A Comparison of Two Methods for Detecting Tropical Forest Change Cover

FAN Ying-long; TAN Bing-xiang

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A Comparison of Two Methods for Detecting Tropical Forest Change Cover

FAN Ying-long¹,
TAN Bing-xiang^1,

1.
Research Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China

Received Date: 2014-06-24

Abstract

Objective In this paper, two methods from two "variant data" to detect tropical forest change were compared. Method After Tasseled Cap transformation, the combine of Brightness index, Greenness index, Wetness index (MKT) and Disturbance index (DI) were obtained by masking the dark object and extracting local histogram threshold. Change information was discovered by the differences of MKT and DI, According to the connection between vegetation cover and Brightness index, Greenness index, Wetness index, the change information was extracted by decision tree classification, and the results were assessed and compared. Result The results showed that they were all able to detect the subtle change of tropical forest, but, MKT-D take an obvious advantage to detect the small change spots, and the total kappa coefficient from MKT-D (0.763 0) was higher than that from MDI-D (0.655 9). For tropical forest change detection in this research, the result from MKT-D was better than that from MDI-D. Conclusion MKT could enhance the effect of near-infrared and short wave infrared band to forest change information. Moreover, MKT-D is accessible to extraction and interpretation of target change information.
- Cambodia,
- tropic forest change,
- remote sensing detection

References

[1]	Holly K G, Sandra B, John O N. Monitoring and estimating tropical forest carbon stocks making REDD a reality[J]. Environmental Research Letters, 2007, 4(2):178-326.
[2]	Britton B S, Kevin R G, Pieter P T. Weak Northern and Strong Tropical Land Carbon Uptake from Vertical Profiles of Atmospheric CO₂[J]. Science, 2007, 316(5832):1732-1735.
[3]	Sean P H, Warren B C, Yang Zhiqiang, et al. Comparison of Tasseled Cap-based Landsat data structures for use in forest disturbance detection[J] Remote Sensing of Environment, 2005, 97: 301-310.
[4]	Cohen W B, Goward S N. Landsat's role in ecological applications of remote sensing[J]. Bioscience, 2004, 54(6), 535-545.
[5]	Catherine B, Hugh E, Beuchle R, et al. Pre-processing of a sample of multi-date Landsat imagery used to monitor forest cover changes over the tropics[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2011, 66(5) 555-563.
[6]	Warren B C, Thomas A S, Ralph J A, et al. Characterizing 23 Years (1972-95) of Stand Replacement Disturbance in Western Oregon Forests with Landsat Imagery[J] Ecosystems, 2002, 5(2)122-137.
[7]	Franklin S E, Lavigne M B, Moskal L M. Interpretation of forest harvest conditions in New Brunswick using Landsat TM Enhanced Wetness Difference Imagery (EWDI)[J].Canadian Journal of Remote Sensing, 2001, 27(2):118-128.
[8]	J.-F.MAS.Monitoring land-cover changes: a comparison of change detection techniques [J].International Journal of Remote Sensing, 1999, 20(1):139-152.
[9]	Voiker C R, David J M L, Mark S.B.Effects of interacting disturbances on landscape patterns: budworm defoliation and salvage logging[J]. Ecological Applications, 2000, 10 (1), 233-247.
[10]	赵英时, 等.遥感应用分析原理与方法[M].北京:科学出版社, 2003.
[11]	John R, Janet F, Dar A R. A comparison of methods for monitoring multitemporal vegetation change using Thematic Mapper imagery[J]. Remote Sensing of Environment, 2001, 80(1):143-156.
[12]	Jin S, Sader S A. Comparison of time-series tasseled cap wetness and the normalized difference moisture index in detecting forest disturbances[J] Remote Sensing of Environment, 2005, 94(3):364-372.
[13]	Lobser S E, Cohen W B.MODIS tasseled cap: Land cover characteristics expressed through transformed MODIS data[J]. International Journal of Remote Sensing, 2007, 28(22):5079-5101.
[14]	Kennedy R E, Cohen W B, Schroeder T A. Trajectory-based change detection for automated characterization of forest disturbance dynamics [J]. Remote Sensing of Environment, 2007, 110(3):370-386.
[15]	Masek J G, Huang C, Cohen W, et al. Mapping North American forest disturbance from a decadal Landsat record: Methodology and initial results[J]. Remote Sensing of Environment, 2008, 112(6):2914-2926.
[16]	Cohen W B, Maiersperger T K, Spies T A. Modelling forest cover attributes as continuous variables in a regional context with Thematic Mapper data[J]. International Journal of Remote Sensing, 2001, 22(12):2279-2310.
[17]	John B C, Curtis E W. An Assessment of Several Linear Change Detection Techniques for Mapping Forest Mortality Using Multitemporal Landsat TM Data[J]. Remote Sensing of Environment, 1996, 56(1):66-77.
[18]	邓书斌.遥感图像处理方法[M].北京:科学出版社, 2010.
[19]	张立彬, 张其前, 胥芳, 等.基于分类回归树(CART)方法的统计解析模型的应用于研究[J].浙江工业大学学报, 2004, 30(4):316-318.
[20]	Jeffrey G M, Chengquan Huang, Robert Wolfe. North American forest disturbance mapped from a decadal Landsat record[J]. Remote Sensing of Environment, 2008, 112(6):2914-2926.
[21]	Chengquan Huang, Samuel N G, Jeffrey G M. An automated approach for reconstructing recent forest disturbance history using dense Landsat time series stacks[J]. Remote Sensing of Environment.2010, 114(1):183-198.
[22]	Hansen M C, Defries R S. Townshend JRG, et al. Global land cover classification at 1 km spatial resolution using a classification tree approach[J]. International Journal of Remote Sensing, 2000, 21(6-7):1331-1364.
[23]	Mark A F, Carla E B, Alan H S. Maximizing Land Cover Classification Accuracies Produced by Decision Trees at Continental to Global Scales[J].Geoscience and Remote Sensing, IEEE Transactions on, 2000, 37(2):969-977.

