Remote sensing change detection tools for natural resource managers: Understanding concepts and tradeoffs in the design of landscape monitoring projects

Kennedy, Robert E.; Townsend, Philip A.; Gross, John E.; Cohen, Warren B.; Bolstad, Paul V.; Wang, Y.Q.; Adams, Phyllis

doi:10.1016/j.rse.2008.07.018

Cited by 305 publications

(140 citation statements)

References 111 publications

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“…In addition to selection of suitable variables with dense dates, different algorithms such as VCT, LandTrendt, and BFAST have been developed for detection of forest disturbance and recovery [11,[23][24][25][26]32,33], but their performances vary, depending on many factors such as the composition of forest species and complexity of the landscape under investigation, the variables used, and parameters in the algorithm. For example, several control parameters are used in the LandTrendr algorithm and they need to be optimized.…”

Section: Discussionmentioning

confidence: 99%

Examining Forest Disturbance and Recovery in the Subtropical Forest Region of Zhejiang Province Using Landsat Time-Series Data

Liu

Wei

et al. 2017

Remote Sensing

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Detection of forest disturbance and recovery has received much attention during the last two decades due to its important influence on forest carbon budget estimation. This research used Landsat time-series data from 1984 to 2015 to examine forest disturbance and recovery in a subtropical region of eastern Zhejiang Province, China, through the LandTrendr algorithm. Field inventory data and high spatial resolution images were used to evaluate the disturbance and recovery results. This research indicates that high producer and user accuracies for both disturbance and recovery classes were obtained and three levels of disturbance and recovery each can be detected. Through incorporation of climate data and disturbance results, drought events also can be successfully detected. More research is needed to incorporate multisource data for detection of forest disturbance types in subtropical regions.

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Section: Discussionmentioning

confidence: 99%

Examining Forest Disturbance and Recovery in the Subtropical Forest Region of Zhejiang Province Using Landsat Time-Series Data

Liu

Wei

et al. 2017

Remote Sensing

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“…Moreover, dividing continuous quantitative information, such as those found in satellite images, into a finite number of discrete land classes that are considered at the outset to be exhaustively defined and mutually exclusive may lend itself to the further loss of information [60]. Such techniques may fail to accurately detect and separate "edge pixels," for example, that exist near the spectral boundaries of different classes [16,61] as well as pixels that exhibit high reflectance variability [62]. An additional constraint can be imposed when spectral signals from the land area represented by a pixel are influenced by signals from immediately surrounding pixels [63].…”

Section: Discussionmentioning

confidence: 99%

“…First, end-users in natural resource monitoring fields may lack a technical understanding of remotely sensed datasets and their associated analyses, and thus rely upon remote sensing scientists to design and implement remote sensing-based projects [16,17]. The high level of expertise necessary to handle remotely sensed data and products is considered an outstanding challenge facing today's ecologists and conservation biologists that has limited their ability to take full advantage of remote sensing technologies as real assets [18].…”

Section: Introductionmentioning

confidence: 99%

A Comparison of Novel Optical Remote Sensing-Based Technologies for Forest-Cover/Change Monitoring

Lui

Coomes

2015

Remote Sensing

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Abstract:Remote sensing is gaining considerable traction in forest monitoring efforts, with the Carnegie Landsat Analysis System lite (CLASlite) software package and the Global Forest Change dataset (GFCD) being two of the most recently developed optical remote sensing-based tools for analysing forest cover and change. Due to the relatively nascent state of these technologies, their abilities to classify land cover and monitor forest dynamics have yet to be evaluated against more established approaches. Here, we compared maps of forest cover and change produced by the more traditional supervised classification approach with those produced by CLASlite and the GFCD, working with imagery collected over Sierra Leone, West Africa. CLASlite maps of forest change from 2001-2007 and 2007-2014 exhibited the highest overall accuracies (79.1% and 89.6%, respectively) and, importantly, the greatest capacity to discriminate natural from planted mature forest growth. CLASlite's comparative advantage likely derived from its more robust sub-pixel classification logic and numerous user-defined parameters, which resulted in classified products with greater site relevance than those of the two other classification approaches. In light of today's continuously growing body of analytical toolsets for remotely sensed data, our study importantly elucidates the ways in which methodological processes and limitations inherent in certain classification tools can impact the maps they are capable of producing, and demonstrates the need to understand and weigh such factors before any one tool is selected for a given application. OPEN ACCESSRemote Sens. 2015, 7 2782

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“…Kennedy [22] built a trajectory-based change detection method for automated characterization of forest disturbance dynamics. Then LandTrendr [23] and TimeSync [24] were further developed to detect trends in forest disturbance and recovery using yearly Landsat time series and applied widely [25][26][27][28][29][30].…”

Section: Introductionmentioning

confidence: 99%

Distribution and Variation of Forests in China from 2001 to 2011: A Study Based on Remotely Sensed Data

Zhao

Zhou

et al. 2013

Forests

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Abstract:Forests are one of the most important components of the global biosphere and have critical influences on the Earth's ecological balance. Regularly updated forest cover information is necessary for various forest management applications as well as climate modeling studies. However, map products are not widely updated at continental or national scales because the current land cover products have overly coarse spatial resolution or insufficiently large training data sets. This study presents the results of forests distribution and variation information over China using Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) time series data with the first layer of MODIS Land Cover Type product (MODIS LC-1). The NDVI time series histogram characteristic curves for forestland were estimated from MODIS LC-1 and MODIS NDVI time series data.

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Remote sensing change detection tools for natural resource managers: Understanding concepts and tradeoffs in the design of landscape monitoring projects

Cited by 305 publications

References 111 publications

Examining Forest Disturbance and Recovery in the Subtropical Forest Region of Zhejiang Province Using Landsat Time-Series Data

Examining Forest Disturbance and Recovery in the Subtropical Forest Region of Zhejiang Province Using Landsat Time-Series Data

A Comparison of Novel Optical Remote Sensing-Based Technologies for Forest-Cover/Change Monitoring

Distribution and Variation of Forests in China from 2001 to 2011: A Study Based on Remotely Sensed Data

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