Anterior|Siguiente

Review of the detection and attribution of multi-type forest disturbances using an ensemble of spatio-temporal-spectral information from remote sensing images

  • role： First author第一作者
  • Affiliation：

    School of Information Engineering, China University of Geosciences, Beijing 100083, China

  • Email：wuling@cugb.edu.cn
  • Introduction：吴伶，研究方向为遥感时序分析与时空数据挖掘。E-mail：wuling@cugb.edu.cn
WU Ling,  
  • role： Corresponding author通信作者
  • Affiliation：

    School of Information Engineering, China University of Geosciences, Beijing 100083, China

  • Email：liuxn@cugb.edu.cn
  • Introduction：刘湘南，研究方向为资源与生态遥感、遥感信息智能挖掘与GIS。E-mail：liuxn@cugb.edu.cn
LIU Xiangnan*,  
  • Affiliation：

    School of Information Engineering, China University of Geosciences, Beijing 100083, China

LIU Meiling,  
  • Affiliation：

    School of Information Engineering, China University of Geosciences, Beijing 100083, China

ZHANG Tingwei,  
  • Affiliation：

    School of Information Engineering, China University of Geosciences, Beijing 100083, China

YANG Baowen,  
  • Affiliation：

    School of Information Engineering, China University of Geosciences, Beijing 100083, China

XU Yuqi

resumen

Remote sensing time series contain information about the changes and differences in forest composition, structure, and function driven by natural factors and human activities. It provides theoretical support for forest disturbance detection and attribution by integrating spatio-temporal-spectral information from remote sensing time series data, which can effectively improve the understanding of the processes of forest succession, developmental trend, and their driving and response mechanism. This paper systematically reviewed the research progress of forest disturbance detection and attribution by integrating spatio-temporal-spectral information from remote sensing time series.The prerequisite for forest disturbance attribution is the detection of forest disturbance events, and the accuracy of disturbance detection directly affects the accuracy of subsequent attribution. In this paper, current forest disturbance detection methods and techniques are highlighted from multiple perspectives, including data (time series observation frequency selection), features (spectral feature selection, fusion of spatial and temporal feature), and algorithms (multi-algorithm integration, forest low-intensity disturbance detection). From the data perspective, based on the frequency of available observations in different regions, the change detection methods for dense and sparse time series are introduced respectively. From the feature perspective, the spectral response characteristics of forest disturbances are summarized. The change detection strategy of multi-spectral feature integration is introduced to address the problems of change detection based on single spectral features. The fusion of temporal and spatial features for forest disturbance detection is summarized. From the algorithmic perspective, to address issues such as differences in the results of different change detection algorithms and the fact that a single algorithm may not be the most efficient way to describe all conditions, two multi-algorithm integration strategies, parallel and serial, are presented. Based on our analysis of the reasons for the poor detection of low-intensity disturbances (e.g., selective logging, pests and diseases, drought, etc.), progress in research on change detection oriented to mid- and low-intensity disturbances in forests is described.The essence of forest disturbance attribution is a classification problem involving multiple types of forest disturbances. This process identifies disturbance types by utilizing remote sensing features of forest disturbances caused by different driving factors as inputs for classification algorithms. In this paper, we first summarized attribution features as the input of forest disturbance attribution, that is, pre-, mid-, and post-disturbance features in chronological order and temporal, spatial, spectral, and topographic features in feature dimensions. Then, according to the condition of whether disturbance detection occurs before the attribution of disturbances, methods for attributing multiple types of forest disturbance are summarized and compared. These methods are based on the spatio-temporal-spectral and topographic features of remote sensing time series, including the direct method and the two-stage method.Lastly, we analyzed the current problems in forest disturbance monitoring using remote sensing and predicted the future research directions, such as the fusion of spatio-temporal-spectral features, simultaneous detection of forest multi-intensity disturbance, and attribution of forest multi-type disturbance under limited sample conditions. We hope this article becomes a reference for the detection and attribution of changes using an ensemble of spatio-temporal-spectral information from remote sensing time series.

palabra clave

forest disturbance;remote sensing time series;spatio-temporal-spectral information;feature ensemble;disturbances detection;attribution of disturbances

