Mapping wildfire and clearcut harvest disturbances in boreal forests with Landsat time series data

Schroeder, Todd A.; Wulder, Michael A.; Healey, Sean P.; Moisen, Gretchen G.

doi:10.1016/j.rse.2011.01.022

Cited by 203 publications

(149 citation statements)

References 82 publications

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“…Data time series are necessary to detect deforestation. Landsat data have been primarily used in monitoring forest disturbance (Griffiths et al 2012;Schroeder et al 2011;Zhu et al 2012;Grinand et al 2013;Huang et al 2010;Gorsevski et al 2012;Renó et al 2011;Goodwin and Collett 2014), mainly due to the long archive, spectral and spatial resolution properties, and the free availability of data. Tasseled Cap Transformation (TCT) indices from Landsat near-annual time series, evaluated under trajectory-based change detection methods, resulted in identifying forest disturbances within 22 years with overall accuracy (OA) 95.72% (Griffiths et al 2012).…”

Section: Degradation / Deforestationmentioning

confidence: 99%

Remote sensing for biodiversity monitoring: a review of methods for biodiversity indicator extraction and assessment of progress towards international targets

2015

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Recognizing the imperative need for biodiversity protection, the Convention on Biological Diversity (CBD) has recently established new targets towards 2020, the so-called Aichi targets, and updated proposed sets of indicators to quantitatively monitor the progress towards these targets. Remote sensing has been increasingly contributing to timely, accurate, and cost-effective assessment of biodiversity-related characteristics and functions during the last years. However, most relevant studies constitute individual research efforts, rarely related with the extraction of widely adopted CBD biodiversity indicators. Furthermore, systematic operational use of remote sensing data by managing authorities has still been limited. In this study, the Aichi targets and the related CBD indicators whose monitoring can be facilitated by remote sensing are identified. For each headline indicator a number of recent remote sensing approaches able for the extraction of related properties are reviewed. Methods cover a wide range of fields, including: habitat extent and condition monitoring; species distribution; pressures from unsustainable management, pollution and climate change; ecosystem service monitoring; and conservation status assessment of protected areas. The advantages and limitations of different remote sensing data and algorithms are discussed. Sorting of the methods based on their reported accuracies is attempted, when possible. The extensive literature survey aims at reviewing highly performing methods that can be used for large-area, effective, and timely biodiversity assessment, to encourage the more systematic use of remote sensing solutions in monitoring progress towards the Aichi targets, and to decrease the gaps between the remote sensing and management communities.

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Section: Degradation / Deforestationmentioning

confidence: 99%

Remote sensing for biodiversity monitoring: a review of methods for biodiversity indicator extraction and assessment of progress towards international targets

2015

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“…These approaches for multi-temporal change detection can be applied, for example, to monitor forest fires, insect-related mortality, or post-disturbance regrowth at an annual time scale (e.g., Coops et al 2010, Schroeder et al 2011, Kennedy et al 2012. However, the spatial resolution of Landsat is limited (28.5 m pixels), and thus these data fail to allow for reconstructing small-scale disturbance processes.…”

Section: Introductionmentioning

confidence: 99%

Gap pattern of the largest primeval beech forest of Europe revealed by remote sensing

et al. 2015

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Abstract. Little is known about the gap pattern of primeval beech forests, since large-scale studies with continuous coverage are lacking. Analyses of forest structural patterns have benefitted from advances in remote sensing, especially with the launch of satellites providing data of submetric ground resolution. These developments can strongly advance our knowledge of natural forest dynamics and disturbance regimes. The Uholka-Shyrokyi Luh forest in the Ukrainian Carpathians, the largest remnant of primeval European beech (Fagus sylvatica L.) covering 102.8 km 2 , is an outstanding object to analyze the frequency distribution of gap sizes and to infer processes of forest dynamics. A stereo pair of very high-resolution WorldView-2 satellite images was used to characterize the forest's gap pattern. Canopy gaps were first digitized stereoscopically based on the image pair. In a second step, spectral properties in the red and yellow frequency bands were used to distinguish the stereoscopically mapped gap areas from non-gap areas, which enabled gap mapping over the entire study area. To validate the spectral gap mapping 338 randomly distributed samples were assigned manually to gap and non-gap areas based on the orthoimages. We found excellent agreement except for an overestimation of gaps close to clouds due to diffuse image areas. The frequency distribution of gap size revealed the forest to be structured by a small-scale mosaic of gaps mainly ,200 m 2 (98% of the gaps). Only a few large, stand-replacing events were detected, most probably caused by a wind storm in March 2007 and a heavy wet snow fall in October 2009. The small canopy gaps reflect fine-scale processes shaping forest structure, i.e., the death of single trees or groups of a few trees and is in line with the findings of the terrestrial forest inventory. We conclude that remote sensing approaches based on very high-resolution satellite images are highly useful to characterize even small-scale forest disturbance regimes and to study long-term gap dynamics. Stereo satellite images provide two viewing angles of the study area, thus allowing for a highly accurate mapping of canopy gaps in forests with a complex topography.

