Multi-Sensor Monitoring System for Forest Cover ... - IEEE Xplore

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, VOL. 6, NO. 1, FEBRUARY 2013

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Multi-Sensor Monitoring System for Forest Cover Change Assessment in Central Africa Baudouin Desclée, Dario Simonetti, Philippe Mayaux, and Frédéric Achard

Abstract—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 Commission’s 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. Index Terms—Change detection, DMC, deforestation, forest mapping and monitoring, Landsat TM, object-based classification, multi-temporal analysis, remote sensing.

I. INTRODUCTION

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ENTRAL Africa contains the second largest area of contiguous moist tropical forest in the world, covering about 2 million km2 [1]. Despite its low level of deforestation, the increasing pressure on the forests of the Congo Basin could lead to considerable forest degradation and reduction of resources for local populations [2]. Monitoring such ecosystems is essential for providing continuous and up-to-date information on tropical Manuscript received May 31, 2012; revised September 28, 2012, December 13, 2012; accepted January 09, 2013. Date of publication January 25, 2013; date of current version March 11, 2013. B. Desclée, P. Mayaux, and F. Achard are with the Institute for Environment and Sustainability of the European Commission’s Joint Research Centre, I-21020 Ispra, Italy (e-mail: [email protected]). D. Simonetti is with Arcadia SIT under contract with the Institute for Environment and Sustainability of the European Commission’s Joint Research Centre, I-21020 Ispra, Italy. Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/JSTARS.2013.2240263

forests resources. In the framework of international policies related to sustainable development and climate change mitigation, one important element to consider is the mechanism (Reduction of Emissions from Deforestation and Degradation) under the United Nations Framework Convention on Climate Change (UNFCCC) [3]. The Joint Research Centre (JRC) is aiming at providing up-to-date information on the status of tropical forest resources at regional to pan-tropical scales and at developing tools for the monitoring of tropical forest ecosystems which can support MRV activities (Monitoring, Reporting and Verification) in the framework. The tools developed at JRC rely mainly on data from earth observing satellites. For Central Africa, information on forest resources is important both for MRV activities and for forest management issues. Pan-tropical measurements of forest cover changes have been provided since the early 2000’s by the JRC through the TREES project [4], [5]. In the framework of this project, forest maps are now being produced for the current and previous decades (from 1990 up to 2010) for improving and updating estimates of forest cover changes at pan-tropical scale. Considering the difficulty to obtain cloud-free images over a large region (such as Central Africa), a sampling approach was selected instead of a wall-to-wall approach. This sampling strategy is aimed at avoiding the need for image compositing and related technical issues, in particular potential bias resulting from the use of image mosaics with non-random cloud distribution at regional level. The TREES approach is based on the analysis of satellite image extracts of 20 km 20 km size located at each latitude/longitude half-degree confluence point over the eight member countries of the Central African Forest Commission (COMIFAC). Two time periods were used in previous surveys: from year 1990 to year 2000 [6], and from year 2000 to year 2005 [7]. Recently, a wall-to wall quantification of forest cover loss was performed over the period 2000 to 2010 for the Democratic Republic of Congo [8]. An object-based automated processing chain has been developed by the TREES project for producing forest cover change maps and estimates over the period 2000–2010 [9]. First, a robust processing module has been developed to pre-process a large amount of multi-date Landsat imagery (from both TM and sensors) [10]. Secondly, an automated segmentation/classification module has been developed for segmenting and classifying this large sample of pre-processed multi-date images [11]. Taking advantage of multi-date segmentation tools [12], this module includes also automated change detection and classification steps based on Tasseled Cap components (TCapbased) which were developed for Landsat images (from both ). TM and

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DESCLÉE et al.: MULTI-SENSOR MONITORING SYSTEM FOR FOREST COVER CHANGE ASSESSMENT IN CENTRAL AFRICA

