A survival guide to Landsat preprocessing Young, Nicholas E.; Anderson, Ryan S.; Chignell, Stephen M. ...
Ecology,
April 2017, Letnik:
98, Številka:
4
Journal Article
Recenzirano
Odprti dostop
Landsat data are increasingly used for ecological monitoring and research. These data often require preprocessing prior to analysis to account for sensor, solar, atmospheric, and topographic effects. ...However, ecologists using these data are faced with a literature containing inconsistent terminology, outdated methods, and a vast number of approaches with contradictory recommendations. These issues can, at best, make determining the correct preprocessing workflow a difficult and time-consuming task and, at worst, lead to erroneous results. We address these problems by providing a concise overview of the Landsat missions and sensors and by clarifying frequently conflated terms and methods. Preprocessing steps commonly applied to Landsat data are differentiated and explained, including georeferencing and co-registration, conversion to radiance, solar correction, atmospheric correction, topographic correction, and relative correction. We then synthesize this information by presenting workflows and a decision tree for determining the appropriate level of imagery preprocessing given an ecological research question, while emphasizing the need to tailor each workflow to the study site and question at hand. We recommend a parsimonious approach to Landsat preprocessing that avoids unnecessary steps and recommend approaches and data products that are well tested, easily available, and sufficiently documented. Our focus is specific to ecological applications of Landsat data, yet many of the concepts and recommendations discussed are also appropriate for other disciplines and remote sensing platforms.
Global estimates of forest aboveground biomass and carbon storage have major discrepancies linked to limitations in tree-level biomass estimates. Robust allometric equations can improve biomass ...estimates; however, destructive sampling to measure single-tree biomass is expensive, challenging, and prone to measurement error. We present a method to efficiently and non-destructively estimate single-tree biomass from terrestrial LiDAR scan data and test the approach on 21 destructively-sampled lodgepole pine (Pinus contorta) trees. The approach estimates branch and foliage volume using voxelization and estimates trunk volume using a method developed in this study called the Outer Hull Model (OHM). The OHM iteratively fits convex hulls, accurately handles noisy scan data, and fits the true shape of the trunk rather than forcing a cylindrical fit. Volume from the LiDAR scans is converted to biomass using density values from the literature and from field sampling to assess model sensitivity to density values. Whole-tree aboveground biomass estimates derived from the LiDAR scans were nearly unbiased and agreed strongly with destructive sampling data (R2=0.98, RMSE=20.4kg). Estimation of the trunk component biomass (R2=0.99, RMSE=12.3kg) was stronger than foliage and needle component estimates (R2=0.54, RMSE=21.4kg). The approach presented in this study accurately and non-destructively estimated the aboveground biomass of needleleaf trees with minimal user input. The promising performance on coniferous trees advances efficient sampling of single-tree biomass.
•A novel algorithm is presented to estimate individual tree biomass from TLS data.•The algorithm uses the shape of the tree rather than forcing a cylinder to fit.•TLS biomass estimates were validated with destructive sampling data.•Estimates of whole-tree and trunk biomass were accurate.•The method advances efficient single-tree biomass estimation, improving allometry.
Background
Biomass maps are valuable tools for estimating forest carbon and forest planning. Individual-tree biomass estimates made using allometric equations are the foundation for these maps, yet ...the potentially-high uncertainty and bias associated with individual-tree estimates is commonly ignored in biomass map error. We developed allometric equations for lodgepole pine (
Pinus contorta)
, ponderosa pine (
P. ponderosa)
, and Douglas-fir (
Pseudotsuga menziesii)
in northern Colorado. Plot-level biomass estimates were combined with Landsat imagery and geomorphometric and climate layers to map aboveground tree biomass. We compared biomass estimates for individual trees, plots, and at the landscape-scale using our locally-developed allometric equations, nationwide equations applied across the U.S., and the Forest Inventory and Analysis Component Ratio Method (FIA-CRM). Total biomass map uncertainty was calculated by propagating errors from allometric equations and remote sensing model predictions. Two evaluation methods for the allometric equations were compared in the error propagation—errors calculated from the equation fit (equation-derived) and errors from an independent dataset of destructively-sampled trees (n = 285).
