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USGS2014

Mapping mountain pine beetle mortality through growth trend analysis of time-series landsat data

Disturbances are key processes in the carbon cycle of forests and other ecosystems. In recent decades, mountain pine beetle (MPB; Dendroctonus ponderosae) outbreaks have become more frequent and extensive in western North America. Remote sensing has the ability to fill the data gaps of long-term infestation monitoring, but the elimination of observational noise and attributing changes quantitatively are two main challenges in its effective application. Here, we present a forest growth trend analysis method that integrates Landsat temporal trajectories and decision tree techniques to derive annual forest disturbance maps over an 11-year period. The temporal trajectory component successfully captures the disturbance events as represented by spectral segments, whereas decision tree modeling efficiently recognizes and attributes events based upon the characteristics of the segments. Validated against a point set sampled across a gradient of MPB mortality, 86.74% to 94.00% overall accuracy was achieved with small variability in accuracy among years. In contrast, the overall accuracies of single-date classifications ranged from 37.20% to 75.20% and only become comparable with our approach when the training sample size was increased at least four-fold. This demonstrates that the advantages of this time series work flow exist in its small training sample size requirement. The easily understandable, interpretable and modifiable characteristics of our approach suggest that it could be applicable to other ecoregions.

Colorado↗

USGS2014

Global land cover mapping: a review and uncertainty analysis

Given the advances in remotely sensed imagery and associated technologies, several global land cover maps have been produced in recent times including IGBP DISCover, UMD Land Cover, Global Land Cover 2000 and GlobCover 2009. However, the utility of these maps for specific applications has often been hampered due to considerable amounts of uncertainties and inconsistencies. A thorough review of these global land cover projects including evaluating the sources of error and uncertainty is prudent and enlightening. Therefore, this paper describes our work in which we compared, summarized and conducted an uncertainty analysis of the four global land cover mapping projects using an error budget approach. The results showed that the classification scheme and the validation methodology had the highest error contribution and implementation priority. A comparison of the classification schemes showed that there are many inconsistencies between the definitions of the map classes. This is especially true for the mixed type classes for which thresholds vary for the attributes/discriminators used in the classification process. Examination of these four global mapping projects provided quite a few important lessons for the future global mapping projects including the need for clear and uniform definitions of the classification scheme and an efficient, practical, and valid design of the accuracy assessment.

Remote Sensing↗

USGS2021

Riparian area changes in greenness and water use on the Lower Colorado River in the USA from 2000-2020

Declines in riparian ecosystem greenness and water use have been observed in the delta of the Lower Colorado River (LCR) since 2000. The purpose of our case study was to measure these metrics on the U.S. side of the border between Hoover and Morelos Dams to see if declining greenness was unique to the portion of the river in Mexico. In this case study, five riparian reaches of the LCR from Hoover to Morelos Dam since 2000 were studied to evaluate trends in riparian ecosystem health. We measure these riparian woodlands using remotely sensed measurements of the two-band Enhanced Vegetation Index (EVI2; a proxy for greenness); daily evapotranspiration (ET; mmd −1 ) using EVI2 (ET(EVI2)); and an annualized ET based on EVI2, the Phenology Assessment Metric (PAM ET), an annualized ET using Landsat time-series. A key finding is that riparian health and its water use has been in decline since 2000 on the U.S. portion of the LCR, depicting a loss of green vegetation over the last two decades. EVI2 results show a decline of −13.83%, while average daily ET(EVI2) between the first and last decade had a decrease of over 1 mmd −1 (−27.30%) and the respective average PAM ET losses were 170.91 mmyr −1 (−17.95%). The difference between the first and last five-year periods, 2000–2005 and 2016–2020, showed the largest decrease in daily ET(EVI) of 1.24 mmd −1 (−32.61%). These declines come from a loss in healthy, green, riparian plant-cover, not a change in plant water use efficiency nor efficient use of managed water resources. Our results suggest further deterioration of biodiversity, wildlife habitat and other key ecosystem services on the U.S. portion of the LCR.

