The extent of emergent vegetation can be a useful indicator of lake health and identify trends and changes over time. However, field data to characterize emergent vegetation may not be available (especially over longe...The extent of emergent vegetation can be a useful indicator of lake health and identify trends and changes over time. However, field data to characterize emergent vegetation may not be available (especially over longer time periods) or may be limited to small, isolated areas. We present a case study using Lands at data to generate indicators that represent emergent vegetation extent in the near-shoreline and tributary delta areas of Malheur Lake, Oregon, USA. Malheur Lake has a large non-native carp population that may significantly affect emergent vegetation and adversely impact reservoir health. This study evaluates long-term trends in emergent vegetation and correlation with common environmental variables other than carp, to determine if emergent vegetation changes can be explained. We selected late June images for this study as vegetation is relatively mature in late June and visible, but has not completely grown-in providing a better indication of vegetation coverage in satellite images. To explore trends in historic emergent vegetation extent, we identified eight regions-of-interest (ROI): three inlet areas, three wet-shore areas (swampy areas), and two dry-shore areas (less swampy areas) around Malheur Lake and computed the Normalized Difference Vegetation Index (NDVI) using 30 years of Lands at images from 1984 to 2013. For each ROI we generated time-series data to quantify the emergent vegetation as determined by the percent of area covered by pixels that had NDVI values greater than 0.2, using cutoff as an indicator of vegetation. For correlation, we produced a corresponding time series of the lake area using the Modified Normalized Difference Water Index (MNDWI) to identify water pixels. We investigated the correlation of vegetation coverage (an indicator of emergent vegetation) with lake area, June precipitation, and average daily maximum temperatures for a period from two months prior to one month after the June collection (April, May, June, and July);all parameters that could affect vegetation growth. We found minimal correlation over time of the vegetative extent in any of the eight ROIs with the selected parameters, indicating that there are other factors which drive emergent vegetation extent in Malheur Lake. This study demonstrates that Landsat data have sufficient spatial and temporal detail to provide insight into ecosystem changes over relatively long periods and offers a method to study historic trends in reservoir health and evaluate potential influences. We expect future work will explore other potential drivers of emergent vegetation extent in Malheur Lake, such as carp populations. Carp were not considered in this study as we did not have access to data that reflect carp numbers over this 30 year period.展开更多
It is widely accepted that natural resources should only be sustainably exploited and utilized to effectively preserve our planet for future generations.To better manage the natural resources,and to better understand ...It is widely accepted that natural resources should only be sustainably exploited and utilized to effectively preserve our planet for future generations.To better manage the natural resources,and to better understand the closely linked Earth systems,the concept of Digital Earth has been strongly promoted since US Vice President Al Gore’s speech in 1998.One core element of Digital Earth is the use and integration of remote sensing data.Only satellite imagery can cover the entire globe repeatedly at a sufficient high-spatial resolution to map changes in land cover and land use,but also to detect more subtle changes related for instance to climate change.To uncover global change effects on vegetation activity and phenology,it is important to establish high quality time series characterizing the past situation against which the current state can be compared.With the present study we describe a time series of vegetation activity at 10-daily time steps between 1998 and 2008 covering large parts of South America at 1 km spatial resolution.Particular emphasis was put on noise removal.Only carefully filtered time series of vegetation indices can be used as a benchmark and for studying vegetation dynamics at a continental scale.Without temporal smoothing,subtle spatio-temporal patterns in vegetation composition,density and phenology would be hidden by atmospheric noise and undetected clouds.Such noise is immanent in data that have undergone solely a maximum value compositing.Within the present study,the Whittaker smoother(WS)was applied to a SPOT VGT time series.The WS balances the fidelity to the observations with the roughness of the smoothed curve.The algorithm is extremely fast,gives continuous control over smoothness with only one parameter,and interpolates automatically.The filtering efficiently removed the negatively biased noise present in the original data,while preserving the overall shape of the curves showing vegetation growth and development.Geostatistical variogram analysis revealed a significantly increased signal-to-noise ratio compared to the raw data.Analysis of the data also revealed spatially consistent key phenological markers.Extracted seasonality parameters followed a clear meridional trend.Compared to the unfiltered data,the filtered time series increased the separability of various land cover classes.It is thus expected that the data set holds great potential for environmental and vegetation related studies within the frame of Digital Earth.展开更多