Given Landsat data availability is limited over Central Africa (due to the combination of SLC failure of the Landsat-7 instrument in 2003 and unavailability of Landsat receiving station in this region), alternative satellite imagery had to be considered for year 2010 to fill the confluence points not covered by any Landsat-5 TM imagery. These sites represent about 60% of the survey sample sites (confluence points) of the region. Among potentially available medium spatial resolution satellite optical sensors, DMC (Disaster Monitoring Constellation including in particular Beijing-1, NigeriaSat-1, Deimos-1, UK-DMC and UK-DMC2 satellites) was considered as good candidate given its short revisiting period (4 days at the equator due to sensors wide swath—up to 650 km), its sensors spatial resolution and spectral bands similar to Landsat TM. Finer resolution satellite imagery from SPOT and RapidEye satellites are intended to be used as complementary imagery in a next phase (these data were not available at the time of the study). Despite their worldwide coverage, DMC imagery has not been extensively exploited by the scientific community. One of the reasons might be the lower product quality, as demonstrated by the assessment of UK-DMC geometric and radiometric quality [13]. In that study, radiometric artefacts like striping, coherent noise, and flat detectors are shown on DMC imagery. A successful application for land use/cover change assessment has been developed from Beijing-1 DMC images over China [14]. However, that study is using object-oriented image classification combined with visual interpretation to handle the poor radiometry of the images. Another study that used NigeriaSat-1 data to monitor mangroves shows lower classification overall accuracy compared to the use of ASTER and Landsat [15]. In west-central Alberta (Canada), the applicability of Beijing-1 DMC imagery was assessed for mapping large-area habitat [16]. Again in this case study, the DMC-based classification shows a lower accuracy compared to the Landsat-based classification (overall accuracy for seven land-cover classes is 10.2% lower with DMC compared to Landsat). As Landsat TM coverage is not available for all Central African sites, our challenge is to exploit DMC images within the TREES approach. Object-Based Image Analysis (OBIA) as defined by [17] relies on spectral, spatial and even textural characteristics of each image object, so-called “features”. It is crucial to select the most appropriate features for land-cover classification. The selection of appropriate features for discriminating land-cover classes has been studied in several pixel-based studies [18], [19]. For object-based approaches, a recent study [20] has compared three feature selection methods and found the Jeffreys-Matusita (JM) distance to be the most attractive and efficient method for computing class distance. Moreover, JM distance is easy to compute using the SEATH tool which exploits data directly exported from eCognition software [21]. With a few input sample objects for every land cover class, the SEATH tool allows analyzing all object features and computing the JM distance for every class combination and every feature. This JM distance is a statistical measure of separability between classes and allows identifying features which best separate land cover classes. Our study aims at adapting the TREES semi-automatic processing chain already developed for Landsat-type imagery to

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the DMC satellite imagery acquired for year 2010. Our goal is to develop an automated multi-sensor processing approach for several reasons: (1) Multi-sensor approaches are needed due to the lack of Landsat high-quality image acquisitions and the evolving earth-observing satellite data availability. (2) DMC imageries have similar spectral and spatial properties, though lower radiometric quality, compared to Landsat and could be used in a continuous time-series with Landsat data. (3) DMC data, however, cannot be used in traditional tasseled-cap classifications because, though similar, DMC has only 3 spectral bands. The approach needs to be flexible in order to incorporate similar or finer resolution satellite imagery from other optical sensors such as from SPOT and RapidEye satellites, as well as from the forthcoming Sentinel-2 satellites. Given that segmentation and change detection steps already provide satisfying results [9], the approach adaptation to DMC data is only dealing with the automatic classification of year 2010 imagery. Focused on a sample of image extracts, this study is performed in two steps: (1) selection of the best object-based parameters (more commonly named ‘object-based features’ as used in the rest of the text) for the land-cover classification by analyzing already validated land-cover maps for 1990 and 2000 and (2) adaptation of the existing processing chain to produce automatically land-cover maps for year 2010 using the characteristics identified in step 1. The accuracy of the resulting land-cover maps for year 2010 (produced from this adapted Multi-Sensor approach) is then assessed and compared to the TCap-based classification. II. DATA A. Study Area The Congo Basin is occupied by vast and still uninterrupted tracts of rainforests from the Gulf of Guinea to the Albertine Rift. Salient features include the presence of the world’s largest tropical swamp forest in the central part of the Congo Basin, and two mountainous regions in Cameroon and in eastern Democratic Republic of Congo [4]. The central region is characterized by low deforestation rates resulting from small localized clearings usually associated with shifting agricultural activities [22], [23]. The situation can be explained in part by the absence of a significant local market for wood products and a poor transportation infrastructure. However, coastal Central Africa has experienced more intensive forest exploitation. Here, population growth and agricultural expansion, as well as emerging market opportunities have exerted a strong pressure on forest resources [24]. Information on forest change in the Congo Basin was based on incomplete and anecdotal information until the mid 1990s, without spatially explicit delineation of forests and statistically robust estimates of forest cover change. The lack of up-to-date and accurate information on the current state and evolution of forested areas in Central Africa has often been cited as a limiting factor in the design of efficient forest management policies. Efforts to improve regional and national capabilities to address the problem of forest and land use monitoring have thus received particular attention in recent years [6].

