Remote Sensing in Mapping Mangrove Ecosystems — An Object-Based Approach

Remote Sensing

Quoc Tuan Vo¹  Natascha Oppelt²  Patrick Leinenkugel¹ 

[1] German Remote Sensing Data Center, German Aerospace Center, Oberpfaffenhofen, D-82234 Wessling, Germany; E-Mails:;Remote Sensing & Environmental Modelling, Department of Geography, Kiel University, Ludewig-Meyn-Str 14, D-24098 Kiel, Germany; E-Mail:

关键词: decision tree;    object-based;    mangrove fraction;    SPOT;   

DOI  :  10.3390/rs5010183

来源: mdpi

【 摘 要 】

Over the past few decades, clearing for shrimp farming has caused severe losses of mangroves in the Mekong Delta (MD) of Vietnam. Although the increasing importance of shrimp aquaculture in Vietnam has brought significant financial benefits to the local communities, the rapid and largely uncontrolled increase in aquacultural area has contributed to a considerable loss of mangrove forests and to environmental degradation. Although different approaches have been used for mangrove classification, no approach to date has addressed the challenges of the special conditions that can be found in the aquaculture-mangrove system in the Ca Mau province of the MD. This paper presents an object-based classification approach for estimating the percentage of mangroves in mixed mangrove-aquaculture farming systems to assist the government to monitor the extent of the shrimp farming area. The method comprises multi-resolution segmentation and classification of SPOT5 data using a decision tree approach as well as local knowledge from the region of interest. The results show accuracies higher than 75% for certain classes at the object level. Furthermore, we successfully detect areas with mixed aquaculture-mangrove land cover with high accuracies. Based on these results, mangrove development, especially within shrimp farming-mangrove systems, can be monitored. However, the mangrove forest cover fraction per object is affected by image segmentation and thus does not always correspond to the real farm boundaries. It remains a serious challenge, then, to accurately map mangrove forest cover within mixed systems.

【 授权许可】

CC BY   
© 2013 by the authors; licensee MDPI, Basel, Switzerland.