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首页> 外文学位 >Soil erosion risk modeling within upland landscapes in Vietnam using remotely sensed data and the RUSLE model.
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Soil erosion risk modeling within upland landscapes in Vietnam using remotely sensed data and the RUSLE model.

机译：使用遥感数据和RUSLE模型对越南高地景观中的土壤侵蚀风险进行建模。

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摘要

Huong Tra is one of the mountainous districts of Thua Thien Hue province, Vietnam, located between two large rivers which ultimately flow into the Tam Giang Lagoon, the largest Lagoon in Southeast Asia with an area of over 220 km2. Water erosion creates negative impacts on agricultural production, aquaculture, infrastructure, and water quality throughout the region. The Revised Universal Soil Loss Equation (RUSLE) is a well tested model for erosion prediction at the field scale. The integration of the RUSLE and remotely sensed data provide a useful tool for regional erosion risk assessment. The aim of this research is to assess soil erosion risks for upland landscapes using the RUSLE model and remotely sensed data within a GIS and to propose several land use scenarios which are able to reduce soil losses. Each erosion factor of the RUSLE model was computed. The Tropical Rainfall Measurement Mission (TRMM) data, provided by point scale, was examined to calculate rainfall and runoff erosivity (R) factors. The monthly TRMM rainfall data and measured rainfall were significantly correlated with a regression correlation coefficient of approximately 0.9. Annual R values range from 960 to 1033 MJ mm ha -1 hr-1, whereas the R values of the wet season are double that of the dry season. Additionally, the TRMM data describe the spatial variation of rainfall in the region better than measured rainfall at meteorological stations. The Digital Elevation Model (DEM) of Shuttle Radar Topographic Mission (STRM), NASA was validated with measured elevation values in official topographic maps. Using this DEM, the topographic LS factors were computed from slope and flow accumulation algorithms. Flow accumulation illustrates the impact of upslope contributing areas to sediment detachment and transportation, so it better reflects the effects of concentrated flow on increased erosion on sloping areas. Landsat ETM+ images were used to calculate C factors by using the Linear Spectral Mixture Analysis (LSMA) model which determines the proportion of each land use type within each pixel. The comparison between bare soil and erosion resistant covers (vegetation cover and non-photosynthetic materials) resulted in C factors for pixels. Soil erodibility factors were computed from readily available soil maps by using a soil erodibilty nomograph. Lastly, soil loss rates were computed for pixels, and erosion risk classes were identified by reclassification. Erosion risks on rice cultivation land were moderate, whereas they were quite high on dry crop, protection forest, and unused land types. The most severe erosion rates occurred on production forest land which had poor vegetation cover. The RUSLE model was also used for developing forest land planning in which the balance between low erosion land area (protection forest) and severe erosion land area (production forest) was taken into account. Erosion risk and slope maps are useful to identify and delineate the spatial allocation of protection forest. In summary, the integration of the RUSLE model and remotely sensed data provides an effective tool for assessing soil erosion risks and selecting appropriate land use scenarios which can reduce soil losses in a large scale.
机译：Huong Tra是越南Thua Thien Hue省的山区之一，位于两条大河之间，最终流入Tam Giang泻湖，Tan Giang泻湖是东南亚最大的泻湖，面积超过220 km2。水蚀对整个地区的农业生产，水产养殖，基础设施和水质产生负面影响。经修订的通用土壤流失方程（RUSLE）是一个经过良好测试的模型，可用于田间尺度的侵蚀预测。 RUSLE和遥感数据的集成为区域侵蚀风险评估提供了有用的工具。这项研究的目的是使用RUSLE模型和GIS中的遥感数据评估高地景观的土壤侵蚀风险，并提出几种能够减少土壤流失的土地利用方案。计算了RUSLE模型的每个腐蚀因子。检查了由点数刻度提供的热带雨量测量任务（TRMM）数据，以计算雨量和径流侵蚀力（R）因子。 TRMM月度降雨数据和实测降雨与回归相关系数约为0.9显着相关。年度R值范围为960至1033 MJ mm ha -1 hr-1，而雨季的R值是旱季的R值的两倍。此外，TRMM数据比气象站的实测降雨更好地描述了该地区降雨的空间变化。 NASA的航天飞机雷达地形任务（STRM）的数字高程模型（DEM）已通过官方地形图中测得的高程值进行了验证。使用此DEM，可根据坡度和流量累积算法计算地形LS因子。流量积聚说明了上坡贡献区对沉积物分离和运输的影响，因此它更好地反映了集中流量对倾斜区侵蚀增加的影响。通过使用线性光谱混合分析（LSMA）模型，使用Landsat ETM +图像来计算C因子，该模型确定每个像素内每种土地使用类型的比例。裸露土壤和抗侵蚀覆盖层（植被覆盖层和非光合材料）之间的比较导致像素的C因子。土壤易蚀性因子是通过使用土壤易蚀性诺模图从易于获得的土壤图计算得出的。最后，以像素为单位计算土壤流失率，并通过重新分类确定侵蚀风险等级。水稻耕地的侵蚀风险中等，而旱作作物，防护林和未利用土地类型的侵蚀风险很高。最严重的侵蚀发生在植被覆盖差的生产林地上。 RUSLE模型还用于制定林地规划，其中考虑了低侵蚀土地面积（保护林）和严重侵蚀土地面积（生产林）之间的平衡。侵蚀风险和坡度图对于识别和描绘防护林的空间分配非常有用。综上所述，RUSLE模型与遥感数据的集成为评估土壤侵蚀风险和选择适当的土地利用情景提供了有效的工具，从而可以大规模减少土壤流失。

著录项

  • 作者

    Pham, Huu Ty.;

  • 作者单位

    Dalhousie University (Canada).;

  • 授予单位 Dalhousie University (Canada).;
  • 学科 Agriculture Soil Science.;Remote Sensing.
  • 学位 M.A.Sc.
  • 年度 2008
  • 页码 87 p.
  • 总页数 87
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 土壤学;遥感技术;
  • 关键词

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