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At least 667 records · Page 37Linked to original sources
USGS2024

A fire-use decision model to improve the United States’ wildfire management and support climate change adaptation

The US faces multiple challenges in facilitating the safe, effective, and proactive use of fire as a landscape management tool. This intentional fire use exposes deeply ingrained communication challenges and distinct but overlapping strategies of prescribed fire, cultural burning, and managed wildfire. We argue for a new conceptual model that is organized around ecological conditions, capacity to act, and motivation to use fire and can integrate and expand intentional fire use as a tool. This result emerges from more considered collaboration and communication of values and needs to address the negative consequences of contemporary fire use. When applied as a communication and translation tool, there is potential to lower barriers to faster and more successful collaboration among stakeholders. Such improvements are a vital part of strategies to address climate adaptation, wildfire mitigation, and the well-being of ecosystems.

Cell Reports Sustainability↗
USGS2017

Low incidence of clonality in cold water corals revealed through the novel use of standardized protocol adapted to deep sea sampling

Sampling in the deep sea is a technical challenge, which has hindered the acquisition of robust datasets that are necessary to determine the fine-grained biological patterns and processes that may shape genetic diversity. Estimates of the extent of clonality in deep-sea species, despite the importance of clonality in shaping the local dynamics and evolutionary trajectories, have been largely obscured by such limitations. Cold-water coral reefs along European margins are formed mainly by two reef-building species, Lophelia pertusa and Madrepora oculata . Here we present a fine-grained analysis of the genotypic and genetic composition of reefs occurring in the Bay of Biscay, based on an innovative deep-sea sampling protocol. This strategy was designed to be standardized, random, and allowed the georeferencing of all sampled colonies. Clonal lineages discriminated through their Multi-Locus Genotypes (MLG) at 6–7 microsatellite markers could thus be mapped to assess the level of clonality and the spatial spread of clonal lineages. High values of clonal richness were observed for both species across all sites suggesting a limited occurrence of clonality, which likely originated through fragmentation. Additionally, spatial autocorrelation analysis underlined the possible occurrence of fine-grained genetic structure in several populations of both L. pertusa and M. oculata . The two cold-water coral species examined had contrasting patterns of connectivity among canyons, with among-canyon genetic structuring detected in M. oculata , whereas L. pertusa was panmictic at the canyon scale. This study exemplifies that a standardized, random and georeferenced sampling strategy, while challenging, can be applied in the deep sea, and associated benefits outlined here include improved estimates of fine grained patterns of clonality and dispersal that are comparable across sites and among species.

Deep-Sea Research Part II: Topical Studies in Ocea↗
USGS2026

Estimating paleotemperature using stable isotopes of soil-formed phyllosilicates from paleosols: A review

Fossilized soils, or paleosols, contain soil-formed phyllosilicates whose stable isotopic compositions may be used to calculate paleotemperature and thus reconstruct ancient terrestrial environments. Though paleosols are common in the geologic record, the use of phyllosilicates as paleotemperature proxies is limited in the literature owing to difficulties with selecting optimal paleosols, isolation from non-clay minerals and organic materials, mixtures of phyllosilicates in natural samples, wide variations of chemical compositions for phyllosilicates, and limited to undefined equilibrium fractionation factors between phyllosilicates-water. Here, we address these challenges by examining and comparing methods used for sample selection, mineral isolation, pretreatments, mineral identification, conventional and developing methods for oxygen and hydrogen isotopic analyses, and determination of phyllosilicate-water equilibrium fractionation factors, concluding with recommendations for best approaches for paleotemperature estimation. Additionally, we discuss how to identify and avoid detrital phyllosilicates, the impacts of diagenesis, comparison of stable isotope and non-isotope paleosol paleotemperature proxies, and challenges and opportunities for broadly using paleosols as paleoclimate archives. With ongoing efforts to refine this multi-faceted paleotemperature approach, the stable isotope geochemistry of soil-formed phyllosilicates continues to be an invaluable proxy system, enhancing our understanding of terrestrial paleoenvironments and paleoclimate.

