Applying remote sensing and gis to classify forest and change detection from 2000 to 2015 in yen nhan thanh hoa

ACKNOWLEDGMENT Firstly, I would like to express my sincere gratitude to my advisor Assoc Prof Tran Quang Bao for the continuous support of my study and related research, for his patience, motivation, and immense knowledge.His guidance helped me in all the time of research and writing of this thesis I could not have imagined having a better advisor and mentor for my study My sincere thanks also goes to Mr Thi, Mr Khang, and Mss Oanh in Institute for Forest Ecology and Environment of Vietnam Forestry University I am extremely thankful and indebted to him for sharing expertise, and sincere and valuable guidance and encouragement extended to me I would like to extend my thanks to the Education Department and Sciences, Technology and International Cooperation Department to give me a change to study in high quality education I also place on record, my sense of gratitude to one and all, who directly or indirectly, have lent their hand in this venture Ha Noi, November/2015 Nguyen Thi Hoa LIST OF CONTENTS ACKNOWLEDGMENT ABSTRACT INTRODUCTION GOALS AND OBJECTIVES 3 STUDY AREA AND DATA 3.1 Study Area 3.2 Data Sources 4 METHODOLOGY 4.1 Image segmentation (Step1) 4.2 Classification and accuracy (Step2) 4.2.1 Classification 4.2.2 Accuracy 10 4.3 Change Detection (Step 3) 12 RESULTSDISCUSSION 13 5.1 Object – Based Classification 13 5.2 Classification 13 5.2.1 Forest classification 13 5.2.2 Accuracy 16 5.3 Land Cover Change 18 5.3.1 Forest Cover 18 5.3.2 Change Detection 21 RECOMENDATIONS 27 CONCLUSIONS AND PERSPECTIVES 28 7.1 Conclusions 28 7.2 Perspective 29 REFERENCES 30 APPENDICES 32 LIST OF FIGURES Figure 1: Location of the Yen Nhan Commune in Thanh Hoa Province Figure 2: Flowchart of research’s methodology Figure 3: Objects with multi-resolution segmentation Figure 4: The distribution of sample points in the study area 11 Figure 5: Object-based classification result (2015) 13 Figure 6: Land areas (ha) in 2015: Forest classification classes (a), their percentages (b) and their proportion forest and non-forest (c) 15 Figure 7: Land use/land cover in Yen Nhan 2015 16 Figure 8: Land cover/land use classification maps of Yen Nhan commune in year 2000, 2005, 2010 and 2015 20 Figure 9: Land cover/land use in Yen Nhan commune from 2000 to 2015: (a) the distribution of land cover/land use types in percent in different years, (b) the evolution of the properties of the major land cover/land use types in hectare, (c) the magnitude of the land cover/land use changes in ha/year for each time interval 21 Figure 10: Spatial distribution of land cover/land use changes in Yen Nhan commune from 2000-2015 24 ABSTRACT Satellite data have become a major application in forest classification and change detection because of representative coverage of the satellites at short intervals Using Remote Sensing and GIS techniques is a cost effective method to map, detect and monitor forest resources The overall objectives of this study are to map out and analyze structural changes of forest cover using SPOT-5 image in 2015 and detect forest cover transformation in Yen Nhan - Thanh Hoa, from 2000 to 2015 ECognition Developer, ArcGIS and MapInfo softwares were used for classification Applying multi-resolution segmentation algorithm of eCognition Developer to segment the imagery of study area into 11,135 deference objects with ranging areas from 0.11 to 112.11 hectare of study area in 2015 Based on the results of classification indices from ground survey, segmented imagery of SPOT-5 were classified into 10 land cover classes: (1) rich evergreen, (2) medium evergreen, (3) poor evergreen, (4) rehabilitation evergreen, (5) bamboo, (6) mixed wood and bamboo, (7) plantation forest, (8) bare land, (9) shrub and grass, and (10) water body The results of the comparison of the four classified images showed that, period 20002005 and period 2010-2015 the forest cover have increased by an amount of 2206.9 and 3321.7 respectively representing 11.7% and 