Arizona, California↗

USGS2013

Semi-automted analysis of high-resolution aerial images to quantify docks in Upper Midwest glacial lakes

Lake resources can be negatively affected by environmental stressors originating from multiple sources and different spatial scales. Shoreline development, in particular, can negatively affect lake resources through decline in habitat quality, physical disturbance, and impacts on fisheries. The development of remote sensing techniques that efficiently characterize shoreline development in a regional context could greatly improve management approaches for protecting and restoring lake resources. The goal of this study was to develop an approach using high-resolution aerial photographs to quantify and assess docks as indicators of shoreline development. First, we describe a dock analysis workflow that can be used to quantify the spatial extent of docks using aerial images. Our approach incorporates pixel-based classifiers with object-based techniques to effectively analyze high-resolution digital imagery. Second, we apply the analysis workflow to quantify docks for 4261 lakes managed by the Minnesota Department of Natural Resources. Overall accuracy of the analysis results was 98.4% (87.7% based on ) after manual post-processing. The analysis workflow was also 74% more efficient than the time required for manual digitization of docks. These analyses have immediate relevance for resource planning in Minnesota, whereas the dock analysis workflow could be used to quantify shoreline development in other regions with comparable imagery. These data can also be used to better understand the effects of shoreline development on aquatic resources and to evaluate the effects of shoreline development relative to other stressors.

Minnesota↗

USGS2015

Distribution of near-surface permafrost in Alaska: estimates of present and future conditions

High-latitude regions are experiencing rapid and extensive changes in ecosystem composition and function as the result of increases in average air temperature. Increasing air temperatures have led to widespread thawing and degradation of permafrost, which in turn has affected ecosystems, socioeconomics, and the carbon cycle of high latitudes. Here we overcome complex interactions among surface and subsurface conditions to map nearsurface permafrost through decision and regression tree approaches that statistically and spatially extend field observations using remotely sensed imagery, climatic data, and thematic maps of a wide range of surface and subsurface biophysical characteristics. The data fusion approach generated medium-resolution (30-m pixels) maps of near-surface (within 1 m) permafrost, active-layer thickness, and associated uncertainty estimates throughout mainland Alaska. Our calibrated models (overall test accuracy of ~85%) were used to quantify changes in permafrost distribution under varying future climate scenarios assuming no other changes in biophysical factors. Models indicate that near-surface permafrost underlies 38% of mainland Alaska and that near-surface permafrost will disappear on 16 to 24% of the landscape by the end of the 21st Century. Simulations suggest that near-surface permafrost degradation is more probable in central regions of Alaska than more northerly regions. Taken together, these results have obvious implications for potential remobilization of frozen soil carbon pools under warmer temperatures. Additionally, warmer and drier conditions may increase fire activity and severity, which may exacerbate rates of permafrost thaw and carbon remobilization relative to climate alone. The mapping of permafrost distribution across Alaska is important for land-use planning, environmental assessments, and a wide-array of geophysical studies.

Alaska↗

USGS2008

Ocean wavenumber estimation from wave-resolving time series imagery

We review several approaches that have been used to estimate ocean surface gravity wavenumbers from wave-resolving remotely sensed image sequences. Two fundamentally different approaches that utilize these data exist. A power spectral density approach identifies wavenumbers where image intensity variance is maximized. Alternatively, a cross-spectral correlation approach identifies wavenumbers where intensity coherence is maximized. We develop a solution to the latter approach based on a tomographic analysis that utilizes a nonlinear inverse method. The solution is tolerant to noise and other forms of sampling deficiency and can be applied to arbitrary sampling patterns, as well as to full-frame imagery. The solution includes error predictions that can be used for data retrieval quality control and for evaluating sample designs. A quantitative analysis of the intrinsic resolution of the method indicates that the cross-spectral correlation fitting improves resolution by a factor of about ten times as compared to the power spectral density fitting approach. The resolution analysis also provides a rule of thumb for nearshore bathymetry retrievals-short-scale cross-shore patterns may be resolved if they are about ten times longer than the average water depth over the pattern. This guidance can be applied to sample design to constrain both the sensor array (image resolution) and the analysis array (tomographic resolution). ?? 2008 IEEE.

