Abstract

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.

Highlights

  • IntroductionThis study extends recent work evaluating long-term reservoir and lake water quality trends by using historic Landsat data to create and analyze time series of various water quality indicators [1]-[3]

  • We evaluated the distribution of Normalized Difference Vegetation Index (NDVI) values in each of the selected ROIs to evaluate whether the ROIs groups we assigned (Inlet, Dry Shore, and Wet Shore) represented similar conditions

  • We do observe a slight trend represented as a small steady decline year after year in the emergent vegetation index in each of the selected ROIs (Figure 5)

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Summary

Introduction

This study extends recent work evaluating long-term reservoir and lake water quality trends by using historic Landsat data to create and analyze time series of various water quality indicators [1]-[3]. This study analyzes the time history and changes in near shore-line emergent vegetation at Malheur Lake from 1984 to 2013. Malheur Lake has a large non-native carp population that affects water quality and emergent vegetation. We do not have data on historic carp population, so this study evaluates whether other environmental drivers could explain trends in emergent vegetation, or if the trends are due to parameters not considered in this study—such as carp. We examined the correlation of emergent vegetation time series with a number of parameters that could influence the extent of emergent vegetation to determine if changes could be attributed to these factors

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