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|Table of Contents|

基于GF-1遥感数据的大兴安岭林区雪水当量反演(PDF/HTML)

《南京林业大学学报(自然科学版)》[ISSN:1000-2006/CN:32-1161/S]

Issue:
2017年03期
Page:
105-111
Column:
研究论文
publishdate:
2017-05-31

Article Info:/Info

Title:
Comparison between GF-1 data and Landsat-8 data in inversing SWE of the forest in the northern of Daxing’an Mountain
Article ID:
1000-2006(2017)03-0105-07
Author(s):
YU Zhengxiang1 HAN Guangzhong2 CAI Tijiu1* JU Cunyong1 ZHU Binbin1
1. School of Forestry, Northeast Forestry University, Harbin 150040, China;
2. Songling Forestry Bureau of Daxing’an Mountain, Songling 165012, China
Keywords:
snow water equivalent remote sense data GF-1 Landsat-8 northern Daxing’an Mountain
Classification number :
S715-3
DOI:
10.3969/j.issn.1000-2006.201605013
Document Code:
-
Abstract:
【Objective】 With a large number of independently developed satellites launched, China has made tremendous advancements in remote sensing. The successful launch of the GF-1 satellite indicates that the observation of earth has entered the “high-resolution epoch” in China. This study was conducted to monitor the snow hydrological process of forests based on data from this Chinese satellite. 【Methods】The accuracy of forest snow characteristics based on the Chinese GF-1 satellite data was estimated by comparison with Landsat-8 satellite data. The research site was the forest of the northern Daxing’an Mountains. Based on the two methods of PLS(partial least squares)and BP(back propagation)neural networks, the linear and nonlinear SWE(snow water equivalent)inversion models were built using synchronous ground SWE field observation data. The models were evaluated through the indicators of E-RMSE, r-RMSE and average estimation accuracy. The best models based on the two types of remote sensing data were used to determine the inverse of the distribution characteristics of the SWE in the study area.【Results】The results showed that the accuracy of the models established using GF-1 data was slightly lower than that of those established using Landsat-8 data. The highest accuracy of a GF-1 model was 80.3%, which was 1.6% lower than that of the equivalent Landsat-8 model. The inverse SWE values based on the data of GM-1 were little higher than that of Landsat-8. The inversed SWE from the two types of remote sensing data showed the same spatial distributions, which showed that the SWE is highly correlated with topography, vegetation and land use type. Both terrain and vegetation are complicated in mountain forests. Snow ablation is relatively fast in spring. Owing to a higher spatial and time resolution than that of Landsat-8, the GF-1 satellite is more efficient for monitoring snow hydrological processes. 【Conclusion】Our results indicated that data from the GF-1 satellite can replace Landsat-8 data in monitoring snow hydrological processes in the northern Daxing’an Mountains.

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Last Update: 2017-05-20