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Remote Sensing for Natural Resources    2023, Vol. 35 Issue (4) : 149-158     DOI: 10.6046/zrzyyg.2022368
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Remote sensing-based monitoring and evaluation of anthropogenic influences on national marine nature reserves in Hainan Province from 2016 to 2020
YIN Yaqiu1,2,3,4,5,6(), WANG Jing2,3,4,5, YANG Jinzhong6, ZHU Xiaohua2,3,4,5, WANG Liwei2,3,4,5, Xing Yu6, LI Tianqi6, YU Yang7,8()
1. Land Consolidation and Rehabilitation Center, Ministry of Natural Resources, Beijing 100035, China
2. Land Science and Technology Innovation Center, Ministry of Natural Resources, Beijing 100035, China
3. Technology Innovation Center for Land Engineering, Ministry of Natural Resources, Beijing 100035, China
4. Key Laboratory of Land Consolidation and Rehabilitation, Ministry of Natural Resources, Beijing 100035, China
5. Key Laboratory of Land Consolidation and Rehabilitation, Ministry of Natural Resources, Beijing 100035, China
6. China Aero Geophysical Survey and Remote Sensing Center for Natural Resources, Beijing 100083, China
7. China Institute of Geo-Environment Monitoring, Beijing 100081, China
8. Key Laboratory of Mine Ecological Effects and Systematic Restoration, Ministry of Natural Resources, Beijing 100081, China
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Abstract  

Nature Reserves are effective means to protect biodiversity and improve the ecological environment. However, frequent human activities have threatened the ecosystem quality and stability of them. In order to study the use of remote sensing means to monitor and evaluate of human activities influence in the protected area, Dongzhaigang, Tongguling, Sanya Coral Reef and Dazhou Island National Marine Nature Reserves in Hainan Provinces were taken as research areas, and the high spatial resolution remote sensing images from 2016 to 2020 were adopted, to obtain the transformation information of artificial and natural factors through image reprocessing, classification system and interpretation signs establishment, human-computer interpretation and other steps. By collecting the topographic and meteorological data, considering the characteristics of human activity and ecological sensitivity of the local area, 11 evaluation factors including topographic, meteorological and land use types were selected to establish the evaluation index system. Analytic Hierarchy Process method was used to evaluate and grade the degree of human activity influence, and the distribution results of severely affected areas, moderately affected areas, mildly affected areas and non-affected areas were obtained. The results were analyzed and the conclusions can be drawn. The results show that from 2016 to 2020, in Dongzhaigang Nature Reserve, human disturbance is strong, but has a tendency to reduce. Severely and moderately affected areas are distributed in Beigang Island, north of the reserve and mildly affected areas are distributed in the edge of the protected area. Destructions in these areas are mainly caused by construction activities of the village. As to Tongguling Nature Reserve, even though it also has human disturbance, but the overall protection is good. Severely and moderately affected areas are mainly located in the rock park in the north and the Tongguling scenic spot in the east. It is mainly caused by the construction of tourism and transportation facilities. Mildly affected areas are located along the coast of Qishui Bay in the west, mainly caused by real estate development. Human activity disturbance in Sanya Coral Reef Nature Reserve is serious, but has a tendency to decrease. Severely affected areas are mainly located in Luhuitou Peninsula and Yulinjiao area, where vigorous development of tourism real estate destroy forests. Moderately affected areas are located in Ximao Island, mainly caused by the construction of residential areas in the north. No land cover changes caused by human activities are found in the Dazhou Island Nature Reserve. The results can provide a scientific basis for the management and protection of national Marine nature reserves in Hainan Province.

Keywords Hainan Province      National Marine Nature Reserve      remote sensing      human activity     
ZTFLH:  TP79  
Issue Date: 21 December 2023
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Yaqiu YIN
Jing WANG
Jinzhong YANG
Xiaohua ZHU
Liwei WANG
Yu Xing
Tianqi LI
Yang YU
Cite this article:   
Yaqiu YIN,Jing WANG,Jinzhong YANG, et al. Remote sensing-based monitoring and evaluation of anthropogenic influences on national marine nature reserves in Hainan Province from 2016 to 2020[J]. Remote Sensing for Natural Resources, 2023, 35(4): 149-158.