IEEE Transactions on Geoscience and Remote Sensing↗

USGS2017

Estimating carbon and showing impacts of drought using satellite data in regression-tree models

Integrating spatially explicit biogeophysical and remotely sensed data into regression-tree models enables the spatial extrapolation of training data over large geographic spaces, allowing a better understanding of broad-scale ecosystem processes. The current study presents annual gross primary production (GPP) and annual ecosystem respiration (RE) for 2000–2013 in several short-statured vegetation types using carbon flux data from towers that are located strategically across the conterminous United States (CONUS). We calculate carbon fluxes (annual net ecosystem production [NEP]) for each year in our study period, which includes 2012 when drought and higher-than-normal temperatures influence vegetation productivity in large parts of the study area. We present and analyse carbon flux dynamics in the CONUS to better understand how drought affects GPP, RE, and NEP. Model accuracy metrics show strong correlation coefficients ( r ) ( r ≥ 94%) between training and estimated data for both GPP and RE. Overall, average annual GPP, RE, and NEP are relatively constant throughout the study period except during 2012 when almost 60% less carbon is sequestered than normal. These results allow us to conclude that this modelling method effectively estimates carbon dynamics through time and allows the exploration of impacts of meteorological anomalies and vegetation types on carbon dynamics.

International Journal of Remote Sensing↗

USGS2021

The effects of urban land cover dynamics on urban heat Island intensity and temporal trends

Assessments of surface urban heat island (UHI) have focused on using remote sensing and land cover data to quantify UHI intensity and spatial distribution within a certain time period by including land cover information. In this study, we implemented a prototype approach to characterize the spatiotemporal variations of UHI using time series of Landsat land surface temperature products and annual land change information. We analyzed UHI distribution and change in Sioux Falls, South Dakota, in the north-central United States and found that the mean UHI intensity in the region was as large as 2.2°C during the period 1986–2017 with an increasing trend of 0.02°C per year within the area with a 5-km non-urban extent. The UHI intensity associated with high intensity urban land cover usually is stronger than with low intensity urban land cover. We evaluated the impact of different non-urban reference extents on UHI variation using different non-urban buffers. The result also suggests that the overall temporal trends of UHI intensity are almost the same when using a 5-km or 10-km non-urban buffer surrounding the urban core. The prototype approach provides a framework to consistently quantify UHI and monitor its change to a large geographic extent.

South Dakota↗

USGS2003

Mapping mine wastes and analyzing areas affected by selenium-rich water runoff in southeast Idaho using AVIRIS imagery and digital elevation data

Remotely sensed hyperspectral and digital elevation data from southeastern Idaho are combined in a new method to assess mine waste contamination. Waste rock from phosphorite mining in the area contains selenium, cadmium, vanadium, and other metals. Toxic concentrations of selenium have been found in plants and soils near some mine waste dumps. Eighteen mine waste dumps and five vegetation cover types in the southeast Idaho phosphate district were mapped by using Airborne Visible-Infrared Imaging Spectrometer (AVIRIS) imagery and field data. The interaction of surface water runoff with mine waste was assessed by registering the AVIRIS results to digital elevation data, enabling determinations of (1) mine dump morphologies, (2) catchment watershed areas above each mine dump, (3) flow directions from the dumps, (4) stream gradients, and (5) the extent of downstream wetlands available for selenium absorption. Watersheds with the most severe selenium contamination, such as the South Maybe Canyon watershed, are associated with mine dumps that have large catchment watershed areas, high stream gradients, a paucity of downstream wetlands, and dump forms that tend to obstruct stream flow. Watersheds associated with low concentrations of dissolved selenium, such as Angus Creek, have mine dumps with small catchment watershed areas, low stream gradients, abundant wetlands vegetation, and less obstructing dump morphologies. ?? 2002 Elsevier Science Inc. All rights reserved.

