Testing a MODIS Global Disturbance Index across North America

Mildrexler, David J.; Zhao, Maosheng; Running, Steven W.

doi:10.1016/j.rse.2009.05.016

Cited by 146 publications

(125 citation statements)

References 61 publications

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“…Therefore, nighttime LST data were used. According to Mildrexler, Zhao, and Running [45], we produced an annual nighttime LST image for 2015 using the MVC method.…”

Section: Datamentioning

confidence: 99%

Urban Land Extraction Using VIIRS Nighttime Light Data: An Evaluation of Three Popular Methods

Dou

Liu

et al. 2017

Remote Sensing

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Abstract:Timely and accurate extraction of urban land area using the Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (VIIRS) nighttime light data is important for urban studies. However, a comprehensive assessment of the existing methods for extracting urban land using VIIRS nighttime light data remains inadequate. Therefore, we first reviewed the relevant methods and selected three popular methods for extracting urban land area using nighttime light data. These methods included local-optimized thresholding (LOT), vegetation-adjusted nighttime light urban index (VANUI), integrated nighttime lights, normalized difference vegetation index, and land surface temperature support vector machine classification (INNL-SVM). Then, we assessed the performance of these methods for extracting urban land area based on the VIIRS nighttime light data in seven evaluation areas with various natural and socioeconomic conditions in China. We found that INNL-SVM had the best performance with an average kappa of 0.80, which was 6.67% higher than the LOT and 2.56% higher than the VANUI. The superior performance of INNL-SVM was mainly attributed to the integration of information on nighttime light, vegetation cover, and land surface temperature. This integration effectively reduced the commission and omission errors arising from the overflow effect and low light brightness of the VIIRS nighttime light data. Additionally, INNL-SVM can extract urban land area more easily. Thus, we suggest that INNL-SVM has great potential for effectively extracting urban land with VIIRS nighttime light data at large scales.

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“…Therefore, nighttime LST data were used. According to Mildrexler, Zhao, and Running [45], we produced an annual nighttime LST image for 2015 using the MVC method.…”

Section: Datamentioning

confidence: 99%

Urban Land Extraction Using VIIRS Nighttime Light Data: An Evaluation of Three Popular Methods

Dou

Liu

et al. 2017

Remote Sensing

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“…As a result, it is more suitable for some regional and global change studies and is particularly well-suited for investigations of drought-induced thermal stress (Wan, Wang, & Li, 2004;Wan, Zhang, et al, 2004). The high-quality satellite-derived LST datasets from MODIS are currently used for a variety of applications including largescale ecosystem disturbance detection (Coops, Wulder, & Iwanicka, 2009;Mildrexler, Zhao, & Running, 2009), drought monitoring (Wan, Wang, et al, 2004), land cover monitoring (Julien & Sobrino, 2008), agro-meteorology studies (Anderson, Norman, Mecikalski, Otkin, & Kustas, 2007), biodiversity studies (Albright et al, 2011), biophysical studies (Li et al, 2015), and have been proposed as an integrative global change metric (Mildrexler et al, 2011b). We derived monthly mean LST from the 1-km 8-day Aqua MODIS LST (MYD11A2) data for further analysis.…”

Section: Aqua Modis Land Surface Temperature (Lst)mentioning

confidence: 99%

A forest vulnerability index based on drought and high temperatures

Mildrexler

Yang

Cohen

et al. 2016

Remote Sensing of Environment

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Increasing forest stress and tree mortality has been directly linked to combinations of drought and high temperatures. The climatic changes expected during the next decades -large increases in mean temperature, increased heat waves, and significant long-term regional drying in the western USA -will likely increase chronic forest stress and mortality. The aim of this research is to develop and apply a new forest vulnerability index (FVI) associated with drought and high temperatures across the Pacific Northwest region (PNW; Oregon and Washington) of the USA during the MODIS Aqua era (since 2003). Our technique incorporates the alterations to canopy water and energy exchange processes caused by drought and high temperatures with spatially continuous MODIS land surface temperature (LST) and evapotranspiration (ET), and with Parameter-elevation Relationships on Independent Slopes Model (PRISM) precipitation (P) data. With P and ET, we calculate a monthly water balance variable for each individual pixel normalized by forest type group (FTG), and then difference the water balance with the corresponding normalized monthly mean LST to calculate a monthly forest stress index (FSI). We then extract the pixel-specific (800-m resolution) statistically significant temporal trends of the FSI from 2003 to 2012 by month (April to October). The FVI is the slope of the monthly FSI across years, such that there is a FVI for each month. Statistically significant positive slopes indicate interannual increases in stress leading to expected forest vulnerability (positive FVI) for a given month. Positive FVI values were concentrated in the months of August and September, with peak vulnerability occurring at different times for different FTGs. Overall, increased vulnerability rates were the highest in drier FTGs such as Ponderosa Pine, Juniper, and Lodgepole Pine. Western Larch and Fir/Spruce/Mountain Hemlock groups occupy moister sites but also had relatively high proportion of positive FVI values. The Douglas-fir group had the second largest total area of increased vulnerability due to its large areal extent in the study area. Based on an analysis using imagery viewed in Google Earth, we confirm that areas with increased vulnerability are associated with greater amounts of stress and mortality. The FVI is a new way to conceptualize and monitor forest vulnerability based on first-order principles and has the potential to be generalized to other geographical areas.