URL:  
https://www.gtzyyg.com/EN/10.6046/zrzyyg.2022368     OR     https://www.gtzyyg.com/EN/Y2023/V35/I4/149
Fig.1  Hainan Island National Marine Nature Reserve distribution map
Fig.2  Technique flowchart
一级分类 二级分类 描述
人工因子 耕地 保护区内人工种植和维护的水田和旱地
园地 保护区内人工种植和维护的果园、茶园等
房屋 保护区内的房屋建筑
道路 保护区内的道路
其他建筑物 保护区内除了房屋、道路之外的其他建筑物
人工湿地 保护区内的水库、养殖场、坑塘、沟渠、废水处理场所等,包括湿地和其附属的人工设施
裸地 保护区内由于人类活动形成的沙地、裸地
天然因子 林地 保护区内自然生长的乔木林、灌木林、竹林等
草地 保护区内自然生长的草地
天然湿地(不包含地表水) 保护区内的海岸带湿地、河流湿地、湖泊湿地、沼泽湿地去除掉地表水的部分,包括海滩、沼泽、河流滩涂、湖泊滩涂、季节性河流、季节性湖泊等
地表水 保护区内天然的河流水面、湖泊水面等
Tab.1  Classification system of land cover change in nature reserves
一级类 二级类 2016—2017年 2017—2018年 2018—2019年 2019—2020年
自然因子->人工因子 林地->其他建筑物 878.04 2 648.94
林地->道路 539.65 1 358.63
林地->房屋 44 680.34 11 340.62 1 623.31
林地->裸地 4 804.22
草地->房屋 2 646.50
天然湿地->房屋 490.20
地表水->其他建筑物 1 186.61 3 276.25
合计 49 931.14 19 114.64 1 623.31 4 804.22
人工因子->自然因子 房屋->林地 294.74
其他建筑物->林地 309.26
合计 604.00
Tab.2  Land cover change of Dongzhaigang Nature Reserve from 2016 to 2020
一级类 二级类 2016—2017年 2017—2018年 2018—2019年 2019—2020年
自然因子->人工因子 林地->其他建筑 9 696.72 3 616.62
林地->道路 73 561.25 448.41 2 005.94
林地->房屋 1 007.06
林地->裸地 514.67
合计 73 561.25 9 696.72 1 970.14 5 622.56
人工因子->自然因子 道路->林地 3 336.84
道路->草地 4 014.59
房屋->林地 27 439.31
房屋->地表水 2 277.12
房屋->天然湿地 202.29
其他建设用地->草地 3 342.82
其他建设用地->林地 62 032.74 732.44
人工湿地->天然湿地 3 529.52
合计 10 694.25 95 480.98 732.44
Tab.3  Land cover change of Tongguling Nature Reserve from 2016 to 2020
一级类 二级类 2016—2017年 2017—2018年 2018—2019年 2019—2020年
自然因子->人工因子 林地->其他建筑物 198 367.55 60 032.25 6 557.36 8 848.13
林地->道路 21 261.12 938.38 5 302.76 886.21
林地->房屋 119 000.04 22 273.45 8 470.29 9 026.15
林地->裸地 31 726.98 20 553.02
草地->其他建筑物 496.84
草地->裸地 2 260.35
地表水->道路 542.12
地表水->其他建筑物 982.24 600.28
地表水->人工湿地 4 386.19
合计 343 997.14 83 844.36 52 057.39 42 612.82
人工因子->自然因子 房屋->林地 625.29 32.07
其他建筑物->草地 39 454.20
其他建筑物->林地 2 797.42
人工湿地->地表水 6 333.45 3 442.74
裸地->林地 36 308.83
合计 9 756.16 42 896.94 36 340.90
Tab.4  Land cover change of Sanya Coral Reef Nature Reserve from 2016 to 2020
评价因素 评价因子 人类活动影响分级
权重 严重 中度 轻度 无影响 自然保护区
地形 坡度/(°) 0.08/0.05 ≥15 [10,15) [5,10) <5 东寨港
≥40 [30,40) [20,30) <20 铜鼓岭、三亚珊瑚礁
坡向 0.08/0.05 阳坡
[135,225)
半阳坡
[225,315)
半阴坡
[45,135)
阴坡
[0,45),(315,360)
高程/m 0.06/0.04 ≥15 [10,15) [5,10) <5 东寨港
≥250 [150,250) [50,150) <50 铜鼓岭
≥200 [100,200) [20,100) <20 三亚珊瑚礁
气候 降雨量/mm 0.06/0.04 [1 600,2 000) [1 200,1 600) [800,1 200) <800
用地类型 居民点/m2 0.11 ≥2 000 [500,2 000) [0,500) 0
交通设施/m2 0.18 ≥2 000 [500,2 000) [0,500) 0
旅游设施/m2 0.21 ≥2 000 [500,2 000) [0,500) 0
养殖场及附属设施/m2 0.08 ≥2 000 [500,2 000) [0,500) 0
农业设施/m2 0.08 ≥2 000 [500,2 000) [0,500) 0
其他建设用地 0.09 ≥2 000 [500,2 000) [0,500) 0
裸地/m2 0.07 ≥2 000 [500,2 000) [0,500) 0
分级赋值 3 2 1 0
Tab.5  Evaluation index system of human activity impact
Fig.3  Impact assessment of human activity in Dongzhaigang National Nature Reserve
Fig.4  Impact assessment of human activity in Tongguling National Nature Reserve
Fig.5  Impact assessment of human activity in Sanya Coral Reef National Nature Reserve
[1] 于博威, 饶恩明, 晁雪林, 等. 海南岛自然保护区对土壤保持服务功能的保护效果[J]. 生态学报, 2016, 36(12):3694-3702.