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using the World Geodetic System 1984 (WGS84) geographic coordinate system with a pixel size of 30 meters in both the X and Y map directions. For the epoch 2000, the only source of imagery is Landsat-7 ETM as described by [26]. C. Disaster Monitoring Constellation

Fig. 1. Distribution of good quality satellite data for year 2010 over the Central Africa systematic sample grid (Landsat Landsat 5 TM; DMC-22 Deimos-1 and UK-DMC2; DMC-32 Beijing-1, NigeriaSat-1 and UK-DMC).

Our forest cover assessment of Central Africa for year 2010 is based on a regional systematic sampling scheme at each 0.5 0.5 confluence points over 8 countries of the COMIFAC. This includes Central African Republic, Republic of Congo, Democratic Republic of Congo, Cameroon, Gabon, Equatorial Guinea, Rwanda and Burundi. The sampling scheme accounts for 1338 sample sites in total. Each sample site has a size of 20 20 km which results in 13% of the total area covered. Satellite imagery is extracted for each sample site from Landsat or DMC imagery depending of the data availability. Over Central Africa, Landsat-5 TM image availability accounts for 40% of the total sample scheme whereas DMC accounts for 25% (Fig. 1). The remaining 35% are expected to be filled in the future by other medium or fine spatial resolution imagery. B. Landsat TM & ETM The principal source of satellite imagery for ‘epoch 2010’ is Landsat-5 TM images (band 1–7) which were acquired between 2008 and 2012. The images are provided in Global Land Survey (GLS) format (L1T level), namely orthorectified scenes [25]. For the orthorectification, NASA used an improved Digital Elevation Model (DEM), specifically the 90 m resolution Shuttle Radar Topography Mission (SRTM) products and a collection of geodetic control points provided by the American National Imagery and Mapping Agency. Overall, the orthorectified data have relative Root Mean Square (RMS) errors of less than 40 m, with absolute RMS errors of less than 64 m. However, individual scenes within the GLS data sets may have in some cases geolocation errors considerably greater than 50 m. The images were resampled to a Universal Transverse Mercator projection

The Disaster Monitoring Constellation (DMC) is a series of Earth Observation satellites which provide worldwide daily revisit capability for disaster responses and deliver high temporal resolution imagery for many applications. DMC has a spatial resolution from 22 to 32 m and 3 spectral bands (green, red and NIR comparable with TM bands 2, 3 and 4). DMC imagery was deliberately designed to be comparable to Landsat imagery, in order to leverage the expertise of the large established remote sensing community used to working with Landsat images. DMC satellites can be divided into two groups depending on image spatial resolution: (1) DMC-22 including Deimos-1 and UK-DMC2 and (2) DMC-32 including the satellites Beijing-1, NigeriaSat-1 and UK-DMC. All these satellites are operated by DMC International Imaging Ltd, except for Deimos-1 operated by DEIMOS Imaging S.L.U. Despite the fact that only few studies have used DMC, one study has assessed the quality of the products from UK-DMC both for geometric and radiometric quality [13]. DMC data products used in this study were delivered at L1T processing level as used by [13]. These products are first radiometrically corrected and orthorectified with respect to the GeoCover reference dataset (wall-to-wall orthorectified Landsat TM & mosaic) [27]. The images are resampled to Universal Transverse Mercator projection using the World Geodetic System 1984 (WGS84) geographic coordinate system and have 22 or 32 meters pixel size (respectively for DMC-22 or DMC-32 groups) in both the X and Y map directions. D. Sub-Sample For adapting the TREES processing chain to DMC data, we use a representative sub-sample of image extracts. From the whole dataset, 52 image extracts (also called boxes) spread over Central Africa was selected empirically to represent the variety of ecosystems and land-cover types (Fig. 2). This sub-sample shows a similar repartition of satellite imagery in terms of ecosystems frequency for the full Central Africa sample scheme (Table I). III. METHODOLOGY The monitoring approach is based on the TREES processing chain [11]. This processing chain includes steps of segmentation and classification of multi-date Landsat imagery. For the period 2000-2010, this object-based methodology is adapted using automatic change detection [9]. The overall processing chain includes 8 steps (Fig. 3): (1) image extraction, (2) visual screening, (3) co-registration, (4) calibration, (5) cloud & shadow masking, (6) segmentation, (7) change detection and (8) classification. The first five steps (pre-processing steps) are described in details by [10], [26]. These operations are first adapted to the DMC data characteristics. The next steps make use of the pre-processed image extracts over the years 2000 and 2010 as well as the available multi-date segmentation


