Assessment and Geospatial Mapping of Soil Organic Carbon in Sirsi Forest Division of Uttara Kannada District Using Remote Sensing Techniques (Record no. 70739)

MARC details
000 -LEADER
fixed length control field 02795nam a2200217 4500
003 - CONTROL NUMBER IDENTIFIER
control field OSt
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20250124184358.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 250124b |||||||| |||| 00| 0 eng d
040 ## - CATALOGING SOURCE
Transcribing agency UAS Dharwad
041 ## - LANGUAGE CODE
Language code English
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 634.9
Author Label AMA
100 ## - MAIN ENTRY--PERSONAL NAME
Name of Author Amarnath A. T.
245 ## - TITLE STATEMENT
Title Assessment and Geospatial Mapping of Soil Organic Carbon in Sirsi Forest Division of Uttara Kannada District Using Remote Sensing Techniques
250 ## - EDITION STATEMENT
Edition Statement M.Sc. (Forest)
260 ## - PUBLICATION, DISTRIBUTION, ETC.
Place of Publisher Dharwad
Name of Publisher University of Agricultural Sciences
Publication Year 2024
300 ## - PHYSICAL DESCRIPTION
Book Pages 73
Book Size 32 Cms
520 ## - SUMMARY, ETC.
Abstract. ABSTRACT<br/><br/> Understanding soil organic carbon (SOC) dynamics is crucial for evaluating forest ecosystems' carbon sequestration potential and overall soil health. This study focuses on the Sirsi forest division, comprising three forest types: evergreen, semi-evergreen and moist deciduous forests. It aims to assess the SOC content, bulk density and SOC stock and to geospatially map SOC stock at three soil depths (0-30, 30-60 and 0-60 cm). Ten replicated composite soil samples were collected from each forest type in two depths to provide a comprehensive analysis.<br/>Results revealed that Evergreen forests recorded the highest SOC content (2.273 %), whereas moist deciduous forests exhibited the lowest SOC (1.87 %). The top soil (0-30 cm) showed the highest SOC (2.23 %) compared 30-60 cm soil depth. It also revealed substantial differences in bulk density among forest types and soil depths. Evergreen forests had the lowest bulk density (1.005 Mg m-3), whereas moist deciduous forests showed the highest bulk density (1.093 Mg m-3). In terms of soil depth, the 0-30 cm had a significantly lower bulk density (1.007 Mg m-3) than the 30-60 cm. Similarly, SOC stock was found to vary significantly among forest types and soil depths. Evergreen forests recorded the highest SOC stock (68.03 Mg ha-1), while moist deciduous forests had the lowest (60.824 Mg ha-1). Among soil depths, the 0-30 cm showed a significantly higher SOC stock (67.15 Mg ha-1) compared to the 30-60 cm depth. The study used Random Forest (RF) model to predict soil organic carbon (SOC) stock across different soil depths. For the 0-30 cm soil depth, the predicted SOC stock ranging from 59.45 to 76.59 Mg ha-1, with an average of 68.02 Mg ha-1. At the 30-60 cm soil depth, SOC stock ranged from 53.4 to 71.87 Mg ha-1, averaging 62.63 Mg ha-1. For combined 0-60 cm, the total SOC stock ranged from 114.75 to 145.89 Mg ha-1, with a mean of 130.3 Mg ha-1. The SOC stock prediction maps demonstrate a notable trend of higher SOC content in the western parts of the study area compared to the eastern parts.<br/>
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Subject Forest Resource Management
700 ## - ADDED ENTRY--PERSONAL NAME
2nd Author, 3rd Author Dasar Gopal V.
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha Item type THESIS
Edition M.Sc. (Forest)
Classification part 634.9
Call number prefix AMA
Suppress in OPAC No
942 ## - ADDED ENTRY ELEMENTS (KOHA)
-- 634_900000000000000
999 ## -
-- 70739
-- 70739
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Home library Current library Date acquired Total checkouts Full call number Barcode Date last seen Copy number Price effective from Koha item type
    Dewey Decimal Classification     University of Agricultural Sciences, Dharwad University of Agricultural Sciences, Dharwad 16/10/2024   634.9/AMA T13972 24/01/2025 1 24/01/2025 THESIS