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通讯作者: 陈斌, bchen63@163.com

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沈阳化工大学材料科学与工程学院沈阳 110142

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A Comparison of Two Methods for Detecting Tropical Forest Change Cover

1. Research Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China

Received Date: 2014-06-24

Abstract: Objective In this paper, two methods from two "variant data" to detect tropical forest change were compared. Method After Tasseled Cap transformation, the combine of Brightness index, Greenness index, Wetness index (MKT) and Disturbance index (DI) were obtained by masking the dark object and extracting local histogram threshold. Change information was discovered by the differences of MKT and DI, According to the connection between vegetation cover and Brightness index, Greenness index, Wetness index, the change information was extracted by decision tree classification, and the results were assessed and compared. Result The results showed that they were all able to detect the subtle change of tropical forest, but, MKT-D take an obvious advantage to detect the small change spots, and the total kappa coefficient from MKT-D (0.763 0) was higher than that from MDI-D (0.655 9). For tropical forest change detection in this research, the result from MKT-D was better than that from MDI-D. Conclusion MKT could enhance the effect of near-infrared and short wave infrared band to forest change information. Moreover, MKT-D is accessible to extraction and interpretation of target change information.

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[1]	Holly K G, Sandra B, John O N. Monitoring and estimating tropical forest carbon stocks making REDD a reality[J]. Environmental Research Letters, 2007, 4(2):178-326.
[2]	Britton B S, Kevin R G, Pieter P T. Weak Northern and Strong Tropical Land Carbon Uptake from Vertical Profiles of Atmospheric CO₂[J]. Science, 2007, 316(5832):1732-1735.
[3]	Sean P H, Warren B C, Yang Zhiqiang, et al. Comparison of Tasseled Cap-based Landsat data structures for use in forest disturbance detection[J] Remote Sensing of Environment, 2005, 97: 301-310.
[4]	Cohen W B, Goward S N. Landsat's role in ecological applications of remote sensing[J]. Bioscience, 2004, 54(6), 535-545.
[5]	Catherine B, Hugh E, Beuchle R, et al. Pre-processing of a sample of multi-date Landsat imagery used to monitor forest cover changes over the tropics[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2011, 66(5) 555-563.
[6]	Warren B C, Thomas A S, Ralph J A, et al. Characterizing 23 Years (1972-95) of Stand Replacement Disturbance in Western Oregon Forests with Landsat Imagery[J] Ecosystems, 2002, 5(2)122-137.
[7]	Franklin S E, Lavigne M B, Moskal L M. Interpretation of forest harvest conditions in New Brunswick using Landsat TM Enhanced Wetness Difference Imagery (EWDI)[J].Canadian Journal of Remote Sensing, 2001, 27(2):118-128.