References

  1. 1.
    Ahern F J. 1988. The effects of bark beetle stress on the foliar spectral reflectance of lodgepole pine. International Journal of Remote Sensing, 9(9): 1451-1468
  2. 2.
    Ahmed O S, Wulder M A, White J C, Hermosilla T, Coops N C and Franklin S E. 2017. Classification of annual non-stand replacing boreal forest change in Canada using Landsat time series: a case study in northern Ontario. Remote Sensing Letters, 8(1): 29-37
  3. 3.
    Awty-Carroll K, Bunting P, Hardy A and Bell G. 2019. An evaluation and comparison of four dense time series change detection methods using simulated data. Remote Sensing, 11(23): 2779
  4. 4.
    Bao Y B, Wang F, Tong S Q, Na L, Han A R, Zhang J Q, Bao Y H, Han Y C and Zhang Q M. 2019. Effect of drought on outbreaks of major forest pests, pine caterpillars (Dendrolimus spp.), in Shandong Province, China. Forests, 10(3): 264
  5. 5.
    Betts M G, Wolf C, Ripple W J, Phalan B, Millers K A, Duarte A, Butchart S H M and Levi T. 2017. Global forest loss disproportionately erodes biodiversity in intact landscapes. Nature, 547(7664): 441-444
  6. 6.
    Bontemps S, Langner A and Defourny P. 2012. Monitoring forest changes in Borneo on a yearly basis by an object-based change detection algorithm using SPOT-VEGETATION time series. International Journal of Remote Sensing, 33(15): 4673-4699
  7. 7.
    Brooks E B, Thomas V A, Wynne R H and Coulston J W. 2012. Fitting the multitemporal curve: a Fourier series approach to the missing data problem in remote sensing analysis. IEEE Transactions on Geoscience and Remote Sensing, 50(9): 3340-3353
  8. 8.
    Brooks E B, Wynne R H, Thomas V A, Blinn C E and Coulston J W. 2014. On-the-fly massively multitemporal change detection using statistical quality control charts and landsat data. IEEE Transactions on Geoscience and Remote Sensing, 52(6): 3316-3332
  9. 9.
    Brooks E B, Yang Z Q, Thomas V A and Wynne R H. 2017. Edyn: dynamic signaling of changes to forests using exponentially weighted moving average charts. Forests, 8(9): 304
  10. 10.
    Bruzzone L, Cossu R and Vernazza G. 2004. Detection of land-cover transitions by combining multidate classifiers. Pattern Recognition Letters, 25(13): 1491-1500
  11. 11.
    Bueno I T, Acerbi Júnior F W, Silveira E M O, Mello J M, Carvalho L M T, Gomide L R, Withey K and Scolforo J R S. 2019. Object-based change detection in the cerrado biome using landsat time series. Remote Sensing, 11(5): 570
  12. 12.
    Bueno I T, McDermid G J, Silveira E M O, Hird J N, Domingos B I and Júnior F W A. 2020. Spatial agreement among vegetation disturbance maps in tropical domains using landsat time series. Remote Sensing, 12(18): 2948
  13. 13.
    Bullock E L, Woodcock C E and Holden C E. 2020a. Improved change monitoring using an ensemble of time series algorithms. Remote Sensing of Environment, 238: 111165
  14. 14.
    Bullock E L, Woodcock C E and Olofsson P. 2020b. Monitoring tropical forest degradation using spectral unmixing and Landsat time series analysis. Remote Sensing of Environment, 238: 110968
  15. 15.
    Cardille J A, Perez E, Crowley M A, Wulder M A, White J C and Hermosilla T. 2022. Multi-sensor change detection for within-year capture and labelling of forest disturbance. Remote Sensing of Environment, 268: 112741
  16. 16.
    Chavana-Bryant C, Malhi Y, Wu J, Asner G P, Anastasiou A, Enquist B J, Cosio Caravasi E G, Doughty C E, Saleska S R, Martin R E and Gerard F F. 2017. Leaf aging of Amazonian canopy trees as revealed by spectral and physiochemical measurements. New Phytologist, 214(3): 1049-1063
  17. 17.
    Chen J, Chen X H, Cui X H and Chen J. 2011. Change vector analysis in posterior probability space: a new method for land cover change detection. IEEE Geoscience and Remote Sensing Letters, 8(2): 317-321
  18. 18.
    Chen X, Zhao W Z, Chen J G, Qu Y, Wu D H and Chen X H. 2021. Mapping large-scale forest disturbance types with multi-temporal CNN framework. Remote Sensing, 13(24): 5177
  19. 19.
    Cohen W B, Healey S P, Yang Z Q, Stehman S V, Brewer C K, Brooks E B, Gorelick N, Huang C, Hughes M J, Kennedy R E, Loveland T R, Moisen G G, Schroeder T A, Vogelmann J E, Woodcock C E, Yang L M and Zhu Z. 2017. How similar are forest disturbance maps derived from different landsat time series algorithms?. Forests, 8(4): 98
  20. 20.
    Cohen W B, Healey S P, Yang Z Q, Zhu Z and Gorelick N. 2020. Diversity of algorithm and spectral band inputs improves landsat monitoring of forest disturbance. Remote Sensing, 12(10): 1673
  21. 21.
    Cohen W B and Goward S N. 2004. Landsat’s role in ecological applications of remote sensing. BioScience, 54(6): 535-545 [DOI: [0535:LRIEAO]2.0.CO;2]
  22. 22.