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namely Brightness and Greenness. In order to adapt this classification for DMC data, an intermediate feature selection step was necessary to identify the appropriate information (object-based features) for the classification instead of tasseled caps components. A. Segmentation & Change Detection

Fig. 2. Mosaic of the 52 image extracts (each 20 by 20 km) for the sub-sample including Landsat TM, DMC-22 and DMC-32 imageries (RGB Red-NIR-Green). TABLE I REPARTITION OF IMAGE EXTRACTS FOR YEAR 2010 USED FOR THE FULL SAMPLE AND SUB-SAMPLE

The first step of an object-based approach is the segmentation of the image into objects. eCognition Developer 8 tool has been used given its success for many applications [17]. The main requirement of the segmentation is to delineate meaningful objects from multi-date images for both change detection and labelling steps. However, for consistency with the 1990–2000 exercise, it is based on multi-temporal segmentation [12], using a Minimum Mapping Unit (MMU) of 5ha. Given that the original segmentation process was based on three spectral bands of Landsat (namely TM bands 3, 4 and 5) [9], the only adaptation was to use the three spectral bands of DMC (namely green, red and NIR bands) to segment these images. In order to obtain consistent objects and avoid sliver polygons when combining the segmentation of imagery of year 2010 with the segmentation for period 1990–2000, the 1990–2000 objects are introduced as a thematic input layer in the segmentation process of 2010 images. This thematic input layer is created by dissolving adjacent segments with the same land cover labels, i.e., the same change trajectory from 1990 to 2000. These dissolved 1990–2000 segments are used as a layer of “super-objects” for the 2010 image segmentation. The thematic information of the 2000 land-cover layer is used in the subsequent steps of change detection and classification. For each object produced through this process (segmentation of 2010 imagery combined with the thematic layer from the period 1990–2000), land cover information from years 1990 and 2000 is copied in the new object-related database. The change detection step identifies automatically objects where land cover change has occurred between 2000 and 2010. The existing change detection module of the TREES processing chain combines image differencing and spectral Euclidian distances calculated on Landsat TM bands [9]. This change detection module is using the 2000 land-cover map as training information and compares the spectral information of images of years 2000 and 2010. The process is performed in sequential steps to separate real land cover changes from natural spectral variability such as seasonality effects. B. Feature Selection

Fig. 3. Scheme of the TREES processing chain with the Multi-Sensor adaptation.

1990–2000 [6]. After segmentation and change detection, a classification of the changed objects is performed based on Tasseled Caps components (TCap-based) for Landsat images,

The TREES processing chain adaptation to DMC data requires first to investigate the potential of DMC spectral information for forest cover monitoring. The characteristics of DMC imagery are compared to Landsat TM imagery characteristics through an analysis of spectral signatures over training sites. For this spectral assessment, we use Landsat TM imagery for years 1990 and 2000 to simulate DMC imagery. From the sub-sample of 52 boxes, 96 object features are extracted from both Landsat datasets from years 1990 and 2000. These features belong to four different categories: spectral (12 features), shape (3 features), texture (72 features) and band combination (9 features). All features are extracted using all objects from the land-cover


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TABLE II OBJECT FEATURES RANKING (BASED ON AVERAGE JM DISTANCE) SIMULATED DMC DATA USING VALIDATED LAND-COVER CLASSIFICATIONS FOR 2000 AND 1990

FOR

maps of years 1990 and 2000 for the sub-sample. The segmentation and feature extraction is performed within eCognition with a Minimum Mapping Unit of 5 ha. In total, the dataset used for this assessment includes 58 651 objects from the 52 boxes with land-cover information available for years 1990 and 2000. Given the large number of object-based features to be assessed, a pre-selection is performed in three steps. In a first step, shape features which do not provide significant differences between land-cover classes are removed from the feature dataset. Removed features include Asymmetry, Border Index and Compactness. In a second step, a Spearman’s correlation analysis is performed to remove correlated features, namely with correlation coefficients greater than 0.9. A number of time consuming textural features are highly correlated to object standard deviation (e.g., Contrast, Dissimilarity, Entropy, Angular 2nd Moment) and are removed from the feature dataset. The features number is consequently reduced to 30. In the last step, we keep only features which can be computed from DMC channels for further analysis. This final step leads to 15 resulting object features (Table II). In order to select the best features for the discrimination between land-cover classes, the 15 resulting features are analyzed using the SEATH tool. This tool computes the inter-class separability, namely the Jeffreys-Matusita (JM) distance for any land-cover class combination [21]. Finally, the JM distances are averaged over all potential class combinations for each of the 15 features. C. Classification The land-cover classification of year 2010 is performed on the basis of the results of change detection. Objects detected as “unchanged” are used as training data (with their 2000 land-cover class label) for the automatic classification of objects detected as “changed” for year 2010. The TREES land-cover legend includes seven land-cover classes: (1) Tree Cover (TC): woody vegetation (canopy density over 10%, high

