Validating land cover maps with Degree Confluence Project information

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1 GEOPHYSICAL RESEARCH LETTERS, VOL. 33, L23404, doi: /2006gl027768, 2006 Validating land cover maps with Degree Confluence Project information Koki Iwao, 1 Kenlo Nishida, 2 Tsuguki Kinoshita, 1 and Yoshiki Yamagata 1 Received 4 August 2006; revised 10 October 2006; accepted 26 October 2006; published 12 December [1] We propose the use of Degree Confluence Project (hereby DCP) information as a new method for validating land cover maps. The DCP is a volunteer-based project that aims to collect onsite information from all the degree confluences (intersections of integer level latitude and longitude gridlines) in the world. We assessed the reliability and effectiveness of DCP-derived data in validating land cover maps. As a result, we obtained land cover validation information superior to the validation information obtained by visual interpretation of Landsat images. By using DCPderived validation information (at 749 confluences), we evaluated existing land cover maps for Eurasia (GLC2000, MOD12, UMD, and GLCC). The agreements between the DCP-derived validation information and the land cover maps were 55% for GLC2000, 58% for MOD12, 54% for UMD, and 50% for GLCC. Although MOD12 and GLC2000 had somewhat better agreements than the other maps, there is no significant difference between the two. Citation: Iwao, K., K. Nishida, T. Kinoshita, and Y. Yamagata (2006), Validating land cover maps with Degree Confluence Project information, Geophys. Res. Lett., 33, L23404, doi: / 2006GL Introduction [2] Land cover maps are one important source of data for discussions of global environmental problems. However, the accuracy of these maps needs to be improved [DeFrise and Belward, 2000; Patenaude et al., 2005]. For example, terrestrial ecosystem models rely on land cover maps for their estimates of total net primary production and spatial distribution; consequently, the accuracy of existing land cover maps needs to be quantitatively evaluated [Ahl et al., 2005; Kok et al., 2001]. [3] Numerous methods have been proposed in the past for validation of land cover maps. For example, many studies have proposed various methods based on interpretation of aerial photographs or satellite imagery. These methods, however, still have problems such as insufficient amount of information and lack of balance in spatial distribution [Hord and Brooner, 1976; Kelly et al., 1999; Rosenfield and Fitzpatrick-Lins, 1986; Strand et al., 2002]. In fact, studies comparing several land cover maps found that the total global areas for each land cover class were similar to one another but varied significantly by region [Giri et al., 2005; McCallum et al., 2006]. These results 1 Center for Global Environmental Research, National Institute for Environmental Studies, Tsukuba, Japan. 2 Institute of Agricultural and Forest Engineering, University of Tsukuba, Tsukuba, Japan. Copyright 2006 by the American Geophysical Union /06/2006GL clearly demonstrate that there has not been enough progress in the validation of the land cover maps. [4] The validation methods proposed in the past focused on statistical evaluation using a small amount of validation information. Properly speaking, numerous, spatially balanced field surveys over the entire earth are preferable [Buckland and Elston, 1994; Tateishi and Hasting, 2002], but it was very difficult for any one researcher or a small group of researchers to conduct a thorough field survey over the entire earth. That is, until the Degree Confluence Project (the DCP ) (DCP website, 1996, org/) was started. The objective of the DCP is for participants to visit the latitude and longitude integer degree intersections (the confluences ) and document the state of the surroundings. In this study, we propose a method for developing validation information for land cover maps based on the information collected by the DCP, and demonstrate the usefulness of the DCP-derived information. Furthermore, we tested existing land cover maps against the DCP-derived information for Eurasia. 