Remote Sensing of Environment↗

USGS2019

Evaluating the temperature difference parameter in the SSEBop model with satellite observed land surface temperature data

The Operational Simplified Surface Energy Balance (SSEBop) model uses the principle of satellite psychrometry to produce spatially explicit actual evapotranspiration (ETa) with remotely sensed and weather data. The temperature difference ( dT ) in the model is a predefined parameter quantifying the difference between surface temperature at bare soil and air temperature at canopy level. Because dT is derived from the average-sky net radiation based primarily on climate data, validation of the dT estimation is critical for assuring a high-quality ETa product. We used the Moderate Resolution Imaging Spectroradiometer (MODIS) data to evaluate the SSEBop dT estimation for the conterminous United States. MODIS data (2008–2017) were processed to compute the 10-year average land surface temperature (LST) and normalized difference vegetation index (NDVI) at 1 km resolution and 8-day interval. The observed dT ( dT o ) was computed from the LST difference between hot (NDVI < 0.25) and cold (NDVI > 0.7) pixels within each 2° × 2° sampling block. There were enough hot and cold pixels within each block to create dT o timeseries in the West Coast and South-Central regions. The comparison of dT o and modeled dT ( dT m ) showed high agreement, with a bias of 0.8 K and a correlation coefficient of 0.88 on average. This study concludes that the dT m estimation from the SSEBop model is reliable, which further assures the accuracy of the ETa estimation.

Conterminous United States↗

USGS2020

Observations and recommendations for coordinated calibration activities of government and commercial optical satellite systems

One of the biggest changes in the world of optical remote sensing over the last several years is the sheer increase in the number of sensors that are imaging the Earth in moderate to high spatial resolution. With respect to the calibration of these sensors, they are broadly classified into two types, namely government systems and commercial systems. Because of the differences in the design and mission of these sensor types, calibration approaches are often substantially different. Thus, an opportunity exists to foster discussion between calibration teams for these sensors with the goal of improving overall sensor calibration and data interoperability. The approach used to accomplish this task was a one-day workshop where team members from both government and commercial sensors could share best practices, discuss methods for collaboration and improvement, and make recommendations for continuing activities. Five major recommendations were developed from the event that focused on coordinated activities using pseudo invariant calibration sites (PICS), broader and more consistent communication, collaboration on specific cross-calibration opportunities, developing a reference sensor for all optical systems, and encouraging the coordinated development of surface reflectance products. Workshop participants concluded that regular interactions between these teams could foster a better calibration of all sensor systems and accelerate the improved interoperability of surface products.

Remote Sensing↗

USGS2021

Monitoring Tamarix changes using WorldView-2 satellite imagery in Grand Canyon National Park, Arizona

Remote sensing methods are commonly used to monitor the invasive riparian shrub tamarisk ( Tamarix spp. ) and its response to the northern tamarisk beetle ( D. carinulata ), a specialized herbivore introduced as a biocontrol agent to control tamarisk in the Southwest USA in 2001. We use a Spectral Angle Mapper (SAM) supervised classification method with WorldView-2 (2 m spatial resolution) multispectral images from May and August of 2019 to map healthy tamarisk, canopy dieback, and defoliated tamarisk over a 48 km segment of the Colorado River in the topographically complex Grand Canyon National Park, where coarse-resolution satellite images are of limited use. The classifications in May and August produced overall accuracies of 80.0% and 83.1%, respectively. Seasonal change detection between May and August 2019 indicated that 47.5% of the healthy tamarisk detected in May 2019 had been defoliated by August 2019 within the WorldView-2 image extent. When compared to a previously published tamarisk map from 2009, derived from multispectral aerial imagery, we found that 29.5% of healthy tamarisk canopy declined between 2009 and 2019. This implies that tamarisk beetle impacts are continuing to accumulate even though land managers have noted the presence of the beetles in this reach of the river for 7 years since 2012.

Arizona↗

USGS2021

Exploring VIIRS continuity with MODIS in an expedited capability for monitoring drought-related vegetation conditions