above 5 m) with tree cover portion in image segment of at least 70%; (2) Tree Cover Mosaic (TCM): woody vegetation (canopy density over 10%, high above 5 m) with tree cover portion in image segment between 30% and 70%; (3) Shrub Cover (SC): including tree regrowth & other woody vegetation below 5 m high; (4) Other Vegetation Cover (OVC): agricultural areas or complex vegetated land cover unfitting TC, TCM or SC criteria; (5) Bare or Artificial areas (BA); (6) Water (WA); (7) Clouds and Shadows (CS). The classification approach is based on an adaptive supervised classification where land-cover class signatures are defined automatically within each image extract. This approach is also adapted to different satellite imageries (namely Landsat and DMC) using the best object-features identified by the here-above JM distance feature selection. After the 2000–2010 change detection step, “unchanged objects” are used to define land-cover class signatures and “changed objects” are classified based on these signatures using Nearest Neighbour (NN) algorithm. The eCognition NN algorithm computes probability membership functions for each object to be classified ( “changed objects”) in the selected object-features space. This object is assigned to the land-cover class corresponding to the nearest land-cover class signature. This approach is applied on the full sub-sample of 52 boxes for producing the 2010 land-cover maps. To compare this adapted approach to the original one, the subsample dataset of 52 boxes is also classified using the TREES TCap-based approach. Whereas TCap-based classification approach is based on similar principle (definition of land-cover signatures based on “unchanged objects”), the input information is different: firstly, the object-features used for the classification are the two first tasseled cap components, namely brightness and greenness; secondly, the definition of land-cover signatures are based membership functions defined by thresholds in the Brightness/Greenness space, namely for each land-cover class (m for mean and std for standard deviation). For matter of comparison, we use also DMC data with the TCap-based approach. As DMC imagery has only three spectral bands, tasseled cap components are computed using a duplication of the three DMC bands to simulate six Landsat TM bands. Missing bands in DMC imageries, corresponding to TM bands 1, 5 and 7, are replaced by DMC bands 2 (Red), 1 (Green) and 3 (NIR) respectively. D. Accuracy Assessment TREES land-cover maps obtained through the automatic classification are interactively corrected by regional and national experts for potential misclassifications due to the automatic procedure. A dedicated graphical user interface has been developed for the visual verification and potential re-assignment of land cover labels [28]. For each sample site, the tool allows to display simultaneously the pair of image subsets (e.g., of 2000 and 2010) and the corresponding land cover maps (automatically produced or after expert correction). The tool offers an optimized set of commands including image enhancement, simultaneous zoom of displayed data, single or multi object selection and relabeling, specific class selection and highlighting. The land cover maps include seven land-cover classes that are


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Fig. 4. Example of land-cover maps produced by (a) TCap-based and (b) Multi-Sensor approaches with DMC image acquired on April 21, 2010 (3 S, 16 E) (TC Tree Cover, TCM Tree Cover Mosaic, SC Shrub Cover, OVC Other Vegetation Cover, BA Bare or Artificial areas, WA Water, CS Clouds & Shadows, UC Unclassified).

controlled and corrected by experts from their local knowledge and ancillary information such as fine resolution images when available from GoogleEarth©. About half of the land-cover maps were evaluated and corrected by visual interpretation using this interactive tool (30 out of the 52 sample sites). This corrected data set is used as validation dataset to assess the performance and accuracy of our results (overall, producer and user accuracies as recommended by [29]), based on objects area (in hectares). This corrected dataset is considered as surrogate for truth maps for our study. For South America and Africa a ‘consistency assessment’ exercise has shown that there is an overall agreement between the TREES land cover maps and reference point data of 96% and 94% for these two regions respectively [30], [31]. Three different products have been assessed: (1) the whole land-cover classification of year 2010, (2) the 2000–2010 change detection (unchanged versus changed), and (3) the land-cover classification of year 2010 restricted to changed objects. This last information is computed to evaluate specifically the performance of the adapted classification.