    Cohen W B, Yang Z Q, Healey S P, Kennedy R E and Gorelick N. 2018. A LandTrendr multispectral ensemble for forest disturbance detection. Remote Sensing of Environment, 205: 131-140
  23. 23.
    Cohen W B, Yang Z Q and Kennedy R. 2010. Detecting trends in forest disturbance and recovery using yearly Landsat time series: 2. TimeSync - Tools for calibration and validation. Remote Sensing of Environment, 114(12): 2911-2924
  24. 24.
    De Marzo T, Gasparri N I, Lambin E F and Kuemmerle T. 2022. Agents of forest disturbance in the argentine dry chaco. Remote Sensing, 14(7): 1758
  25. 25.
    De Oliveira Silveira E M, De Mello J M, Júnior F W A and de Carvalho L M T. 2018. Object-based land-cover change detection applied to Brazilian seasonal savannahs using geostatistical features. International Journal of Remote Sensing, 39(8): 2597-2619
  26. 26.
    Desclée B, Bogaert P and Defourny P. 2006. Forest change detection by statistical object-based method. Remote Sensing of Environment, 102(1-2): 1-11
  27. 27.
    DeVries B, Verbesselt J, Kooistra L and Herold M. 2015. Robust monitoring of small-scale forest disturbances in a tropical montane forest using landsat time series. Remote Sensing of Environment, 161: 107-121
  28. 28.
    Esteban J, Fernández-Landa A, Tomé J L, Gómez C and Marchamalo M. 2021. Identification of silvicultural practices in mediterranean forests integrating landsat time series and a single coverage of ALS data. Remote Sensing, 13(18): 3611
  29. 29.
    Feng L Y, Tan B X, Wang X H, Chen X Y, Zeng W S and Qi Z. 2020. Object-oriented rapid forest change detection based on distribution function. Remote Sensing for Land and Resources, 32(2): 73-80
  30. 30.
    Feng Y, Ziegler A D, Elsen P R, Liu Y, He X Y, Spracklen D V, Holden J, Jiang X, Zheng C M and Zeng Z Z. 2021. Upward expansion and acceleration of forest clearance in the mountains of Southeast Asia. Nature Sustainability, 4(10): 892-899
  31. 31.
    Fornacca D, Ren G P and Xiao W. 2021. Small fires, frequent clouds, rugged terrain and no training data: a methodology to reconstruct fire history in complex landscapes. International Journal of Wildland Fire, 30(2): 125-138
  32. 32.
    Fu S L and Fu B J. 2019. Concept clarification of eco-geography and analysis of related classical studies. Scientia Geographica Sinica, 39(1): 70-79
  33. 33.
    Gao F, Hilker T, Zhu X L, Anderson M, Masek J, Wang P J and Yang Y. 2015. Fusing Landsat and MODIS data for vegetation monitoring. IEEE Geoscience and Remote Sensing Magazine, 3(3): 47-60
  34. 34.
    Ghaderpour E, Pagiatakis S D and Hassan Q K. 2021. A survey on change detection and time series analysis with applications. Applied Sciences, 11(13): 6141
  35. 35.
    Ghaderpour E and Vujadinovic T. 2020. Change detection within remotely sensed satellite image time series via spectral analysis. Remote Sensing, 12(23): 4001
  36. 36.
    Gómez C, White J C, Wulder M A and Alejandro P. 2015. Integrated object-based spatiotemporal characterization of forest change from an annual time series of Landsat image composites. Canadian Journal of Remote Sensing, 41(4): 271-292
  37. 37.
    Goodwin N R, Coops N C, Wulder M A, Gillanders S, Schroeder T A and Nelson T. 2008. Estimation of insect infestation dynamics using a temporal sequence of Landsat data. Remote Sensing of Environment, 112(9): 3680-3689
  38. 38.
    Gu X Z, Chen E X, Li Z Y, Zhao L, Fan Y X and Wang Y H. 2019. Forest cover change detection method using multi-polarization space-borne SAR. Scientia Silvae Sinicae, 55(5): 74-84
  39. 39.
    Hais M, Jonášová M, Langhammer J and Kučera T. 2009. Comparison of two types of forest disturbance using multitemporal Landsat TM/ETM+ imagery and field vegetation data. Remote Sensing of Environment, 113(4): 835-845
  40. 40.
    Hamunyela E, Brandt P, Shirima D, Do H T T, Herold M and Roman-Cuesta R M. 2020. Space-time detection of deforestation, forest degradation and regeneration in montane forests of Eastern Tanzania. International Journal of Applied Earth Observation and Geoinformation, 88: 102063
  41. 41.
    Hamunyela E, Verbesselt J and Herold M. 2016. Using spatial context to improve early detection of deforestation from Landsat time series. Remote Sensing of Environment, 172: 126-138
  42. 42.
    Hansen M C, Potapov P V, Moore R, Hancher M, Turubanova S A, Tyukavina A, Thau D, Stehman S V, Goetz S J, Loveland T R, Kommareddy A, Egorov A, Chini L, Justice C O and Townshend J R G. 2013. High-resolution global maps of 21st-century forest cover change. Science, 342(6160): 850-853
  43. 43.
    Hartmann H, Moura C F, Anderegg W R L, Ruehr N K, Salmon Y, Allen C D, Arndt S K, Breshears D D, Davi H, Galbraith D, Ruthrof K X, Wunder J, Adams H D, Bloemen J, Cailleret M, Cobb R, Gessler A, Grams T E E, Jansen S, Kautz M, Lloret F and O'Brien M. 2018b. Research frontiers for improving our understanding of drought-induced tree and forest mortality. New Phytologist, 218(1): 15-28