Results
Tree-scale error and bias of allometric equations varied dramatically between species, but local equations were generally most accurate. Depending on allometric equation and evaluation method, allometric uncertainty contributed 30–75% of total uncertainty, while remote sensing model prediction uncertainty contributed 25–70%. When using equation-derived allometric error, local equations had the lowest total uncertainty (root mean square error percent of the mean % RMSE = 50%). This is likely due to low-sample size (10–20 trees sampled per species) allometric equations and evaluation not representing true variability in tree growth forms. When independently evaluated, allometric uncertainty outsized remote sensing model prediction uncertainty. Biomass across the 1.56 million ha study area and uncertainties were similar for local (2.1 billion Mg; % RMSE = 97%) and nationwide (2.2 billion Mg; % RMSE = 94%) equations, while FIA-CRM estimates were lower and more uncertain (1.5 billion Mg; % RMSE = 165%).
Conclusions
Allometric equations should be selected carefully since they drive substantial differences in bias and uncertainty. Biomass quantification efforts should consider contributions of allometric uncertainty to total uncertainty, at a minimum, and independently evaluate allometric equations when suitable data are available.
Abstract
The influence of forest treatments on wildfire effects is challenging to interpret. This is, in part, because the impact forest treatments have on wildfire can be slight and variable across ...many factors. Effectiveness of a treatment also depends on the metric considered. We present and define human–fire interaction, fire behavior, and ecological metrics of forest treatment effects on wildfire and discuss important considerations and recommendations for evaluating treatments. We demonstrate these concepts using a case study from the Cameron Peak Fire in Colorado, USA. Pre-fire forest treatments generally, but not always, experienced reduced burn severity, particularly when surface fuels were reduced. Treatments in the Cameron Peak Fire have also been documented as increasing tree survivorship, aiding suppression efforts, promoting firefighter safety, and influencing fire spread. However, the impacts of pre-fire management on primary landscape-scale objectives, like watershed protection, are unknown. Discussions about the influence of pre-fire treatments on fire effects must define the indicator(s) being assessed, as the same treatment may be considered successful under one measure but not others. Thus, it is critical to bring a common language and understanding to conversations about treatment effects and advance efforts to evaluate the range of treatment effects, thus supporting treatment planning.
Here we present “CO-RIP”, a novel spatial dataset delineating riparian corridors and riparian vegetation along large streams and rivers in the United States (US) portion of the Colorado River Basin. ...The consistent delineation of riparian areas across large areas using remote sensing has been a historically complicated process partially due to differing definitions in the scientific and management communities regarding what a “riparian corridor” or “riparian vegetation” represents. We use valley-bottoms to define the riparian corridor and establish a riparian vegetation definition interpretable from aerial imagery for efficient, consistent, and broad-scale mapping. Riparian vegetation presence and absence data were collected using a systematic, flexible image interpretation process applicable wherever high resolution imagery is available. We implemented a two-step approach using existing valley bottom delineation methods and random forests classification models that integrate Landsat spectral information to delineate riparian corridors and vegetation across the 12 ecoregions of the Colorado River Basin. Riparian vegetation model accuracy was generally strong (median kappa of 0.80), however it varied across ecoregions (kappa range of 0.42–0.90). We offer suggestions for improvement in our current image interpretation and modelling frameworks, particularly encouraging additional research in mapping riparian vegetation in moist coniferous forest and deep canyon environments. The CO-RIP dataset created through this research is publicly available and can be utilized in a wide range of ecological applications.
Non-native and invasive tamarisk (Tamarix spp.) and Russian olive (Elaeagnus angustifolia) are common in riparian areas of the Colorado River Basin and are regarded as problematic by many land and ...water managers. Widespread location data showing current distribution of these species, especially data suitable for remote sensing analyses, are lacking. This dataset contains 3476 species occurrence and absence point records for tamarisk and Russian olive along rivers within the Colorado River Basin in Arizona, California, Colorado, Nevada, New Mexico, and Utah. Data were collected in the field in the summer of 2017 with high-resolution imagery loaded on computer tablets. This dataset includes status (live, dead, defoliated, etc.) of observed tamarisk to capture variability in tamarisk health across the basin, in part attributable to the tamarisk beetle (Diorhabda spp.). For absence points, vegetation or land cover were recorded. These data have a range of applications including serving as a baseline for the current distribution of these species, species distribution modeling, species detection with remote sensing, and invasive species management.