1990 (Table II). The feature ranking of year 2000 is very close to the ranking for year 1990. The colour index hue is the best object feature whereas the other colour indices, intensity and saturation, have lower ranks (respectively 4th and 8th). These three indices already provide efficient discrimination between land-cover classes. The object standard deviations in green and red spectral bands are ranked second and third. The object standard deviation in NIR band has a lower impact for the discrimination of land-cover classes: this is probably due to similar variability of NIR values within objects for all vegetation types. The object means in the three DMC spectral bands are ranked 5th to 7th. The lowest feature ranks include textural information such as homogeneity and Correlation for bands green and red. The textural information from NIR band has very low significance (ranks 13th and 14th). Based on this ranking, we decided to select the features with an average JM values higher than 0.4 for our adapted classification approach (“Multi-Sensor approach”), namely 8 object-features: Hue, Saturation, Intensity, Mean of all DMC bands, Standard deviation of DMC bands 1 and 2.

IV. RESULTS A. Selection of Best Features for DMC Imagery For adapting the original TREES processing chain to DMC imagery, the first step was the selection of best object-features. This study was applied on 15 pre-selected features using the 1990 and 2000 validated land-cover maps as reference datasets and Landsat images to simulate DMC. For each potential landcover combinations (e.g., from TC to TCM, from TC to SC, from TC to OVC ), the average JM distance was calculated and averaged by object-features. The 15 pre-selected object-features are ranked by decreasing significance for years 2000 and

B. Multi-Sensor and TCap-Based Comparison Land-cover maps are produced for all 52 boxes of the subsample dataset through our Multi-Sensor approach. For each box of this dataset, imagery has been acquired and processed from Landsat TM sensor for around-year 2000 and a mix of Landsat TM (42%), DMC-22 (44%) and DMC-32 (13%) for around-year 2010. For comparison purpose, TCap-based approach was also applied on the same dataset. An illustration of land-cover maps produced by both classification approaches using DMC imagery of year 2010 is provided in Fig. 4.

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TABLE III CONFUSION MATRIX OF THE 2010 LAND-COVER CLASSIFICATION USING MULTI-SENSOR APPROACH

TABLE IV CONFUSION MATRIX OF THE 2010 LAND-COVER CLASSIFICATION USING TCAP-BASED APPROACH

Tables III and IV present the accuracy assessment results of the automatic land-cover classifications of year 2010. The overall accuracy comparison between the two approaches shows a limited improvement using Multi-Sensor approach (95.4%) compared to TCap-based one (93.3%). Considering the user accuracy, the main improvements are observed for classes TCM, OVC and BA. Given that land-cover maps are produced within two steps (change detection and classification), it is important to assess them separately. For the change detection step, the overall accuracy is assessed at 90.6% (Table V). This high overall accuracy is linked to the important weight of “unchanged objects”. When focused only on “changed objects”, we observe a high commission error (78%). However, after change detection, all “changed objects” are classified into land-cover class (thanks to the NN classification). They could finally be reconsidered correctly classified as “unchanged objects” if the land-cover has not changed between 2000 and 2010. Indeed after this classification step, this commission error drops from 78% (after change detection step) to 64% for the TCap-based approach and to 42% for our Multi-Sensor approach. In the other side, the overall omission error increases slightly from 18% to 20% (TCap-based) and 32% (Multi-Sensor).

TABLE V CONFUSION MATRIX OF CHANGE DETECTION FOR PERIOD 2000–2010

In order to evaluate specifically the improvement of our Multi-Sensor approach, a complementary accuracy assessment was performed on the classification step. This classification has been applied only on objects identified as changed by the previous change detection step. Tables VI and VII present the classification accuracy of these “changed objects”. In the Multi-Sensor approach, overall accuracy reaches 83%. This overall accuracy is a weighted average of 85.4% accuracy for sites with Landsat TM imagery and 81.7% accuracy for sites with DMC imagery in year 2010. This Multi-Sensor


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Fig. 5. User’s and Producer’s Accuracies (respectively U.A. and P.A.) of each land-cover classes derived from the changed objects accuracy assessment classified either by Multi-Sensor or TCap-based approach. TABLE VI CONFUSION MATRIX OF THE 2010 LAND-COVER CLASSIFICATION USING MULTI-SENSOR APPROACH, RESTRICTED TO CHANGED OBJECTS OVER 2000–2010

