Dario Simonetti

Determination of tropical deforestation rates and related carbon losses from 1990 to 2010

We estimate changes in forest cover (deforestation and forest regrowth) in the tropics for the two last decades (1990–2000 and 2000–2010) based on a sample of 4000 units of 10 ×10 km size. Forest cover is interpreted from satellite imagery at 30 × 30 m resolution. Forest cover changes are then combined with pan-tropical biomass maps to estimate carbon losses. We show that there was a gross loss of tropical forests of 8.0 million ha yr−1 in the 1990s and 7.6 million ha yr−1 in the 2000s (0.49% annual rate), with no statistically significant difference. Humid forests account for 64% of the total forest cover in 2010 and 54% of the net forest loss during second study decade. Losses of forest cover and Other Wooded Land (OWL) cover result in estimates of carbon losses which are similar for 1990s and 2000s at 887 MtC yr−1 (range: 646–1238) and 880 MtC yr−1 (range: 602–1237) respectively, with humid regions contributing two-thirds. The estimates of forest area changes have small statistical standard errors due to large sample size. We also reduce uncertainties of previous estimates of carbon losses and removals. Our estimates of forest area change are significantly lower as compared to national survey data. We reconcile recent low estimates of carbon emissions from tropical deforestation for early 2000s and show that carbon loss rates did not change between the two last decades. Carbon losses from deforestation represent circa 10% of Carbon emissions from fossil fuel combustion and cement production during the last decade (2000–2010). Our estimates of annual removals of carbon from forest regrowth at 115 MtC yr−1 (range: 61–168) and 97 MtC yr−1 (53–141) for the 1990s and 2000s respectively are five to fifteen times lower than earlier published estimates.

Land Cover Change Monitoring Using Landsat MSS/TM Satellite Image Data over West Africa between 1975 and 1990

Monitoring land cover changes from the 1970s in West Africa is important for assessing the dynamics between land cover types and understanding the anthropogenic impact during this period. Given the lack of historical land cover maps over such a large area, Landsat data is a reliable and consistent source of information on land cover dynamics from the 1970s. This study examines land cover changes occurring between 1975 and 1990 in West Africa using a systematic sample of satellite imagery. The primary data sources for the land cover classification were Landsat Multispectral Scanner (MSS) for 1975 and Landsat Thematic Mapper (TM) for the 1990 period. Dedicated selection of the appropriate image data for land cover change monitoring was performed for the year 1975. Based on this selected dataset, the land cover analysis is based on a systematic sample of 220 suitable Landsat image extracts (out of 246) of 20 km × 20 km at each one degree latitude/longitude intersection. Object-based classification, originally dedicated for Landsat TM land cover change monitoring and adapted for MSS, was used to produce land cover change information for four different land cover classes: dense tree cover, tree cover mosaic, other wooded land and other vegetation cover. Our results reveal that in 1975 about 6% of West Africa was covered by dense tree cover complemented with 12% of tree cover mosaic. Almost half of the area was covered by other wooded land and the remaining 32% was represented by other vegetation cover. Over the 1975–1990 period, the net annual change rate of dense tree cover was estimated at −0.95%, at −0.37% for the other wooded land and very low for tree cover mosaic (−0.05%). On the other side, other vegetation cover increased annually by 0.70%, most probably due to the expansion of agricultural areas. This study demonstrates the potential of Landsat MSS and TM data for large scale land cover change assessment in West Africa and highlights the importance of consistent and systematic data processing methods with targeted image acquisition procedures for long-term monitoring.

Operational Two-Stage Stratified Topographic Correction of Spaceborne Multispectral Imagery Employing an Automatic Spectral-Rule-Based Decision-Tree Preliminary Classifier

The increasing amount of remote sensing (RS) imagery acquired from multiple platforms and the recent announcements that scientists and decision makers around the world will soon have unrestricted access at no charge to large-scale spaceborne multispectral (MS) image databases make urgent the need to develop easy-to-use, effective, efficient, robust, and scalable satellite-based measurement systems. In these scientific and industrial contexts, it is well known that, to date, the operational performance of existing stratified non-Lambertian (anisotropic) topographic correction (SNLTOC) algorithms has been limited by the need for a priori knowledge of structural landscape characteristics, such as surface roughness which is land cover class specific. In practice, to overcome the circular nature of the SNLTOC problem, a mutually exclusive and totally exhaustive land cover classification map of a spaceborne MS image is required before SNLTOC takes place. This system requirement is fulfilled by the original operational automatic two-stage SNLTOC approach presented in this paper which comprises, in cascade, 1) an automatic stratification first stage and 2) a second-stage ordinary SNLTOC method selected from the literature. The former combines 1) four subsymbolic digital-elevation-model-derived strata, namely, horizontal areas, self-shadows, and sunlit slopes either facing the sun or facing away from the sun, and 2) symbolic (semantic) strata generated from the input MS image by an operational fully automated spectral-rule-based decision-tree preliminary classifier recently presented in RS literature. In this paper, first, previous works related to the TOC subject are surveyed, and next, the novel operational two-stage SNLTOC system is presented. Finally, the original two-stage SNLTOC system is validated in up to 19 experiments where the systems capability of reducing within-stratum spectral variance while preserving pixel-based spectral patterns (shapes) is assessed quantitatively.

