Browsing by Subject "forest degradation"

Sort by: Order: Results:

Now showing items 1-2 of 2
  • Pienimäki, Arttu (Helsingfors universitet, 2014)
    The most extensive dry forest and woodland formation in sub-Saharan Africa, including Mozambique, is formed by miombo woodlands. Because of their wide distribution, the miombo woodlands carry significance in global carbon cycle. Previous studies have indicated that while the miombo aboveground carbon stocks appear modest in comparison with tropical rainforests, they have a potential to retain high stocks of soil organic carbon. The miombo landscape is nowadays characterized by widespread deforestation and forest degradation, with woodlands being replaced by anthropogenic land uses such as small-scale agriculture and charcoal harvesting. A new land use type spreading in northern Mozambique is formed by industrial forest plantations. The emerging plantations further change the landscape in transition, allegedly affecting the carbon stocks in the process as well. The purpose of this study was to quantify carbon stocks on locally relevant land use classes in Niassa province, northern Mozambique, and evaluate the change of carbon stocks caused by forest plantations. Six major land use classes were identified: dense miombo, open miombo, other woody vegetation, fallow land, eucalypt plantations and pine plantations. A sample plot grid was laid on chosen areas representing each of the classes. Vegetation aboveground carbon stocks (trees, shrubs and herbaceous vegetation) were recorded in the inventory and topsoil (30 cm) was sampled for soil organic carbon content, to be determined in laboratory. Vegetation belowground carbon stocks were calculated based on existing root to shoot ratios. Since plantations were generally juvenile on the study area, their average yield during rotation period was estimated based on growth models to provide comparable results. Forest plantations were found to have carbon stocks of the same order of magnitude as the two miombo land use classes. Open and dense miombo carried mean vegetation aboveground carbon stocks of 27.47 ± 5.77 and 37.65 ± 7.20 Mg ha-1 respectively, and mean total carbon stocks of 67.81 ± 17.09 and 86.81 ± 18.91 Mg ha-1 respectively, which was consistent with pre-existing results. Pine plantations placed in between with a partially modelled total aboveground mean carbon stock of 34.59 Mg ha-1, whereas the corresponding figure for eucalypt plantations was 21.04 Mg ha-1. Dense miombo had the highest mean total carbon stock of all the land use classes, and fallow land the smallest with 42.59 Mg ha-1. Soil organic carbon did not demonstrate statistically significant differences between any of the land use classes. The result was unexpected, and may be explained either by (i) limited time frame since the land use conversions or (ii) soil mineralogical properties buffering carbon stock changes.
  • Langner, Andreas; Miettinen, Jukka; Kukkonen, Markus; Vancutsem, Christelle; Simonetti, Dario; Vieilledent, Ghislain; Verhegghen, Astrid; Gallego, Javier; Stibig, Hans-Juergen (2018)
    This study presents an approach to forest canopy disturbance monitoring in evergreen forests in continental Southeast Asia, based on temporal differences of a modified normalized burn ratio (NBR) vegetation index. We generate NBR values from each available Landsat 8 scene of a given period. A step of ' self-referencing' normalizes the NBR values, largely eliminating illumination/topography effects, thus maximizing inter-comparability. We then create yearly composites of these self-referenced NBR (rNBR) values, selecting per pixel the maximum rNBR value over each observation period, which reflects the most open canopy cover condition of that pixel. The ArNBR is generated as the difference between the composites of two reference periods. The methodology produces seamless and consistent maps, highlighting patterns of canopy disturbances (e. g., encroachment, selective logging), and keeping artifacts at minimum level. The monitoring approach was validated within four test sites with an overall accuracy of almost 78% using very high resolution satellite reference imagery. The methodology was implemented in a Google Earth Engine (GEE) script requiring no user interaction. A threshold is applied to the final output dataset in order to separate signal from noise. The approach, capable of detecting sub-pixel disturbance events as small as 0.005 ha, is transparent and reproducible, and can help to increase the credibility of monitoring, reporting and verification (MRV), as required in the context of reducing emissions from deforestation and forest degradation (REDD+).