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方天纵,秦朋遥,王黎明,李晓松.高时空分辨率植被覆盖获取方法及其在土壤侵蚀监测中的应用.生态学报,2019,39(15):5679~5689 本文二维码信息
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高时空分辨率植被覆盖获取方法及其在土壤侵蚀监测中的应用
High temporal-and spatial-resolution green vegetation coverage generation and its application in soil erosion monitoring
投稿时间:2018-06-11  修订日期:2019-03-21
DOI: 10.5846/stxb201806111302
关键词高空间分辨率  土壤侵蚀  绿色植被覆盖度  CSLE
Key Wordshigh spatial resolution  soil erosion  green vegetation coverage  CSLE
基金项目国家重点研发计划(2016YFC0500806);国家自然科学基金项目(41571421)
作者单位E-mail
方天纵 天津市水务局, 天津 300074  
秦朋遥 中国林业科学研究院资源信息研究所, 北京 100091
中国科学院遥感与数字地球研究所数字地球重点实验室, 北京 100094 
 
王黎明 天津市蓟州区水务局, 天津 301900  
李晓松 中国科学院遥感与数字地球研究所数字地球重点实验室, 北京 100094 lixs@radi.ac.cn 
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摘要:
土壤侵蚀是全球性生态问题,准确监测区域土壤侵蚀状况是评估区域生态质量和生态保护成效的基础。准确获取高时空分辨率植被覆盖信息并与降水动态匹配是土壤侵蚀准确监测的关键。然而,受卫星传感器限制,大区域高时间分辨率与高空间分辨率遥感数据无法同时获取,高空间分辨率植被动态遥感监测面临巨大挑战。为解决这一问题,本研究提出了一套多源遥感数据融合的高时空分辨率绿色植被覆盖度(半月尺度,空间分辨率2 m)获取方法,并与半月尺度的降水因子匹配应用于CSLE开展了天津市蓟州区的土壤侵蚀监测。研究结果表明:1)降雨和植被覆盖度因子在一年之内变异较大,半月降雨量的平均值为43.32 mm,变异系数可达150%,绿色植被半月植被覆盖度的平均值为54.74%,变异系数为18%。考虑土地覆盖类型的高时空分辨率绿色植被覆盖度融合方法,可以获取合理的高空间分辨率绿色植被覆盖度动态,为高空间分辨率土壤侵蚀监测提供了一个有效手段;2)土壤侵蚀发生范围与强度与降水及植被因子在年内的动态匹配高度相关,土壤侵蚀发生范围最大为10月上半月,发生面积为137.55 km2,土壤侵蚀发生强度最为严重为7月下半月,25 t/hm2以上土壤侵蚀发生面积为12.70 km2;3)高时空分辨率植被与降水因子耦合下的土壤侵蚀监测结果与地面一致性较好(判定系数可达0.88),明显好于仅用一期高空间分辨率植被因子的土壤侵蚀监测结果(判定系数仅为0.097),采用高时空分辨率植被与降水因子耦合的土壤侵蚀监测方法可以大幅度提高土壤侵蚀监测的准确性,本研究为其他区域准确开展土壤侵蚀监测提供了一套有效的方法。
Abstract:
Soil erosion is a global ecological problem. Its accurate monitoring is necessary for safeguarding regional ecological safety and assessing ecological protection effectiveness. Accurately obtaining high temporal-resolution vegetation coverage information and matching it with precipitation dynamics plays a critical role in accurate soil erosion monitoring. However, limited by satellite sensors, large-area remote sensing data with both high temporal-resolution and high spatial-resolution cannot be acquired at the same time. To solve this problem, this study proposes an approach to acquire green vegetation coverage with high spatial-temporal resolution (semi-month scale, 2 m spatial resolution) based on the fusion of multi-source remote sensing data. Then, the temporal-series green vegetation coverage dataset is utilized in Chinese Soil Loss Equation (CSLE) through matching with the semi-month precipitation factor to evaluate its effectiveness. Rainfall and vegetation coverage factors were highly variable within a year. The average value of the semi-month rainfall was 43.32 mm, and the coefficient of variation was 150%. The mean green vegetation coverage was 54.74%, and the coefficient of variation was 54.74%. The fusion approach of high spatial and temporal resolution data is feasible to obtain vegetation dynamics at high spatial resolution, which provides an effective means for soil erosion monitoring. Precipitation and vegetation factors are highly dynamic during the year, and the range and intensity of soil erosion were highly correlated with these dynamic factors. CSLE using vegetation and precipitation factors with high temporal and spatial resolution can better reflect the intensity of soil erosion in the study area. The coefficient of determination between CSLE estimations and field observations of soil erosion can reach 0.88, whereas it is only 0.097 between CSLE estimations using static vegetation coverage and field observations. Our results suggest that incorporating high temporal and spatial resolution green vegetation coverage could improve soil erosion monitoring accuracy.
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