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题名: 基于植被指数的大熊猫栖息地动态监测评价研究
作者: 袁秀
学位类别: 博士后
答辩日期: 2015-09
授予单位: 中国科学院研究生院
授予地点: 北京
导师: 陈利顶 ; 马克明
关键词: 大熊猫栖息地、NDVI、时间序列、基于像元的变化监测、原因识别,Giant panda habitats, NDVI, time series, pixel based change detection, identify disturbances
其他题名: Using vegetaion index to monitoring and assessment the habitat of Giant Panda
学位专业: 环境科学与工程
中文摘要:       栖息地退化一直是大熊猫保护面临的重要威胁。快速评估其栖息地变化过程及成因,提出系统性、针对性的保护管理需求,是及时并长期有效保护大熊猫栖息地的关键。本文利用2000-2014 年基于像元的植被归一化植被指数(NDVI)及其衍生值宽域植被指数(WDRVI)时间序列值,对大熊猫分布区的变化地点、变化时间、变化原因进行了监测,评价导致栖息地植被变化的各因素后果。
      应用BFAST 模型和Mann-kendll 检验探测每个像元的植被指数时间序列值变化趋势及其显著性;根据像元植被指数时间序列的变化类型(匀速、断点、组成)、大小(变化的幅度、变化前后值大小)、趋势(变化前后植被盖度趋势),判断变化原因。结果显示:(1)大熊猫分布区植被盖度变化的地区共99.6 万公顷,其中植被盖度减少的地区57.2 万公顷,增加的42.3 万公顷。植被变化地区占变化栖息地比例最大(86.2%),断点变化占变化栖息地的28.3%,组成变化占变化栖息地的0.3%。植被盖度减少较多的市县主要包括:汶川、宝兴、峨边、天全、彭州等。(2)大熊猫栖息地内植被自然波动的面积较大,而导致植被突然减少的主要原因包括地震引起的滑坡、竹子开花、自然扰动、水电站建设、采矿、道路建设等。(3)大多数地震和竹子开花地区植被盖度在减少后都能自然恢复;地震引起滑坡、采矿、水电站建设引起植被盖度减少到0.4 左右地区植被恢复困难,需要人工保护和恢复干预;水电站建设、采矿、道路建设过程中的永久占地无法恢复。(4)植被盖度自然恢复过程中NDVI 值年增加幅度大多在0.01-0.015 间,每年植被增长幅度主要由植被发生增长前植被盖度、植被受干扰前植被盖度、干扰因素引起植被盖度减少幅度决定。
      基于植被指数时间序列建立一套经济、快速、客观、大范围监测大熊猫栖息地变化的有效方法,可为大熊猫栖息地保护和恢复提供科学依据。
英文摘要:       The degradation of habitats were main threats of giant panda's protection for a long
time. Monitoring the changes of giant panda's habitats in time, provide scientific management recommendations, it's the keys to protection the habitas of giant pands.
Using the 2000-2014 time series data of Normalized Difference Vegetation Index (NDVI) and the Wide Dynamic Range Vegetation Index(WDRVI)which derived from NDVI, monitoring the change of giant panda's habitat, including the distribution, time and cause of change, assessment the results of changes.
      Breaks For Additive Seasonal and Trend (BFAST) model and Mann-kendll test were used to detect the change of time series vegetation index for each pixelTrend of the time series data, amount and extent of the time series data changed were used to assessment the area which improved or degraded, and also were used to assessment the effects of factors which have influenced the vegetation cover. The results show that: (1) 996 000 ha Giant Panda's habitat vegetation cover have significant changed, in which there are 572 000 ha vegetation cover trend were in decreasing trend, 423 000 ha habitat vegetation cover trend is in increasing. 86.2% of signicant changed habitat shows that the overall trend of vegetation cover were signicant changed, 28.3% of signicant changed habitat shows that the trend of vegetation cover have abrupt break points, 0.3% of significantly changed habitat shows that the composition of vegetation have changed. Most vegetation cover of habitat in Wenchuan, Baoxing, Ebianyisuzizhi, Tianquan, Pengzhou city shows decreasing. (2)The main reasons influnce the vegetation cover are those factors effect the vegetation cover gradually, include those factors improve the vegetation cover and wreck the vegetation cover.
Most of the habitat influced by natural disturbances. The main reasons make the vegetation cover abrupt changed were earthquake, bamboo flowering, natural disturbances, hydropower construction, mining, road construction. (3) Most area will recovery after nature disturbance, such as earthquake and bamboo flowering; but the habitats which NDVI value was decreased to 0.4 after damage by eathquake, mining and hydropower construction, were very diffult to recovery by itself, need resotration method; the area which habitat was change to other landuse can not recovery. (4) The NDVI value will increase about 0.01-0.015 each year during the recovery period, the amount of NDVI value increased was related to vegetation cover before decreasing, vegetation cover before increasing, vegetation cover decreased. Based on vegetation time series data, a method can economic, fast, objective,
larg-scale monitoring habitat of gaint panda was build, this could be helpful to protected the Giant Panda scientificly.
内容类型: 学位论文
URI标识: http://ir.rcees.ac.cn/handle/311016/34460
Appears in Collections:城市与区域生态国家重点实验室_学位论文

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Recommended Citation:
袁秀. 基于植被指数的大熊猫栖息地动态监测评价研究[D]. 北京. 中国科学院研究生院. 2015.
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