Exploring Emergent Vegetation Time-History at Malheur Lake, Oregon Using Remote Sensing


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.

Share and Cite:

Adjei, Z. , Thyfault, M. and Williams, G. (2015) Exploring Emergent Vegetation Time-History at Malheur Lake, Oregon Using Remote Sensing. Natural Resources, 6, 553-565. doi: 10.4236/nr.2015.612053.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] Hansen, C., Swain, N., Munson, K., Adjei, Z., Williams, G.P. and Miller, W. (2013) Development of Sub-Seasonal Remote Sensing Chlorophyll—A Detection Models. American Journal of Plant Sciences, 4, 21-26.
[2] Hansen, C.H., Williams, G.P. and Adjei, Z. (2015) Long-Term Application of Remote Sensing Chlorophyll Detection Models: Jordanelle Reservoir Case Study. Natural Resources, 6, 123.
[3] Hansen, C.H., Williams, G.P., Adjei, Z., Barlow, A., Nelson, E.J. and Woodruff Miller, A. (2015) Reservoir Water Quality Monitoring Using Remote Sensing with Seasonal Models: Case Study of Five Central-Utah Reservoirs. Lake and Reservoir Management, 31, 225-240.
[4] Roberts, J., Chick, A., Oswald, L. and Thompson, P. (1995) Effect of Carp, Cyprinus carpio L., on Exotic Benthivorous Fish, on Aquatic Plants and Water Quality in Experimental Ponds. Marine and Freshwater Research, 46, 1171- 1180.
[5] Dekker, A.G. (1993) Detection of Optical Water Quality Parameters for Eutrophic Waters by High Resolution Remote Sensing. Ph.D. Thesis, Earth and Life Sciences, Amsterdam, The Netherlands. Proefschrift Vrije Universiteit (Free University).
[6] Fuller, D.O. (1998) Trends in NDVI Time Series and Their Relation to Rangeland and Crop Production in Senegal, 1987-1993. International Journal of Remote Sensing, 19, 2013-2018.
[7] Sawaya, K.E., Olmanson, L.G., Heinert, N.J., Brezonik, P.L. and Bauer, M.E. (2003) Extending Satellite Remote Sensing to Local Scales: Land and Water Resource Monitoring Using High-Resolution Imagery. Remote Sensing of Environment, 88, 144-156.
[8] Hansen, C., Williams, G.P., Miller, W. and Adjei, Z. (2014) Development of Sub-Seasonal Remote Sensing Chlorophyll—A Detection Models. Utah Conference on Undergraduate Research, Provo, Utah.
[9] Han, L. and Rundquist, D.C. (1997) Comparison of NIR/RED Ratio and First Derivative of Reflectance in Estimating Algal-Chlorophyll Concentration: A Case Study in a Turbid Reservoir. Remote Sensing of Environment, 62, 253-261.
[10] Mishra, S. and Mishra, D.R. (2012) Normalized Difference Chlorophyll Index: A Novel Model for Remote Estimation of Chlorophyll—A Concentration in Turbid Productive Waters. Remote Sensing of Environment, 117, 394-406.
[11] Kutser, T., Metsamaa, L., Strombeck, N. and Vahtmae, E. (2006) Monitoring Cyanobacterial Blooms by Satellite Remote Sensing. Estuarine, Coastal and Shelf Science, 67, 303-312.
[12] Anyamba, A. and Tucker, C.J. (2005) Analysis of Sahelian Vegetation Dynamics Using NOAA-AVHRR NDVI Data from 1981-2003. Journal of Arid Environments, 63, 596-614.
[13] Chen, J., Jonsson, P., Tamura, M., Gu, Z., Matsushita, B. and Eklundh, L. (2004) A Simple Method for Constructing a High-Quality NDVI Time-Series Data Set Based on the Savitzky-Golay Filter. Remote Sensing of Environment, 91, 332-344.
[14] Pettorelli, N., Vik, J.O., Mysterud, A., Gaillard, J.-M., Tucker, C.J. and Stenseth, N.C. (2005) Using the Satellite-Derived NDVI to Assess Ecological Responses to Environmental Change. Trends in Ecology and Evolution, 20, 503-510.
[15] Justice, C.O., Townsend, J.R.G., Holben, B.N. and Tucker, C.J. (1985) Analysis of the Phenology of Global Vegetation Using Meteorological Satellite Data. International Journal of Remote Sensing, 6, 1271-1318.
