Estimation of accumulated soil organic carbon stock in tropical forest using geospatial strategy

被引:48
|
作者
Kumar, Pavan [1 ]
Pandey, Prem Chandra [2 ]
Singh, B. K. [3 ]
Katiyar, Swati [4 ]
Mandal, V. P. [5 ]
Rani, Meenu [5 ]
Tomar, Vandana [6 ]
Patairiya, Shashikanta [7 ]
机构
[1] Kumaun Univ, Dept Remote Sensing & GIS, Almora, Uttarakhand, India
[2] Univ Leicester, CLCR, Dept Geog, Leicester LE1 7RH, Leics, England
[3] BIT Extens Ctr, Remote Sensing Div, Jaipur, Rajasthan, India
[4] Banasthali Univ, Dept Remote Sensing, Newai, Rajasthan, India
[5] Indian Inst Farming Syst Res, Meerut, Uttar Pradesh, India
[6] Haryana Inst Publ Adm, Gurgaon, Haryana, India
[7] Haryana Space Applicat Ctr, Hisar, Haryana, India
关键词
Soil organic carbon; Geographical science; Remote sensing; Indices; NDVI; bare soil index; Digital soil carbon map; Regression analysis;
D O I
10.1016/j.ejrs.2015.12.003
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Soil organic carbon (SOC) is a dynamic soil property that represents the key component of the forest ecosystems. The main objective of the present study is to evaluate SOC using the remote sensing images as well as field methods at Ranthambhore Tiger Reserve Forest area. The soil samples were collected randomly from the region at several field locations, to estimate the surface soil carbon concentrations in the laboratory. The study derived results for bare soil index, NDVI, SOC and relationship of SOC with NDVI using regression analysis, while comparing reference SOC (field measured SOC) and predicted SOC (estimated from satellite image). The remote sensing images were used to predict the precise carbon content associated with organic matter in the soil using NDVI and related equations, to prepare digital soil organic carbon map. The relationship between the NDVI and both reference/predicted SOC is established using the equation to derive the digital SOC for the study area using remote sensing data. The statistical relationship between reference SOC, pH concentrations, and NDVI values were presented against the predicted SOC to provide the variation between each variable. (C) 2015 National Authority for Remote Sensing and Space Sciences. Production and hosting by Elsevier B.V.
引用
收藏
页码:109 / 123
页数:15
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