Tag: deforestation
Implementing the ACIAR Trees for Food Security 2 Project: Our Observations, Experiences and Lessons from Uganda
Converting natural forest to rubber plantations affects soil CO2 and CH4 fluxes
Adoption and Economic Impact of Briquettes as Cooking Fuel: The Case of Women Fish Smokers in Ghana
Community Vulnerability and Adaptation to the Dynamic of Water Resources in Rejoso Watershed, East Java, Indonesia
Shifting discourse, shifting power: how is climate change mitigation and justice negotiated in Indonesia?
Scenarios and Models to Support Global Conservation Targets
Expanding networks of field hedges in densely populated landscapes in the Sahel
Changing woody plant density across agricultural landscapes of the Sudano-Sahelian region is a debated issue. This paper reports the results of an investigation on the contribution of field hedges to overall woody plant density. Hedges separating individual cropped parcels were studied within village agropastoral territories in the Dantiandou district in western Niger. In 1992, field hedges were mapped over the study area using aerial photography and in 2016, using high resolution Google Earth imagery. In 1992, field hedge length was equal to 1006 km within 435 km2 of croplands, equivalent to 2.3 km km−2. In 2016, 17.5% of these hedges had disappeared, but 1591 km of new hedges were observed resulting in an increased density of hedges to 5.6 km km−2. In 24 years, hedges had increased at a mean annual rate of 3.7% likely associated with the splitting of crop fields by inheritance. The composition and productivity of hedges were also monitored in 1996, 2010 and 2015. All trees (maximum height ≥ 4 m), shrubs (<4 m) and tussock perennial grasses were recorded within twelve field hedge samples of 200 m each. Field measurements were used to estimate basal area, crown area, foliage mass and wood mass of each woody plant within the sampled hedges. No significant trends were found between 1996 and 2015 in woody plant density, basal area, crown cover, wood and foliage masses across all monitored hedges. However, overall means hide a slight decrease in tree contributions, while shrub contributions first increased and then decreased. They also mask contradictory trends among sites most likely related to different rates of shrub coppicing and tree cutting. The woody species composition of the hedges is poor with an increase of Combretum glutinosum to the detriment of Guiera senegalensis over the study period. Multiplying the hedge sample statistics by the changing lengths of field hedges in the study area provides estimates of the contribution of the hedge woody plants to the woody plant population at a landscape scale. Between 1992 and 2016, field hedges contributed to increases of woody plant density by 3.9%, basal areas by 5.4%, crown cover by 2.7%, leading to 6.1% annual increase in foliage mass and 8.8% increase in wood mass.
Delineation of Cocoa Agroforests Using Multiseason Sentinel-1 SAR Images: A Low Grey Level Range Reduces Uncertainties in GLCM Texture-Based Mapping
Delineating the cropping area of cocoa agroforests is a major challenge in quantifying the contribution of land use expansion to tropical deforestation. Discriminating cocoa agroforests from tropical transition forests using multispectral optical images is difficult due to the similarity of the spectral characteristics of their canopies. Moreover, the frequent cloud cover in the tropics greatly impedes optical sensors. This study evaluated the potential of multiseason Sentinel-1 C-band synthetic aperture radar (SAR) imagery to discriminate cocoa agroforests from transition forests in a heterogeneous landscape in central Cameroon. We used an ensemble classifier, Random Forest (RF), to average the SAR image texture features of a grey level co-occurrence matrix (GLCM) across seasons. We then compared the classification performance with results from RapidEye optical data. Moreover, we assessed the performance of GLCM texture feature extraction at four different grey levels of quantization: 32 bits, 8 bits, 6 bits, and 4 bits. The classification’s overall accuracy (OA) from texture-based maps outperformed that from an optical image. The highest OA (88.8%) was recorded at the 6 bits grey level. This quantization level, in comparison to the initial 32 bits in the SAR images, reduced the class prediction error by 2.9%. The texture-based classification achieved an acceptable accuracy and revealed that cocoa agroforests have considerably fragmented the remnant transition forest patches. The Shannon entropy (H) or uncertainty provided a reliable validation of the class predictions and enabled inferences about discriminating inherently heterogeneous vegetation categories.
Remote Sensing and Climate Data for Targeting Landscape Restoration in Africa
Tackling land degradation and restoring degraded landscapes require information on areas of priority intervention, since it is not economi-cally and technically possible to manage all areas affected. Recent developments in data availability and improved computational power have enhanced our understanding of the major regional drivers of land degradation and possible remedial measures at different scales. In this study, we have used land degradation hotspots, which were identified using satellite and climate data covering the period of 1982–2003 (Vlek et al. 2010). We then simulated the potentials of different management measures in tackling land degradation in Sub-Saharan Africa (SSA). Scenario analysis results show that about 14 million people can benefit from the application of sustainable land management (e.g., integrated soil fertility management, conservation agriculture, and soil and water conservation) techniques targeted to improve the productivity of croplands. Fallowing degraded areas and allowing them to recover (e.g., through exclosures and agroforestry) could improve land productivity. However, this intervention requires appropriate and improved methods that can accommodate the needs of about 8.7 million people who utilize those “marginal” areas for crop production or livestock grazing. This chapter presents the benefits of utilizing long-term satellite data to analyze the potentials of targeted land management and restoration measures for improv-ing land productivity in SSA. This approach and framework can also be used to design suitable land-use planning for the restoration of degraded areas and to perform detailed cost-benefit and trade-off analysis of various interventions.