17.56% However, from 2005 to 2010, the forest cover has decreased significantly by 641.7 ha, representing 3.4% Generally, the results indicate that from 2000 to 2015, forest cover increased 4886.9 ha, representing 25.88% Overlay of the reserved forest of 2000, 2005, 2010 and the classified map of 2015 shows vegetation changed during 2000-2015 remarkably Keywords: Remote Sensing;GIS, SPOT5; Change Detection; Forest Classification; INTRODUCTION Forest ecosystem is responsible for much of our climate physiology, plays an important roles for human and animals According to the U.N FAO[16], 44.5% or about 13,797,000 of Viet Nam is forested Of this 0.6% (80,000 ha) is classified as primary forest, the most bio-diverse and carbon-dense form of forest.However, looking back to the year 1943, one can find that the forest cover at that time was not less than 43% Primary forest cover in 2000 was 187,000 hectares, in 2005 was 85,000 hectares, annual change is 10.91% Studies of Rosyadi (1986) [15] or Ringrose (1997) [14] or Boakey (2008) [6] have performed to identify factors that cause changes in forest cover in developing countries One of those factors is unappropriated agricultural technology used in farm lands located around the forest area (Angelsen, 2001) [1] The misuse of forest resources due to the centralization of forest management policy is considered as another factor of deforestation (Rosyadi, 1986) [15] Moreover, Boltz et al [7] mentioned that conventional logging operation with unplanned selective logging method also becomes one factor of deforestation However, the most importance factor that causes deforestation comes from illegal logging and trade (Atmopawiro, 2004) [2] Forest cover change is one of the main driving forces of global environmental change, is central to the sustainable development debate Forest cover changes have impacts on a wide range of environmental and landscape attributes including the quality of water, land and air resources, ecosystem processes and function, and the climate system itself through greenhouse gas fluxes and surface albedo effects Hence general information about change is necessary for updating forest cover maps and the management or natural resources From 1990 to 2010, Vietnam have done four forest inventory surveys In these surveys, Landsat and SPOT imageries were used (Bao, et al., 2010) Currently, the forest inventory program is conducting and using SPOT-5, SPOT-6 and VNREDSAT imageries In the previous years, to investigate forest resources are mainly based on survey and mapping by manual methods It requires a lot of time, money, effort and low accuracy, information is not often updated because forest cover change over time In recent years, geographical information systems (GIS) and remote sensing are well-established information technologies, the value of which for applications in land and natural resources management are now widely recognized (Bao et al., 2013) [5] Recent improvements in satellite image quality and availability have made it possible to perform image analysis at much larger scale than in past Forest have long been regarded as a nation treasure in Thanh Hoa and in addition timber; those forests provide such resources as grazing land for animals, wildlife habitat, water resources, non-timber or timber products for local people However, farming activities and illegal logging are posing a serious threat to quality and quantity of forest Mapping forest cover changes maps is the standard way to monitor changes, a change detection analysis was performed to determine the nature; extent and rate of forest cover change over time and space.The results will quantify the forest cover change patterns in the area and demonstrate the potential of multi-temporal satellite data to map and analyze changes in forest spatial temporal framework This can be used as inputs to land management and policy decisions with regard to varied themes have link with space such as urbanization, water management, deforestation and forest degradation.This research investigated the spatial temporal