Changes between Version 32 and Version 33 of UserApp/Proba-V

Timestamp:

12/16/11 13:02:46 (12 years ago)

Author:

Iliyankatsarski

Comment:

UserApp/Proba-V

@@ -475 +475 @@
 
 [http://events.eoportal.org/presentations/7111/10001905.html For further info File:ProbaV Auto6.jpeg]
+
+= PROBA-V PERFORMANCE ASSESSMENT =
+
+= ABSTRACT =
+
+The Belgian Federal Science Policy Office (BELSPO) has initiated a Preparatory Evalua-tion/Validation Programme for 
+the products of the new PROBA-V satellite to be launched in 2012. The satellite will allow daily monitoring of terrestrial 
+vegetation cover through remote sensing, and will cover the data provision gap between the closure of the SPOT/VEGETATION 
+Programme and the launch of the SENTINEL-3 mission. The aim of this study is to evaluate the improvements that PROBA-V will 
+bring along for forest monitoring in the Atlantic Biogeographical Region of Europe, and lies within the objectives of the 
+FM@PROBA-V project. A representative site in Northern Por-tugal is selected for this reason. VEGETATION, LANDSAT-ÔÌ5, and 
+MODIS data along with the JRC Forest Cover Map are used to train the classifiers, simulate PROBA-V data, apply the classi-fiers 
+at 250 m, 1/3 of a km, and 1 km pixels, and validate the results, while quantifying the accura-cies. Maximum Likelihood (ML), 
+Artificial Neural Networks (ANN), and Support Vector Machine (SVM) methods were tested. From the confusion matrices the best 
+result is obtained by MODIS 2 bands with ANN classifier. Further analysis on the base of those confusion matrices will be 
+applied to define the best classifier taking into account all the parameters of the matrices. The best per-forming classifier 
+will then be recommended to examine its robustness against sudden disastrous events, like fire, in the same area, performing 
+change detection between sequential dates (before and after the event). The performance of the data and classifiers are 
+demonstrated, and the pre-liminary results are discussed.The Belgian Federal Science Policy Office (BELSPO) has initiated a 
+Preparatory Evalua-tion/Validation Programme for the products of the new PROBA-V satellite to be launched in 2012. The satellite 
+will allow daily monitoring of terrestrial vegetation cover through remote sensing, and will cover the data provision gap between
+the closure of the SPOT/VEGETATION Programme and the launch of the SENTINEL-3 mission. The aim of this study is to evaluate the 
+improvements that PROBA-V will bring along for forest monitoring in the Atlantic Biogeographical Region of Europe, and lies within 
+the objectives of the FM@PROBA-V project. A representative site in Northern Por-tugal is selected for this reason. VEGETATION, 
+LANDSAT-ÔÌ5, and MODIS data along with the JRC Forest Cover Map are used to train the classifiers, simulate PROBA-V data, apply 
+the classi-fiers at 250 m, 1/3 of a km, and 1 km pixels, and validate the results, while quantifying the accura-cies. Maximum 
+Likelihood (ML), Artificial Neural Networks (ANN), and Support Vector Machine (SVM) methods were tested. From the confusion 
+matrices the best result is obtained by MODIS 2 bands with ANN classifier. Further analysis on the base of those confusion 
+matrices will be applied to define the best classifier taking into account all the parameters of the matrices. The best 
+per-forming classifier will then be recommended to examine its robustness against sudden disastrous events, like fire, in the same area, 
+performing change detection between sequential dates (before and after the event). The performance of the data and classifiers are 
+demonstrated, and the pre-liminary results are discussed.
+= INTRODUCTION =
+
+
+World forests cover roughly 31% of land area and in the last decade it has been reduced at a rate of 13 million hectares 
+per year (1). According to the global forest resources assessment 2010 of the Food and Agriculture Organization, significant 
+progress has been made towards reversing the overall trend of forest area loss and a positive trend over time has been shown 
+in some countries and regions such as in Europe where the forest area continued to expand.
+
+Considerable efforts are needed to improve or at least maintain this positive trend. The forest monitoring gives crucial data 
+to decision makers helping them allocate appropriate financial re-sources for effective forest conservation and management plans. 
+One of these crucial data is For-est Area, which has been selected as one of the 60 indicators for monitoring progress towards 
+the Millennium Development Goals, the 2010 Biodiversity Target and the Global Objectives on For-ests. Forest Area and its changes 
