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In Burkina Faso, within the Pô-Nazinga-Sissili ecological complex, known as PONASI in the south-central part of the country, rapid population growth and climatic variability lead to frequent, but generally unplanned, changes in land use. The present study assesses the landscape dynamics around Corridor 1 of the PONASI ecological complex based on the processing of Landsat images from 1996, 2006, 2016 and 2020, complemented by landscape ecology analysis tools. The results reveal that in 24 years (from 1996 to 2020), the areas of pasture, gallery forest and fallow land have regressed by 50 %, 40 % and 98 % respectively through fragmentation and suppression processes. Conversely, the fields have expanded in the landscape, their surface being multiplied by 11, as a consequence of the creation and aggregation of tasks. The landscape dynamics is thus materialized by the replacement of pastures, the matrix of the landscape until 2006, by the fields that constituted the new matrix of the landscape from 2016. Consequently, the anthropization of the landscape induced by agricultural development is illustrated by the level of landscape disturbance that has increased from 0.30 to 1.83, a consequence of the absence of accompanying measures of land use plans in a context of rapid population growth. Our observations imply the need to develop a participatory natural resource management policy taking into account the socio-economic context of the study area. [ABSTRACT FROM AUTHOR]

The objective of this study was to understand the need for the preservation of lemurs (V. v. subcincta and E. albifrons) and to provide an updated inventory of the situation and evolution of the dense forest of Nosy Mangabe from 1997 to 2017. The data obtained will be used to strengthen conservation strategies for the lemur population. The study was carried out from January 27th to September 19th 2017 and from January 28th to September 22nd 2017 in the Nosy Mangabe service area. For estimation of the density and abundance of the two species, the line transect method was used. The results obtained in the service area show a density of 5.62 ind/ha for Eulemur albifrons and a density of 2.87 ind/ha for V. v. subcincta. In the core part, the results show a density of 3.62 ind/ha for E. albifrons and a density of 1.25 ind/ha for V. v. subcincta. Land cover conversion over a period of 20 years was mapped using multi-date images from the Landsat satellite. The unsupervised classification method was used using ArcGis 10.4.1 software. A few GPS points were recorded for each type of land use to validate the cartographic results. The results obtained indicate that the dense forest lost an area of 1.47 km² (26%) between the year 1997 and the year 2017. Cyclones and tourism policy are the main natural causes threatening conservation. The landscape of the site is in full transformation and dominated by dense degraded forest. [ABSTRACT FROM AUTHOR]

Context and background: In Cameroon, the pressure on wetlands, which cover nearly 70% of the national territory, appears to be increasing, whether for subsistence needs, firewood, grazing, logging or expansion of development projects. Currently, in terms of land use, forest has decreased by 619 km² and cultivated land has increased by 321 km². The surface area of degraded forests and land is estimated at around 12 million hectares, with a general trend towards an increase in the phenomenon due to both natural and anthropogenic factors. It is obvious that approaches in favour of wetlands are widely engaged in the different basins of Cameroon. However, the management strategies for these areas are based on knowledge that is certainly extensive, but still patchy. There is a serious lack of overview of the location of degraded lands, the intensity of this degradation and the way farmers are addressing this problem through sustainable land management. Goal and objectives: The objective of this study is to characterize the dynamics of land cover from multi)date satellite data in the Noun floodplain of Cameroon. Methodology The methodology relies on the use of remote sensing and GIS to identify spatial units and detect changes over a twenty-two year period (1999 to 2021). The land cover maps were produced from an unsupervised classification with maximum likelihood. Results: The results identified eight classes: herbaceous savannahs with shrubs, forest galleries, fields and plantations, herbaceous tannas, young fallows, mineralized and built-up soils, bare soils and surface waters. It appears that in 1999, the landscape was dominated by natural vegetation (72.6%) located from north to south of the Noun plain. However, since 2004, the landscape has been dominated by agricultural areas (56.8%). Natural formations have been progressively reduced in space over time. The evolution of the Noun floodplain landscape reveals that 14.3% of the space has remained stable. These are fields and plantations, young fallows, mineralized soils and surface water. This space has not migrated to other classes. While about 73.9% of the area has moved to higher classes, of which 35.6% to herbaceous tannas and 26% to fields and plantations. On the other hand, 72.6% of the area (herbaceous savannahs and forests gallery) has been heavily degraded. These results indicate that the landscape of the Noun floodplain is marked by a high level of agricultural activity, which is at the origin of the progressive degradation of the land. They suggest an effective awareness of the level of land degradation and a better integrated management of land at the local level. [ABSTRACT FROM AUTHOR]