CONFUSION MATRIX

TABLE VII CLASSIFICATION USING TCAP-BASED APPROACH, OBJECTS OVER 2000–2010

OF THE 2010 LAND-COVER RESTRICTED TO CHANGED

performance (83%) is an important improvement compared to the 56% achieved by TCap-based approach. The comparison

of the two approaches is also analysed for each land-cover class (Fig. 5). In general, both user and producer accuracies

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are improved when using Multi-Sensor approach instead of TCap-based. This improvement is higher for TC, SC, OVC and WA. For TCM and BA, user accuracies increase whereas producer ones reduce. The low representation of BA can explain this effect and the proximity of TCM to SC in terms of land-cover signature makes their classification complex. V. DISCUSSION Forest cover monitoring in Central Africa is challenging due to the permanent cloud coverage that limits data availability and also to the small fragmented nature of the change. DMC data are found to be acceptable alternatives for filling Landsat data gaps for the year 2010. Despite DMC lower radiometric quality compared to Landsat TM & ETM, the challenge was to find a way to process both Landsat TM and DMC with the same methodology. The Multi-Sensor approach takes advantage of an automated supervised classification produced on-the-fly for each sample site using existing historical land-cover map (year 2000) as training set. For the 2000–2010 forest cover change exercise, the required data are thus the 2000 validated land-cover map as well as 2000 and 2010 remote sensing images. The radiometric quality issue of DMC imagery is partly handled through the consideration of local spectral signatures (i.e., at level of single sample site). This strength allows to process images from the different DMC sensors. One limitation of this on-the-fly aspect is that under-represented land-cover classes for year 2000 within an image extract are difficult to classify for year 2010. Indeed, if the number of training areas is low and does not represent the variability of the land-cover class, the other classes can occupy large part of the multi-dimensional feature space in the NN classification. The object-based features, provided by the eCognition software are numerous and it is often difficult to select the appropriate ones. A feature selection exercise is very important for ensuring high classification accuracy. In many studies, this preliminary step is often minimized or even skipped leading to sub-optimal classification accuracy. However, feature selection tools are usually not appropriate to provide rapidly significant and clear trends. Thanks to the study comparing different feature selection approaches [20], the JM distance was found efficient. Using SEATH tool [21], this selection could be realized in an efficient way as it can analyze object-based data directly exported from eCognition. Apart from the means of each spectral band which are commonly used, the results of object-feature selection illustrate the importance of spectral indices as hue, saturation and intensity as well as standard deviations of red and NIR channels for automatic land-cover classification. The importance of hue, saturation and intensity indices has already being demonstrated by other authors [32]. Whereas textural features were expected to be useful for the land-cover classification, object standard deviations are shown to be the main significant textural features in our study. A different conclusion can be expected for textural features when using finer spatial resolution imagery from which individual tree crowns can play a stronger textural role within objects. The added-value of the Multi-Sensor approach is important given the large data set to analyze (about 1400 image extracts

for Central Africa). The automated classification is applied only to objects detected as changed because the user accuracy is already very high (99.4%) for “unchanged objects”. An alternative way to map could have been to reclassify all objects, both changed and unchanged. However, we would most probably have to face to important commission errors of objects newly classified as changed given to the important weight of “unchanged objects”. Another option would have been to copy the 2000 land-cover information and perform a visual interpretation for identifying land-cover changes. However, if we consider our analysis over 52 image extracts including 41 742 objects, we would have to correct by visual interpretation about 2072 “changed objects” instead of the 1793 wrongly classified objects by the automated Multi-Sensor classification process. The amount of objects for which labels have to be corrected (1793) is then reduced compared to the amount of objects which would need to be relabelled if no automatic re-classification would be applied (2072). This automated process, now adapted to DMC, allows producing more efficiently 2010 land-cover maps with satisfying overall accuracy. VI. CONCLUSION TREES forest monitoring processing chain based on Landsat TM imagery has been successfully adapted to DMC imagery. This adaptation concerns mainly the classification step and is based on an appropriate object-features selection using largest JM distances between land-cover classes. The eight most significant object-features have been integrated in the nearest neighbour classification procedure in order to optimize the potential of DMC imagery for the classification. The resulting automated land-cover maps produced for year 2010 through this adapted processing chain show improvements in overall accuracies compared to the original Landsat based tasseled caps approach (from 93 to 95%). When considering only changed objects between 2000 and 2010, the improvement in accuracies between the TCap-based and Multi-Sensor approaches is much larger (from 56% to 83%). Although pre-processing steps have been adapted to the DMC characteristics, there is still room for further improvements, mainly for the cloud and shadows mask. Moreover, the under-representation of certain land-cover class in some of the sample sites is problematic for the automated classification as it requires a minimum presence of all land-cover classes within each single site (i.e., a minimum number of objects of each class). In order to be more robust over a larger set of ecosystems (e.g., dry tropical Africa), this Multi-Sensor approach will be evaluated in the future on a larger number of sample sites and with other sources of satellite data such as SPOT-HRV or RapidEye. This is intended to adapt the approach to a broader set of earth observation sensors in particular in the perspective of future Sentinel-2 data. ACKNOWLEDGMENT The Landsat data source for year 2010 is from the National Aeronautics and Space Administration Global Land Survey data set (GLS-2010). The DMC data were obtained in the framework


