Estimation of Aboveground Biomass in Agroforestry Systems over Three Climatic Regions in West Africa Using Sentinel-1, Sentinel-2, ALOS, and GEDI Data.

Agroforestry systems (AFS) offer viable solutions for climate change because of the aboveground biomass (AGB) that is maintained by the tree component. Therefore, spatially explicit estimation of their AGB is crucial for reporting emission reduction efforts, which can be enabled using remote sensing (RS) data and methods. However, multiple factors including the spatial distributions within the AFS, their structure, their composition, and their variable extents hinder an accurate RS-assisted estimation of the AGB across AFS. The aim of this study is to (i) evaluate the potential of spaceborne optical, SAR and LiDAR data for AGB estimations in AFS and (ii) estimate the AGB of different AFS in various climatic regions. The study was carried out in three climatic regions covering Côte d'Ivoire and Burkina Faso. Two AGB reference data sources were assessed: (i) AGB estimations derived from field measurements using allometric equations and (ii) AGB predictions from the GEDI level 4A (L4A) product. Vegetation indices and texture parameters were generated from optical (Sentinel-2) and SAR data (Sentinel-1 and ALOS-2) respectively and were used as predictors. Machine learning regression models were trained and evaluated by means of the coefficient of determination (R2) and the RMSE. It was found that the prediction error was reduced by 31.2% after the stratification based on the climatic conditions. For the AGB prediction, the combination of random forest algorithm and Sentinel-1 and -2 data returned the best score. The GEDI L4A product was applicable only in the Guineo-Congolian region, but the prediction error was approx. nine times higher than the ground truth. Moreover, the AGB level varied across AFS including cocoa (7.51 ± 0.6 Mg ha-1) and rubber (7.33 ± 0.33 Mg ha-1) in the Guineo-Congolian region, cashew (13.78 ± 0.98 Mg ha-1) and mango (12.82 ± 0.65 Mg ha-1) in the Guinean region. The AFS farms in the Sudanian region showed the highest AGB level (6.59 to 82.11 Mg ha-1). AGB in an AFS was mainly determined by the diameter (R2 = 0.45), the height (R2 = 0.13) and the tree density (R2 = 0.10). Nevertheless, RS-based estimation of AGB remain challenging because of the spectral similarities between AFS. Therefore, spatial assessment of the prediction uncertainties should complement AGB maps in AFS.

Forest structure following natural disturbances and early succession provides habitat for two avian flagship species, capercaillie (Tetrao urogallus) and hazel grouse (Tetrastes bonasia).

Boreal and mountainous forests are a primary focus of conservation efforts and are naturally prone to large-scale disturbances, such as outbreaks of bark beetles. Affected stands are characterised by biological legacies which persist through the disturbance and subsequent succession. The lack of long-term monitoring data on post-disturbance forest structure precludes understanding of the complex pathways by which natural disturbances affect forest structure and subsequently species presence. We analysed the response of capercaillie (Tetrao urogallus) and hazel grouse (Tetrastes bonasia) to bark beetle infestations. We combined high-resolution airborne light detection and ranging (LiDAR) with a 23-year time series of aerial photography to quantify present-day forest structure and stand disturbance history. Species presence was assessed by collecting droppings of hazel grouse and capercaillie in a citizen science project. Structural equation models showed that the probability of hazel grouse presence increased with increasing disturbance, and the probability of both hazel grouse and capercaillie presence increased with succession. Indirect effects of bark beetle infestations, such as a reduced abundance of deciduous trees and an enhanced herb layer cover, were positively associated with capercaillie presence. Decreasing canopy cover increased the probability of hazel grouse presence. The high temporal and spatial heterogeneity of bark beetle infestations created forest structures that meet the contrasting habitat requirements of both, capercaillie and hazel grouse. This heterogeneity resulted from biological legacies such as decomposing snags, and the simultaneous regrowth of natural regeneration. A benign-neglect strategy towards bark beetle infestations could hence foster capercaillie and hazel grouse in mountainous forests.

Role of African protected areas in maintaining connectivity for large mammals.

The African protected area (PA) network has the potential to act as a set of functionally interconnected patches that conserve meta-populations of mammal species, but individual PAs are vulnerable to habitat change which may disrupt connectivity and increase extinction risk. Individual PAs have different roles in maintaining connectivity, depending on their size and location. We measured their contribution to network connectivity (irreplaceability) for carnivores and ungulates and combined it with a measure of vulnerability based on a 30-year trend in remotely sensed vegetation cover (Normalized Difference Vegetation Index). Highly irreplaceable PAs occurred mainly in southern and eastern Africa. Vegetation cover change was generally faster outside than inside PAs and particularly so in southern Africa. The extent of change increased with the distance from PAs. About 5% of highly irreplaceable PAs experienced a faster vegetation cover loss than their surroundings, thus requiring particular conservation attention. Our analysis identified PAs at risk whose isolation would disrupt the connectivity of the PA network for large mammals. This is an example of how ecological spatial modelling can be combined with large-scale remote sensing data to investigate how land cover change may affect ecological processes and species conservation.

Spatial characterization of bark beetle infestations by a multidate synergy of SPOT and Landsat imagery.

Biological infestations in forests, e.g. the insect outbreaks, have been shown as favoured by future climate change trends. In Europe, the European spruce bark beetle (Ips typographus L.) is one of the main agents causing substantial economic disturbances in forests. Therefore, studies on spatio-temporal characterization of the area affected by bark beetle are of major importance for rapid post-attack management. We aimed at spatially detecting damage classes by combining multidate remote sensing data and a non-parametric classification. As study site served a part of the Bavarian Forest National Park (Germany). For the analysis, we used 10 geometrically rectified scenes of Landsat and SPOT sensors in the period between 2001 and 2011. The main objective was to explore the potential of medium-resolution data for classifying the attacked areas. A further aim was to explore if the temporally adjacent infested areas are able to be separated. The random forest (RF) model was applied using the reference data drawn from high-resolution aerial imagery. The results indicate that the sufficiently large patches of visually identifiable damage classes can be accurately separated from non-attacked areas. In contrast to those, the other mortality classes (current year, current year 1 and current year 2 infested classes) were mostly classified with higher commission or omission errors as well as higher classification biases. The available medium-resolution satellite images, combined with properly acquired reference data, are concluded to be adequate tools to map area-based infestations at advanced stages. However, the quality of reference data, the size of infested patches and the spectral resolution of remotely sensed data are the decisive factors in case of smaller areas. Further attempts using auxiliary height information and spatially enhanced data may refine such an approach.