[1] Yu B W, Rao E M, Chao X L, et al. Evaluating the effectiveness of nature reserves in soil conservation on Hainan Island[J]. Acta Ecologica Sinica, 2016. 36(12):3694-3702.
[2] 吴钟解, 李成攀, 陈敏, 等. 大洲岛国家级自然保护区海洋资源调查及其管理保护机制探讨[J]. 海洋开发与管理, 2012, 29(7):97-100.
[2] Wu Z J, Li C P, Chen M, et al. Investigation of marine resources in Dazhou Island National Nature Reserve and discussion on its management and protection mechanism[J]. Ocean Development and Management, 2012, 29(7):97-100.
[3] 杜春松. 我国第一批国家级海洋自然保护区[J]. 生物学教学, 2004, 29(7):58-59.
[3] Du C S. The first batch of national marine nature reserves in China[J]. Bioligy Teaching, 2004, 29(7):58-59.
[4] 韩淑梅, 何平, 黄勃, 等. 东寨港典型红树林区底栖动物多样性特征指数比较研究[J]. 西北林学院学报, 2010, 25(1):123-126,161.
[4] Han S M, He P, Huang B, et al. Comparative study on the diversity of Macrobenthos in typical mangrove regions of Dongzhai Harbor,Hainan Island[J]. Journal of Northwest Forestry University, 2010, 25(1):123-126,161.
[5] 王涛, 侯立平. 我国自然保护区管理中存在的问题及其对策探究[J]. 南方农业, 2021, 15(23):214-216.
[5] Wang T, Hou L P. Problems and countermeasures in the management of nature reserves in China[J]. South China Agriculture, 2021, 15(23):214-216.
[6] 殷亚秋, 蒋存浩, 鞠星, 等. 海南岛2018年矿山地质环境遥感评价和生态修复对策[J]. 自然资源遥感, 2022, 34(2):194-202.doi:10.6046/zyzyyg.2021136.
[6] Yin Y Q, Jiang C H, Ju X, et al. Remote sensing evaluation of mine geological environment of Hainan Island in 2018 and ecological restoration countermeasures[J]. Remote Sensing for Natural Resources, 2022, 34(2):194-202.doi:10.6046/zyzyyg.2021136.
[7] 张明莎, 刘乾飞, 王敬文, 等. 1992—2018年轿子山自然保护区人为活动遥感监测[J]. 生态与农村环境学报, 2020, 36(9):1097-1105.
[7] Zhan M S, Liu Q F, Wang J W, et al. Monitoring human activities in Jiaozi Mountain Nature Reserve based on remote sensing during 1992—2018[J]. Journal of Ecology and Rural Environment, 2020, 36(9):1097-1105.
[8] 孔梅, 孟祥亮, 高洁, 等. 山东省省级自然保护区人类活动遥感监测与评价[J]. 环境监控与预警, 2020, 12(1):16-19.
[8] Kong M, Meng X L, Gao J, et al. Remote sensing monitoring and assessment for human activities of Shandong provincial nature reserves[J]. Environmental Monitoring and Forewarning, 2020, 12(1):16-19.
[9] 何柏华, 张晓勉, 薛晓坡, 等. 自然保护区人类活动遥感监测效果分析——以广西为例[J]. 安徽林业科技, 2020, 46(3):3-8.
[9] He B H, Zhang X M, Xue X P, et al. Study on the present situation and management countermeasures of Dongzhaigang national nature reserve[J]. Anhui Forestry Science and Technology, 2020, 46(3):3-8.
[10] 赵玉灵, 杨金中, 殷亚秋, 等. 海南岛东部滨海锆钛砂矿开发状况遥感监测与生态恢复治理对策研究[J]. 国土资源遥感, 2019, 31(4):143-150.doi:10.6046/zyzyyg.2019.04.19.
[10] Zhao Y L, Yang J Z, Yin Y Q, et al. Research on remote sensing monitoring of zirconium-titanium sand mine exploitation and strategies of ecological restoration on the eastern beach of Hainan Island[J]. Remote Sensing for Land and Resources, 2019, 31(4):143-150.doi:10.6046/zyzyyg.2019.04.19.
[11] 汪洁, 殷亚秋, 于航, 等. 基于RS和GIS的浙江省矿山地质环境遥感监测[J]. 国土资源遥感, 2020, 32(1):232-236.doi:10.6046/zyzyyg.2020.01.31.
