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

Deforestation and forest degradation in the Amazon - Updated status and trends for the year 2021

The Amazon forest is the largest tropical rainforest in the world, which houses about 10% of the Earth’s biodiversity and 16% of the world’s total river discharge into the oceans. However, the Amazon forest has already lost up to 20% of its original area since the 1970s and is under constant threat of ongoing deforestation and forest degradation. Disturbances in the forest cover lead to carbon emissions, endanger the livelihoods of indigenous people, and threaten biodiversity in the Amazon. In 2021, according to JRC-TMF data, the Pan-Amazon region showed a 37% decrease in forest disturbances (comprising deforestation and forest degradation) compared to the previous year (23,383 km2 of new disturbances in 2021 vs. 37,293 km2 in 2020). This decrease can be mainly explained by the decrease in forest fires in 2021, which apparently has been a year of high precipitation during the ‘fire season’ (July-November) in the Southern and Eastern Amazon regions. However, the decrease of burned areas in 2021 has been measured also in many other Amazon countries, leading to a significant decrease of forest disturbances in Colombia (38%), Ecuador (39%), Bolivia (59%), and Venezuela (85%). For the first seven months in 2022 (January–July) the INPE-DETER alert system shows a deforestation increase of 7% for the Brazilian Legal Amazon, compared to the previous year, while forest degradation increased by 27% for the same period.JRC.D.1 - Bio-econom

Earth Observation for Biodiversity

The Knowledge Centre on Earth Observation (KCEO) assessed the current and potential use of Earth Observation (EO) in support of EU policies related to biodiversity - The KCEO DD assessment on EO in support of biodiversity-related policies, was rooted on the questions and policy information needs raised by the European Commission Directorate Generals and the implementation of the EU Biodiversity Strategy for 2030 - Examining biodiversity-related concerns using EO brought to light numerous synergies across different policy themes - EO applications emerged to be crucial in monitoring and reporting in common policy contexts - The opportunities presented by the Copernicus products and services are therefore a potential to streamline and enhance EO uptake across EU policies areasJRC.D.6 - Nature Conservation and Observation

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

Deforestation and forest degradation in the Amazon - Update for year 2022 and link to soy trade

During year 2022, the JRC-TMF dataset on tropical moist forests reports a 14.9% increase in forest disturbances in the Pan-Amazon region compared to year 2021 (35,480 km2 of new disturbances in 2022 vs. 30,089 km2 in 2021) - disturbances including both deforestation and forest degradation). The Amazon countries show different trends in forest cover change. Forest disturbances in Ecuador, the Guiana Shield countries (Guyana, Suriname and French Guiana) and in Bolivia increased significantly in 2022 compared to 2021 (by + 172.9%, 87.6% and 38.5% respectively). While forest disturbances in all moist forests of Brazil stayed roughly on the same level between 2022 and 2021 (1.8% increase), they increased by 9.3% in the Brazilian Legal Amazon (BLA). Forest disturbances decreased in Colombia, Venezuela and Peru by 16.2%, 14.9% and 7.0%, respectively. This report provides also an overview regarding forest disturbances in the Brazilian Amazon for the first seven months in 2023, as reported by the INPE-DETER alert system. This alert system shows decreases of 47.4% and 29.9% between 2022 and 2023 (January-July period) for deforestation and forest degradation (by selective logging and forest fires), respectively. A second, independent deforestation alert system for the Brazilian Amazon, IMAZON-SAD, reports a 63.2% decrease between 2022 and 2023 (period January to July) for deforestation.JRC.D.1 - Forests and Bio-Econom

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