change detection of forest cover of Yen Nhan – Thuong Xuan in period 2000-2015 2 GOALS AND OBJECTIVES This goal of project is to classify forest and detect the changes from 2000 to 2015 in Yen Nhan commune, Thuong Xuan district, Thanh Hoa province The objectives were to:  To segment SPOT imagery based on object-based classification  To classify and map out the different land use / land cover and their spatial distribution in Yen Nhan in 2015  To identity, quantify and map out the forest cover changes in Yen Nhan from 2000 to 2015 3 STUDY AREA AND DATA 3.1 Study Area Yen Nhan is a mountainous area commune of Thanh Hoa Province The commune is a mixture of diverse land covers/land uses Statistical report in web portal of Yen Nhan [17] showed that Yen Nhan have total 19,094.63 and a population of 4850 people; the calculated population density is 0.25 person/ha There are four seasons: spring, summer, autumn and winter with an average annual temperature about 23-24oC and an average annual rainfall about 1600-2000 mm Figure 1: Location of the Yen Nhan Commune in Thanh Hoa Province 3.2 Data Sources The remote sensing data available for this study consist of multi-spectral SPOT5 data The scene was stored in GeoTIFF format and featured in a UTM map projection (UTM-48N, WGS-84 datum) Table presents configuration details of the SPOT5 imagery A provincial forest maps for 2000, 2005, 2010 and 2015 were provided by Institute for Forest Ecology and Environmentof Vietnam Forestry University together with an administrative map of the study area Table Technical parameters and properties of the sensors used in this study SPOT-5 (HRG) 480–710 (pan) 500–590 (green) 610–680 (red) Band Wavelength (nm) 790–890 (NIR) 1580–1750 (mid IR) Spatial Resolution (m) 1.5 × 1.5 (m/pxl) Pixel Size 1.5 (m) Project Size 10476 × 14707 (pixels) METHODOLOGY The proposed methodology is illustrated in Figure Software eCognition Developer v8.9, ArcGIS desktop 10.1 and MapInfo Professional 10.5 were used.For change detection, using existing maps in period 2000-2010 from Institute for Forest Ecology and Environment of Vietnam Forestry University Figure 2: Flowchart of research’s methodology This study used an object-based approach to map forest cover at Yen Nhan in 2015 based on SOPT data (Step and Step 2) After that, we performed a change-detection analysis based on object-based mapping results and map from period time (2000-2010) (Step 3) Each step was described below 4.1 Image segmentation (Step1) The segmentation was performed using the eCognition v8.9 image analysis software (Baatz et al., 2004) [3] The first step in object-based image analysis is segmentation In general, image segmentation is defined as the process of partitioning an image into separated regions based on parameters specified (Myint et al., 2008) [12] These parameters often consider the homogeneity/heterogeneity of regions (Pal et al., 1993) [14] The segmentation algorithm applied in this study is the so-called “multiresolution segmentation” The algorithm was applied to all four SPOT bands (green, Figure 8: Land cover/land use classification maps of Yen Nhan commune in year 2000, 2005, 2010 and 2015 20 5.3.2 Change Detection 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Shrub and Grass Water body Bare Land Bamboo Forest Plantation Forest Mixed Forest Rehabiliation Evergreen Forest Poor Evergreen Forest Area 2000 Area 2005 Area 2010 Area 2015 Medium Evergreen Forest Rich Evergreen Forest (a) 7000 6000 5000 4000 3000 2000 1000 AREA 2000 AREA 2005 AREA 2010 Rich Evergreen Forest Poor Evergreen Forest Mixed Forest Bamboo Forest Water body AREA 2015 Medium Evergreen Forest Rehabiliation Evergreen Forest Plantation Forest Bare Land Shrub and Grass (b) Figure 9: Land cover/land use in Yen Nhan commune from 2000 to 2015: (a) the distribution of land cover/land use types in percent in different years, (b) the evolution of the properties of 21 the major land cover/land use types in hectare, (c) the magnitude of the land cover/land use changes in ha/year for each time interval Figure 9a, shows