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“…The Landsat series of instruments at a 30-m resolution provide a critical tool to understand forest changes relevant to the C-cycle from harvest [18], wildfire [19,20] and insect outbreaks [21] by extending observational studies to areas that have poor records [22]. North American studies have used the decadal epoch of Landsat data, GeoCover or the Global Land Survey (GLS) [23] to quantify forest disturbances [24,25], while others have used dense Landsat time-series stack (LTSS) data focused on specific regions to evaluate change vectors to understand forest disturbance dynamics [20,[26][27][28][29]. These LTSS data with ecosystem models could be used to monitor the productivity of forests pre-and post-disturbance [30].…”

Section: Introductionmentioning

confidence: 99%

“…Due to the ease of extraction, many remote sensing studies using Landsat have focused on fire disturbance and logging [20,[31][32][33][34][35]. Given the difficulty of discriminating background populations from outbreak events, few have classified insect, pest and pathogen events in Landsat data [21,36].…”

Section: Introductionmentioning

confidence: 99%

An Automated Approach to Map the History of Forest Disturbance from Insect Mortality and Harvest with Landsat Time-Series Data

et al. 2014

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Forests contain a majority of the aboveground carbon (C) found in ecosystems, and understanding biomass lost from disturbance is essential to improve our C-cycle knowledge. Our study region in the Wisconsin and Minnesota Laurentian Forest had a strong decline in Normalized Difference Vegetation Index (NDVI) from 1982 to 2007, observed with the National Ocean and Atmospheric Administration's (NOAA) series of Advanced Very High Resolution Radiometer (AVHRR). To understand the potential role of disturbances in the terrestrial C-cycle, we developed an algorithm to map forest disturbances from either harvest or insect outbreak for Landsat time-series stacks. We merged two image analysis approaches into one algorithm to monitor forest change that included: (1) multiple disturbance index thresholds to capture clear-cut harvest; and (2) a OPEN ACCESS Remote Sens. 2014, 6 2783 spectral trajectory-based image analysis with multiple confidence interval thresholds to map insect outbreak. We produced 20 maps and evaluated classification accuracy with air-photos and insect air-survey data to understand the performance of our algorithm. We achieved overall accuracies ranging from 65% to 75%, with an average accuracy of 72%. The producer's and user's accuracy ranged from a maximum of 32% to 70% for insect disturbance, 60% to 76% for insect mortality and 82% to 88% for harvested forest, which was the dominant disturbance agent. Forest disturbances accounted for 22% of total forested area (7349 km 2 ). Our algorithm provides a basic approach to map disturbance history where large impacts to forest stands have occurred and highlights the limited spectral sensitivity of Landsat time-series to outbreaks of defoliating insects. We found that only harvest and insect mortality events can be mapped with adequate accuracy with a non-annual Landsat time-series. This limited our land cover understanding of NDVI decline drivers. We demonstrate that to capture more subtle disturbances with spectral trajectories, future observations must be temporally dense to distinguish between type and frequency in heterogeneous landscapes.

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Mapping wildfire and clearcut harvest disturbances in boreal forests with Landsat time series data

Cited by 203 publications

References 82 publications

Remote sensing for biodiversity monitoring: a review of methods for biodiversity indicator extraction and assessment of progress towards international targets

Remote sensing for biodiversity monitoring: a review of methods for biodiversity indicator extraction and assessment of progress towards international targets

Gap pattern of the largest primeval beech forest of Europe revealed by remote sensing

An Automated Approach to Map the History of Forest Disturbance from Insect Mortality and Harvest with Landsat Time-Series Data

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