Tropical Geography ›› 2019, Vol. 39 ›› Issue (4): 583-596.doi: 10.13284/j.cnki.rddl.003161

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Estimating the Net Primary Productivity of Typical Mangrove and Archipelago Ecosystems in the Beibu Gulf Based on Unmanned Aerial Vehicle Imagery

Tian Yichao1ab, Huang Yuanlin1a, Tao Jin1a, Zhang Qiang1a, Wu Bin1a, Zhang Yali1a, Huang Hu1b, Liang Mingzhong1b and Zhou Guoqing2   

  1. (1. a. College of Resources and Environment,; b. Key Laboratory of Marine Geographic Information Resources Development and Utilization in the Beibu Gulf, Beibu Gulf University, Qinzhou 535000, China; 2. Guangxi Key Laboratory for Geospatial Informatics and Geomatics Engineering, Guilin University of Technology, Guilin 541004, China)
  • Online:2019-07-10 Published:2019-07-10

Abstract:

The net primary productivity (NPP) of vegetation is an important component of the carbon cycle and carbon budget balance in global ecosystems. Its accurate assessment depends on high-resolution satellite data. Previous studies have mostly estimated NPP using remote sensing data from moderate resolution imaging spectroradiometers (250-8000 m) and thematic mappers (30 m). Due to the limitations and influences of spatial resolution and weather factors in the Beibu Gulf, it is difficult to accurately assess the NPP of mangrove and archipelago ecosystems. This study is the first to use aerial images from unmanned aerial vehicles (UAV) as a data source and to use the CART method in ENVI5.3 to interpret the landscapes of mangrove and archipelago ecosystems. Using python language, the CASA model was implemented and introduced in our study to obtain spatial NPP distribution characteristics in different islands and mangrove ecosystems. We found that: 1) The VDVI index ranged from -1 to 1 and its vegetation index form was similar to the normalized differential vegetation index (NDVI). The threshold value for vegetation and non-vegetation segmentation was 0, which should be selected as the input parameter for the vegetation index in the CASA model. 2) The NPP carbon density in the study area ranged from 0 to 1437.12 g·C/m2, with an average value of 399.85 g·C/m2. NPP spatial distribution was divided by the mangrove ecosystem in the north and Guixian island in the south. The NPP of the mangrove ecosystem in the north was higher than that of the south. 3) The seasonal order of NPP carbon density was as follows: summer (191.35 g·C/m2) > autumn (116.55 g·C/m2) > spring (62.98 g·C/m2) > winter (28.94 g·C/m2). The percentage of the average annual NPP for each season was 47.86%, 29.15%, 15.75%, and 7.24%, respectively. 4) The maximum carbon density per unit area of mangrove ecosystem in the study area was 545.56 g·C·m-2, followed by Leeward Pier Island (535.90 g·C/m2), whilst Guixian Island was the lowest with a value of 413.54 g·C·m-2. The average NPP carbon density in typical mangrove and archipelago ecosystems estimated by this method was similar to the national average, lower than that of coastal provinces on the same latitude, but higher than that of provinces such as Hunan and Yunnan. The results of this study provide a reference and basis for further small-scale studies of NPP and its rapid estimation and also provide a new international research perspective. In the future, the Li-6400XT portable photosynthesis measurement system could be used to determine the maximum light use efficiency of different vegetation types to examine the NPP of mangroves in island groups and accurately assess the model. At the same time, high resolution satellite data and UAV data could be used to combine spatial and temporal data, thus improving both the spatial resolution and range of the vegetation index.

Key words: Unmanned Aerial Vehicle remote sensing, landscape pattern, Carnegie Ames Stanford Approach model, Net Primary Productivity, mangrove and archipelago ecosystems, Beibu Gulf of Guangxi