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“…Previous studies estimating national and global forest disturbance patterns have used coarse--resolution (250m -1km) satellite imagery (NOAA AVHRR and NASA EOS MODIS) (Potter et al, 2005;Mildrexler et al, 2009;Potapov et al, 2009). Although suitable for detection of large--area disturbances such as fire and large--scale clearcuts, coarse--resolution imagery is less capable of detecting forest management activities than finer--grained observations from satellites such as Landsat (Skole and Tucker, 1993;Tucker and Townshend, 2000;Bucha & Stibig, 2008;Wulder et al, 2008;Potapov et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

United States Forest Disturbance Trends Observed Using Landsat Time Series

et al. 2013

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Disturbance events strongly affect the composition, structure, and function of forest ecosystems; however, existing U.S. land management inventories were not designed to monitor disturbance. To begin addressing this gap, the North American Forest Dynamics (NAFD) project has examined a geographic sample of 50 Landsat satellite image time series to assess trends in forest disturbance across the conterminous United States for 1985--2005. The geographic sample design used a probability--based scheme to encompass major forest types and maximize geographic dispersion. For each sample location disturbance was identified in the Landsat series using the Vegetation Change Tracker (VCT) algorithm.The NAFD analysis indicates that, on average, 2.77 Mha/yr of forests were disturbed annually, representing 1.09%/yr of US forestland. These satellite--based national disturbance rates estimates tend to be lower than those derived from land management inventories, reflecting both methodological and definitional differences. In particular the VCT approach used with a biennial time step has limited sensitivity to low--intensity disturbances. Unlike prior satellite studies, our biennial forest disturbance rates vary by nearly a factor of two between high and low years. High western US disturbance rates were associated with active fire years and insect activity, while variability in the east is more strongly related to harvest rates in managed forests. We note that generating a geographic sample based on representing forest type and variability may be problematic since the spatial pattern of disturbance does not necessarily correlate with forest type. We also find that the prevalence of diffuse, non--stand clearing disturbance in US forests makes the application of a biennial geographic sample problematic. Future satellite--based studies of disturbance at regional and national scales should focus on wall--to--wall analyses with annual time step for improved accuracy. IntroductionChange is ubiquitous in forest ecosystems. Forests experience both seasonality as well as long--term growth cycles that can vary in duration between 50 years and 500 or more years (Waring & Running, 2007). These long--term changes are punctuated by mostly short--term disturbances from fire, insects, disease, and harvest which strongly alter the state and functioning of the forest (He & Mlandenoff, 1999). Both climate change and the increasing global demand for wood and fiber products are likely to drive increases in forest disturbance rates Nepstad et al., 2008). These changes in disturbance will alter the water and carbon cycles of forest stands as well as impact the habitat and biodiversity of these ecosystems (Lindenmayer et al., 2006;Gardner et al., 2009). With respect to the carbon cycle, forest disturbance is now recognized as a major driver of non--fossil--fuels--related terrestrial fluxes to the atmosphere (Running, 2008; Amiro et al., 2010).To effectively understand how forest disturbance impacts forest state and functioning, disturbance rates...

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Testing a MODIS Global Disturbance Index across North America

Cited by 146 publications

References 61 publications

Urban Land Extraction Using VIIRS Nighttime Light Data: An Evaluation of Three Popular Methods

Urban Land Extraction Using VIIRS Nighttime Light Data: An Evaluation of Three Popular Methods

A forest vulnerability index based on drought and high temperatures

United States Forest Disturbance Trends Observed Using Landsat Time Series

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