Kansas↗
USGS2021

Synthesizing and analyzing long-term monitoring data: A greater sage-grouse case study

Long-term monitoring of natural resources is imperative for increasing the understanding of ecosystem processes, services, and how to manage those ecosystems to maintain or improve function. Challenges with using these data may occur because methods of monitoring changed over time, multiple organizations collect and manage data differently, and monetary resources fluctuate, affecting many aspects of data. Because many species respond to changes in habitat conditions and predator-prey relationships across different spatial scales that span management boundaries, greater efforts for collaborating are essential. We demonstrate the challenges and methods for standardizing greater sage-grouse ( Centrocercus urophasianus ) long-term monitoring data across the species range in the western United States to inform population modeling needs identified by the Western Association of Fish and Wildlife Agencies. We used automated and repeatable methods of standardizing data via custom open-source software ( grsg_lekdb ) to improve the scientific integrity of future sage-grouse population assessments within and among states. Data standardization included reconciling uses of different terminology and expunging unusable data, resulting in the removal of 26% of data records due to database insertion errors and modifications to >1 million values to correct formatting and typing errors. Our approaches maximized the inclusion of usable data and identified data that could inform detection probabilities, population trends, and monitoring guidelines. Using sage-grouse databases as an example, we identified the importance of data management and how quality assurance and quality control measures can improve the usefulness of these data for future research needs. Our methods of using informatics and concluding recommendations can support similar endeavors of flora and fauna monitoring programs, whether those efforts are to use existing data or support new monitoring programs.

Ecological Informatics↗
USGS2018

Assessing the influence of multiple stressors on stream diatom metrics in the upper Midwest, USA

Water resource managers face increasing challenges in identifying what physical and chemical stressors are responsible for the alteration of biological conditions in streams. The objective of this study was to assess the comparative influence of multiple stressors on benthic diatoms at 98 sites that spanned a range of stressors in an agriculturally dominated region in the upper Midwest, USA. The primary stressors of interest included: nutrients, herbicides and fungicides, sediment, and streamflow; although the influence of physical habitat was incorporated in the assessment. Boosted Regression Tree was used to examine both the sensitivity of various diatom metrics and the relative importance of the primary stressors. Percent Sensitive Taxa, percent Highly Motile Taxa, and percent High Phosphorus Taxa had the strongest response to stressors. Habitat and total phosphorous were the most common discriminators of diatom metrics, with herbicides as secondary factors. A Classification and Regression Tree (CART) model was used to examine conditional relations among stressors and indicated that fine-grain streams had a lower percentage of Sensitive Taxa than coarse-grain streams, with Sensitive Taxa decreasing further with increased water temperature (>30 °C) and triazine concentrations (>1500 ng/L). In contrast, streams dominated by coarse-grain substrate contained a higher percentage of Sensitive Taxa, with relative abundance increasing with lower water temperatures (<29 °C) and shallower water depth (<0.3 m). Quantile regression indicated that maximum water temperature appears to be a major limiting factor in Midwest streams; whereas both total phosphorus and percent fines showed a slight subsidy-stress response. While using benthic algae for assessing stream quality can be challenging, field-based studies can elucidate stressor effects and interactions when the response variables are appropriate, sufficient stressor resolution is achieved, and the number and type of sites represent a gradient of stressor conditions and at least a quasi-factorial design.

Ecological Indicators↗
USGS2021

Assessment of sea lamprey (Petromyzon marinus) diet using DNA metabarcoding of feces