+are relatively easy to measure, especially nowadays with the technological progress made in remote sensing fields.
+
+The available high temporal resolution images allow operational and near real-time applications at global, continental and regional 
+scales for forest area mapping and monitoring. The VEGETATION instruments, which were developed with the objective to provide data 
+specifically for vegetation canopy monitoring, are on board two SPOT5 satellites and will be available until 2013. The instru-ments 
+of this programme (VEGETATION 1 and VEGETATION 2) have monitored and mapped the worldwide vegetation on a daily basis for more than 
+12 years now. They provide essential infor-mation on terrestrial vegetation cover for a large community of users. Therefore, to ensure 
+the continuity of the data after 2012, the SENTINEL mission, which will be launched at the earliest at the end of 2013 (2), will provide 
+data which will fulfil the needs of the current VEGETATION data users. To fill the data gap between VEGETATION-2 and SENTINEL-3, the 
+Belgian Federal Sci-ence Policy Office (BELSPO) has decided to build a satellite mission called PROBA-V which is expected to meet all 
+the specifications of the vegetation user community (2). PROBA-V will have an increased spatial resolution in comparison to the 
+VEGETATION instrument, an enhancement expected to provide new opportunities for forest monitoring.
+
+The aim of this study is to evaluate forest cover classification methods on the European Atlantic biogeographical region by applying the 
+Maximum Likelihood (ML), Artificial Neural Network (ANN) and Support Vector Machine (SVM) classification algorithms on simulated PROBA-V, 
+VEGETATION and MODIS data, in order to determine the optimal classification methodology for the region. This work is carried out within 
+the framework of the FM@PROBA-V project1 towards evaluation and quantification of the improvements of the products for forest monitoring 
+offered by PROBA-V, in relation to its predecessor, VEGETATION. The preliminary results of the study are presented and discussed in this paper.
+== Study area ==
+
+
+The area of interest is the southern part of the Atlantic biogeographical region of Europe (Figure 1) which is located 
+in the north of Portugal. The region is one of the rare mountainous areas in this region, and mountains in the area can 
+reach an altitude of 1500 m. Forest cover is sparse and agriculture is not very intensive in the region
+ 
+= Methods =
+
+== Dataset and preprocessing ==
+
+
+The data used in this study included the images used for the evaluation process of the forest cover mapping in the European Atlantic 
+biogeographical region and the data used to create the refer-ence data for the classifications. The reference data originated from the 
+classification of a Landsat TM image, acquired on 11 July 2009, using training samples and reference points identified from the JRC 
+Forest Cover Map 2006 and Google Earth images. A small subset (20 „e 20 km2) of that scene was used to simulate a part of a PROBA-V 
+scene, a process undertaken by VITO NV, Bel-gium. The datasets used to evaluate classification algorithms included the simulated 
+PROBA-V image (Red, NIR, approximated SWIR), a 10-day synthesis VEGETATION (VGT) image at 1000 m spatial resolution with 3 bands (Red, NIR, SWIR), 
+and an 8-day synthesis MODIS image at 250 m spatial resolution (again Red, NIR, resampled/ upgraded SWIR). The simulated PROBA-V 
+subset was relatively small (20 „e 20 km2) and the results were expected to be of questionable statistical significance. Hence the 
+MODIS image was additionally evaluated, as its similar charac-teristics with PROBA-V data (Table 1) would allow better evaluation of 
+the potential of the upcom-ing sensor, and its comparison with VGT data. Since only the red and NIR channels are available at 250 m 
+resolution, the SWIR 500 m channel was resampled to 250 m and two MODIS datasets were evaluated, with and without the SWIR data. All 
+images, including the JRC forest cover map where registered to the Landsat TM image in UTM WGS84, Zone 29N projection, WGS84 datum. 
+The extent of MODIS and VGT images was reduced to a subset matching the extent of Landsat TM, which was topographically normalized with the ASTER GDEM.
+
+
+{| border="1"
+|+ Image data characteristics
+! !! VEGETATION II !! Modis !! Proba V 
+|-
+! Blue
+| 0.43 – 0.47 || 0.45 - 0.47 || 0.44 – 0.48
+|-
+! Red
+| 0.61 – 0.68 || 0.62 – 0.67 || 0.62 – 0.69
+|-
+! NIR
+|0.78 – 0.89 || 0.84 - 087 || 0.79 – 0.90 || 800 x 800 x 1000 mm
+|-
+! SWIR
+| 1.58 – 1.75 || 1.62 –1.65 || 1.56 – 1.65
+|-
+! Spatial Resolution
+| 1.15 km || 250m (R, NIR), 500m (SWIR) || 300m (VNIR), 600m (SWIR)
+|}
 = User Segment =