Protection Reduces Loss of Natural Land-Cover at Sites of Conservation Importance across Africa

There is an emerging consensus that protected areas are key in reducing adverse land-cover change, but their efficacy remains difficult to quantify. Many previous assessments of protected area effectiveness have compared changes between sets of protected and unprotected sites that differ systematically in other potentially confounding respects (e.g. altitude, accessibility), have considered only forest loss or changes at single sites, or have analysed changes derived from land-cover data of low spatial resolution. We assessed the effectiveness of protection in reducing land-cover change in Important Bird Areas (IBAs) across Africa using a dedicated visual interpretation of higher resolution satellite imagery. We compared rates of change in natural land-cover over a c. 20-year period from around 1990 at a large number of points across 45 protected IBAs to those from 48 unprotected IBAs. A matching algorithm was used to select sample points to control for potentially confounding differences between protected and unprotected IBAs. The rate of loss of natural land-cover at sample points within protected IBAs was just 42% of that at matched points in unprotected IBAs. Conversion was especially marked in forests, but protection reduced rates of forest loss by a similar relative amount. Rates of conversion increased from the centre to the edges of both protected and unprotected IBAs, but rates of loss in 20-km buffer zones surrounding protected IBAs and unprotected IBAs were similar, with no evidence of displacement of conversion from within protected areas to their immediate surrounds (leakage).

Fast and Robust Topographic Correction Method for Medium Resolution Satellite Imagery Using a Stratified Approach

Several preprocessing steps have to be performed to reliably study mountainous terrains with satellite imagery, and one of the most important is topographic correction. The illumination conditions of these images often vary due to unequal physical properties, such as sun elevation angles and different illumination levels, while the temporal resolution of the imagery has to be accounted for as well. Two digital elevation models, a pre-classification/stratification approach and several correction methods were tested on selected medium resolution sensors. The processed images were selected to encompass different land cover types and temporal variations in solar illumination and a range of topography. It has been demonstrated over several study sites that the empirical-statistical method in combination with a pre-classification/stratification approach provided exceptional results in correcting topographic effects of the satellite imagery using the ASTER Global Digital Elevation Model. The pre-classification/stratification approach was used to split the different land cover types into “strata” which were corrected individually with the selected topographic correction method to achieve better reduction of the terrain effects.

Effect of land-cover change on Africa’s burnt area

As Africa contributes some 64% of the global extent of area burnt annually, uncertainty concerning fire activity in the continent is an important issue. In this study, we quantify the effect of land-cover conversion from natural vegetation to agriculture on burnt area extent. This is based on the comparison of contemporary fire distribution in 189 protected areas where agricultural activity is largely absent with that occurring in the surrounding regions, where agriculture is practised. Results indicate a decrease in the total area burnt annually in Africa linked to the loss of natural vegetation communities due to expanding agricultural lands. Land-use change within the savanna vegetation units of Africa has led to a decrease in burnt area in the order of ,8 � 10 5 hayear � 1 , which corresponds to 0.4% of the area currently burnt in Africa. The resulting decrease in the quantity of biomass burnt in any year would be between 3.4 and 9Tg,dependingontheestimatesofabovegroundfuelbiomass.Deforestationinthehumidtropicalforestdomainsmayact as a small counterbalance to the trend of decreasing burnt area linked to land-use change in the short term. Additional keywords: climate change, fire regime, land-use change, protected areas. Received 23 September 2011, accepted 29 March 2012, published online 5 September 2012

Interannual Changes of Fire Activity in the Protected Areas of the SUN Network and Other Parks and Reserves of the West and Central Africa Region Derived from MODIS Observations

Time series of fire occurrence, derived from MODIS data, have been used to characterise the spatio-temporal distribution of fire events during the 2004–2009 period in 17 protected areas (PAs) of West and Central Africa, with particular attention to those of the SUN network in Senegal, Burkina Faso, Benin and Niger. The temporal distribution of the fire activity and the number of fire occurences are quite different inside the PAs and in their surrounding area. A progressive increase of the length of the burning season is observed in the West Africa PAs. Quantitatively, the general trend over the last five years is an increase of the fire density (+22%) inside the PAs and a decrease (−27%) outside. The results indicate that the capacity of the PAs to maintain the biological diversity of the region is probably decreasing due to the combined effects of the anthropic pressure inside the PAs and of an on-going isolation process.