Vegetation has been effectively monitored using remote sensing time-series vegetation index (VI) data for several decades. Drought monitoring has been a common application with algorithms tuned to capturing anomalous temporal and spatial vegetation patterns. Drought stress models, such as the Vegetation Drought Response Index (VegDRI), often use VIs like the Normalized Difference Vegetation Index (NDVI). The EROS expedited Moderate Resolution Imaging Spectroradiometer (eMODIS)-based, 7-day NDVI composites are integral to the VegDRI. As MODIS satellite platforms (Terra and Aqua) approach mission end, the Visible Infrared Imaging Radiometer Suite (VIIRS) presents an alternate NDVI source, with daily collection, similar band passes, and moderate spatial resolution. This study provides a statistical comparison between EROS expedited VIIRS (eVIIRS) 375-m and eMODIS 250-m and tests the suitability of replacing MODIS NDVI with VIIRS NDVI for drought monitoring and vegetation anomaly detection. For continuity with MODIS NDVI, we calculated a geometric mean regression adjustment algorithm using 375-m resolution for an eMODIS-like NDVI (eVIIRS’) eVIIRS’ = 0.9887 × eVIIRS − 0.0398. The resulting statistical comparisons (eVIIRS’ vs. eMODIS NDVI) showed correlations consistently greater than 0.84 throughout the three years studied. The eVIIRS’ VegDRI results characterized similar drought patterns and hotspots to the eMODIS-based VegDRI, with near zero bias.

Kansas↗

USGS2026

Multi-temporal mapping and analyses of post-wildfire surface soil moisture modeled from Landsat Thermal-IR and Sentinel-1 SAR

Wildfires alter surface soil moisture by consuming vegetation and exposing the soil to increased solar radiation, increased precipitation throughfall, and reduced infiltration from soil hydrophobicity. This can affect the water balance within the watershed and, in turn, influence post-fire nutrient cycling and ecosystem succession. Modeling surface soil moisture over large areas using satellite remote sensing can aid monitoring of soil moisture patterns and dynamics. We trained a random forest regression model using an in situ soil moisture network and examined pre- and post-fire soil moisture dynamics for three wildfires that burned in 2016 and 2017 in coastal California, U.S.A. Model inputs included Sentinel-1A synthetic aperture radar (SAR) and Landsat thermal infrared imagery (TIR), which produced a high resolution (30 m) soil moisture map time series with 18–20 estimates across 2 years. An unbiased root mean square error of 0.065 and an r 2 of 0.624 indicates a good fit between the model and reference data. We found a significant interaction between treatment (wildfire or unburned) and period (before or after wildfire) by examining the modeled surface soil moisture in high-severity burned areas. Before-after control-impact metrics showed that modeled surface soil moisture was lower after each fire within wildfire areas compared to unburned controls, and overall changes in soil moisture were greater in the wildfire perimeters than unburned areas. These results demonstrate the potential of Sentinel-1 SAR and Landsat TIR data for the development of high-resolution soil moisture time series products to support monitoring and assessment of post-fire soil hydrologic conditions.

California↗

USGS2021

Rangeland fractional components across the western United States from 1985 to 2018

Monitoring temporal dynamics of rangelands to detect and understand change in vegetation cover and composition provides a wealth of information to improve management and sustainability. Remote sensing allows the evaluation of both abrupt and gradual rangeland change at unprecedented spatial and temporal extents. Here, we describe the production of the National Land Cover Database (NLCD) Back in Time (BIT) dataset which quantified the percent cover of rangeland components (bare ground, herbaceous, annual herbaceous, litter, shrub, and sagebrush ( Artemisia spp. Nutt.) across the western United States using Landsat imagery from 1985 to 2018. We evaluate the relationships of component trends with climate drivers at an ecoregion scale, describe the nature of landscape change, and demonstrate several case studies related to changes in grazing management, prescribed burns, and vegetation treatments. Our results showed the net cover of shrub, sagebrush, and litter significantly ( p < 0.01) decreased, bare ground and herbaceous cover had no significant change, and annual herbaceous cover significantly ( p < 0.05) increased. Change was ubiquitous, with a mean of 92% of pixels with some change and 38% of pixels with significant change ( p < 0.10). However, most change was gradual, well over half of pixels have a range of less than 10%, and most change occurred outside of known disturbances. The BIT data facilitate a comprehensive assessment of rangeland condition, evaluation of past management actions, understanding of system variability, and opportunities for future planning.

