کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
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4460015 | 1621310 | 2009 | 14 صفحه PDF | دانلود رایگان |
![عکس صفحه اول مقاله: Integrating spectral indices into prediction models of soil phosphorus in a subtropical wetland Integrating spectral indices into prediction models of soil phosphorus in a subtropical wetland](/preview/png/4460015.png)
Remote sensing, in combination with multivariate geostatistical methods, has the potential to improve the prediction of soil properties at landscape scales. In the Everglades region, and particularly in Water Conservation Area 2A (WCA-2A), phosphorus enrichment has drawn a lot of attention and has led to an extensive documentation of different aspects of the degradation of the system. This study presents a hybrid geospatial modeling approach to predict soil total phosphorus (TP) using remotely-sensed data and ancillary landscape properties as supporting variables. Two remote sensors, Landsat 7 Enhanced Thematic Mapper (ETM)+ and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), were used to investigate relationships between spectral data and indices and soil TP. A variation of a vegetation index (Normalized Difference Vegetation Index – NDVI green) was found to be the most effective in predicting floc TP values, due to its capacity to capture small variations in chlorophyll a that are associated to TP levels in periphyton, especially in aquatic/non-impacted areas. On the other hand, NDVI, a more traditionally used vegetation index, was still a good indicator of TP variability, particularly in the soil surface layer, due to its stronger relationship with impacted areas dominated by cattail (Typha domingensis Pers.).Findings from this study indicate that: a) remote sensing can play an important role in optimizing monitoring of environmental variables, particularly below-ground properties of floc and soils; b) because of limitations about the numbers and frequency of soil samples that can be taken, the combination of remote sensing and geostatistics could represent a non-invasive and cost-effective method to monitor soil nutrient status in complex wetland systems, and c) variations of traditional remote sensing indices such as NDVI can be used to better capture the spatial variability associated with soil and periphyton TP.
Journal: Remote Sensing of Environment - Volume 113, Issue 11, 16 November 2009, Pages 2389–2402