[16] Ackleson, S.G. and Klemas, V. (1987) Remote Sensing of Submerged Acquatic Vegetation in Lower Chesapeake Bay: A Comparison of Landsat MSS to TM Imagery. Remote Sensing of Environment, 22, 235-248.
[17] Dervieux, A. and Tamisier, A. (1987) Submerged Macrophyte Beds of Camargue Wetlands: Estimation of Their Distribution and Size by the Interpretation of Air Photos. ACTA Oecologica—International Journal of Ecology, 8, 371-385.
[18] Singh, A. (1989) Digital Change Detection Techniques Using Remotely-Sensed Data. International Journal of Remote Sensing, 10, 989-1003.
[19] Tarpley, J., Schneider, S. and Money, R.L. (1984) Global Vegetation Indices from the NOAA-7 Meteorological Satellite. Journal of Climate and Applied Meteorology, 23, 491-494.
[20] Townshend, J.R., Goff, T.E. and Tucker, C.J. (1985) Multitemporal Dimensionality of Images of Normalized Difference Vegetation Index at Continental Scales. IEEE Transactions on Geoscience and Remote Sensing, GE-23, 888-895.
[21] Middleton, E. and Anuta, M. (1984) Evaluation of the Normalized Difference Vegetation Index with Thematic Mapper Data. 10th International Symposium on Machine Processing of Remotely Sensed Data with Special Emphasis on Thematic Mapper Data and Geographic Information Systems, Purdue University, Laboratory for Applications of Remote Sensing, West Lafayette, 12-14 June 1984, 92.
[22] Penuelas, J., Gamon, J.A., Griffin, K.L. and Field, C.B. (1993) Assessing Community Type, Plant Biomass, Pigment Composition, and Photosynthetic Efficiency of Aquatic Vegetation from Spectral Reflectance. Remote Sensing of Environment, 46, 110-118.
[23] Xu, H. (2006) Modification of Normalised Difference Water Index (NDWI) to Enhance Open Water Features in Remotely Sensed Imagery. International Journal of Remote Sensing, 27, 3025-3033.
[24] USGS (2015) USGS: Remote Sensing Phenology. US Geological Survey.
[25] Exelis (2015) Exelis Learn: Tutorials. Exelis Visual Information Solutions.
[26] Lyon, J.G., Yuan, D., Lunetta, S.R. and Chris, E.D. (1998) A Change Detection Experiment Using Vegetation Indices. Photogrammetric Engineering & Remote Sensing, 64, 143-150.
[27] Myneni, R.B. and Hall, F.G. (1995) The Interpretation of Spectral Vegetation Indexes. IEEE Transactions on Geoscience and Remote Sensing, 33, 481-486.
[28] Holben, B.N. (1986) Characteristics of Maximum-Value Composite Images from Temporal AVHRR Data. International Journal of Remote Sensing, 7, 1417-1434.
[29] Wang, J., Rich, P.M. and Price, K.P. (2003) Temporal Responses of NDVI to Precipitation and Temperature in the Central Great Plains, USA. International Journal of Remote Sensing, 24, 2345-2364.
[30] Yu, F., Price, K.P., Ellis, J. and Shi, P. (2003) Response of Seasonal Vegetation Development to Climatic Variations in Eastern Central Asia. Remote Sensing of Environment, 87, 42-54.
[31] McFeeters, S.K. (1996) The Use of the Normalized Difference Water Index (NDWI) in the Delineation of Open Water Features. International Journal of Remote Sensing, 17, 1425-1432.
[32] Lu, S., Ouyang, N., Wu, B., Wei, Y. and Tesemma, Z. (2013) Lake Water Volume Calculation with Time Series Remote-Sensing Images. International Journal of Remote Sensing, 34, 7962-7973.
[33] Ouma, Y.O. and Tateishi, R. (2006) A Water Index for Rapid Mapping of Shoreline Changes of Five East African Rift Valley Lakes: An Emperical Analysis Using Landsat TM and ETM+ Data. International Journal of Remote Sensing, 27, 3153-3181.
[34] OCS (2015) Oregon Climate Data. Oregon Climate Service.

Copyright © 2022 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.