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

Abstract: 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.JRC.H.5-Land Resources Managemen

State and evolution of the African rainforests between 1990 and 2010

This paper presents a 2005 map of Africa’s rainforests with new levels of spatial and thematic detail, being derived from 250m resolution MODIS data, and having an overall accuracy of 84%. A systematic sample of Landsat images (with supplemental data from equivalent platforms to fill sample gaps) is used to produce a consistent assessment of deforestation between 1990, 2000 and 2010 for West Africa, Central Africa and Madagascar. Net deforestation is estimated at 0.28% yr-1 for the period 1990-2000 and 0.14% yr-1 for the period 2000-2010. West Africa and Madagascar exhibit a much higher deforestation rate than the Congo Basin. Based on a simple analysis of the variance over the Congo Basin, we show that expanding agriculture and increasing fuelwood demands are key drivers of deforestation while well-controlled timber exploitation programmes have little or no direct influence on forest-cover reduction at present. Rural and urban population concentrations and fluxes are identified as strong underlying causes of deforestation in this study.JRC.H.5-Land Resources Managemen

Automated object-based change detection for forest monitoring by satellite remote sensing : applications in temperate and tropical regions/

Forest ecosystems have recently received worldwide attention due to their biological diversity and their major role in the global carbon balance. Detecting forest cover change is crucial for reporting forest status and assessing the evolution of forested areas. However, existing change detection approaches based on satellite remote sensing are not quite appropriate to rapidly process the large volume of earth observation data. Recent advances in image segmentation have led to new opportunities for a new object-based monitoring system. This thesis aims at developing and evaluating an automated object-based change detection method dedicated to high spatial resolution satellite images for identifying and mapping forest cover changes in different ecosystems. This research characterized the spectral reflectance dynamics of temperate forest stand cycle and found the use of several spectral bands better for the detection of forest cover changes than with any single band or vegetation index over different time periods. Combining multi-date image segmentation, image differencing and a dedicated statistical procedure of multivariate iterative trimming, an automated change detection algorithm was developed. This process has been further generalized in order to automatically derive an up-to-date forest mask and detect various deforestation patterns in tropical environment. Forest cover changes were detected with very high performances (>90 %) using 3 SPOT-HRVIR images over temperate forests. Furthermore, the overall results were better than for a pixel-based method. Overall accuracies ranging from 79 to 87% were achieved using SPOT-HRVIR and Landsat ETM imagery for identifying deforestation for two different case studies in the Virunga National Park (DRCongo). Last but not least, a new multi-scale mapping solution has been designed to represent change processes using spatially-explicit maps, i.e. deforestation rate maps. By successfully applying these complementary conceptual developments, a significant step has been done toward an operational system for monitoring forest in various ecosystems.(AGRO 3)--UCL, 200

Forest change detection by statistical object-based method

Forest monitoring requires more automated systems to analyse the large amount of remote sensing data. A new method of change detection is proposed for identifying forest land cover change using high spatial resolution satellite images. Combining the advantages of image segmentation, image differencing and stochastic analysis of the multispectral signal, this OB-Reflectance method is object-based and statistically driven. From a multidate image, a single segmentation using region-merging technique delineates multidate objects characterised by their reflectance differences statistics. Objects considered as outliers from multitemporal point of view are successfully discriminated thanks to a statistical procedure, i.e., the iterative trimining. Based on a chi-square test of hypothesis, abnormal values of reflectance differences statistics are identified and the corresponding objects are labelled as change. The object-based method performances were assessed using two sources of reference data, including one independent forest inventory, and were compared to a pixel-based method using the RGB-NDVI technique. High detection accuracy (> 90%) and overall Kappa (> 0.80) were achieved by OB-Reflectance method in temperate forests using three SPOT-HRV images covering a 10-year period. (c) 2006 Elsevier Inc. All rights reserved

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 resources status and provide up-to-date estimates of deforestation rates. Based on a systematic sample of 20 × 20 km size sites where medium resolution satellite imagery has been acquired, a processing chain had been developed at 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 (TC-based). We adapted this module to DMC imagery. The most suitable object-based features for the classification are selected through the analysis of a sub-sample of the existing Land Cover (LC) maps for years 1990 and 2000. The processing chain is adapted for the production of 2000-2010 LC change maps and the resulting 2010 LC maps produced by the two methods, original TC-based and adapted Multi-Sensor, are assessed and compared. The overall accuracies are 90% and 92% for the TC-based and Multi-Sensor approaches respectively, with significant improvement when considering only changed objects (46% and 72% respectively). These results show that, even with DMC imagery lower radiometric quality (compared to Landsat) automated classification can provide accurate LC maps thanks to an appropriate features selection. Similar adaptation need to be developed for other satellite imagery such as SPOT and RapidEye.JRC.H.3-Forest Resources and Climat