  44. 44.
    Hartmann H, Schuldt B, Sanders T G M, Macinnis-Ng C, Boehmer H J, Allen C D, Bolte A, Crowther T W, Hansen M C, Medlyn B E, Ruehr N K and Anderegg W R L. 2018a. Monitoring global tree mortality patterns and trends. Report from the VW symposium ‘crossing scales and disciplines to identify global trends of tree mortality as indicators of forest health’. New Phytologist, 217(3): 984-987
  45. 45.
    Healey S P, Cohen W B, Yang Z Q, Kenneth Brewer C, Brooks E B, Gorelick N, Hernandez A J, Huang C, Hughes M J, Kennedy R E, Loveland T R, Moisen G G, Schroeder T A, Stehman S V, Vogelmann J E, Woodcock C E, Yang L M and Zhu Z. 2018. Mapping forest change using stacked generalization: an ensemble approach. Remote Sensing of Environment, 204: 717-728
  46. 46.
    Healey S P, Cohen W B, Yang Z Q and Krankina O N. 2005. Comparison of Tasseled Cap-based Landsat data structures for use in forest disturbance detection. Remote Sensing of Environment, 97(3): 301-310
  47. 47.
    Hemati M, Hasanlou M, Mahdianpari M and Mohammadimanesh F. 2021. A systematic review of landsat data for change detection applications: 50 years of monitoring the earth. Remote Sensing, 13(15): 2869
  48. 48.
    Hermosilla T, Wulder M A, White J C, Coops N C and Hobart G W. 2015. Regional detection, characterization, and attribution of annual forest change from 1984 to 2012 using Landsat-derived time-series metrics. Remote Sensing of Environment, 170: 121-132
  49. 49.
    Hilker T, Wulder M A, Coops N C, Linke J, McDermid G, Masek J G, Gao F and White J C. 2009. A new data fusion model for high spatial- and temporal-resolution mapping of forest disturbance based on Landsat and MODIS. Remote Sensing of Environment, 113(8): 1613-1627
  50. 50.
    Hislop S, Haywood A, Alaibakhsh M, Nguyen T H, Soto-Berelov M, Jones S and Stone C. 2021. A reference data framework for the application of satellite time series to monitor forest disturbance, International Journal of Applied Earth Observation and Geoinformation, 105: 102636
  51. 51.
    Hislop S, Jones S, Soto-Berelov M, Skidmore A, Haywood A and Nguyen T H. 2019. A fusion approach to forest disturbance mapping using time series ensemble techniques. Remote Sensing of Environment, 221: 188-197
  52. 52.
    Hu S Y, Pang Y, Meng S L and Yue C R. 2020. Annual forest disturbance detection using time series landsat 8 OLI data. Forest Research, 33(6): 65-72
  53. 53.
    Huang C B, Dian Y Y, Zhou Z X, Wang D and Chen R D. 2015. Forest change detection based on time series images with statistical properties. Journal of Remote Sensing, 19(4): 657-668
  54. 54.
    Huang C Y and Anderegg W R L. 2012. Large drought-induced aboveground live biomass losses in southern Rocky Mountain aspen forests. Global Change Biology, 18(3): 1016-1027
  55. 55.
    Huang C Y, Anderegg W R L and Asner G P. 2019. Remote sensing of forest die-off in the Anthropocene: from plant ecophysiology to canopy structure. Remote Sensing of Environment, 231: 111233
  56. 56.
    Huang C Q, Goward S N, Masek J G, Thomas N, Zhu Z L and Vogelmann J E. 2010. An automated approach for reconstructing recent forest disturbance history using dense Landsat time series stacks. Remote Sensing of Environment, 114(1): 183-198
  57. 57.
    Huang L. 2021. A review on the climatic regulation effects of afforestation and its impact mechanisms. Acta Ecologica Sinica, 41(2): 469-478
  58. 58.
    Huo L Z, Boschetti L and Sparks A M. 2019. Object-based classification of forest disturbance types in the conterminous United States. Remote Sensing, 11(5): 477
  59. 59.
    Jentsch P C, Bauch C T and Anand M. 2021. Fire mitigates bark beetle outbreaks in serotinous forests. Theoretical Ecology, 14(4): 611-621
  60. 60.
    Kennedy R E, Yang Z Q, Braaten J, Copass C, Antonova N, Jordan C and Nelson P. 2015. Attribution of disturbance change agent from Landsat time-series in support of habitat monitoring in the Puget Sound region, USA. Remote Sensing of Environment, 166: 271-285
  61. 61.
    Kennedy R E, Yang Z Q and Cohen W B. 2010. Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. LandTrendr - Temporal segmentation algorithms. Remote Sensing of Environment, 114(12): 2897-2910
  62. 62.
    Kong Y L, Huang Q Q, Wang C Y, Chen J B, Chen J S and He D X. 2018. Long short-term memory neural networks for online disturbance detection in satellite image time series. Remote Sensing, 10(3): 452
  63. 63.
    Lei M, Tian W X, Ren D and Dong T. 2019. Forest change detection method for remote sensing images based on object classification. Journal of Forest and Environment, 39(6): 641-646
  64. 64.