[8]	J.-F.MAS.Monitoring land-cover changes: a comparison of change detection techniques [J].International Journal of Remote Sensing, 1999, 20(1):139-152.
[9]	Voiker C R, David J M L, Mark S.B.Effects of interacting disturbances on landscape patterns: budworm defoliation and salvage logging[J]. Ecological Applications, 2000, 10 (1), 233-247.
[10]	赵英时, 等.遥感应用分析原理与方法[M].北京:科学出版社, 2003.
[11]	John R, Janet F, Dar A R. A comparison of methods for monitoring multitemporal vegetation change using Thematic Mapper imagery[J]. Remote Sensing of Environment, 2001, 80(1):143-156.
[12]	Jin S, Sader S A. Comparison of time-series tasseled cap wetness and the normalized difference moisture index in detecting forest disturbances[J] Remote Sensing of Environment, 2005, 94(3):364-372.
[13]	Lobser S E, Cohen W B.MODIS tasseled cap: Land cover characteristics expressed through transformed MODIS data[J]. International Journal of Remote Sensing, 2007, 28(22):5079-5101.
[14]	Kennedy R E, Cohen W B, Schroeder T A. Trajectory-based change detection for automated characterization of forest disturbance dynamics [J]. Remote Sensing of Environment, 2007, 110(3):370-386.
[15]	Masek J G, Huang C, Cohen W, et al. Mapping North American forest disturbance from a decadal Landsat record: Methodology and initial results[J]. Remote Sensing of Environment, 2008, 112(6):2914-2926.
[16]	Cohen W B, Maiersperger T K, Spies T A. Modelling forest cover attributes as continuous variables in a regional context with Thematic Mapper data[J]. International Journal of Remote Sensing, 2001, 22(12):2279-2310.
[17]	John B C, Curtis E W. An Assessment of Several Linear Change Detection Techniques for Mapping Forest Mortality Using Multitemporal Landsat TM Data[J]. Remote Sensing of Environment, 1996, 56(1):66-77.
[18]	邓书斌.遥感图像处理方法[M].北京:科学出版社, 2010.
[19]	张立彬, 张其前, 胥芳, 等.基于分类回归树(CART)方法的统计解析模型的应用于研究[J].浙江工业大学学报, 2004, 30(4):316-318.
[20]	Jeffrey G M, Chengquan Huang, Robert Wolfe. North American forest disturbance mapped from a decadal Landsat record[J]. Remote Sensing of Environment, 2008, 112(6):2914-2926.
[21]	Chengquan Huang, Samuel N G, Jeffrey G M. An automated approach for reconstructing recent forest disturbance history using dense Landsat time series stacks[J]. Remote Sensing of Environment.2010, 114(1):183-198.
[22]	Hansen M C, Defries R S. Townshend JRG, et al. Global land cover classification at 1 km spatial resolution using a classification tree approach[J]. International Journal of Remote Sensing, 2000, 21(6-7):1331-1364.
[23]	Mark A F, Carla E B, Alan H S. Maximizing Land Cover Classification Accuracies Produced by Decision Trees at Continental to Global Scales[J].Geoscience and Remote Sensing, IEEE Transactions on, 2000, 37(2):969-977.

A Comparison of Two Methods for Detecting Tropical Forest Change Cover

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通讯作者: 陈斌, bchen63@163.com

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A Comparison of Two Methods for Detecting Tropical Forest Change Cover

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