Detecting long-term changes to vegetation in northern Canada using the Landsat satellite image archive

Fraser, Robert; Olthof, Ian; Carriere, Melanie; Deschamps, A.; Pouliot, Darren

doi:10.1088/1748-9326/6/4/045502

Cited by 125 publications

(96 citation statements)

References 32 publications

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“…At the 32-year scale, such changes are most likely due to the proliferation of relatively fast-growing shrubs in tundra and open forests. This finding is indirectly supported by numerous studies conducted throughout the circumpolar Arctic (e.g., [7,11,17,[54][55][56][57]). Specifically, within this study area a comparative analysis was done between high-resolution Gambit imagery from the 1960s and contemporary GeoEye-1 imagery over a 58 km 2 area in the vicinity of Dudinka (Figure 1).…”

Section: Discussionsupporting

confidence: 68%

Land Cover Change in the Lower Yenisei River Using Dense Stacking of Landsat Imagery in Google Earth Engine

et al. 2018

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Climate warming is occurring at an unprecedented rate in the Arctic due to regional amplification, potentially accelerating land cover change. Measuring and monitoring land cover change utilizing optical remote sensing in the Arctic has been challenging due to persistent cloud and snow cover issues and the spectrally similar land cover types. Google Earth Engine (GEE) represents a powerful tool to efficiently investigate these changes using a large repository of available optical imagery. This work examines land cover change in the Lower Yenisei River region of arctic central Siberia and exemplifies the application of GEE using the random forest classification algorithm for Landsat dense stacks spanning the 32-year period from 1985 to 2017, referencing 1641 images in total. The semiautomated methodology presented here classifies the study area on a per-pixel basis utilizing the complete Landsat record available for the region by only drawing from minimally cloudand snow-affected pixels. Climatic changes observed within the study area's natural environments show a statistically significant steady greening (~21,000 km 2 transition from tundra to taiga) and a slight decrease (~700 km 2 ) in the abundance of large lakes, indicative of substantial permafrost degradation. The results of this work provide an effective semiautomated classification strategy for remote sensing in permafrost regions and map products that can be applied to future regional environmental modeling of the Lower Yenisei River region.

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Section: Discussionsupporting

confidence: 68%

Land Cover Change in the Lower Yenisei River Using Dense Stacking of Landsat Imagery in Google Earth Engine

et al. 2018

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“…Other studies confirm trend estimates based on the GIMMS dataset: Despite of some regional differences in areas at very high latitudes with low vegetation cover, NDVI trends from the GIMMS dataset agree with trends from MODIS data (Moderate Resolution Imaging Spectrometer) [17,18,21]. Trends from the GIMMS dataset compare well with trends computed from Landsat imagery [22]. Changes in tree rings [23,24], temperature-induced drought stress or insect disturbances [25] were also observed in regions with browning NDVI trends.…”

Section: Introductionsupporting

confidence: 71%

Trend Change Detection in NDVI Time Series: Effects of Inter-Annual Variability and Methodology

et al. 2013

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Changing trends in ecosystem productivity can be quantified using satellite observations of Normalized Difference Vegetation Index (NDVI). However, the estimation of trends from NDVI time series differs substantially depending on analyzed satellite dataset, the corresponding spatiotemporal resolution, and the applied statistical method. Here we compare the performance of a wide range of trend estimation methods and demonstrate that performance decreases with increasing inter-annual variability in the NDVI time series. Trend slope estimates based on annual aggregated time series or based on a seasonal-trend model show better performances than methods that remove the seasonal cycle of the time series. A breakpoint detection analysis reveals that an overestimation of breakpoints in NDVI trends can result in wrong or even opposite trend estimates. Based on our results, we give practical recommendations for the application of trend methods on long-term NDVI time series. Particularly, we apply and compare different methods on NDVI time series in Alaska, where both greening and browning trends have been previously observed. Here, the multi-method uncertainty of NDVI trends OPEN ACCESSRemote Sens. 2013, 5 2114 is quantified through the application of the different trend estimation methods. Our results indicate that greening NDVI trends in Alaska are more spatially and temporally prevalent than browning trends. We also show that detected breakpoints in NDVI trends tend to coincide with large fires. Overall, our analyses demonstrate that seasonal trend methods need to be improved against inter-annual variability to quantify changing trends in ecosystem productivity with higher accuracy.

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“…Long-term Earth Observation utilizing numerous satellite sensors is an effective way to monitor and characterize variations in land surface [1][2][3][4][5][6]. Vegetation indices (VIs) derived from satellite data have been widely used to assess variations in the physiological state and biophysical properties of vegetation [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Comparison of the Continuity of Vegetation Indices Derived from Landsat 8 OLI and Landsat 7 ETM+ Data among Different Vegetation Types

She

Zhang

et al. 2015

Remote Sensing

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Landsat 8, the most recently launched satellite of the series, promises to maintain the continuity of Landsat 7. However, in addition to subtle differences in sensor characteristics and vegetation index (VI) generation algorithms, VIs respond differently to the seasonality of the various types of vegetation cover. The purpose of this study was to elucidate the effects of these variations on VIs between Operational Land Imager (OLI) and Enhanced Thematic Mapper Plus (ETM+). Ground spectral data for vegetation were used to simulate the Landsat at-senor broadband reflectance, with consideration of sensor band-pass differences. Three band-geometric VIs (Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), Enhanced Vegetation Index (EVI)) and two band-transformation VIs (Vegetation Index based on the Universal Pattern Decomposition method (VIUPD), Tasseled Cap Transformation Greenness (TCG)) were tested to evaluate the performance of various VI generation algorithms in relation to multi-sensor continuity. Six vegetation types were included to evaluate the continuity in different vegetation types. Four pairs of data during four seasons were selected to evaluate continuity with respect to seasonal variation. The simulated data showed that OLI largely inherits the band-pass characteristics of ETM+. Overall, the continuity of band-transformation derived VIs was higher than OPEN ACCESSRemote Sens. 2015, 7 13486 band-geometry derived VIs. VI continuity was higher in the three forest types and the shrubs in the relatively rapid growth periods of summer and autumn, but lower for the other two non-forest types (grassland and crops) during the same periods.

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Detecting long-term changes to vegetation in northern Canada using the Landsat satellite image archive

Cited by 125 publications

References 32 publications

Land Cover Change in the Lower Yenisei River Using Dense Stacking of Landsat Imagery in Google Earth Engine

Land Cover Change in the Lower Yenisei River Using Dense Stacking of Landsat Imagery in Google Earth Engine

Trend Change Detection in NDVI Time Series: Effects of Inter-Annual Variability and Methodology

Comparison of the Continuity of Vegetation Indices Derived from Landsat 8 OLI and Landsat 7 ETM+ Data among Different Vegetation Types

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