    Li C G and Dai H B. 2017. Statistical object-based method for forest change detection using high-resolution remote sensing images. Scientia Silvae Sinicae, 53(5): 74-81
  65. 65.
    Li C G and Liang W H. 2017. Forest change detection using remote sensing image based on object-oriented change vector analysis. Remote Sensing for Land and Resources, 29(3): 77-84
  66. 66.
    Li L X, Shen R P, Li X H and Guo J. 2016. Comparison of forest disturbance indices based on MODIS time-series data. Remote Sensing Technology and Application, 31(6): 1083-1090
  67. 67.
    Li Y T, Wu Z Z, Xu X, Fan H, Tong X J and Liu J. 2021. Forest disturbances and the attribution derived from yearly Landsat time series over 1990-2020 in the Hengduan Mountains Region of Southwest China. Forest Ecosystems, 8(1): 73
  68. 68.
    Lin Y K, Zhang L F, Wang N, Zhang X, Cen Y and Sun X J. 2020. A change detection method using spatial-temporal-spectral information from Landsat images. International Journal of Remote Sensing, 41(2): 772-793
  69. 69.
    Liu G F, Guan R M, Xia M L, Lu H L, Xu M and Zheng H. 2022. Comprehensive assessment on both carbon and ecosystem-based benefits from Xishuangbanna’s tropical forests changes. Acta Ecologica Sinica, 42(3): 1118-1129
  70. 70.
    Liu M L, Liu X N, Wu L, Tang Y B, Li Y, Zhang Y Q, Ye L and Zhang B Y. 2021. Establishing forest resilience indicators in the hilly red soil region of southern China from vegetation greenness and landscape metrics using dense Landsat time series. Ecological Indicators, 121: 106985
  71. 71.
    Liu W W, Wang X K, Lu F and Ouyang Z Y. 2016. Influence of afforestation, reforestation, forest logging, climate change, CO2 concentration rise, fire, and insects on the carbon sequestration capacity of the forest ecosystem. Acta Ecologica Sinica, 36(8): 2113-2122
  72. 72.
    Lu D, Mausel P, Brondízio E and Moran E. 2004. Change detection techniques. International Journal of Remote Sensing, 25(12): 2365-2401
  73. 73.
    Lu M, Pebesma E, Sanchez A and Verbesselt J. 2016. Spatio-temporal change detection from multidimensional arrays: detecting deforestation from MODIS time series. ISPRS Journal of Photogrammetry and Remote Sensing, 117: 227-236
  74. 74.
    Lü Y Y, Ren X Y and Li M S. 2014. Assessing forest disturbance patterns over the three forested areas of Nanjing using multi-temporal TM/ETM + imagery. Journal of Nanjing Forestry University (Natural Sciences Edition), 38(1): 77-82
  75. 75.
    McDowell N G, Coops N C, Beck P S A, Chambers J Q, Gangodagamage C, Hicke J A, Huang C Y, Kennedy R, Krofcheck D J, Litvak M, Meddens A J H, Muss J, Negrón-Juarez R, Peng C H, Schwantes A M, Swenson J J, Vernon L J, Williams A P, Xu C G, Zhao M S, Running S W and Allen C D. 2015. Global satellite monitoring of climate-induced vegetation disturbances. Trends in Plant Science, 20(2): 114-123
  76. 76.
    Mei S H, Fan C C and Liao Y S. 2019. Forestland change detection based on spectral and texture features. Bulletin of Surveying and Mapping, (8): 140-143
  77. 77.
    Meng R, Gao R J, Zhao F, Huang C Q, Sun R, Lv Z G and Huang Z H. 2022. Landsat-based monitoring of southern pine beetle infestation severity and severity change in a temperate mixed forest. Remote Sensing of Environment, 269: 112847
  78. 78.
    Meng Y Y, Liu X N, Wang Z, Ding C and Zhu L H. 2021. How can spatial structural metrics improve the accuracy of forest disturbance and recovery detection using dense Landsat time series?. Ecological Indicators, 132: 108336
  79. 79.
    Meng Y Y, Liu X N, Wu L, Liu M L, Zhang B Y and Zhao S. 2019. Spatio-temporal variation indicators for landscape structure dynamics monitoring using dense normalized difference vegetation index time series. Ecological Indicators, 107: 105607
  80. 80.
    Murillo-Sandoval P J, Hilker T, Krawchuk M A and Van Den Hoek J. 2018. Detecting and attributing drivers of forest disturbance in the Colombian andes using landsat time-series. Forests, 9(5): 269
  81. 81.
    National Remote Sensing Center of China. 2021. 2021 annual reports on global ecosystem and environment observation released [EB/OL]. [2021-12-20].
  82. 82.
    Nguyen T H, Jones S D, Soto-Berelov M, Haywood A and Hislop S. 2018. A spatial and temporal analysis of forest dynamics using Landsat time-series. Remote Sensing of Environment, 217: 461-475
  83. 83.
    Oeser J, Heurich M, Senf C, Pflugmacher D and Kuemmerle T. 2021. Satellite-based habitat monitoring reveals long-term dynamics of deer habitat in response to forest disturbances. Ecological Applications, 31(3): e2269
  84. 84.
    Oeser J, Pflugmacher D, Senf C, Heurich M and Hostert P. 2017. Using intra-annual Landsat time series for attributing forest disturbance agents in Central Europe. Forests, 8(7): 251
  85. 85.
