Jennifer Rover

Estimating aboveground biomass in interior Alaska with Landsat data and field measurements

a b s t r a c t Terrestrial plant biomass is a key biophysical parameter required for understanding ecological systems in Alaska. An accurate estimation of biomass at a regional scale provides an important data input for ecological modeling in this region. In this study, we created an aboveground biomass (AGB) map at 30-m resolution for the Yukon Flats ecoregion of interior Alaska using Landsat data and field measurements. Tree, shrub, and herbaceous AGB data in both live and dead forms were collected in summers and autumns of 2009 and 2010. Using the Landsat-derived spectral variables and the field AGB data, we generated a regression model and applied this model to map AGB for the ecoregion. A 3-fold cross-validation indicated that the AGB estimates had a mean absolute error of 21.8 Mg/ha and a mean bias error of 5.2 Mg/ha. Additionally, we validated the mapping results using an airborne lidar dataset acquired for a portion of the ecoregion. We found a significant relationship between the lidar-derived canopy height and the Landsat-derived AGB (R 2 = 0.40). The AGB map showed that 90% of the ecoregion had AGB values ranging from 10 Mg/ha to 134 Mg/ha. Vegetation types and fires were the primary factors controlling the spatial AGB patterns in this ecoregion. Published by Elsevier B.V.

Establishing water body areal extent trends in interior Alaska from multi-temporal Landsat data

An accurate approach is needed for monitoring, quantifying and understanding surface water variability due to climate change. Separating inter- and intra-annual variances from longer-term shifts in surface water extents due to contemporary climate warming requires repeat measurements spanning a several-decade period. Here, we show that trends developed from multi-date measurements of the extents of more than 15,000 water bodies in central Alaska using Landsat Multispectral Scanner (MSS), Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) data (1979–2009) were highly influenced by the quantity and timing of the data. Over the 30-year period from 1979 to 2009, the study area had a net decrease (p < 0.05) in the extents of 3.4% of water bodies whereas 86% of water bodies exhibited no significant change. The Landsat-derived dataset provides an opportunity for additional research assessing the drivers of lake and wetland change in this region.

Classifying the Hydrologic Function of Prairie Potholes with Remote Sensing and GIS

A sequence of Landsat TM/ETM+ scenes capturing the substantial surface water variations exhibited by prairie pothole wetlands over a drought to deluge period were analyzed in an attempt to determine the general hydrologic function of individual wetlands (recharge, flow-through, and discharge). Multipixel objects (water bodies) were clustered according to their temporal changes in water extents. We found that wetlands receiving groundwater discharge responded differently over the time period than wetlands that did not. Also, wetlands located within topographically closed discharge basins could be distinguished from discharge basins with overland outlets. Field verification data showed that discharge wetlands with closed basins were most distinct and identifiable with reasonable accuracies (user’s accuracy = 97%, producer’s accuracy = 71%). The classification of other hydrologic function types had lower accuracies reducing the overall accuracy for the four hydrologic function classes to 51%. A simplified classification approach identifying only two hydrologic function classes was 82%. Although this technique has limited success for detecting small wetlands, Landsat-derived multipixel-object clustering can reliably differentiate wetlands receiving groundwater discharge and provides a new approach to quantify wetland dynamics in landscape scale investigations and models.

On the terminology of the spectral vegetation index (NIR − SWIR)/(NIR + SWIR)

The spectral vegetation index (ρNIR − ρSWIR)/(ρNIR + ρSWIR), where ρNIR and ρSWIR are the near-infrared (NIR) and shortwave-infrared (SWIR) reflectances, respectively, has been widely used to indicate vegetation moisture condition. This index has multiple names in the literature, including infrared index (II), normalized difference infrared index (NDII), normalized difference water index (NDWI), normalized difference moisture index (NDMI), land surface water index (LSWI) and normalized burn ratio (NBR). After reviewing each terms definition, associated sensors and channel specifications, we found that the index consists of three variants, differing only in the SWIR region (1.2–1.3, 1.55–1.75 or 2.05–2.45 μm). Thus, three terms are sufficient to represent these three SWIR variants; other names are redundant and therefore unnecessary. Considering the spectral representativeness, the terms popularity and the ‘rule of priority’ in scientific nomenclature, NDWI, NDII and NBR, each corresponding to the three SWIR regions, are more preferable terms.

A self-trained classification technique for producing 30 m percent-water maps from Landsat data

Small bodies of water can be mapped with moderate-resolution satellite data using methods where water is mapped as subpixel fractions using field measurements or high-resolution images as training datasets. A new method, developed from a regression-tree technique, uses a 30 m Landsat image for training the regression tree that, in turn, is applied to the same image to map subpixel water. The self-trained method was evaluated by comparing the percent-water map with three other maps generated from established percent-water mapping methods: (1) a regression-tree model trained with a 5 m SPOT 5 image, (2) a regression-tree model based on endmembers and (3) a linear unmixing classification technique. The results suggest that subpixel water fractions can be accurately estimated when high-resolution satellite data or intensively interpreted training datasets are not available, which increases our ability to map small water bodies or small changes in lake size at a regional scale.