Sea lamprey ( Petromyzon marinus ) are invasive in the Laurentian Great Lakes, parasitize large-bodied fishes, and therefore are the focus of an international control program. However, damage caused by sea lamprey to modern day fish stocks remains uncertain because diet analysis of juvenile sea lamprey has been challenging; they feed on blood and are difficult to randomly sample in the lakes. Here, both challenges were addressed by showing that DNA metabarcoding of fecal material can be used to identify the diet of actively feeding juvenile sea lamprey, and can also be used to determine what non-feeding adult sea lamprey captured in streams fed on while parasitizing fish. Fecal samples from juvenile sea lamprey that were feeding on lake trout in northern Lake Huron overwhelmingly contained lake trout ( Salvelinus namaycush ) DNA (90%), while smaller percentages contained lake whitefish ( Coregonus clupeaformis ; 5%) and longnose sucker ( Catostomus catostomus ; 5%) DNA. Fecal samples from adult sea lamprey captured from a tributary to northern Lake Huron overwhelmingly contained longnose and white sucker DNA ( Catostomus spp.; 80%), while a smaller percentage contained lake trout DNA (10%). Diet composition of adult sea lamprey sampled in the tributary (Black Mallard Creek) was more diverse than juvenile diet composition. DNA metabarcoding suggests that Catostomus spp. may be an important host fish in northern Lake Huron for sea lamprey prior to spawning. Future research could investigate how diet varies across years and lakes and the prevalence and sources of DNA contamination. Application of DNA metabarcoding for diet assessment may be practical for identifying populations of invasive sea lamprey that feed on highly valued fishes and help guide restoration of lampreys worldwide.

Michigan↗
USGS2025

Evaluating freshwater mussel sampling methodologies using a simulation model

Field surveys form the basis of many research efforts and are the foundation for estimates of population size and density that inform conservation and management practices for imperiled species. As a result, evaluating the performance of different survey methods across a range of conditions that may be encountered in the field can increase understanding of the time and effort that may be required to ensure that survey results are sufficiently accurate and reliable for conservation goals. We used a spatially explicit agent-based model to simulate four commonly used freshwater mussel field survey methodologies: simple random sampling (SRS), transect random sampling (TRS), adaptive cluster sampling (ACS), and qualitative timed searches (QTS) to investigate the influence of sampling method, spatial distribution, and mussel density on the performance ( i.e., accuracy, precision, and detection rate) of survey techniques. Our analysis suggests that mussel density, spatial distribution, and sampling effort influence sampling accuracy, precision, and species detection for all sampling methods. QTS produces highly variable catch-per-unit-effort (CPUE) metrics when mussels are dense and/or clustered, indicating the technique may be unreliable as a proxy for density. Quantitative methods like SRS and TRS may be well-suited for estimating population characteristics, but a high level of effort may be needed to obtain reasonable accuracy when mussels occur at low densities. ACS may be more efficient for mussels at low densities, but it can be challenging to plan for the level of effort required to complete an ACS protocol. Designing an ecological survey requires careful consideration of research objectives and available resources. Future research may consider the performance of qualitative and quantitative surveys in combination as a means of overcoming some of the practical challenges of applying individual survey methods.

Ecological Indicators↗
USGS2018

Quantifying population-level effects of water temperature, flow velocity and chemical-induced reproduction depression: A simulation study with smallmouth bass

Evaluating stochastic abiotic factors and their combined effects on fish and wildlife populations have been challenging in environmental sciences. Contributing to this challenge is the paucity of data describing how observations made on individuals related to exposure to chemical compounds ultimately effect population vital rates, and how this exposure interacts with other abiotic drivers. Using three smallmouth bass populations in Pennsylvania as a case study, we explored both single-factor and combined effects of water temperature, flow velocity and chemical exposure on populations through a model simulation. Although there are many pathways through which chemicals can affect population vital rates, we focused on one where chemical exposure leads to reduced reproduction. Among the three populations considered, two (the Juniata and Susquehanna populations) have experienced adverse health, including the potential adverse effects of environmental stress and chemical contamination that may cause disease and mortality of young-of-year (YOY), various skin lesions and a high prevalence of intersex or testicular oocytes in adults. The third population (The Alleghany population) has not encountered mortality events of YOY and intersex prevalence is much lower. The simulation involved projecting populations using a length-based model under different environmental conditions. In the simulations, abiotic factors influenced population dynamics through their impacts on growth, survival and recruitment. Response to the same environmental stress varied by population and life-stage of the species. Factors affecting young adult and adult life-stages had great influence on proportional stock density (PSD) and the probability of having PSD within the suggested range (PSD probability). Increases in water temperature had a negative effect and dominant role in the combined effect on population size structure (e.g., PSD and PSD probability) – increases in flow velocity during the spring season also had a negative effect on abundance. Populations with high recruitment rates sustained relatively large population size, even under high water temperature and/or high flow velocity, which suggests that factors and management strategies that benefit recruitment (such as reduced chemical contaminants) may compensate for the negative effects of warming water temperatures and high spring flow velocity on population size.