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Baudouin Desclée is post-doc scientist at the Joint Research Centre (JRC) of the European Commission. In 2002, he received the Bio-Engineer M.S. degree specialized in Forestry at the University catholique of Louvain (UCL, Belgium). His master thesis was focused on mapping forest in the Philippines using one of the first Very High Resolution (VHR) satellite images in the tropics. He continued to gain forest mapping expertise and in 2007, he obtained his Ph.D. degree in forest monitoring by developing automated change detection methodology. He continued to work and manage research projects on precision forest and agriculture mapping in private companies for 3 years. Since March 2011, he is involved in the TREES-3 project at the JRC for mapping and monitoring forest cover in Central Africa. He contributes to forest cover change assessment from Earth Observation data in the frame of the Observatory for Central African Forests (OFAC).

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Dario Simonetti was born in Varese, Italy, in 1981. He received the Laurea (M.S.) degree in informatics from the Universita’ dell’Insubria, Facolta’ di Scienze Matematiche, Fisiche e Naturali, Varese, in 2004. His thesis focused on the development of improved classification algorithms for near-real-time fire detection at global scale from satellite optical imagery. He is currently with the Forest Resources and Climate Unit, Institute for Environment and Sustainability, Joint Research Centre, European Commission, Ispra, Italy, where he was an Analyst—Programmer Consultant from 2004 to 2012. His main activities are focused on automatic near-real-time analysis of fire occurrences in African protected areas; development and assessment of processing chains for calibration, co-registration, topographic correction, and classification of satellite imagery; and development of user-friendly geographic information systems application for validation of satellite imagery classification across different epochs. He is contributing in delivering products and services derived from the analysis of satellite remote sensing data, with special emphasis on biodiversity and land cover change detection.

Philippe Mayaux was born in 1963 in Belgium. He received the Engineer in Forestry degree in 1986, the M.S. degree in land planning in 1988, and the Ph.D. degree in 1999 from the University of Louvain, Louvain-la-Neuve, Belgium. In the early 1990s, he worked for three years in field projects in Africa. He has worked for over 23 years in land cover mapping and tropical ecosystem monitoring from satellite imagery. He coordinated the African TREES project’s activities, produced the Global Land Cover 2000 Map of Africa and Australia and is involved in many projects dealing with biodiversity and land-cover changes, in particular, in Central Africa. His research focused also on the scaling issues in remote sensing and on validation of regional to global scale land-cover products. He is currently responsible of a research team dealing with the assessment of natural resources in Africa, Caribbean and Pacific countries. Dr. Mayaux is member of the Scientific and Technical Board of the GOFCGOLD project.

Frédéric Achard is senior scientist at the Joint Research Centre (JRC), Ispra, Italy. He first worked in optical remote sensing at the Institute for the International Vegetation Map (CNRS/University) in Toulouse. Having joined the JRC in 1992, he started a research activity over Southeast Asia in the framework of the TREES project. His current research interests include the development of Earth observation techniques for global and regional forest monitoring, and the assessment of the implications of forest cover changes in the Tropics and boreal Eurasia on the global carbon budget. Dr. Achard received his Ph.D. degree in tropical ecology and remote sensing from Toulouse University, France, in 1989. He has co-authored over 50 scientific peer-reviewed papers in leading scientific journals including Nature, Science, International Journal of Remote Sensing, Forest Ecology and Management, Global Biogeochemical Cycles and Remote Sensing of Environment.