2. Methodology [5] The DCP was initiated by Alex Jarrett in February Since then, volunteers have conducted visits at confluences around the world. Visitors to any of the confluences may use the DCP website to register photographs taken at the confluence together with text about their visit, which collectively can form the basis for a Current Site Description (CSD) of the confluence. Any number of CSDs may be registered for a single confluence by anyone who has visited the site. This provides information regarding that confluence over varying periods. [6] The WGS84 system is used for locating the confluence. For all of the 64,442 possible confluences, 16,180 meet the goals of the project confluence. Near the poles and oceans are discounted from having a CSD. Positional errors of the visits must be within 100 m. As of May 2006, a total of 4,962 confluences have been visited at least once, which covers about 20% of all possible confluences (24,482) on or close to land. Compared to the validation information published by Boston University (IGBP land cover validation confidence sites at Boston University: Sample index, 2005, which covers 413 sites (as of May 2006), the DCP covers almost 10 times more. The DCP requires participants to take a minimum of two photographs within a range of 100 m of the confluence, and recommends four additional photographs be taken in each of four directions from the confluence. The regional coordinators of the DCP check the particulars of a submission prior to registration. These policies of the DCP ensure that field visits are of a global scale, numerous, spatially balanced, and quality-checked, all of which are necessary for validation of land cover maps. L of5

2 Table 1. Relationships Between the Classification Schemes of Land Cover Maps (GLC2000, MOD12, UMD, and GLCC) and the 6 LULUCF Land-Use Classes a Unified Classification Class (Class No.) Land Cover Classification Scheme (GLC2000) IGBP Class Scheme (MOD12) Classification Scheme Simplified IGBP Class Scheme (UMD) Olson Global Ecosystem Legend (GLCC) Forest land (1) , 11, 16 18, 20 29, 32 34, 46 48, 51, 52, 54, 59, 60 64, 77 79, Cropland (2) , , 19, 30, 31, 35 39, 55 58, 76, Grassland (3) , 10 8, 9, 10 2, 7, 40, 41, 42, 43, 87 Wetlands (4) 20 0, 11, , 13 15, 44, 45, 53, 65 68, 72 75, Settlements (5) Other land (6) 19, 21 15, 16, 254, , 254 8, 12, 49, 50, 69, 70, 71 a Class numbers correspond to class numbers assigned in each of the land cover maps. [7] In order to develop validation information, a land cover classification scheme needs to be established first. In this study, so that the information can be utilized for carbon management policy and other purposes, we adopted 6 classes, forest land, cropland, grassland, wetlands, settlements, and other land of the LULUCF (Land Use, Land Use Change and Forestry) classification scheme established by the Intergovernmental Panel of Climate Change (IPCC) (Good practice guidance for land use, land-use change, and forestry, 2003, gpglulucf/gpglulucf_contents.htm). [8] We attempted visual interpretation of land class and its spatial uniformity at each DCP from its CSDs on the website. A pilot study using three test subjects (an ecologist, a land cover map researcher, and a clerical staff member) confirmed that the results of land cover classification from visual interpretation of CSDs (hereby DCP-derived validation information) did not rely on the expertise of the interpreters, that is, the interpretation were objective and reproducible independently of the expertise of the classifiers. Therefore, we adopted the majority opinion of the three visual interpreters of the CSDs as the information by which to validate land cover maps. Based on this method, we then developed DCP-derived validation information for 749 confluences for Eurasia (i.e., the region covered from 80 N to10 S and from 20 E to 180 E). [9] Next, we used three different methods to evaluate the DCP-derived validation information. First, to check the positioning accuracy of visits to a confluence, we focused on eight confluences which had been visited more than four times and investigated the consistency among the CSDs for each confluence. We believed that if the contents of the CSDs resembled each other for a confluence, we could trust the positioning information contained in the CSDs. We further examined the representativeness over time by confirming whether there were any changes in land cover over the multiple visits to each of the eight confluences. Second, to evaluate the reliability of the DCP-derived validation information, we compared it with the information developed by conventional methods based on satellite imagery, using the ortho-geo rectified Landsat imagery published on the website of the Earth Science Data Interface (University of Maryland, Earth science data