Ecological Modelling↗
USGS2017

Prior knowledge-based approach for associating contaminants with biological effects: A case study in the St. Croix River basin, MN, WI, USA

Evaluating potential adverse effects of complex chemical mixtures in the environment is challenging. One way to address that challenge is through more integrated analysis of chemical monitoring and biological effects data. In the present study, water samples from five locations near two municipal wastewater treatment plants in the St. Croix River basin, on the border of MN and WI, USA, were analyzed for 127 organic contaminants. Known chemical-gene interactions were used to develop site-specific knowledge assembly models (KAMs) and formulate hypotheses concerning possible biological effects associated with chemicals detected in water samples from each location. Additionally, hepatic gene expression data were collected for fathead minnows ( Pimephales promelas ) exposed in situ , for 12 d, at each location. Expression data from oligonucleotide microarrays were analyzed to identify functional annotation terms enriched among the differentially-expressed probes. The general nature of many of the terms made hypothesis formulation on the basis of the transcriptome-level response alone difficult. However, integrated analysis of the transcriptome data in the context of the site-specific KAMs allowed for evaluation of the likelihood of specific chemicals contributing to observed biological responses. Thirteen chemicals (atrazine, carbamazepine, metformin, thiabendazole, diazepam, cholesterol, p-cresol, phenytoin, omeprazole, ethyromycin, 17β-estradiol, cimetidine, and estrone), for which there was statistically significant concordance between occurrence at a site and expected biological response as represented in the KAM, were identified. While not definitive, the approach provides a line of evidence for evaluating potential cause-effect relationships between components of a complex mixture of contaminants and biological effects data, which can inform subsequent monitoring and investigation.

Minnesota, Wisconsin↗
USGS2021

Understanding collaborative governance from a communication network perspective: A case study of the Atlantic Salmon recovery framework

Atlantic salmon populations in Maine remain critically low despite extensive hatchery supplementation and habitat improvement efforts. In 2000, the Gulf of Maine Distinct Population Segment was listed as Endangered under the ESA with joint listing authority shared by the National Oceanic and Atmospheric Administration (NOAA) and the United States Fish and Wildlife Service (USFWS). Because, regulators, and tribal, federal and state managers operate with independent authorities, recovery decisions depend upon effective communication and coordination among groups. Using a mixed-methods approach, we surveyed (n = 41) and interviewed (n = 28) members of the Atlantic Salmon Recovery Framework (ASRF), the joint governance structure responsible for Atlantic salmon management in Maine. We used survey results to examine the communication between members of ASRF through communication network analysis. While there is a relatively high network density for individual communication (56 %), connections are decentralized, a characteristic that can be incompatible with some organizational structures. Challenges reported by members fit into three general categories; i. slow and ineffective decision-making, ii. confusion surrounding leadership and accountability, and iii. low adaptive capacity. Despite these challenges, participants reported a commitment to maintaining a collaborative governance structure and a long history of inter-organizational relationships. This introspective effort has led to a reorganization effort to optimize communication pathways.

Maine↗
USGS2012

Automating calibration, sensitivity and uncertainty analysis of complex models using the R package Flexible Modeling Environment (FME): SWAT as an example

