Dynamics of national forests assessed using the Landsat record: Case studies in eastern United States

Huang, Chengquan; Goward, Samuel N.; Schleeweis, Karen; Thomas, Nancy E.; Masek, Jeffrey G.; Zhu, Zhiliang

doi:10.1016/j.rse.2008.06.016

Cited by 140 publications

(71 citation statements)

References 25 publications

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“…It also is a challenge to validate forest disturbance assessments due to difficulty in collecting ground-truth data. High spatial resolution images such as QuickBird with a multitemporal scale were often used for this validation, presenting an overall accuracy of 60%-80% [17][18][19]41].…”

Section: Introductionmentioning

confidence: 99%

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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: Introductionmentioning

confidence: 99%

“…Many previous studies on forest disturbance assessments have focused on coniferous forests and mixed evergreen and deciduous forests in North America [9,[17][18][19]24,48,53,54]. In the subtropical forest regions of China, detection of forest disturbance and recovery has not received much attention.…”

Section: Introductionmentioning

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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“…Persistent cloud is one major limiting factor for land surface observation in humid tropical regions [28,29] like the Lake Kivu region. The region also suffers from low Landsat acquisition frequency for the early Landsat sensors because of lack of ground station coverage in central Africa and limited onboard image storage capacity, reducing temporal coverage for the application of land change methods [30][31][32]. Furthermore, the Landsat ETM+ SLC-off acquisitions suffer from wedge shape gaps reducing 22% of the usable data after 2003 [28,33,34].…”

Section: Introductionmentioning

confidence: 99%

Tracking Land Use/Land Cover Dynamics in Cloud Prone Areas Using Moderate Resolution Satellite Data: A Case Study in Central Africa

Basnet

Vodacek

2015

Remote Sensing

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Tracking land surface dynamics over cloud prone areas with complex mountainous terrain is an important challenge facing the Earth Science community. One such region is the Lake Kivu region in Central Africa. We developed a processing chain to systematically monitor the spatio-temporal land use/land cover dynamics of this region over the years 1988, 2001, and 2011 using Landsat data, complemented by ancillary data. Topographic compensation was performed on Landsat reflectances to avoid the strong illumination angle impacts and image compositing was used to compensate for frequent cloud cover and thus incomplete annual data availability in the archive. A systematic supervised classification was applied to the composite Landsat imagery to obtain land cover thematic maps with overall accuracies of 90% and higher. Subsequent change analysis between these years found extensive conversions of the natural environment as a result of human related activities. The gross forest cover loss for 1988-2001 and 2001-2011 period was 216.4 and 130.5 thousand hectares, respectively, signifying significant deforestation in the period of civil war and a relatively stable and lower deforestation rate later, possibly due to conservation and reforestation efforts in the region. The other dominant land cover changes in the region were aggressive subsistence farming and urban expansion displacing natural vegetation and arable lands. Despite limited data availability, this study fills the gap of much needed detailed and updated land cover change information for this biologically important region of Central Africa. These multi-temporal datasets will be a valuable baseline for land use managers in the region OPEN ACCESSRemote Sens. 2015, 7 6684 interested in developing ecologically sustainable land management strategies and measuring the impacts of biodiversity conservation efforts.

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“…Although many successful applications of satellite monitoring of forest cover change are carried out at sparse temporal intervals (e.g., 5 yr, 10 yr or longer) [11][12][13][14][15][16][17], forest change rates have been shown to vary substantially from one year to another [18][19][20]. Hence, change products derived at sparse temporal intervals cannot capture such temporal dynamics, especially when forest cover change is caused by harvest and other land management practices [21].…”

Section: Introductionmentioning

confidence: 99%

“…In particular, a number of novel techniques have recently emerged for reconstructing forest change history using dense time series of Landsat images [24][25][26][27]. Consisting of "clear-view" Landsat observations every year or every two years [19], such image stacks allow forest change mapping at annual or biennial time steps. Although Landsat provides one of the longest and most consistent satellite records of the land surface with a spatial resolution suitable for monitoring many types of anthropogenic land cover change [28], dense time series of Landsat observations do not exist in many areas outside the U.S. [29].…”

Section: Introductionmentioning

confidence: 99%

Annual Detection of Forest Cover Loss Using Time Series Satellite Measurements of Percent Tree Cover

et al. 2014

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Abstract:We introduce and test a new method to detect annual forest cover loss from time series estimates of percent tree cover. Our approach is founded on two realistic assumptions: (1) land cover disturbances are rare events over large geographic areas that occur within a short time frame; and (2) spatially discrete land cover disturbances are continuous processes over time. Applying statistically rigorous algorithms, we first detect disturbance pixels as outliers of an underlying chi-square distribution. Then, we fit nonlinear, logistic curves for each identified change pixel to simultaneously characterize the magnitude and timing of the disturbance. Our method is applied using the yearly Vegetation Continuous Fields (VCF) tree cover product from Moderate Resolution Imaging Spectroradiometer (MODIS), and the resulting disturbance-year estimates are evaluated using a large sample of Landsat-based forest disturbance data. Temporal accuracy is ~65% at 250-m, annual resolution and increases to >85% when temporal resolution is relaxed to ±1 yr. The r 2 of MODIS VCF-based disturbance rates against Landsat ranges from 0.7 to 0.9 at 5-km spatial resolution. The general approach developed in this study can be potentially applied at a global scale and to other land cover types characterized as continuous variables from satellite data.

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Dynamics of national forests assessed using the Landsat record: Case studies in eastern United States

Cited by 140 publications

References 25 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

Tracking Land Use/Land Cover Dynamics in Cloud Prone Areas Using Moderate Resolution Satellite Data: A Case Study in Central Africa

Annual Detection of Forest Cover Loss Using Time Series Satellite Measurements of Percent Tree Cover

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