Logo Lanfrica
  • Home
  • Atlas
  • Insights
  • Docs
  • Sign in

© 2026 Lanfrica. All rights reserved. All copyrights of the resources shown on the Lanfrica website belong to the original copyright holders, unless explicitly stated otherwise.

Vegetation structure and greenness in Central Africa from Modis multi-temporal data.

Domain:

geospatialenvironment and energy

Record type:

paperdataset
Creator:
GonFayolle, AdelinePenCornu, Guillaume
Editor:
BieGemSysJRC
Publisher:
CCSDRoy
Host:avatar
8 pages International audience African forests within the Congo Basin are generally mapped at a regional scale as broad-leaved evergreen forests, with the main distinction being between terra-firme and swamp forest types. At the same time, commercial forest inventories, as well as national maps, have highlighted a strong spatial heterogeneity of forest types. A detailed vegetation map generated using consistent methods is needed to inform decision makers about spatial forest organization and their relationships with environmental drivers in the context of global change. We propose a multi-temporal remotely sensed data approach to characterize vegetation types using vegetation index annual profiles. The classifications identified 22 vegetation types (six savannas, two swamp forests, 14 forest types) improving existing vegetation maps. Among forest types, we showed strong variations in stand structure and deciduousness, identifying (i) two blocks of dense evergreen forests located in the western part of the study area and in the central part on sandy soils; (ii) semi-deciduous forests are located in the Sangha River interval which has experienced past fragmentation and human activities. For all vegetation types enhanced vegetation index profiles were highly seasonal and strongly correlated to rainfall and to a lesser extent, to light regimes. These results are of importance to predict spatial variations of carbon stocks and fluxes, because evergreen/deciduous forests (i) have contrasted annual dynamics of photosynthetic activity and foliar water content and (ii) differ in community dynamics and ecosystem processes.

Visit

hal.science

Languages

Dogon, Toro So

Tags

tropical rainforestCentral Africaremote sensing[SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces, environment[SDE.MCG]Environmental Sciences/Global Changes[SDE]Environmental Sciences

Similar

Thermal modelling of Africa from multi-temporal MODIS LSΤ imagery.Spatio-Temporal Patterns of Drought and Impact on Vegetation in North and West Africa Based on Multi-Satellite DataLearning to forecast vegetation greenness at fine resolution over Africa with ConvLSTMsWoody vegetation cover monitoring with multi-temporal Landsat data and Random Forests: the case of the Northwest Province (South Africa)Data from MODIS images classificationA deep-learning approach for multi-temporal savannah woody vegetation density assessment with Earth Observation data

Thermal modelling of Africa from multi-temporal MODIS LSΤ imagery.

Papasotirakopoulos S., Miliaresis G., Tsatsaris A., 2010. Thermal modelling of Africa from multi-

Spatio-Temporal Patterns of Drought and Impact on Vegetation in North and West Africa Based on Multi-Satellite Data

Studying the significant impacts of drought on vegetation is crucial to understand its dynamics and

Learning to forecast vegetation greenness at fine resolution over Africa with ConvLSTMs

Forecasting the state of vegetation in response to climate and weather events is a major challenge.

Woody vegetation cover monitoring with multi-temporal Landsat data and Random Forests: the case of the Northwest Province (South Africa)

Land degradation and desertification (LDD) are serious global threats to humans and the environment.

Data from MODIS images classification

This data is related to MODIS images classification in Ghana (Guinea-savannah and Forest-savannah)

A deep-learning approach for multi-temporal savannah woody vegetation density assessment with Earth Observation data

Bush encroachment in African savannahs has been identified as a land degradation process, mainly due