SIELETERS is an airborne hyperspectral imaging system that covers the mid- and long-wavelength infrared bands. It is part of the SYSIPHE system that covers bands from 0.4 μm to 11.8 μm. The ground sampling distance of the SIELETERS instrument is 0.5 m when 2000 m above the ground and the spectral resolution is between 6.7 cm-1 and 14.4 cm-1. In this article, we introduce the SYSIPHE project, the SIELETERS system and we show the latest results that we have obtained with the system. Our recent studies led to significant improvements on the quality of the hyperspectral images and of the spectra. The hyperspectral images from the previous airborne campaigns are now compatible with atmospheric compensations, data classifications and anomaly detection. The system is opened to external users. [ABSTRACT FROM AUTHOR]

The objective of this research is to quantify, in a region considered as the most important in terms of date palms, the changes that have affected oases during 30 years and to show the importance of remote sensing as a tool facilitating oasis monitoring. The study of change detection conducted in Zeb El Gherbi oases located near Biskra wilaya, it realized by differentiating images using Normalized Difference Vegetation Index (NDVI), which calculated on three satellite images, Landsat 5TM for 1984 and 2001 as well as Landsat OLI for 2014. Field missions conducted to validate work. The results indicated an increase in the area of date palm plantations with 6123.15 ha between 1984 and 2001 and 6996.78 ha between 2001 and 2014. On the other hand, the study showed a reduction of date palms vigour in the ancient oases, probably caused by urbanisation and abandonment of date palms. [ABSTRACT FROM AUTHOR]

Context and background Forest ecosystems play a key role in the regulation of carbon cycle. The sustainability of This ecosystemic service is a necessity in the context of global changes. The spatial distribution of carbon stock is a useful tool of decisionmaking. Which contribute to improve forest management practices. Goal and Objectives: The aim was to model the spatial distribution of the aboveground carbon stock in the Goungoun Forest Reserve and its surrounding lands in northern Benin. Methodology: Systematic stratified sampling was used for the forest inventory. One hundred and fifty circular sampling plots with a radius of 18 m were inventoried. In each plot, all trees with a DBH ≥ 10 cm were identified at species level and measured using compasses. The structural parameters of trees were calculated. The spatial data analysis tool was used to extract spectral indices from remote sensing images. In addition, road density, distance to road network and distance to water stream were calculated. Aboveground biomass was obtained using the allometric model. The conversion factor (CF = 0.50) was used to obtain the carbon stock. The Pearson correlation test was used to select the best environmental variable for carbon distribution modelling. The Radom Forest algorithm was used to predict the spatial distribution of aboveground carbon. The performance of model prediction was evaluated by cross-validation with 100 iterations. The coefficient of determination (R²), root mean square error (RMSE), prediction bias and Akaike Information Criterion (AIC) were used to measure model performance. Results: Results showed that the spatial distribution of carbon stock was strongly influenced by basal area, mean diameter, tree density, forest strats and water stress index. The relative importance of these variables was ranged from 4 to 96%. The model was very accurate (bias = -0.03%, RMSE = 3.24 t/ha and R² = 98.93%). The quantity of carbon observed was ranged from 0.26 to 76.52 t/ha. While, the carbon predicted was varied from 0.24 to 64.66 t/ha for all stands. The carbon stock of forest stands was 2.11 times higher than that of the forest surrounding lands. The sites with high carbon stock were located in savannah woodlands. Anthropogenic pressure triggered the carbon storage potential of the FCGTR. Therefore, we had to strengthen the monitoring of plant species for sustainable forest management. [ABSTRACT FROM AUTHOR]