interface, umd.edu/index.shtml). We visually interpreted Landsat false-color images (red, band 4; green, band 3; and blue, band 2) at 30 confluences for Thailand based on topography, color and texture. Third, to confirm the results of the visual interpretation of CSDs and Landsat images with respect to land cover and spatial representativeness, we made actual visits to some confluences including a confluence which could not be interpreted from its CSD (13 N, 100 E), a confluence which appeared to be typical cropland from its CSD (14 N, 100 E), and a confluence which indicated that the contents in the texts had changed during the intervals between three visits (13 N, 101 E). [10] Also, we compared DCP-derived validation information and existing land cover maps for Eurasia. In this study, we tested four land cover maps. They were Global Land Cover 2000 (GLC2000) (Joint Research Centre, Global land cover 2000, MODIS Land Cover (MOD12) (Boston University, Land cover and land cover dynamics products user guide, 2003, available at index.html), the University of Maryland s 1-km Global Land Cover products (UMD) [Hansen et al., 2000], and Global Land Cover Characteristics Data Base Version 2.0 (GLCC) (U.S. Geological Survey, Global land cover validation exercise, 1999, globdoc2_0.html#valid). These land cover maps had been developed according to different classification schemes; accordingly, we adapted them to the 6 LULUCF land classes through a method proposed by Sato and Tateishi [2001]. Table 1 shows the relationship between the classes before and after they were adapted. 3. Results [11] Based on the CSDs, we categorized the land cover of each of 749 confluences as either forest land (239 confluences), cropland (195 confluences), grassland (184 confluences), wetlands (76 confluences), settlements (11 confluences), or other land (44 confluences) (Figure 1). Some photographs in the CSDs revealed landscapes over several kilometers although the DCP policy required visitors to characterize landscapes only within 100 m from a confluence. Conversely, it was difficult to obtain a visibility 2of5

3 Figure 1. Distribution of confluences examined in this study. Forest land (green), 239 confluences; cropland (yellow), 195 confluences; grassland (orange), 184 confluences; wetlands (blue), 76 confluences; settlements (red), 11 confluences; other land (grey), 44 confluences. Total confluences, 749. of 100 m in most confluences in forests. In some cases, however, it was possible to infer spatial representativeness by taking into account the verbal descriptions of text in a CSD. With respect to temporal representativeness of the land cover classes, we confirmed through multiple visits that seven of eight confluences (55 N, 83 E; 56 N, 49 E; 56 N, 37 E; 60 N, 27 E; 35 N, 137 E; 21 N, 106 E; 40 N, 116 E; 23 N, 114 E) had had no change in land cover since We also confirmed the remaining confluence (56 N, 49 E) to be temporally invariant by ignoring the CSDs of two incomplete visits (i.e. the visitors could not reach within 100 m of that confluence). In addition, from the CSD of the eight confluences, we were able to infer that all the visitors had reached the same confluences. [12] We confirmed the consistency between interpretation results of CSDs and Landsat images and found that they agreed at 20 confluences out of 30 confluences. Seven confluences out of the 30 confluences could not be interpreted from Landsat images but were interpretable from CSDs. For confluence 18 N, 99 E, the land cover type could not be estimated even with Landsat images taken in two different seasons. Confluence 14 N, 101 E was visually interpreted to be forest land from the CSD, but the Landsat imagery suggested that it was likely to be cropland. Confluence 13 N, 100 E, which could not be visually interpreted from the CSD, was interpreted using the Landsat imagery to be most likely cropland. In contrast to interpretation of Landsat images, the visual interpretation of CSDs sometimes allows the identification of sub-classes, such as tree species or type of agriculture. [13] Based on these results combined with the results of the pilot test, the DCP-derived validation information is likely to be the same as or better than the validation information from visual interpretation of Landsat imagery in terms of objectivity and reproducibility independent of the expertise of the classifiers. In particular, the DCPderived validation information is much more reliable than the results