Arizona, California, Colorado, Idaho, Kansas, Mont↗

USGS2022

Mapping the probability of freshwater algal blooms with various spectral indices and sources of training data

Algal blooms are pervasive in many freshwater environments and can pose risks to the health and safety of humans and other organisms. However, monitoring and tracking of potentially harmful blooms often relies on in-person observations by the public. Remote sensing has proven useful in augmenting in situ observations of algal concentration, but many hurdles hinder efficient application by end users. First, numerous approaches to estimate aquatic chlorophyll-a are available and can produce inconsistent results. Second, lack of quantitative in situ observations limits opportunities to train models for specific waterbodies, such that models developed for other systems must be used instead. We (1) implement univariate and multivariate logistic regression models to estimate the probability that aquatic chlorophyll-a concentrations exceed an accepted threshold beyond which harmful effects become likely and (2) evaluate the use of visually classified bloom/no-bloom satellite imagery to augment in situ training data. Using a binary classification of aquatic chlorophyll-a exceeding 10 μg / L, we found that (1) logistic regression models were ∼80 % accurate, (2) univariate models trained with visually classified data produce nearly the same accuracy (79%) as models trained with in situ observations (80%), and (3) augmenting in situ chlorophyll-a observations with visual classifications outperformed (82% accuracy) models trained on in situ observations alone (80% accuracy). These results provide a framework for evaluating multiple spectral indices in retrieving algal bloom presence or absence and illustrate that training data derived directly from satellite imagery can be useful in augmenting in situ observations.

Idaho, Oregon↗

USGS2019

UAV-derived estimates of forest structure to inform ponderosa pine forest restoration

Restoring forest ecosystems has become an increasingly high priority for land managers across the American West. Millions of hectares of forest are in need of drastic yet strategic reductions in density (e.g., basal area). Meeting the restoration and management goals requires quantifying metrics of vertical and horizontal forest structure, which has relied upon field‐based measurements, manned airborne or satellite remote sensing datasets. We used unmanned aerial vehicle (UAV ) image‐derived Structure‐from‐Motion (SfM) models and high‐resolution multispectral orthoimagery in this study to quantify vertical and horizontal forest structure at both the fine‐ (<4 ha) and mid‐scales (4–400 ha) across a forest density gradient. We then used these forest structure estimates to assess specific objectives of a forest restoration treatment. At the fine‐scale, we found that estimates of individual tree height and canopy diameter were most accurate in low‐density conditions, with accuracies degrading significantly in high‐density conditions. Mid‐scale estimates of canopy cover and forest density followed a similar pattern across the density gradient, demonstrating the effectiveness of UAV image‐derived estimates in low‐ to medium‐density conditions as well as the challenges associated with high‐density conditions. We found that post‐treatment conditions met a majority of the prescription objectives and demonstrate the UAV image application in quantifying changes from a mechanical thinning treatment. We provide a novel approach to forest restoration monitoring using UAV ‐derived data, one that considers varying density conditions and spatial scales. Future research should consider a more spatially extensive sampling design, including different restoration treatments, as well as experimenting with different combinations of equipment, flight parameters, and data processing workflows.

Western United States↗

USGS2013

An approach for characterizing the distribution of shrubland ecosystem components as continuous fields as part of NLCD

Characterizing and quantifying distributions of shrubland ecosystem components is one of the major challenges for monitoring shrubland vegetation cover change across the United States. A new approach has been developed to quantify shrubland components as fractional products within National Land Cover Database (NLCD). This approach uses remote sensing data and regression tree models to estimate the fractional cover of shrubland ecosystem components. The approach consists of three major steps: field data collection, high resolution estimates of shrubland ecosystem components using WorldView-2 imagery, and coarse resolution estimates of these components across larger areas using Landsat imagery. This research seeks to explore this method to quantify shrubland ecosystem components as continuous fields in regions that contain wide-ranging shrubland ecosystems. Fractional cover of four shrubland ecosystem components, including bare ground, herbaceous, litter, and shrub, as well as shrub heights, were delineated in three ecological regions in Arizona, Florida, and Texas. Results show that estimates for most components have relatively small normalized root mean square errors and significant correlations with validation data in both Arizona and Texas. The distribution patterns of shrub height also show relatively high accuracies in these two areas. The fractional cover estimates of shrubland components, except for litter, are not well represented in the Florida site. The research results suggest that this method provides good potential to effectively characterize shrubland ecosystem conditions over perennial shrubland although it is less effective in transitional shrubland. The fractional cover of shrub components as continuous elements could offer valuable information to quantify biomass and help improve thematic land cover classification in arid and semiarid areas.

Arizona, Florida, Texas↗