    Otero V, Van De Kerchove R, Satyanarayana B, Mohd-Lokman H, Lucas R and Dahdouh-Guebas F. 2019. An analysis of the early regeneration of mangrove forests using landsat time series in the matang mangrove forest reserve, peninsular Malaysia. Remote Sensing, 11(7): 774
  86. 86.
    Pasquarella V J, Bradley B A and Woodcock C E. 2017. Near-real-time monitoring of insect defoliation using Landsat time series. Forests, 8(8): 275
  87. 87.
    Piao S L, Wang X H, Park T, Chen C, Lian X, He Y, Bjerke J W, Chen A P, Ciais P, Tømmervik H, Nemani R R and Myneni R B. 2020. Characteristics, drivers and feedbacks of global greening. Nature Reviews Earth and Environment, 1(1): 14-27
  88. 88.
    Platt R V, Ogra M V, Badola R and Hussain S A. 2016. Conservation-induced resettlement as a driver of land cover change in India: an object-based trend analysis. Applied Geography, 69: 75-86
  89. 89.
    Platt R V and Schoennagel T. 2009. An object-oriented approach to assessing changes in tree cover in the Colorado Front Range 1938–1999. Forest Ecology and Management, 258(7): 1342-1349
  90. 90.
    Puhm M, Deutscher J, Hirschmugl M, Wimmer A, Schmitt U and Schardt M. 2020. A near real-time method for forest change detection based on a structural time series model and the kalman filter. Remote Sensing, 12(19): 3135
  91. 91.
    Qin X L, Chen E X, Li Z Y and Yi H R. 2006. Forest cover change monitoring using MODIS data. Remote Sensing Technology and Application, 21(3): 178-183
  92. 92.
    Reiche J, Hamunyela E, Verbesselt J, Hoekman D and Herold M. 2018. Improving near-real time deforestation monitoring in tropical dry forests by combining dense Sentinel-1 time series with Landsat and ALOS-2 PALSAR-2. Remote Sensing of Environment, 204: 147-161
  93. 93.
    Rodman K C, Andrus R A, Veblen T T and Hart S J. 2021. Disturbance detection in landsat time series is influenced by tree mortality agent and severity, not by prior disturbance. Remote Sensing of Environment, 254: 112244
  94. 94.
    Rogers B M, Soja A J, Goulden M L and Randerson J T. 2015. Influence of tree species on continental differences in boreal fires and climate feedbacks. Nature Geoscience, 8(3): 228-234
  95. 95.
    Saxena R, Watson L T, Wynne R H, Brooks E B, Thomas V A, Yang Z Q and Kennedy R E. 2018. Towards a polyalgorithm for land use change detection. ISPRS Journal of Photogrammetry and Remote Sensing, 144: 217-234
  96. 96.
    Schleeweis K G, Moisen G G, Schroeder T A, Toney C, Freeman E A, Goward S N, Huang C Q and Dungan J L. 2020. US national maps attributing forest change: 1986-2010. Forests, 11(6): 653
  97. 97.
    Schroeder T A, Wulder M A, Healey S P and Moisen G G. 2011. Mapping wildfire and clearcut harvest disturbances in boreal forests with Landsat time series data. Remote Sensing of Environment, 115(6): 1421-1433
  98. 98.
    Sebald J, Senf C and Seidl R. 2021. Human or natural? Landscape context improves the attribution of forest disturbances mapped from Landsat in Central Europe. Remote Sensing of Environment, 262: 112502
  99. 99.
    Senf C and Seidl R. 2021a. Mapping the forest disturbance regimes of Europe. Nature Sustainability, 4(1): 63-70
  100. 100.
    Senf C and Seidl R. 2021b. Storm and fire disturbances in Europe: distribution and trends. Global Change Biology, 27(15): 3605-3619
  101. 101.
    Shen W J and Li M S. 2017. Mapping disturbance and recovery of plantation forests in southern China using yearly Landsat time series observations. Acta Ecologica Sinica, 37(5): 1438-1449
  102. 102.
    Shen W J, Li M S and Huang C Q. 2018. Review of remote sensing algorithms for monitoring forest disturbance from time series and multi-source data fusion. Journal of Remote Sensing, 22(6): 1005-1022
  103. 103.
    Shimizu K, Ota T, Mizoue N and Yoshida S. 2019. A comprehensive evaluation of disturbance agent classification approaches: strengths of ensemble classification, multiple indices, spatio-temporal variables, and direct prediction. ISPRS Journal of Photogrammetry and Remote Sensing, 158: 99-112
  104. 104.
    Silveira E M O, Bueno I T, Acerbi-Junior F W, Mello J M, Scolforo J R S and Wulder M A. 2018. Using spatial features to reduce the impact of seasonality for detecting tropical forest changes from landsat time series. Remote Sensing, 10(6): 808
  105. 105.
    Smith V, Portillo-Quintero C, Sanchez-Azofeifa A and Hernandez-Stefanoni J L. 2019. Assessing the accuracy of detected breaks in landsat time series as predictors of small scale deforestation in tropical dry forests of Mexico and Costa Rica. Remote Sensing of Environment, 221: 707-721
  106. 106.
    Sun D, Liu S P, Shen W J, Rao X Q, Guo Z F and Li Y Q. 2018. Soil and water conservation effects of different plantations in southern subtropical area of China. Science of Soil and Water Conservation, 16(1): 72-79
