, et al . 2008. Identifying mangrove species and their surrounding land use and land cover classes using an object-oriented approach with a lacunarity spatial measure. GIScience & Remote Sensing, 45(2): 188-208.
[21] Phan M T, Jacques P. 2007. Status and changes of mangrove forest in Mekong delta: case study in Tra Vinh,Vietnam. Estuarine Coastal and Shelf Science, 71(1-2): 98-109.
[22] Prasad Rama Chandra, P Sudhakar Reddy, C Sundara Rajan, et al . 2009. Assessment of tsunami and anthropogenic impacts on the forest of the North Andaman Islands, India. International Journal of Remote Sensing, 30(5): 1235-1249.
[23] Rasolofoharinoro M, Blasco F, Bellan M F, et al . 1998. A remote sensing based methodology for mangrove studies in Madagascar. International Journal of Remote Sensing, 19(10): 1873-1886.
[24] Seto K C, Fragkias M. 2007. Mangrove conversion and aquaculture development in Vietnam: A remote sensing-based approach for evaluating the Ramsar Convention on Wetlands. Global Environmental Change, 17(3-4): 486-500.
[25] Vaiphasa C, Andrew S K, Willem F B. 2006. A post-classifier for mangrove mapping using ecological data. ISPRS Journal of Photogrammetry & Remote Sensing, 61(1): 1-10.
[26] Wang L., Sousa W., Gong P. 2004. Integration of object-based and pixel- based classification for mangrove mapping with IKONOS imagery. International Journal of Remote Sensing, 25(24): 5655-5668.
[27] Zhang X. 2011. Remote sensing information extraction of mangrove based on knowledge and rules. Journal of Nanjing University of Information Science and Technology, 3(4): 341-345. (in Chinese)
[28] Zhang X., Tian Q. 2012. Application of the temperature –moisture index to the improvement of remote sensing identification accuracy of mangrove. Remote Sensing for Land & Resources, 23(3): 65-70. (in Chinese)
[29] Zhang X., Zhou J., Wei Y . , et al. 2013. Remote sensing identification of mangrove forest combined tidal level information. Journal of Nanjing University of Information Science and Technology, 5(6): 501-507. (in Chinese)