Virtual fencing (VF) is a rapidly expanding technology that uses global positioning technologies to send audible and electrical cues to livestock that create invisible boundaries to replace physical ...fencing. The technology portends several benefits, from replacing costly and hazardous physical fencing to being an additional tool to contain, exclude, or move livestock. While researchers and VF providers work to improve the technology and applications, little is known about producer perceptions of its capabilities and what they most want in a system. We conducted phone and in-person interviews with beef cattle producers to ask them about their views and experiences related to virtual fencing technology. We included producers that already use the technology (including producers currently installing the technology) and producers not actively considering or using the technology. Our findings identify benefits and barriers of VF from the cattle producers’ perspective. These perspectives can guide new research, improve VF technology, guide educational programs, and help producers considering a VF system. Survey responses are organized into eight themes: animal stress and welfare; effectiveness, function, and technology; management impacts; financial and economic perspectives; improvements and advice; learning; privacy; and implementation. Producers who use the technology had greater optimism about the applications and economics and have found creative applications of VF specific to their operations. While they have more confidence in the technology, they still report issues such as collars falling off or base stations not working. Producers new to VF should expect a learning period both for themselves and their animals. Producers from all groups cite potential benefits from better use of forages, reduced wildlife conflicts, more flexibility and convenience, to the ability to better manage sensitive landscapes such as riparian areas or other areas affected by fire or drought.
Abstract The invasive shrub, Russian olive ( Elaeagnus augustifolia ), is widely established within riparian areas across North America and eastern Europe. Limited information on its distribution and ...invasion dynamics in northern regions has hampered understanding and management efforts. Given this lack of spatial and ecological information we worked with local stakeholders and developed two main objectives: (1) map the distribution of Russian olive along the Powder River (Montana and Wyoming, United States) as of 2020 with field data and remote sensing; and (2) relate that distribution to environmental variables to understand its habitat suitability and community/invasion dynamics. Field data showed Russian olive has reached near equal canopy cover (18.3%) to native Plains cottonwood ( Populus deltoides; 19.1%) and has a broader distribution. At the watershed scale, we modeled Russian olive distribution using field surveys, ocular sampling of aerial imagery, and spectral variables from Sentinel-2 MultiSpectral Instrument using a random forest model (RMSE = 15.42, R 2 = 0.64). A statistical model linking the resulting Russian olive percent cover detection map to environmental variables for the entire watershed indicated Russian olive cover increased with flow accumulation and decreased with elevation, and was associated with poorer soil types. We attribute the success of Russian olive to its broad habitat suitability combined with changing hydrologic conditions favoring it over natives. The maps of Russian olive cover along the Powder River and its main tributaries in northern Wyoming and southern Montana revealed regions of the watershed with high and low cover, which can guide landscape-scale management prioritization. This study provides a repeatable Russian olive detection method due to the use of Sentinel-2 imagery that is available worldwide and provides insight into Russian olive’s ecological relationships and success with relevance for management across areas with similar environmental conditions.
•Patterns of mountain pine beetle-killed lodgepole pine are analyzed.•Proportion of basal area killed was mapped using Landsat 7 imagery.•Mortality ranged from 0 to 99% of stand basal area, ...proportional to pine abundance.•Pine mortality in regenerating clearcuts was lower than in old-growth stands.
The recent mountain pine beetle (Dendroctonus ponderosae Hopkins) outbreaks had unprecedented effects on lodgepole pine (Pinus contorta var. latifolia) in western North America. We used data from 165 forest inventory plots to analyze stand conditions that regulate lodgepole pine mortality across a wide range of stand structure and species composition at the Fraser Experimental Forest in Colorado, USA. Forest inventory data were then combined with Landsat 7 Enhanced Thematic Mapper Plus (ETM+) imagery and boosted regression trees modeling to map outbreak severity (proportion of basal area killed). The outbreak severity map was evaluated with training (pseudo-R2=0.63, RMSE=0.13) and independent test plots (pseudo-R2=0.42, RMSE=0.27). This map was used to compare pine mortality in regenerating clearcuts and mature stands, which would have been problematic otherwise since regenerating clearcuts were underrepresented in the forest inventory data. Mortality spanned from 0 to 99% of stand basal area, proportional to the abundance of pine in surveyed stands. During the outbreak, mortality was highest for larger-diameter trees; however, contrary to earlier outbreaks, beetles also attacked younger stands. Pine mortality was lower in stands regenerating from clearcut harvests conducted between 1954 and 1985 than in mature stands and was more closely related to topographic factors than stand age or clearcut size; mortality was highest on southerly aspects and lower elevation sites, favorable to lodgepole pine. The best predictors for mapping outbreak severity were the change in the Normalized Difference Moisture Index between pre- and end-of-outbreak imagery and the end-of-outbreak ETM+ band 5. A better understanding of mortality patterns relative to forest management can inform management planning and assessment of the influence of bark beetle outbreaks on ecosystem processes.
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