of the visual interpretation of Landsat imagery because of the availability of a description of the confluence based on actual visits. [14] From the results of personal field visits by one of the authors to three confluences, we confirmed that the CSDs alone could be used to accurately interpret the land cover. Confluence 14 N, 100 E, which was interpreted to be typical paddy fields according to the CSD, was confirmed to be indeed a paddy field. Confluence 13 N, 101 E had been visited three times as of April 2006 (in May 2001, October 2004, and in October 2005), and photographs taken on each of the visits showed similar land cover (cropland), although there were some discrepancies (e.g. type of crop) in the text. Our field visit to the confluence confirmed its classification as cropland. There was, however, a discrepancy in the description of the type of crop (i.e. the crop, identified as rubber trees in one text, was actually coconuts). Visual interpretation of the CSD was not possible for confluence 13 N, 100 E because, although the photographs included showed a farm house and a reservoir, the text contained little information regarding land cover. The field visit, however, confirmed that it was cropland as indicated by the visual interpretation of the Landsat imagery. Furthermore, the combined use of the Landsat imagery for the confluences for which the CSD could not be interpreted easily was found to be useful in improving the interpretation. [15] Figure 2 illustrates how the DCP-derived validation information developed from the CSD agrees with several land cover maps. The blank spots on the maps indicate the confluences of disagreement. Comparison of the DCPderived validation information with existing land cover maps produced agreement rates of 55% for GLC2000, 58% for MOD12, 54% for UMD, 50% for GLCC (Table 2). We did not include confluences which the DCP has not yet visited in the statistical analysis. After the number of the DCP-derived validation information developed from the CSDs increases, this result may change. If a binominal distribution can be assumed, there would be a standard error of about 2 percentage points. The above results indicate that although MOD12 had the highest agreement, there is no significant difference between MOD12 and GLC2000. MOD12, however, is superior to UMD and GLCC with an agreement rate more than four percentage points higher. UMD gave the best agreement in identifying forest and grassland, while GLCC showed the best agreement in identifying cropland. 4. Discussion [16] If the confluence and its surrounding 1-km square area contain more than one type of land cover, it is not always possible to determine whether the DCP-derived validation information faithfully represents the land cover of a 1-km square surrounding the confluence. For example, the visual interpretation of Landsat imagery and the DCPderived validation information did not agree for confluence 14 N, 101 E, possibly owing to the fact that the land cover at this confluence did not reflect typical land cover in the 1-km square surrounding the confluence. Since, however, DCP data can be regarded as a random sampling of the land cover within a 1km 2 area; therefore, on average it faithfully represents the dominant land cover a 1km 2 area. Accordingly, the use of a large number of confluences could statistically ensure their reliability. In fact, the interpretation results of the 22 confluences showed agreements for 21 3of5

4 Figure 2. Comparison of DCP data with that of various land cover maps (GLC2000, MOD12, UMD, and GLCC). Forest land (green), cropland (yellow), grassland (orange), wetlands (blue), settlements (red), other land (grey), disagreement (white). The points which DCP project has not visited yet are not shown. confluences with the results of a visual interpretation of Landsat imagery. The agreement may be improved by combining DCP-derived validation information with visual interpretation of Landsat imagery when the DCP interpretation does not match any other land cover maps. [17] Because DCP is trying to cover the entire world with a consistent protocol, our approach can be applicable worldwide in principle. The biggest obstacle for this is the less availability of the DCP data in some particular places such as Africa, South America, and the central Asia. This may be simply due to difficulty of secure travel in some countries in those areas because of the tough natural/ political environment. In order to