Annual mapping of large forest disturbances across Canada's forests using 250 m MODIS imagery from 2000 to 2011

被引:53
|
作者
Guindon, L. [1 ]
Bernier, P. Y. [1 ]
Beaudoin, A. [1 ]
Pouliot, D. [2 ]
Villemaire, P. [1 ]
Hall, R. J. [3 ]
Latifovic, R. [2 ]
St-Amant, R. [1 ]
机构
[1] Nat Resources Canada, Canadian Forest Serv, Laurentian Forestry Ctr, Stn St Foy, PQ G1V 4C7, Canada
[2] Canada Ctr Mapping & Earth Observat, Ottawa, ON K1A 0E4, Canada
[3] Nat Resources Canada, Canadian Forest Serv, Northern Forestry Ctr, Edmonton, AB T6H 3S5, Canada
关键词
boreal forest; National Burned Area Composite; remote sensing; regression tree; decision tree; change detection; LAND-COVER; BOREAL FOREST; AREA; RESOLUTION; ACCURACY;
D O I
10.1139/cjfr-2014-0229
中图分类号
S7 [林业];
学科分类号
0829 ; 0907 ;
摘要
Disturbances such as fire and harvesting shape forest dynamics and must be accounted for when modelling forest properties. However, acquiring timely disturbance information for all of Canada's large forest area has always been challenging. Therefore, we developed an approach to detect annual forest change resulting from fire, harvesting, or flooding using Moderate Resolution Imaging Spectroradiometer (MODIS) imagery at 250 m spatial resolution across Canada and to estimate the withinpixel fractional change (FC). When this approach was applied to the period from 2000 to 2011, the accuracy of detection of burnt, harvested, or flooded areas against our validation dataset was 82%, 80%, and 85%, respectively. With FC, 77% of the area burnt and 82% of the area harvested within the validation dataset were correctly identified. The methodology was optimized to reduce the commission error but tended to omit smaller disturbances as a result. For example, the omitted area for harvest blocks greater than 80 ha was less than 14% but increased to between 38% and 50% for harvest blocks of 20 to 30 ha. Detection of burnt and harvested areas in some regions was hindered by persistent haze or cloud cover or by insect outbreaks. All resulting data layers are available as supplementary material.
引用
收藏
页码:1545 / 1554
页数:10
相关论文
共 29 条
  • [21] Large-area crop mapping using time-series MODIS 250 m NDVI data: An assessment for the US Central Great Plains
    Wardlow, Brian D.
    Egbert, Stephen L.
    REMOTE SENSING OF ENVIRONMENT, 2008, 112 (03) : 1096 - 1116
  • [22] Mapping Annual Cropping Pattern from Time-Series MODIS EVI Using Parameter-Tuned Random Forest Classifier
    Praveen, Alex
    Jeganathan, C.
    Mondal, Saptarshi
    JOURNAL OF THE INDIAN SOCIETY OF REMOTE SENSING, 2023, 51 (05) : 983 - 1000
  • [23] Mapping Annual Cropping Pattern from Time-Series MODIS EVI Using Parameter-Tuned Random Forest Classifier
    Alex Praveen
    C. Jeganathan
    Saptarshi Mondal
    Journal of the Indian Society of Remote Sensing, 2023, 51 : 983 - 1000
  • [24] Mapping Clearances in Tropical Dry Forests Using Breakpoints, Trend, and Seasonal Components from MODIS Time Series: Does Forest Type Matter?
    Grogan, Kenneth
    Pflugmacher, Dirk
    Hostert, Patrick
    Verbesselt, Jan
    Fensholt, Rasmus
    REMOTE SENSING, 2016, 8 (08)
  • [25] Mapping Forest Characteristics at Fine Resolution across Large Landscapes of the Southeastern United States Using NAIP Imagery and FIA Field Plot Data
    Hogland, John
    Anderson, Nathaniel
    St Peter, Joseph
    Drake, Jason
    Medley, Paul
    ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION, 2018, 7 (04)
  • [26] Large-Scale Mapping of Complex Forest Typologies Using Multispectral Imagery and Low-Density Airborne LiDAR: A Case Study in Pinsapo Fir Forests
    Ariza-Salamanca, Antonio Jesus
    Gonzalez-Moreno, Pablo
    Lopez-Quintanilla, Jose Benedicto
    Navarro-Cerrillo, Rafael Maria
    REMOTE SENSING, 2024, 16 (17)
  • [27] Spectral matching techniques (SMTs) and automated cropland classification algorithms (ACCAs) for mapping croplands of Australia using MODIS 250-m time-series (2000-2015) data
    Teluguntla, Pardhasaradhi
    Thenkabail, Prasad S.
    Xiong, Jun
    Gumma, Murali Krishna
    Congalton, Russell G.
    Oliphant, Adam
    Poehnelt, Justin
    Yadav, Kamini
    Rao, Mahesh
    Massey, Richard
    INTERNATIONAL JOURNAL OF DIGITAL EARTH, 2017, 10 (09) : 944 - 977
  • [28] Monitoring Annual Forest Cover Fraction Change During 2000-2020 in China's Han River Basin Using Time-Series MODIS NDVI, VCF and Spatio-Temporal Regression
    Zhong, Xinyan
    Du, Yun
    Wang, Xia
    Li, Xiaodong
    Zhao, Wenqiong
    Zhang, Yihang
    Atkinson, Peter M.
    IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2024, 17 : 12092 - 12111
  • [29] Mapping 30 m Fractional Forest Cover over China's Three-North Region from Landsat-8 Data Using Ensemble Machine Learning Methods
    Liu, Xiaobang
    Liang, Shunlin
    Li, Bing
    Ma, Han
    He, Tao
    REMOTE SENSING, 2021, 13 (13)