First Results From the Phenology-Based Synthesis Classifier Using Landsat 8 Imagery

A fully automatic phenology-based synthesis (PBS) classification algorithm was developed to map land cover based on medium spatial resolution satellite data using the Google Earth Engine cloud computing platform. Vegetation seasonality, particularly in the tropical dry regions, can lead conventional algorithms based on a single date image classification to “misclassify” land cover types, as the selected date might reflect only a particular stage of the natural phenological cycle. The PBS classifier operates with occurrence rules applied to a selection of single date image classifications of the study area to assign the most appropriate land cover class. Since the launch of Landsat 8 in 2013, it has been possible to acquire imagery at any point on the Earth every 16 days with exceptional radiometric quality. The relatively high global acquisition frequency and the open data policy allow near-real-time land cover mapping and monitoring with automated tools such as the PBS classifier. We mapped four protected areas and their 20-km buffer zones from different ecoregions in Sub-Saharan Africa using the PBS classifier to present its first results. Accuracy assessment was carried out through a visual interpretation of very high resolution images using a Web geographic information system interface. The combined overall accuracy was over 90%, which demonstrates the potential of the classifier and the power of cloud computing in geospatial sciences.

Multi-Sensor Monitoring System for Forest Cover Change Assessment in Central Africa

Forest monitoring from earth observation is crucial over tropical regions to assess forest extent and provide up-to-date estimates of deforestation rates. Based on a systematic sample of 20x20 km size sites, a processing chain has been developed at the European Commissions Joint Research Centre (JRC) for producing deforestation estimates between years 1990, 2000 and 2005. Whereas this monitoring exercise was based on Landsat imagery, limitations in Landsat availability over Central Africa for year 2010 required alternative imagery such as the Disaster Monitoring Constellation (DMC). The classification module of the existing JRC processing chain is based on tasseled caps analysis (TCap-based). We adapted this module to DMC imagery by selecting the most suitable object-features through their assessments using a sub-sample of existing land-cover maps of years 1990 and 2000. The processing chain is adapted for the production of land-cover change maps between years 2000 and 2010. The accuracy of the land-cover maps produced for year 2010 with the two methods (original TCap-based and adapted Multi-Sensor) is assessed through a reference dataset. Overall accuracies are similar for both approaches (93% and 95% respectively), but the Multi-Sensor approach shows a significant improvement when considering only changed objects (83% overall accuracy versus 56% for TCap-based). Our results show that, even by using DMC imagery with lower radiometric quality (compared to Landsat) an automated classification can provide land-cover maps with similar accuracy thanks to an appropriate object-features selection. Similar adaptations need to be developed for other satellite imagery such as SPOT and RapidEye.

Estimating Burned Area in Mato Grosso, Brazil, Using an Object-Based Classification Method on a Systematic Sample of Medium Resolution Satellite Images

This paper presents a new approach for estimating burned areas at a regional scale, using a systematic sample of medium spatial resolution satellite images. This approach is based on a pan-tropical deforestation survey developed by the Joint Research Centre. We developed and tested our approach over Mato Grosso State, located in the Brazilian Legal Amazon region, with a total area of 903 366 km2. We analyze Landsat-5 TM imagery over 77 sample sites (20 km × 20 km in size) located at each full degree confluence of latitude and longitude. Our new approach leads to an estimate of burned area for year 2010 at 66 368 km2, representing approximately 7.3% of the Mato Grosso area. This estimate is compared to estimates from two different approaches: 1) from a method developed by the Brazilian Institute for Space Research, applied to a wall-to-wall coverage of Landsat-5 TM imagery and 2) from a method using MODIS MCD64A1 products of the University of Maryland, resulting in 70 232 and 55 157 km2 of burned area, respectively (representing 7.8% or 6.1% of Mato Grosso area). Our method produces statistically valid estimates of burned areas for the Brazilian State of Mato Grosso in a more efficient manner than previous methods and enables the inclusion of small burn scars typically missed by coarse resolution satellites. This approach can be applied for regional and global assessments as well as for refining and evaluating burned area products based on coarse spatial resolution imagery like MODIS or SPOT-VEGETATION.