the surface distribution (in percent) and the evolution of the proportion of each land cover/land use class in the different time periods Shrub and grass, bare land, bamboo forest and rich evergreen forest decreased gradually, but by the end of the study period, bare land and rich evergreen forest have become smallest land cover type in commune that cannot see clearly in the graph Trends can be observed in the land cover classes undergoing the largest change namely rich evergreen forest, poor evergreen forest, bamboo, rehabilitation evergreen forest, bare land and shrub and grass from 2000-2015 (figure 9b) Shrub, grass and poor evergreen forest decreased in the first time interval, whereas mixed forest and rehabilitation forest increase In the period 2005-2010, shrub, grass and poor evergreen forest increased gradually, but bamboo and rehabilitation forest increase slowly However, in the end of time period of study area, shrub, grass and bamboo forest decreased sharply; and bared land and poor evergreen forest increased greatly In the other hand, rich evergreen forest and bare land were not changed much in the first two time intervals However, both of them rapid decreased from 2010 to 2015 Medium evergreen forest was quite stable from 2000 to 2010, after that, it and plantation forest increased significantly Water body stayed and mixed forest represent a stable percentage of the study area throughout the 15 years Figure 9c illustrates the magnitudes of changes in hectares per year, standardizing the absolute change by the duration of each year analyzed interval for the main land cover/land use categories Only the plantation areas class shows a continuous increasingly trend The other land cover/land use types fluctuated over the different time periods Rich forest, poor forest, rehabilitation forest, bamboo forest, shrub and grass, and bare land had higher 22 magnitudes of change than the limited changes for medium forest, mixed forest and the negligible variation of water bodies Figure 10 illustrates the spatial distribution of changes over different time interval From 2000-2005, the changes occurred in large patches and scattered throughout the commune From 2005 to 2010, the changes were more fragmented In the end of study time period 2010-2015, the changes occurred in large scale 23 Figure 10: Spatial distribution of land cover/land use changes in Yen Nhan commune from 2000-2015 24 The detailed dynamics of the land cover/land use changes in Yen Nhan commune from 2000 to 2015 is shown in Table (the values on the diagonal represent the amount of each land cover/land use class that did not change, while the remaining values refer to the expansion or reduction of the class) The table presents all the results of the cross tabulation matrices of the land cover/land use change, showing the conversion from each class to another class For example, total rich evergreen forest in 2000 was 700 But there is only 5.9 remain in 2015, 457.1 is converted to medium evergreen forest, 133.8 is converted to mixed forest In the other hand, total rich evergreen forest in 2015 is 11.3 It comes from 5.9 remaining from 2000 and 2.4 of new area is from poor evergreen forest and 1.9 from shrub and grass During this period of 15 years, the areas of rich forest, poor forest, bamboo forest and shrub and grass experienced the greatest absolute reduction in area, with 694.1 ha, 1158.8 ha, 3029.8 and 6076.3 In relative term, the reduction of rich forest and shrub and grass are 99% and poor forest and bamboo is greater than 97% In other hand, 670.5 of plantation forest was developed mostly from 298.6 of shrub and grass, 151.1 of mixed forest, 139.5 of bamboo forest Moreover, water bodies were lost about 56 percent (representing 50 ha) mostly to bare land (48.1 ha) 25 Table 5: Natural of land cover/land use changes in Yen Nhan commune from 2000 to 2015 in ha(figure are rounds up to entire numbers) Period 2000-2015 RG TB RN HG PH TN DT DK MN Total Expansion RG 5.9 - 2.4 0.1 - 1.0 1.9 - - 11.3 5.4 TB 457.1 192.4 249.3 185.2 165.0 133.9 74.4 2.2 - 1459.4 1267.1 RN 86.5 132.2 505.7 801.2 566.2 746.6 1592.9 122.5 0.4 4554.3 4048.6 HG 133.8 413.7 379.6 772.3 629.3 546.8 752.5 16.6 0.7 3645.3 2873.0 PH 12.2 79.5 463.3 1003.3 1050.8 1142.7 2551.9 76.6 0.6 6380.9 5330.1 TN 1.0 - 0.5 42.7 1.3 64.2 32.3 - - 142.0 77.8 RT - - 9.4 151.1 31.2 139.5 298.6 40.6 0.1 670.5 670.5 DT - - 5.9 2.2 9.9 53.0 9.5 80.5 27.6 DK 3.5 2.2 193.2 103.3 306.3 704.3 364.7 48.1 1779.8 1415.2 MN 0.6 5.0 3.2 67.5 0.6 38.5 115.4 76.9 Total 700.0 820.0 1664.5 3155.5 2554.5 3093.9 6129.3 633.3 88.5 Reduction 694.1 627.6 1158.8 2383.3 1503.7 3029.8 6076.3 268.7 50.0 2015 54.3 (RG: Rich Evergreen Forest, TB: Medium Evergreen Forest, RN: Poor Evergreen Forest, HG: Mixed Wood-Bamboo Forest, PH: Rehabilitation Evergreen Forest, TN: Bamboo Forest, RT: Plantation Forest, DT: Shrub and Grass, DK: Bare Land, MN: Water Body) 26 RECOMENDATIONS Results show that rich evergreen forest, bamboo forest, shrub and grass are decreased dramatically in Yen Nhan commune It reduced by over 97% between 2000 and 2015 The major factors that might have contributed to the loss of rich forest and bamboo forest cover are timber and non-timber forest production extraction The expansion of rehabilitation evergreen, poor ever forest, bare land (roads, residential areas) and expansion of plantation forest are the main factors have contributed to loss of bare land.Bare land increased throughout study period 2000-2015 because of population growth, the development of new road and socio-economic development.From 2000, plantation forest expanded because of government policies as 327, 661 and 147 programs.Land use/land cover changes reflect the dynamics observed in the socio-economic condition of the study area As saying up the changes might due to the government policies that aim to balance the need to encourage rural development, the removal of compulsory grain crop quotas, promoting livestock with ecological stability 27 CONCLUSIONS AND PERSPECTIVES 7.1 Conclusions The object-based change detection method proposed in this studyproved to be very efficient to identify forest land cover changes in both deciduous and coniferous stands An overall kappa was 0.73 between 0.40 and 0.80 (40 to 80%) represents moderate agreement In conclusion, the results of study demonstrate that Yen Nhan commune is diverse land cover/land use pattern This research is the first detailed land cover/land use analysis of Yen Nhan commune The object-oriented method use in this study provided results is better than the pixel-based classification Because pixel-based classifications misclassify pixels, particularly in land covers that are spectrally heterogeneous The main conclusions are that (i) the main land cover/land use categories of Yen Nhan commune, for the last 15 years from 2000 to 2015, include rich evergreen, medium evergreen, poor evergreen, rehabilitation evergreen, bamboo, mixed wood and bamboo, plantation forest, bare land, shrub and grass, and water body; (ii) while plantation forest increased significantly, rich evergreen forest and bare land decreased greatly in the last 15 years; (iii) almost all land cover/land use types display variations in magnitude over the different time periods, with rich evergreen, poor evergreen, rehabilitation evergreen, bamboo and shrub, grass changing more drastically compared to the other types; (iv) plantation forest was converted from poor evergreen forest, mixed forest, rehabilitation evergreen forest, bamboo forest, shrub and grass and bare land Only the plantation areas class shows a continuous increasingly trend The other land cover/land use types fluctuated over the different time periods Rich forest, poor forest, rehabilitation forest, bamboo forest, shrub and grass, and bare land had higher magnitudes of change than the limited changes for medium forest, mixed forest and the negligible variation of water bodies 28 7.2 Perspective These results of land cover/land use maps and change detection may be used to help in understanding the impact of past policies and the role of several factors such as