Deforestation in Central Africa: Estimates at Regional, National and Landscape Levels by Advanced Processing of Systematically-distributed Landsat Extracts

Accurate land-cover change estimates are among the headline indicators set by the Convention on Biological Diversity to evaluate the progress toward its 2010 target concerning habitat conservation. Tropical deforestation is of prime interest since it threatens the terrestrial biomes with highest biodiversity rates. Local forest change dynamics, detected over very large extents, are necessary to derive regional and national figures for multilateral environmental agreements and sustainable forest management. Current deforestation estimates in Central Africa were derived either from coarse to medium resolution imagery or from wall-to-wall coverage of limited area. Whereas the first approach cannot grasp small forest changes widely spread across a landscape, the operational costs limit the mapping extent in the second approach. This research developed and implemented a new cost-effective approach to derive area estimates of land cover change by combining a systematic regional sampling scheme based on high spatial resolution imagery with object-based unsupervised classification techniques. A multi-date segmentation is obtained by grouping pixels with similar land cover change trajectories which are then classified by unsupervised procedures. The interactive part of the processing chain is therefore limited to land cover class labelling of object clusters. The adequate combination of automated image processing and interactive labelling renders this method cost-efficent. The proposed approach was operationally applied to the entire Congo River basin to accurately estimate deforestation at regional, national and landscape levels. The survey was composed of 10 × 10 km sampling sites systematically distributed every 0.5¿ over the whole forest domain of Central Africa, corresponding to a sampling rate of 3.3%. For each of the 571 sites, subsets were extracted from both Landsat TM and ETM+ imagery acquired in 1990 and 2000 respectively. Around 60% of the 390 cloud-free images do not show any forest cover change. For the other 165 sites, the results are resumed by a change matrix for every sample site describing 4 land cover change processes, e.g. deforestation, reforestation, forest degradation and forest recovery. This unique exercise estimates the deforestation rate at 0.21% per year, while the forest degradation rate is close to 0.15% per year. The results also show that the Landscapes designed as high priority conservation zones by the Congo Basin Forest Partnership had undergone significantly less deforestation and forest degradation between 1990 and 2000 than the rest of the Central African forest.JRC.H.3-Global environement monitorin

Deforestation in Central Africa: Estimates at regional, national and landscape levels by advanced processing of systematically-distributed Landsat extracts

Accurate land cover change estimates are among the headline indicators set by the Convention on Biological Diversity to evaluate the progress toward its 20 10 target concerning habitat conservation. Tropical deforestation is of prime interest since it threatens the terrestrial biomes hosting the highest levels of biodiversity. Local forest change dynamics, detected over very large extents, are necessary to derive regional and national figures for multilateral environmental agreements and sustainable forest management. Current deforestation estimates in Central Africa are derived either from coarse to medium resolution imagery or from wall-to-wall coverage of limited areas. Whereas the first approach cannot detect small forest changes widely spread across a landscape, operational costs limit the mapping extent in the second approach. This research developed and implemented a new cost-effective approach to derive area estimates of land cover change by combining a systematic regional sampling scheme based on high spatial resolution imagery with object-based unsupervised classification techniques. A multi-date segmentation is obtained by grouping pixels with similar land cover change trajectories which are then classified by unsupervised procedures. The interactive part of the processing chain is therefore limited to land cover class labelling of object clusters. The combination of automated image processing and interactive labelling renders this method cost-efficient. The approach was operationally applied to the entire Congo River basin to accurately estimate deforestation at regional, national and landscape levels. The survey was composed of 10 x 10 km sampling sites systematically-distributed every 0.5 degrees over the whole forest domain of Central Africa, corresponding to a sampling rate of 3.3%. For each of the 571 sites, subsets were extracted from both Landsat TM and ETM+ imagery acquired in 1990 and 2000 respectively. Approximately 60% of the 390 cloud-free samples do not show any forest cover change. For the other 165 sites, the results are depicted by a change matrix for every sample site describing four land cover change processes: deforestation, reforestation, forest degradation and forest recovery. This unique exercise estimates the deforestation rate at 0.21% per year, while the forest degradation rate is close to 0.15% per year. However, these figures are less reliable for the coastal region where there is a lack of cloud-free imagery. The results also show that the Landscapes designated after 2000 as high priority conservation zones by the Congo Basin Forest Partnership had undergone significantly less deforestation and forest degradation between 1990 and 2000 than the rest of the Central African forest. (C) 2008 Elsevier Inc. All rights reserved