[11] Wang J, Yin Y Q, Yu H, et al. Remote sensing monitoring of mine geological environment in Zhejiang Province based on RS and GIS[J]. Remote Sensing for Land and Resources, 2020, 32(1):232-236.doi:10.6046/zyzyyg.2020.01.31.
[12] 杨金中, 邢宇, 赵玉灵, 等. 资源环境遥感监测图集[M]. 北京: 地质出版社, 2022.
[12] Yang J Z, Xing Y, Zhao Y L, et al. Remote sensing monitoring atlas of resources and environment[M]. Beijing: Geological Publishing House, 2022.
[13] 吴瑞, 王道儒. 东寨港国家级自然保护区现状与管理对策研究[J]. 海洋开发与管理, 2013, 25(8):73-75.
[13] Wu R, Wang D R. Study on the present situation and management strategy of Dongzhaigang National Nature Reserve[J]. Ocean Development and Management, 2013, 25(8):73-75.
[14] 林雪云. 浅谈东寨港保护区的资源保护及管理对策[J]. 热带林业, 2019, 47(4):62-65.
[14] Lin X Y. Discussion on resource protection and management countermeasures of Dongzhaigang National Reserve[J]. Topical Forestry, 2019, 47(4):62-65.
[15] 权佳, 金羽, 徐卫华, 等. 铜鼓岭国家级自然保护区管理问题与对策研究[J]. 生态经济, 2009(4):104-107.
[15] Quan J, Jin Y, Xu W H, et al. Problems and countermeasures on management of Tongguling Nature Reserves[J]. Ecological Economy, 2009(4):104-107.
[16] 桑潇, 国巧真, 乔悦, 等. 基于多源数据的山西省长治市宜居性研究[J]. 国土资源遥感, 2020, 32(3):200-207.doi:10.6046/gtzyyg.2020.03.26.
[16] Sang X, Guo Q Z, Qiao Y, et al. Research on livability in Changzhi City of Shanxi Province based on multi-source data[J]. Remote Sensing for Land and Resources, 2020, 32(3):200-207.doi:10.6046/gtzyyg.2020.03.26.
[17] 赵龙贤, 代晶晶, 赵元艺, 等. 基于RS和GIS技术的西藏多龙矿集区矿山选址研究[J]. 国土资源遥感, 2021, 33(2):182-191.doi:10.6046/gtzyyg.2020200.
[17] Zhao L X, Dai J J, Zhao Y Y, et al. A study of mine site selection of the Duolong ore concentration area in Tibet based on RS and GIS technology[J]. Remote Sensing for Land and Resources, 2021, 33(2):182-191.doi:10.6046/gtzyyg.2020200.
[18] 姚昆, 张存杰, 何磊, 等. 雅砻江中上游流域生态环境脆弱性动态评价及预测[J]. 国土资源遥感, 2020, 32(4):199-208.doi:10.6046/gtzyyg.2020.04.25.
[18] Yao K, Zhang C J, He L, et al. Dynamic evaluation and prediction of ecological environment vulnerability in the middle-upper reaches of the Yalong River[J]. Remote Sensing for Land and Resources, 2020, 32(4):199-208.doi:10.6046/gtzyyg.2020.04.25.
[19] 宋晓龙, 李晓文, 白军红, 等. 黄河三角洲国家级自然保护区生态敏感性评价[J]. 生态学报, 2009, 29(9):4836-4846.
[19] Song X L, Li X W, Bai J H, et al. The ecological sensitivity evaluation in Yellow River Delta National Natural Reserve[J]. Acta Ecologica Sinica, 2009, 29(9):4836-4846.
[20] 崔宁, 于恩逸, 李爽, 等. 基于生态系统敏感性与生态功能重要性的高原湖泊分区保护研究——以达里湖流域为例[J]. 生态学报, 2021, 41(3):949-958.
[20] Cui N, Yu E Y, Li S, et al. Protection measures of plateau lake based on ecosystem sensitivity and importance of ecosystem function:The case of Lake Dalinor Basin[J]. Acta Ecologica Sinica, 2021, 41(3):949-958.
[21] 郭金玉, 张忠彬, 孙庆云. 层次分析法的研究与应用[J]. 中国安全科学学报, 2008, 18(5):148-153.
[21] Guo J Y, Zhang Z B, Sun Q Y. Study and application of analytic hierarchy process[J]. China Safety Science Journal, 2008, 18(5):148-153.
[22] 兰继斌, 徐扬, 霍良安, 等. 模糊层次分析法权重研究[J]. 系统工程理论与实践, 2006, 26(9):107-112.
doi: 10.12011/1000-6788(2006)9-107
[22] Lan J B, Xu Y, Huo L A, et al. Research on the priorities of fuzzy analytical hierarchy process[J]. Systems Engineering-Theory & Practice, 2006, 26(9):107-112.
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