  107. 107.
    Tang D M, Fan H and Zhang Y. 2017. Review on Landsat time series change detection methods. Journal of Geo-information Science, 19(8): 1069-1079
  108. 108.
    Thom D and Seidl R. 2016. Natural disturbance impacts on ecosystem services and biodiversity in temperate and boreal forests. Biological Reviews, 91(3): 760-781
  109. 109.
    Tong X W, Brandt M, Yue Y M, Ciais P, Rudbeck Jepsen M, Penuelas J, Wigneron J P, Xiao X M, Song X P, Horion S, Rasmussen K, Saatchi S, Fan L, Wang K L, Zhang B, Chen Z C, Wang Y H, Li X J and Fensholt R. 2020. Forest management in southern China generates short term extensive carbon sequestration. Nature Communications, 11(1): 129
  110. 110.
    Townshend J R, Masek J G, Huang C Q, Vermote E F, Gao F, Channan S, Sexton J O, Feng M, Narasimhan R, Kim D, Song K, Song D X, Song X P, Noojipady P, Tan B, Hansen M C, Li M X and Wolfe R E. 2012. Global characterization and monitoring of forest cover using landsat data: opportunities and challenges. International Journal of Digital Earth, 5(5): 373-397
  111. 111.
    Tyukavina A, Hansen M C, Potapov P V, Stehman S V, Smith-Rodriguez K, Okpa C and Aguilar R. 2017. Types and rates of forest disturbance in Brazilian Legal Amazon, 2000-2013. Science Advances, 3(4): e1601047
  112. 112.
    Verbesselt J, Hyndman R, Newnham G and Culvenor D. 2010a. Detecting trend and seasonal changes in satellite image time series. Remote Sensing of Environment, 114(1): 106-115
  113. 113.
    Verbesselt J, Hyndman R, Zeileis A and Culvenor D. 2010b. Phenological change detection while accounting for abrupt and gradual trends in satellite image time series. Remote Sensing of Environment, 114(12): 2970-2980
  114. 114.
    Verbesselt J, Zeileis A and Herold M. 2012. Near real-time disturbance detection using satellite image time series. Remote Sensing of Environment, 123: 98-108
  115. 115.
    Vogeler J C, Slesak R A, Fekety P A and Falkowski M J. 2020. Characterizing over four decades of forest disturbance in Minnesota, USA. Forests, 11(3): 362
  116. 116.
    Vogelmann J E, Gallant A L, Shi H and Zhu Z. 2016. Perspectives on monitoring gradual change across the continuity of Landsat sensors using time-series data. Remote Sensing of Environment, 185: 258-270
  117. 117.
    Wang Q K, Yu X F and Shu Q T. 2017. Forest burned scars area extraction using time series remote sensing data. Journal of Natural Disasters, 26(1): 1-10
  118. 118.
    Wang Z H, Yao W Y, Tang Q H, Liu L Y, Xiao P Q, Kong X B, Zhang P, Shi F X and Wang Y J. 2018. Continuous change detection of forest/grassland and cropland in the loess plateau of China using all available Landsat data. Remote Sensing, 10(11): 1775
  119. 119.
    Woodcock C E, Loveland T R, Herold M and Bauer M E. 2020. Transitioning from change detection to monitoring with remote sensing: a paradigm shift. Remote Sensing of Environment, 238: 111558
  120. 120.
    Woźniak E and Aleksandrowicz S. 2019. Self-adjusting thresholding for burnt area detection based on optical images. Remote Sensing, 11(22): 2669
  121. 121.
    Wu L, Li Z L, Liu X N, Zhu L H, Tang Y B, Zhang B Y, Xu B L, Liu M L, Meng Y Y and Liu B Y. 2020. Multi-type forest change detection using BFAST and monthly landsat time series for monitoring spatiotemporal dynamics of forests in subtropical wetland. Remote Sensing, 12(2): 341
  122. 122.
    Wu L, Liu X N, Liu M L, Yang J H, Zhu L H and Zhou B T. 2022. Online forest disturbance detection at the sub-annual scale using spatial context from sparse Landsat time series. IEEE Transactions on Geoscience and Remote Sensing, 60: 4507814
  123. 123.
    Wulder M A, Loveland T R, Roy D P, Crawford C J, Masek J G, Woodcock C E, Allen R G, Anderson M C, Belward A S, Cohen W B, Dwyer J, Erb A, Gao F, Griffiths P, Helder D, Hermosilla T, Hipple J D, Hostert P, Hughes M J, Huntington J, Johnson D M, Kennedy R, Kilic A, Li Z, Lymburner L, Mccorkel J, Pahlevan N, Scambos T A, Schaaf C, Schott J R, Sheng Y W, Storey J, Vermote E, Vogelmann J, White J C, Wynne R H and Zhu Z. 2019. Current status of Landsat program, science, and applications. Remote Sensing of Environment, 225: 127-147
  124. 124.
    Wulder M A, White J C, Loveland T R, Woodcock C E, Belward A S, Cohen W B, Fosnight E A, Shaw J, Masek J G and Roy D P. 2016. The global Landsat archive: status, consolidation, and direction. Remote Sensing of Environment, 185: 271-283
  125. 125.
    Xiao P F, Sheng G W, Zhang X L, Liu H and Guo R. 2021. Direction-dominated change vector analysis for forest change detection. International Journal of Applied Earth Observations and Geoinformation, 103: 102492
  126. 126.
    Xie F D, Yu S S and Yang J. 2018. A new semi-supervised change detection method of fusing spatial information. Bulletin of Surveying and Mapping, (1): 50-54