Effects of disturbance and climate change on ecosystem performance in the Yukon River Basin boreal forest

A warming climate influences boreal forest productivity, dynamics, and disturbance regimes. We used ecosystem models and 250 m satellite Normalized Difference Vegetation Index (NDVI) data averaged over the growing season (GSN) to model current, and estimate future, ecosystem performance. We modeled Expected Ecosystem Performance (EEP), or anticipated productivity, in undisturbed stands over the 2000–2008 period from a variety of abiotic data sources, using a rule-based piecewise regression tree. The EEP model was applied to a future climate ensemble A1B projection to quantify expected changes to mature boreal forest performance. Ecosystem Performance Anomalies (EPA), were identified as the residuals of the EEP and GSN relationship and represent performance departures from expected performance conditions. These performance data were used to monitor successional events following fire. Results suggested that maximum EPA occurs 30–40 years following fire, and deciduous stands generally have higher EPA than coniferous stands. Mean undisturbed EEP is projected to increase 5.6% by 2040 and 8.7% by 2070, suggesting an increased deciduous component in boreal forests. Our results contribute to the understanding of boreal forest successional dynamics and its response to climate change. This information enables informed decisions to prepare for, and adapt to, climate change in the Yukon River Basin forest.

Spatially explicit estimation of aboveground boreal forest biomass in the Yukon River Basin, Alaska

Quantification of aboveground biomass (AGB) in Alaska’s boreal forest is essential to the accurate evaluation of terrestrial carbon stocks and dynamics in northern high-latitude ecosystems. Our goal was to map AGB at 30 m resolution for the boreal forest in the Yukon River Basin of Alaska using Landsat data and ground measurements. We acquired Landsat images to generate a 3-year (2008–2010) composite of top-of-atmosphere reflectance for six bands as well as the brightness temperature (BT). We constructed a multiple regression model using field-observed AGB and Landsat-derived reflectance, BT, and vegetation indices. A basin-wide boreal forest AGB map at 30 m resolution was generated by applying the regression model to the Landsat composite. The fivefold cross-validation with field measurements had a mean absolute error (MAE) of 25.7 Mg ha−1 (relative MAE 47.5%) and a mean bias error (MBE) of 4.3 Mg ha−1 (relative MBE 7.9%). The boreal forest AGB product was compared with lidar-based vegetation height data; the comparison indicated that there was a significant correlation between the two data sets.

Controls on the Geochemical Evolution of Prairie Pothole Region Lakes and Wetlands Over Decadal Time Scales

One hundred sixty-seven Prairie Pothole lakes, ponds and wetlands (largely lakes) previously analyzed chemically during the late 1960’s and early to mid-1970’s were resampled and reanalyzed in 2011–2012. The two sampling periods differed climatically. The earlier sampling took place during normal to slightly dry conditions, whereas the latter occurred during and immediately following exceptionally wet conditions. As reported previously in Mushet et al. (2015), the dominant effect was expansion of the area of these lakes and dilution of their major ions. However, within that context, there were significant differences in the evolutionary pathways of major ions. To establish these pathways, we employed the inverse modeling computer code NetpathXL. This code takes the initial and final lake composition and, using mass balance constrained by the composition of diluting waters, and input and output of phases, calculates plausible geochemical evolution pathways. Despite the fact that in most cases major ions decreased, a subset of the lakes had an increase in SO42−. This distinction is significant because SO42− is the dominant anion in a majority of Prairie Pothole Region wetlands and lakes. For lakes with decreasing SO42−, the proportion of original lake water required for mass balance was subordinate to rainwater and/or overland flow. In contrast, lakes with increasing SO42− between the two sampling episodes tended to be dominated by original lake water. This suite of lakes tended to be smaller and have lower initial SO42− concentrations such that inputs of sulfur from dissolution of the minerals gypsum or pyrite had a significant impact on the final sulfur concentration given the lower dilution factors. Thus, our study provides context for how Prairie Pothole Region water bodies evolve geochemically as climate changes. Because wetland geochemistry in turn controls the ecology of these water bodies, this research contributes to the prediction of the impact of climate change on this important complex of ecosystems.

Monitoring algal blooms in drinking water reservoirs using the Landsat-8 Operational Land Imager

ABSTRACT In this study, we demonstrated that the Landsat-8 Operational Land Imager (OLI) sensor is a powerful tool that can provide periodic and system-wide information on the condition of drinking water reservoirs. The OLI is a multispectral radiometer (30 m spatial resolution) that allows ecosystem observations at spatial and temporal scales that allow the environmental community and water managers another means to monitor changes in water quality not feasible with field-based monitoring. Using the provisional Land Surface Reflectance product and field-collected chlorophyll-a (chl-a) concentrations from drinking water monitoring programs in North Carolina and Rhode Island, we compared five established approaches for estimating chl-a concentrations using spectral data. We found that using the three band reflectance approach with a combination of OLI spectral bands 1, 3, and 5 produced the most promising results for accurately estimating chl-a concentrations in lakes (R2 value of 0.66; root mean square error value of 8.9 µg l−1). Using this model, we forecast the spatial and temporal variability of chl-a for Jordan Lake, a recreational and drinking water source in piedmont North Carolina and several small ponds that supply drinking water in southeastern Rhode Island.