overcome this issue, we Table 2. Agreement Rates Between Global Land Cover Maps and the Results of Visual Interpretation of DCP Data DCP GLC2000 MOD12 UMD GLCC Forest land Cropland Grassland Wetland Settlements Other Total Agreements, % would like to attract attentions of many people and organizations in various social circumstances to the importance of the field survey activity such as DCP. 5. Conclusion [18] We have developed a method for the validation of land cover maps using the Degree Confluence Project information. It is evident that the DCP-derived validation information has the same or a higher level of reliability than validation information based on conventional visual interpretation with Landsat imagery. The validation data for land cover maps for Eurasia at 749 locations indicate agreement rates for existing land cover maps of 55% for Global Land Cover 2000, 58% for MODIS Land Cover 12, 54% for University of Maryland Data, and 50% for GLCC. The agreement rates were higher for MOD12 and GLC2000 than for other data sets, although there were no significant differences between MOD12 and GLC2000. [19] Acknowledgments. We acknowledge with thanks that this study was supported by the S-1: Integrated Study for Terrestrial Carbon Management of Asia in the 21st Century Based on Scientific Advancements program funded by the Global Environmental Research Fund of the Ministry of the Environment of Japan (study leader: Takehisa Oikawa). We also thank the founder, the organizers, and all participants of the Degree Confluence Project. 4of5

5 References Ahl, D. E., S. T. Gower, D. S. Mackay, S. N. Burrows, J. M. Norman, and G. R. Diak (2005), The effects of aggregated land cover data on estimating NPP in northern Wisconsin, Remote Sens. Environ., 97, Buckland, S. T., and D. A. Elston (1994), Use of groundtruth data to correct land cover area estimates from remotely sensed data, Int. J. Remote Sens., 15(6), DeFrise, R. S., and A. S. Belward (2000), Global and regional land cover characterization from satellite data: An introduction to the special issue, Int. J. Remote Sens., 21(6 7), Giri, C., Z. Zhu, and B. Reed (2005), A comparative analysis of the Global Land Cover 2000 and MODIS land cover data sets, Remote Sens. Environ., 94, Hansen, M., R. DeFries, J. R. G. Townshend, and R. Sohlberg (2000), Global land cover classification at 1km resolution using a decision tree classifier, Int. J. Remote Sens., 21(6 7), Hord, R. M., and W. Brooner (1976), Land-use map accuracy criteria, Photogramm. Eng. Remote Sens., 42, Kelly, M. K., J. E. Estes, and K. A. Knight (1999), Image interpretation keys for validation of global land-cover data sets, Photogramm. Eng. Remote Sens., 65, Kok, K. K., A. Farrow, A. Velkamp, and P. H. Verburg (2001), A method and application of multi-scale validation in spatial land use models, Agric. Ecosyst. Environ., 85, McCallum, I., M. Obersteiner, S. Nilsson, and A. Shvidenko (2006), A spatial comparison of four satellite derived 1 km global land cover datasets, Int. J. Appl. Earth Obs. Geoinf., in press. Patenaude, G., R. Milne, and T. P. Dawson (2005), Synthesis of remote sensing approaches for forest carbon estimation: Reporting to the Kyoto Protocol, Environ. Sci. Policy, 8, Rosenfield, G. H., and K. Fitzpatrick-Lins (1986), A coefficient of agreement as a measure of thematic classification accuracy, Photogramm. Eng. Remote Sens., 52, Sato, H., and R. Tateishi (2001), The review of a global land use, land cover, and vegetation classification system, Geogr. Surv. Inst. Annu. Rep., 96, Strand, G.-H., W. Dramstad, and G. Engan (2002), The effect of field experience on the accuracy of identifying land cover types in aerial photographs, Int. J. Appl. Earth Obs. Geoinf., 4, Tateishi, R., and D. Hasting (Eds.) (2002), Global Environmental Databases, Geocarto Int. Cent., Hong Kong. K. Iwao, T. Kinoshita, and Y. Yamagata, Center for Global Environmental Research, National Institute for Environmental Studies, 16-2 Onogawa, Tsukuba , Japan. (iwao.koki@nies.go.jp) K. Nishida, Institute of Agricultural and Forest Engineering, University of Tsukuba, Tennoudai, Tsukuba , Japan. 5of5

Land cover/land use mapping and cha Mongolian plateau using remote sens. Title. Author(s) Bagan, Hasi; Yamagata, Yoshiki. Citation Japan.

Title Land cover/land use mapping and cha Mongolian plateau using remote sens Author(s) Bagan, Hasi; Yamagata, Yoshiki International Symposium on "The Imp Citation Region Specific Systems". 6 Nove Japan.