Modelling grass land carrying capacity from satellite remote sensing

Developments in Remote Sensing (RS) satellite technology have made it possible to apply RS products for agricultural purposes, including modelling grassland carrying capacity (CC) of grazing land. However, determining the grazing land CC using pixel based approach is relatively new. This study model...

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Main Authors: Zumo, I. M., Hashim, M., Hassan, N. D.
Format: Conference or Workshop Item
Language:English
Published: 2021
Subjects:
Online Access:http://eprints.utm.my/95993/1/IsaMuhammadZumo2021_ModellingGrassLandCarryingCapacity.pdf
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author Zumo, I. M.
Hashim, M.
Hassan, N. D.
author_facet Zumo, I. M.
Hashim, M.
Hassan, N. D.
author_sort Zumo, I. M.
collection ePrints
description Developments in Remote Sensing (RS) satellite technology have made it possible to apply RS products for agricultural purposes, including modelling grassland carrying capacity (CC) of grazing land. However, determining the grazing land CC using pixel based approach is relatively new. This study modelled CC using pixel based approach and later compare it with the convetional method. Sentinel 2A MSI, in-situ Grass Above-ground Biomass (GAB) of 30 sample points and livestock data were used for the modelling CC of Daware grazing land northeast Nigeria. The result indicate that the available grass in the grazing land can only support 2,377,419 goats/sheep for 6 months or 4909 cattle for 1 month. This indicates that the grazing land was over grazed. The result of this study shows areas of grass available for rotational grazing throughout the season, thereby contributing to accurate modelling of grazing lands in Savannah and similar eco system.
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spelling utm.eprints-959932022-07-01T07:52:01Z http://eprints.utm.my/95993/ Modelling grass land carrying capacity from satellite remote sensing Zumo, I. M. Hashim, M. Hassan, N. D. G70.39-70.6 Remote sensing Developments in Remote Sensing (RS) satellite technology have made it possible to apply RS products for agricultural purposes, including modelling grassland carrying capacity (CC) of grazing land. However, determining the grazing land CC using pixel based approach is relatively new. This study modelled CC using pixel based approach and later compare it with the convetional method. Sentinel 2A MSI, in-situ Grass Above-ground Biomass (GAB) of 30 sample points and livestock data were used for the modelling CC of Daware grazing land northeast Nigeria. The result indicate that the available grass in the grazing land can only support 2,377,419 goats/sheep for 6 months or 4909 cattle for 1 month. This indicates that the grazing land was over grazed. The result of this study shows areas of grass available for rotational grazing throughout the season, thereby contributing to accurate modelling of grazing lands in Savannah and similar eco system. 2021 Conference or Workshop Item PeerReviewed application/pdf en http://eprints.utm.my/95993/1/IsaMuhammadZumo2021_ModellingGrassLandCarryingCapacity.pdf Zumo, I. M. and Hashim, M. and Hassan, N. D. (2021) Modelling grass land carrying capacity from satellite remote sensing. In: 7th International Conference on Geomatics and Geospatial Technology, GGT 2021, 23 March 2021 - 24 March 2021, Shah Alam, Virtual. http://dx.doi.org/10.1088/1755-1315/767/1/012044
spellingShingle G70.39-70.6 Remote sensing
Zumo, I. M.
Hashim, M.
Hassan, N. D.
Modelling grass land carrying capacity from satellite remote sensing
title Modelling grass land carrying capacity from satellite remote sensing
title_full Modelling grass land carrying capacity from satellite remote sensing
title_fullStr Modelling grass land carrying capacity from satellite remote sensing
title_full_unstemmed Modelling grass land carrying capacity from satellite remote sensing
title_short Modelling grass land carrying capacity from satellite remote sensing
title_sort modelling grass land carrying capacity from satellite remote sensing
topic G70.39-70.6 Remote sensing
url http://eprints.utm.my/95993/1/IsaMuhammadZumo2021_ModellingGrassLandCarryingCapacity.pdf
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