  127. 127.
    Xu R, Li Y, Teuling A J, Zhao L, Spracklen D V, Garcia-Carreras L, Meier R, Chen L, Zheng Y T, Lin H Q and Fu B J. 2022. Contrasting impacts of forests on cloud cover based on satellite observations. Nature Communications, 13(1): 670
  128. 128.
    Yang C, Shen R P, Yu D W, Liu R G and Chen J M. 2013. Forest disturbance monitoring based on the time-series trajectory of remote sensing index. Journal of Remote Sensing, 17(5): 1246-1263
  129. 129.
    Yang Y, Anderson M, Gao F, Hain C, Noormets A, Sun G, Wynne R, Thomas V and Sun L. 2020. Investigating impacts of drought and disturbance on evapotranspiration over a forested landscape in North Carolina, USA using high spatiotemporal resolution remotely sensed data. Remote Sensing of Environment, 238: 111018
  130. 130.
    Ye S, Rogan J, Zhu Z, Hawbaker T J, Hart S J, Andrus R A, Meddens A J H, Hicke J A, Eastman J R and Kulakowski D. 2021. Detecting subtle change from dense Landsat time series: case studies of mountain pine beetle and spruce beetle disturbance. Remote Sensing of Environment, 263: 112560
  131. 131.
    Yin Q D, Liu C X and Tian Y. 2020. Detecting dynamics of vegetation disturbance in forest natural reserve using Landsat imagery and LandTrendr algorithm: the case of Chaisong and Taibaishan Natural Reserves in Shaanxi, China. Acta Ecologica Sinica, 40(20): 7343-7352
  132. 132.
    Zhan Q B, Xu Y and Zhang S Y. 2016. Interaction between pest insect outbreaks and forest fires. Forest Pest and Disease, 35(1): 8-12
  133. 133.
    Zhang B Y. 2019. Change Detection of Semi-Arid Forest Ecosystem based on Remote Sensing Time Series Analysis. Beijing: China University of Geosciences (Beijing)
  134. 134.
    Zhang J T, Lin J and Li X. 2008. SAR image change detection based on fractal dimension. Journal of Xi'an Technological University, 28(5): 466-470
  135. 135.
    Zhang L F, Wang S, Liu H L, Lin Y K, Wang J N, Zhu M, Gao L R and Tong Q X. 2021. From spectrum to spectrotemporal: research on time series change detection of remote sensing. Geomatics and Information Science of Wuhan University, 46(4): 451-468
  136. 136.
    Zhang L P and Wu C. 2017. Advance and future development of change detection for multi-temporal remote sensing imagery. Acta Geodaetica et Cartographica Sinica, 46(10): 1447-1459
  137. 137.
    Zhang X, Chen G S, Cai L X, Jiao H B, Hua J W, Luo X F and Wei X L. 2021. Impact assessments of typhoon lekima on forest damages in subtropical China using machine learning methods and Landsat 8 OLI imagery. Sustainability, 13(9): 4893
  138. 138.
    Zhang Y and Ding Y F. 2019. Analysis on spatial distribution of forest disasters in China. Journal of Beijing Forestry University, 41(3): 68-79
  139. 139.
    Zhang Y T, Woodcock C E, Chen S J, Wang J A, Sulla-Menashe D, Zuo Z P, Olofsson P, Wang Y T J and Friedl M A. 2022. Mapping causal agents of disturbance in boreal and arctic ecosystems of North America using time series of landsat data. Remote Sensing of Environment, 272: 112935
  140. 140.
    Zhang Z L and Lu P. 2019. Object oriented classification of high -resolution remote sensing image for monitoring the change of forest vegetation by comparison. Shaanxi Forest Science and Technology, 47(3): 47-50
  141. 141.
    Zhao Z M, Meng Y, Yue A Z, Huang Q Q, Kong Y L, Yuan Y, Liu X Y, Lin L and Zhang M M. 2016. Review of remotely sensed time series data for change detection. Journal of Remote Sensing, 20(5): 1110-1125
  142. 142.
    Zheng Z H, Bai W L and Jiang T. 2021. An improved VCT long time series forest disturbance method. Journal of Geomatics Science and Technology, 38(1): 39-43
  143. 143.
    Zhu Z. 2017. Change detection using landsat time series: a review of frequencies, preprocessing, algorithms, and applications. ISPRS Journal of Photogrammetry and Remote Sensing, 130: 370-384
  144. 144.
    Zhu Z and Woodcock C E. 2014. Continuous change detection and classification of land cover using all available Landsat data. Remote Sensing of Environment, 144: 152-171
  145. 145.
    Zhu Z, Wulder M A, Roy D P, Woodcock C E, Hansen M C, Radeloff V C, Healey S P, Schaaf C, Hostert P, Strobl P, Pekel J F, Lymburner L, Pahlevan N and Scambos T A. 2019. Benefits of the free and open landsat data policy. Remote Sensing of Environment, 224: 382-385
  146. 146.
    Zhu Z, Zhang J X, Yang Z Q, Aljaddani A H, Cohen W B, Qiu S and Zhou C L. 2020. Continuous monitoring of land disturbance based on Landsat time series. Remote Sensing of Environment, 238: 111116

Leer el texto completo

The above content is generated by Large Model Translation. The translated content is for reference only. We do not assume any commercial or legal responsibilty for any consequences arising from the use of our website