socioeconomic trends and environmental changes in controlling the dynamics on land use changes Understanding the drivers of land use change might in turn contribute to model future evolution of land use patterns and better steer future land use planning policies at the district or province level Future research will focus on the identification of the drivers and impacts of land cover/land use change in the case study area There is a need to further understand the drivers controlling theindividuals’ decision to convert their land use and their spatial patterns Further, it would be interesting to research the cause of rapid land use dynamics, their impacts on the environment, on the livelihoods and access to natural resources for local people, and on their vulnerability to natural hazards and expected environment changes 29 REFERENCES Angelsen, A The Causes of Land Use and Land Cover Change: Moving beyond the Myths Global Environmental Change, 2001, pp.261-69 Atmopawiro, V.P Detection of Single Tree Felling in the Tropical Forest Using Optical Satellite Data and Image classification Techniques (a case study in the Labanan concession, East Kalimantan, Indonesia), MSc Thesis, ITC, the Netherlands, Enscheda, 2004, pp.91 Baatz, M.; Benz, U.; Dehghani, S.; Heynen, M.; Astrid, H.; Hofmann, P.; Lingenfelder, I.; Mimler, M.; Sohlbach, M.; Weber, M.; Willhauck, G User Guide 4—Introducing EcognitionElements;2004 Available online: swww.gis.unbc.ca/help/software/ecognition4/ELuserguide.pdf (accessed on 25 June 2011) Baatz, M.; Schäpe, A Multiresolution Segmentation: An Optimization Approach for High Quality Multi-Scale Image Segmentation In Angewandte Geographische Informations verarbeitung XII: Beiträge zum AGIT-Symposium Salzburg 2000 (German Edition); Strobl, J., Blaschke, T., Griesebner, G., Eds.; Wichmann-Verlag: Heidelberg, Germany, 2000; pp 12–23 Bao, T.Q et al., GIS and Remote Sensing, Vietnam Forestry University Textbook, 2013 Boakey, E.: Odai, S N; Adjei, K A., ad Annor, F O Landsat Images for Assessment of the Impact of Land use and Land Cover Changes on the Barekese Catchment in Ghana European Journal of Scientific Research, Vol.22 No.2, 2008, pp.269-278 Boltz, F.; Holmes, T P.; And Cater, D R Economic andEnvironmental Impacts of Conventional and Reducedimpact Logging in Tropical South America: A Comparative Review Forest Policy and Economics, 5(1),2003, pp 69-81 Congalton, R G A Review of Assessing the Accuracy of Classification of Remotely Sensed Data, Remote Sensing of Environment, Vol 37, 1991, pp 35–46 30 Congalton, R G Using Spatial Autocorrelation Analysisto Explore the Error in Maps Generated from RemotelySensed Data, Photogrammetric Engineering andRemote Sensing, 54, 1988, pp 587-592 10 Foody, G On the Compensation for Chance Agreementin Image Classification Accuracy Assessment Photogrammetric Engineering and Remote Sensing Vol 58, No 10, 1992, pp 1459-1460 11 Jensen J R., and Cowen D C Remote Sensing of UrbanSuburban Infrastructure and Socioeconomic Attributes, Photogrammetric Engineering and Remote Sensing, 65, 1999, pp 611-622 12 Myint, S.W.; Giri, C.P.; Wang, L.; Zhu, Z.; Gillette, S.C Identifying mangrove species and their surrounding land use and land cover classes using an objectoriented approach with a lacunarity spatial measure GIScience Remote Sens 2008, 45, 188–208 13 Pal, N.R.; Pal, S.K A review on image segmentation techniques Pattern Recog 1993, 26, 1277–1294 14 Ringrose, S., Vamderpost, C.,and Maheson, W Use of Image Processing and GIS Technique to determine the Extent and Possible Causes of Land Management/Fenceline Induced Degradation Problems in the Okavango Area, Northern Botswana International Journal of Remote Sensing, Vol 28, NO.11, 1997, pp 2337-2364 15 Rosyadi, S.; Birner, R and Zeller, M (2005) Creatingpolitical capital to promote devolution in the forestrysector – a case study of the forest communities in Banyumas District, Central Java, Indonesia ForestPolicy and Economics, Vol.7, 1986, pp 313226 16 Web:http://rainforests.mongabay.com/deforestation/2000/Vietnam.htm 17 