A Vectorial Capacity Product to Monitor Changing Malaria Transmission Potential in Epidemic Regions of Africa

Rainfall and temperature are two of the major factors triggering malaria epidemics in warm semi-arid (desert-fringe) and high altitude (highland-fringe) epidemic risk areas. The ability of the mosquitoes to transmit Plasmodium spp. is dependent upon a series of biological features generally referred to as vectorial capacity. In this study, the vectorial capacity model (VCAP) was expanded to include the influence of rainfall and temperature variables on malaria transmission potential. Data from two remote sensing products were used to monitor rainfall and temperature and were integrated into the VCAP model. The expanded model was tested in Eritrea and Madagascar to check the viability of the approach. The analysis of VCAP in relation to rainfall, temperature and malaria incidence data in these regions shows that the expanded VCAP correctly tracks the risk of malaria both in regions where rainfall is the limiting factor and in regions where temperature is the limiting factor. The VCAP maps are currently offered as an experimental resource for testing within Malaria Early Warning applications in epidemic prone regions of sub-Saharan Africa. User feedback is currently being collected in preparation for further evaluation and refinement of the VCAP model

Harmonizing and combining existing land cover/land use datasets for cropland area monitoring at the African continental scale

Mapping cropland areas is of great interest in diverse fields, from crop monitoring to climate change and food security. Recognizing the value of a reliable and harmonized crop mask that entirely covers the African continent, the objectives of this study were to (i) consolidate the best existing land cover/land use datasets, (ii) adapt the Land Cover Classification System (LCCS) for harmonization, (iii) assess the final product, and (iv) compare the final product with two existing datasets. Ten datasets were compared and combined through an expert-based approach to create the derived map of cropland areas at 250m covering the whole of Africa. The resulting cropland mask was compared with two recent cropland extent maps at 1km: one derived from MODIS and one derived from five existing products. The accuracy of the three products was assessed against a validation sample of 3591 pixels of 1km regularly distributed over Africa and interpreted using high resolution images, which were collected using the Geo-Wiki tool. The comparison of the resulting crop mask with existing products shows that it has a greater agreement with the expert validation dataset, in particular for places where the cropland represents more than 30% of the area of the validation pixel.JRC.H.4-Monitoring Agricultural Resource

Harmonizing and combining existing land cover and land use datasets for cropland area monitoring at the African continental scale

Mapping cropland areas is of great interest in diverse fields, from crop monitoring to climate change and food security. Recognizing the value of a reliable and harmonized crop mask that entirely covers the African continent, the objectives of this study were to (i) consolidate the best existing land cover/land use datasets, (ii) adopt the Land Cover Classification System (LCCS) for harmonization and (iii) assess the final product. Ten datasets were compared and combined through an expert-based approach to create the derived map of cropland areas at 250m covering the whole of Africa. The resulting cropland mask was compared with two recent cropland extent maps at 1km: one derived from MODIS and one derived from five existing products. The accuracy of the three products was assessed against a validation sample of 3591 pixels of 1km² regularly distributed over Africa and interpreted using high resolution images, which were collected using the agriculture.geo.wiki.org tool. The comparison of the resulting crop mask with existing products shows that it has a greater agreement with the expert validation dataset, in particular for cropland above 30%.JRC.H.4-Monitoring Agricultural Resource

Assessment of Above-Ground Biomass of Borneo Forests through a New Data-Fusion Approach Combining Two Pan-Tropical Biomass Maps

This study investigates how two existing pan-tropical above-ground biomass (AGB) maps (Saatchi 2011, Baccini 2012) can be combined to derive forest ecosystem specific carbon estimates. Several data-fusion models which combine these AGB maps according to their local correlations with independent datasets such as the spectral bands of SPOT VEGETATION imagery are analyzed. Indeed these spectral bands convey information about vegetation type and structure which can be related to biomass values. Our study area is the island of Borneo. The data-fusion models are evaluated against a reference AGB map available for two forest concessions in Sabah. The highest accuracy was achieved by a model which combines the AGB maps according to the mean of the local correlation coefficients calculated over different kernel sizes. Combining the resulting AGB map with a new Borneo land cover map (whose overall accuracy has been estimated at 86.5%) leads to average AGB estimates of 279.8 t/ha and 233.1 t/ha for forests and degraded forests respectively. Lowland dipterocarp and mangrove forests have the highest and lowest AGB values (305.8 t/ha and 136.5 t/ha respectively). The AGB of all natural forests amounts to 10.8 Gt mainly stemming from lowland dipterocarp (66.4%), upper dipterocarp (10.9%) and peat swamp forests (10.2%). Degraded forests account for another 2.1 Gt of AGB. One main advantage of our approach is that, once the best fitting data-fusion model is selected, no further AGB reference dataset is required for implementing the data-fusion process. Furthermore, the local harmonization of AGB datasets leads to more spatially precise maps. This approach can easily be extended to other areas in Southeast Asia which are dominated by lowland dipterocarp forest, and can be repeated when newer or more accurate AGB maps become available.JRC.H.3-Forest Resources and Climat

Towards Operational Monitoring of Forest Canopy Disturbance in Evergreen Rain Forests : A Test Case in Continental Southeast Asia