文摘The extent of emergent vegetation can be a useful indicator of lake health and identify trends and changes over time. However, field data to characterize emergent vegetation may not be available (especially over longer time periods) or may be limited to small, isolated areas. We present a case study using Lands at data to generate indicators that represent emergent vegetation extent in the near-shoreline and tributary delta areas of Malheur Lake, Oregon, USA. Malheur Lake has a large non-native carp population that may significantly affect emergent vegetation and adversely impact reservoir health. This study evaluates long-term trends in emergent vegetation and correlation with common environmental variables other than carp, to determine if emergent vegetation changes can be explained. We selected late June images for this study as vegetation is relatively mature in late June and visible, but has not completely grown-in providing a better indication of vegetation coverage in satellite images. To explore trends in historic emergent vegetation extent, we identified eight regions-of-interest (ROI): three inlet areas, three wet-shore areas (swampy areas), and two dry-shore areas (less swampy areas) around Malheur Lake and computed the Normalized Difference Vegetation Index (NDVI) using 30 years of Lands at images from 1984 to 2013. For each ROI we generated time-series data to quantify the emergent vegetation as determined by the percent of area covered by pixels that had NDVI values greater than 0.2, using cutoff as an indicator of vegetation. For correlation, we produced a corresponding time series of the lake area using the Modified Normalized Difference Water Index (MNDWI) to identify water pixels. We investigated the correlation of vegetation coverage (an indicator of emergent vegetation) with lake area, June precipitation, and average daily maximum temperatures for a period from two months prior to one month after the June collection (April, May, June, and July);all parameters that could affect vegetation growth. We found minimal correlation over time of the vegetative extent in any of the eight ROIs with the selected parameters, indicating that there are other factors which drive emergent vegetation extent in Malheur Lake. This study demonstrates that Landsat data have sufficient spatial and temporal detail to provide insight into ecosystem changes over relatively long periods and offers a method to study historic trends in reservoir health and evaluate potential influences. We expect future work will explore other potential drivers of emergent vegetation extent in Malheur Lake, such as carp populations. Carp were not considered in this study as we did not have access to data that reflect carp numbers over this 30 year period.
文摘It is widely accepted that natural resources should only be sustainably exploited and utilized to effectively preserve our planet for future generations.To better manage the natural resources,and to better understand the closely linked Earth systems,the concept of Digital Earth has been strongly promoted since US Vice President Al Gore’s speech in 1998.One core element of Digital Earth is the use and integration of remote sensing data.Only satellite imagery can cover the entire globe repeatedly at a sufficient high-spatial resolution to map changes in land cover and land use,but also to detect more subtle changes related for instance to climate change.To uncover global change effects on vegetation activity and phenology,it is important to establish high quality time series characterizing the past situation against which the current state can be compared.With the present study we describe a time series of vegetation activity at 10-daily time steps between 1998 and 2008 covering large parts of South America at 1 km spatial resolution.Particular emphasis was put on noise removal.Only carefully filtered time series of vegetation indices can be used as a benchmark and for studying vegetation dynamics at a continental scale.Without temporal smoothing,subtle spatio-temporal patterns in vegetation composition,density and phenology would be hidden by atmospheric noise and undetected clouds.Such noise is immanent in data that have undergone solely a maximum value compositing.Within the present study,the Whittaker smoother(WS)was applied to a SPOT VGT time series.The WS balances the fidelity to the observations with the roughness of the smoothed curve.The algorithm is extremely fast,gives continuous control over smoothness with only one parameter,and interpolates automatically.The filtering efficiently removed the negatively biased noise present in the original data,while preserving the overall shape of the curves showing vegetation growth and development.Geostatistical variogram analysis revealed a significantly increased signal-to-noise ratio compared to the raw data.Analysis of the data also revealed spatially consistent key phenological markers.Extracted seasonality parameters followed a clear meridional trend.Compared to the unfiltered data,the filtered time series increased the separability of various land cover classes.It is thus expected that the data set holds great potential for environmental and vegetation related studies within the frame of Digital Earth.