Web:http://thuongxuan.thanhhoa.gov.vn/vivn/thuongxuan/Pages/Article.aspx?ChannelId=1&articleID=2 31 APPENDICES Appendix 1: Natural of land cover/land use changes in Yen Nhan commune from 2000 to 2005 in (figure are rounds up to entire numbers) RG TB RN HG PH TN DT DK MN Total Expansion RG 622.7 10.9 0.1 5.0 5.9 10.1 3.7 - - 658.4 35.7 TB 69.3 753.8 4.6 8.5 11.2 1.5 11.1 - - 860.0 106.2 RN 0.2 1.9 1084.3 12.0 84.4 14.0 32.8 - - 1229.5 145.2 HG 1.9 11.6 32.4 2364.4 19.1 417.1 534.6 26.0 - 3407.1 1042.8 PH 4.4 9.7 282.3 51.3 2067.5 22.8 1656.0 32.3 - 4126.3 2058.8 TN 0.1 4.5 33.2 405.3 25.0 2032.4 1346.6 67.5 - 3914.5 1882.1 DT 1.5 27.6 217.7 235.0 336.3 575.1 2460.0 51.5 - 3904.7 1444.7 DK - - 10.3 74.0 5.1 21.1 83.6 456.0 1.0 651.0 195.0 MN - - - 0.0 0.0 0.0 1.1 0.0 87.5 88.6 1.1 Total 700.0 820.0 1664.8 3155.5 2554.5 3094.1 6129.5 633.3 88.5 Reduction 77.3 66.2 580.5 791.2 487.0 1061.7 3669.4 177.3 1.0 Period 2000-2005 2005 (RG: Rich Evergreen Forest, TB: Medium Evergreen Forest, RN: Poor Evergreen Forest, HG: Mixed Wood-Bamboo Forest, PH: Rehabilitation Evergreen Forest, TN: Bamboo Forest, RT: Plantation Forest, DT: Shrub and Grass, DK: Bare Land, MN: Water Body) 32 Appendix 2: Natural of land cover/land use changes in Yen Nhan commune from 2005 to 2010 in (figure are rounds up to entire numbers) RG TB RN HG PH TN DT DK MN Total Expansion RG 504.9 2.2 0.3 0.9 0.7 0.1 - - - 509.1 4.2 TB 30.7 744.6 1.0 2.5 25.2 2.0 13.5 0.4 - 819.9 75.3 RN 19.9 50.2 947.0 31.1 242.0 11.5 136.4 0.0 0.1 1438.1 491.1 HG 12.3 8.1 22.9 2614.9 90.4 404.0 138.2 73.4 - 3364.2 749.3 PH 28.4 27.5 85.4 71.1 2903.5 121.4 670.9 27.8 0.1 3936.1 1032.6 TN 16.9 11.0 28.5 296.5 133.6 2580.5 325.6 19.1 0.2 3411.8 831.3 RT - - - 6.8 25.3 1.5 31.0 10.3 - 74.9 74.9 DT 45.2 13.8 144.1 329.4 662.2 659.5 2312.6 143.5 0.4 4310.8 1998.2 DK - 2.5 0.3 53.7 42.3 132.4 276.0 374.7 0.2 882.0 507.3 MN - - - 0.1 1.1 1.5 0.2 1.5 87.6 92.0 4.4 Total 658.4 860.0 1229.5 3407.0 4126.2 3914.3 3904.3 650.8 88.5 Reduction 153.5 115.4 282.5 792.1 1222.7 1333.8 1591.7 276.0 0.9 Period 2005-2010 2010 (RG: Rich Evergreen Forest, TB: Medium Evergreen Forest, RN: Poor Evergreen Forest, HG: Mixed Wood-Bamboo Forest, PH: Rehabilitation Evergreen Forest, TN: Bamboo Forest, RT: Plantation Forest, DT: Shrub and Grass, DK: Bare Land, MN: Water Body) 33 Appendix 3: Natural of land cover/land use changes in Yen Nhan commune from 2010 to 2015 in (figure are rounds up to entire numbers) RG TB RN HG PH TN RT DT DK MN Total Expansion RG 5.7 - - 0.4 2.5 - - 2.8 - - 11.3 5.6 TB 344.4 252.9 172.1 200.4 307.3 42.2 - 136.3 3.8 - 1459.4 1206.5 RN 32.9 131.8 463.8 795.4 988.4 672.3 12.4 1339.3 117.4 0.4 4554.0 4090.2 HG 123.6 363.0 390.9 773.4 709.3 782.7 - 490.6 11.0 0.7 3645.1 2871.7 PH 2.5 71.5 373.4 1217 1693.5 1501.7 7.1 1472.3 41.0 0.6 6380.7 4687.2 TN - - 0.0 43.7 5.4 68.5 - 19.2 5.2 - 142.0 73.5 RT - - 12.1 140.8 107.6 144.6 42.6 193.6 29.0 0.1 670.5 627.9 DT - - - 3.6 5.1 7.1 0.8 20.8 42.4 0.6 80.4 59.6 DK - 0.7 25.5 187.7 116.9 185.9 8.3 609.3 596.9 48.3 1779.5 1182.6 MN - - - 1.8 - 6.8 3.6 26.4 35.3 41.4 115.3 73.9 Total 509.1 819.9 1437.9 3364.2 3936.0 3411.8 74.9 4310.6 882.0 92.0 Reduction 503.4 567.0 974.1 2590.8 2242.5 3343.3 32.3 4289.8 285.1 50.6 Period 2010-2015 2015 (RG: Rich Evergreen Forest, TB: Medium Evergreen Forest, RN: Poor Evergreen Forest, HG: Mixed Wood-Bamboo Forest, PH: Rehabilitation Evergreen Forest, TN: Bamboo Forest, RT: Plantation Forest, DT: Shrub and Grass, DK: Bare Land, MN: Water Body) 34 ...  To classify and map out the different land use / land cover and their spatial distribution in Yen Nhan in 2015  To identity, quantify and map out the forest cover changes in Yen Nhan from 2000. .. structural changes of forest cover using SPOT-5 image in 2015 and detect forest cover transformation in Yen Nhan - Thanh Hoa, from 2000 to 2015 ECognition Developer, ArcGIS and MapInfo softwares... This goal of project is to classify forest and detect the changes from 2000 to 2015 in Yen Nhan commune, Thuong Xuan district, Thanh Hoa province The objectives were to:  To segment SPOT imagery