Combinig Multi-Sensor Medium Resolution Satellite Imagery for Forest Cover Change Assessment in Central Africa

Forest monitoring is crucial over tropical regions to assess the evolution of forest cover changes and give figures about deforestation. Based on a systematic sample of image extracts, a processing chain had been developed for producing deforestation estimates over the years 1990-2000-2005. Whereas this monitoring exercise was based on Landsat images, limitations in Landsat image availability for the year 2010 over Central Africa required alternative imagery. Given its high revisit period and characteristics close to the Landsat TM sensor, DMC images are considered in this paper to replace Landsat TM data gaps over Central Africa. However the classification module of the existing processing chain is based on Tasseled Caps (TC-based) analysis of Landsat TM imagery and needs to be adapted to such data in order to be sensor-independent. This adaptation is described in this paper. A sub-sample of the available image extracts has been used for the selection of the best object-based features through the analysis of existing Land-Cover maps for 1990 and 2000. The processing chain has been adapted for the production of Land-Cover change maps for year 2010. The resulting maps from the two methods, original TC-based classification and adapted Multi-Sensor approach, have been compared and evaluated. The overall accuracies of the 2010 Land-Cover classification results are 90% for the TC-based approach and 92% for the Multi-Sensor approach. When considering only objects for which label is changing between 2000 and 2010, the accuracies of the 2010 LC classifications are 45% and 72% for the TC-based and Multi-Sensor approaches respectively. These results show that, even with lower radiometric quality of DMC imagery the performance of the automated classification has been improved thanks to an appropriate selection of object-based features. As similar adaptation will be developed for other satellite imagery such as SPOT and Rapid-Eye in order to be sensor-independent, the future adaptation to Sentinel-2 data will be very easy using the same approach.JRC.H.3-Forest Resources and Climat

Contribution of the Geographical Information Systems to the measure of multidimensional poverty thought the systemic approach: the illustrative case study of the Biosphere Reserve of Luki (DRC)

For decades, poverty is mainly measured based on variables directly related te income and / or consumption of the agents studied. In parallel, and since the 1980s, researchers are looking at the significance of addressing poverty in a more holistic and complex. Poverty cannot be studied in only one dimension; the many dimensions (environmental, socio-economic, cultural, etc.) cannot be neglected, neither the obvious interactions between these dimensions. Morever, poverty is flot a static phenomenon. The spatio-temporal dynamics that characterize it must be modeled to produce relevant analytical results. In this context, the research question is: how can we overcome the limitations of the multidimensionality of poverty today? The systems approach and geo-spatial analysis tools can certainly help answer this question. Measuring poverty by recognizing the multidimensional nature of poverty implies that environmental risks and socio-economic risks faced by the populations are taken into account and directly incorporated into the analysis. The issue of sustainable development in all its magnitude cannot be approached differently today. Meaning without considering the interactions between the environment and agents that live there. The interest of the systems approach in measuring multidimensional poverty is intuitive, but in the light of literature available on the subject, the contribution and relevance of this approach are not yet proven. Multi-agent systems modeling and simulation are more and more in line with the systems approach. This technique can rcpresent a specific environment and its observed characteristics. It can also be coupled with Geographic Information Systems. The review of the literature on the subject shows that the multi-agent systems have been widely used for analysis of natural resource management in the field of life sciences. Nevertheless, it seems that the concept of multidimensional poverty has been very little, if at all, studied with the multi-agent systems. It is therefore necessary, through modeling and simulations, to identify the elements and variables that strongly influence the vulnerability and the risks people poorer. Today, as the organizational system of many populations living in rich environment (like in our case in a Biosphere Reserve) is moving towards capitalization, the environment is perceived as a source of income. The goal of all activities in this environment becomes more and more income generation. The environment is at risk because of trading and economic interaction. Issues of sustainable management of natural resources and environment are flot a priority in the actions of agents. Often, the perception of tenure rules by agents do not guarantee a reasonable consumption of resources offered by the environment. Especially since agents tend to think that the resources offered by the environment are inexhaustible..