Logo Lanfrica
  • Accueil
  • Atlas
  • Analyses
  • Documentation
  • Sign in

© 2026 Lanfrica. Tous droits réservés. Tous les droits d'auteur des ressources affichées sur le site Web Lanfrica appartiennent aux détenteurs de droits d'auteur d'origine, sauf indication contraire explicite.

Quantifying post logging biomass loss using satellite images and ground measurements in Southeast Cameroon

Domaine:

environment and energygeospatial

Type de record:

paperdataset
Créateur:
SufSonEbaMoa
Éditeur:
EspCenUni
Éditeur:
CCSDSpringer-Verlag
Hôte:avatar
International audience Forest logging in the Congo Basin has led to forest fragmentation due to logging infrastructures and felling gaps. In the same vein, forest concessions in the Congo Basin have increasing interest in the REDD+ mechanism. However, there is little information or field data on carbon emissions from forest degradation caused by logging. To help fill this gap, Landsat 7 and 8 and SPOT 4 images of the East Region of Cameroon were processed and combined with field measurements (measurement of forest roads widths, felling gaps and log yards) to assess all disturbed areas. Also, measurements of different types of forest infrastructures helped to highlight emission factors. Forest contributes to 5.18 % of the degradation of the annual allowable cut (AAC) (84.53 ha) corresponding to 4.09 % of forest carbon stock (6.92 t ha−1). Felling gaps constitute the primary source of degradation, represented an estimated area of 32.41 ha (2 % of the cutting area) far ahead of primary roads (18.44 ha) and skid trails (16.36 ha). Assessment of the impact of degradation under the canopy requires the use of high resolution satellite images and field surveys.

Visit

shs.hal.science

Tags

Forest loggingEastern CameroonCarbonSatellite imagesREDD+[SHS]Humanities and Social Sciences[SHS.ENVIR]Humanities and Social Sciences/Environmental studies[SHS.GEO]Humanities and Social Sciences/Geography

Similaires

Transport and Deposition of Saharan Dust Observed from Satellite Images and Ground MeasurementsUSING LEARNING MACHINES TO CREATE SOLAR RADIATION MAPS FROM NUMERICAL WEATHER PREDICTION MODELS, GROUND MEASUREMENTS AND SATELLITE IMAGESBias Adjustment of Four Satellite-Based Rainfall Products Using Ground-Based Measurements over SudanEstimation of aboveground biomass in East region of Cameroon from satellite data using quaternion-based texture analysis of multi chromatic images Assessing Forest Structure and Biomass Loss in Mount Cameroon National Park Using Remote Sensing and Machine LearningLinear vs non-linear learning methods A comparative study for forest above ground biomass, estimation from texture analysis of satellite images

Transport and Deposition of Saharan Dust Observed from Satellite Images and Ground Measurements

Haboob occurrence strongly impacts the annual variability of airborne desert dust in North Africa. I

USING LEARNING MACHINES TO CREATE SOLAR RADIATION MAPS FROM NUMERICAL WEATHER PREDICTION MODELS, GROUND MEASUREMENTS AND SATELLITE IMAGES

International audience This paper describes the first steps given in order to develop

Bias Adjustment of Four Satellite-Based Rainfall Products Using Ground-Based Measurements over Sudan

Satellite-based rainfall estimates (SREs) represent a promising alternative dataset for climate and

Estimation of aboveground biomass in East region of Cameroon from satellite data using quaternion-based texture analysis of multi chromatic images

International audience In recent years, first approaches using quaternion numbers to

Assessing Forest Structure and Biomass Loss in Mount Cameroon National Park Using Remote Sensing and Machine Learning

International audience Tropical montane forests of Cameroon represent disproportionat

Linear vs non-linear learning methods A comparative study for forest above ground biomass, estimation from texture analysis of satellite images

International audience The aboveground biomass estimation is an important question in