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+).Peer reviewe

Long-term monitoring of tropical moist forest extent (from 1990 to 2019): Description of the dataset

The need for accurate information on the state and evolution of tropical forest types at regional and continental scales is widely recognized, particularly to analyze the forest diversity and dynamics, to assess degradation and deforestation processes and to better manage these natural resources. Here we document the approach that was developed by JRC to map and monitor the extent of moist tropical forests and their changes (degradation, deforestation and regrowth) over the last three decades (1990-2020) at fine spatial resolution (30 m × 30 m). The approach is based on the analysis of each valid observation from the Landsat archive and allows to capture disturbances with a short-duration appearance on satellite imagery such as selective logging, fires, and severe weather events (hurricanes, dryness). This new approach allows characterizing the sequential dynamics of forest cover changes by providing transition stages from the initial observation period to the most recent year (2019 for this report). For the first time at the pantropical scale the occurrence and extent of forest degradation can be documented on an annual basis in addition to the monitoring of deforestation. After a short introduction (chapter 1), this technical report describes the study area (chapter 2), the input data (chapter 3), the method that has been developed (chapter 4), and the outcomes of this study (chapter 5). A discussion is also provided regarding the specificities and added value of the outcomes (chapter 6), and the known limitations and future expected improvements (chapter 7). This new pan-tropical scale deforestation and forest degradation monitoring system will contribute to the EU Observatory on deforestation, forest degradation, changes in the world’s forest cover, and associated drivers, which is an action being implemented in the framework of the Communication from the Commission to step up EU action to protect and restore the World’s forests (COM(2019) 352).JRC.D.1-Bio-econom

Persistent collapse of biomass in Amazonian forest edges following deforestation leads to unaccounted carbon losses

Deforestation is the primary driver of carbon losses in tropical forests, but it does not operate alone. Forest fragmentation, a resulting feature of the deforestation process, promotes indirect carbon losses induced by edge effect. This process is not implicitly considered by policies for reducing carbon emissions in the tropics. Here, we used a remote sensing approach to estimate carbon losses driven by edge effect in Amazonia over the 2001 to 2015 period. We found that carbon losses associated with edge effect (947 Tg C) corresponded to one-third of losses from deforestation (2592 Tg C). Despite a notable negative trend of 7 Tg C year(-1) in carbon losses from deforestation, the carbon losses from edge effect remained unchanged, with an average of 63 +/- 8 Tg C year(-1). Carbon losses caused by edge effect is thus an additional unquantified flux that can counteract carbon emissions avoided by reducing deforestation, compromising the Paris Agreement's bold targets.Peer reviewe

Revisiting satellite time series compositing for earth observation applications

Time series acquired from environmental satellites provide important information for the monitoring of earth surfaces. However, their use requires the application of various processes such as the compositing, i.e. the combination of several observations within a given period in order to produce consistent and cloud-free temporal syntheses. Given the limitations of current compositing strategies based on a physically-sound approach, an alternative method namely mean compositing (MC) was proposed so as to produce consistent time series while requiring few cloud-free observations, consuming little computation time, and being user friendly. The spatial and temporal consistency of MC syntheses was assessed by comparison with 5 other strategies including the more advanced techniques based on the inversion of BRDF models. The quantitative analysis was carried out for the VEGETATION and MERIS sensors. The second objective consisted of designing and implementing a decision-support tool to improve the exploitation of temporal syntheses by the adjustment of the compositing parameters to the regional constraints and to the application needs. The tool's potential to improve the relevancy and quality of time series was demonstrated through 2 applications. Finally, the proposed methods were applied to one practical situation which consists of producing a land cover map from time series in restrictive conditions. The development of a semi-automated methodology combining the advantages of MC with a stratification and the ecological knowledge from botanists, allowed to production of a spatially consistent map of the DRCongo with a high thematic level, and a phenological and physionomical description of each vegetation type. A comparative analysis of this map with the Africover and GLC2000 products was realised and discussed.Doctorat en sciences agronomiques et ingénierie biologique (AGRO 3)--UCL, 200

A decision support tool for the optimization of compositing parameters

Temporal syntheses of surface reflectance are one of the most common data products from high temporal resolution instruments. Such an image combination procedure is sensitive to several control parameters, including the compositing period. Unfortunately, these choices usually rely on a unique and global solution delivered by the data provider to ensure temporal and spatial consistency across applications. However, many applications require customized composites to achieve their goals and meet regional constraints. Although this need is now widely recognized by the scientific community, practitioners still rely on trial and error and ad hoc adjustments in designing their approach. The objective of this paper is to design, implement, and test a decision support tool that can find the most appropriate compositing parameters for coarse- to medium-resolution sensors. The key innovation of this approach is that it incorporates external data on the cloud cover and seasonality of the region studied. The algorithm can be applied to any region of the globe and to any optical satellite instrument recording surface reflectance over time. The tool allows data users to optimize the compositing parameters to their application subject and to regional conditions. It can also be used to determine the feasibility of a proposed compositing process. The potential of this methodology to improve the relevancy and the quality of time series products is demonstrated by testing it on two specific applications