Parameter optimization and uncertainty issues are a great challenge for the application of large environmental models like the Soil and Water Assessment Tool (SWAT), which is a physically-based hydrological model for simulating water and nutrient cycles at the watershed scale. In this study, we present a comprehensive modeling environment for SWAT, including automated calibration, and sensitivity and uncertainty analysis capabilities through integration with the R package Flexible Modeling Environment (FME). To address challenges (e.g., calling the model in R and transferring variables between Fortran and R) in developing such a two-language coupling framework, 1) we converted the Fortran-based SWAT model to an R function (R-SWAT) using the RFortran platform, and alternatively 2) we compiled SWAT as a Dynamic Link Library (DLL). We then wrapped SWAT (via R-SWAT) with FME to perform complex applications including parameter identifiability, inverse modeling, and sensitivity and uncertainty analysis in the R environment. The final R-SWAT-FME framework has the following key functionalities: automatic initialization of R, running Fortran-based SWAT and R commands in parallel, transferring parameters and model output between SWAT and R, and inverse modeling with visualization. To examine this framework and demonstrate how it works, a case study simulating streamflow in the Cedar River Basin in Iowa in the United Sates was used, and we compared it with the built-in auto-calibration tool of SWAT in parameter optimization. Results indicate that both methods performed well and similarly in searching a set of optimal parameters. Nonetheless, the R-SWAT-FME is more attractive due to its instant visualization, and potential to take advantage of other R packages (e.g., inverse modeling and statistical graphics). The methods presented in the paper are readily adaptable to other model applications that require capability for automated calibration, and sensitivity and uncertainty analysis.

Environmental Modelling and Software↗
USGS2020

A non-intrusive approach for efficient stochastic emulation and optimization of model-based nitrate-loading management decision support

Use of physically-motivated numerical models like groundwater flow-and-transport models for probabilistic impact assessments and optimization under uncertainty (OUU) typically incurs such a computational burdensome that these tools cannot be used during decision making. The computational challenges associated with these models can be addressed through emulation. In the land-use/water-quality context, the linear relation between nitrate loading and surface-water/groundwater nitrate concentrations presents an opportunity for employing an efficient model emulator through the application of impulse-response matrices. When paired with first-order second-moment techniques, the emulation strategy gives rise to the “stochastic impulse-response emulator” (SIRE). SIRE is shown to facilitate non-intrusive, near-real time, and risk-based evaluation of nitrate-loading change scenarios, as well as nitrate-loading OUU subject to surface-water/groundwater concentration constraints in high decision variable and parameter dimensions. Two case studies are used to demonstrate SIRE in the nitrate-loading context.

Environmental Modelling and Software↗
USGS2022

Complex magmatic-tectonic interactions during the 2020 Makushin Volcano, Alaska, earthquake swarm

On June 15, 2020, at 21:16 UTC, a locally-felt earthquake of magnitude 4.2 struck Unalaska Island, Alaska, ∼15 km west of the town of Unalaska and the large fishing port of Dutch Harbor. The event was followed by a M4.1 earthquake at 00:34 UTC and several M3+ aftershocks, initiating a prolific sequence with hundreds of earthquakes recorded into late December. The earthquakes all locate about 12 km southeast of the summit of Makushin Volcano at 7 to 10 km depth. To date, no eruptive activity or other surface changes have been observed at the volcano in webcam images, GPS or InSAR. Seismic bursts close to volcanoes are often associated with the onset of unrest that can lead to eruption. However, determining whether seismicity reflects magmatic rather than tectonic stresses is often challenging, although critical for hazard assessments and risk management strategies. To investigate the triggering mechanisms of the recent Makushin seismicity, we integrate information from space-time patterns of the earthquake hypocenters with their fault-plane solutions. We relocate the swarm events using double-difference relocation techniques and a 3D velocity model and find that the earthquakes, although they seem to follow two predominant orientations (NW-SE and SW-NE), do not show clear clustering into preferred alignments. Similarly, we do not observe pronounced migration in time and space. Fault-plane solutions (FPS) for all but one M2.5+ earthquakes have P-axis orientations consistent with subhorizontal NW-SE oriented regional maximum compression, whereas many of the lower-magnitude earthquakes have P-axes perpendicular to regional maximum compression. This provides evidence for the presence of a local stress field likely induced by magma intrusion. Results from Coulomb stress modeling are also consistent with dike inflation modulated by stresses induced by the M4+ earthquakes. The seismic swarm is thus likely linked to a superposition of driving stresses from both magmatic and tectonic processes on pre-existing faults. The case of the 2020 Makushin swarm, with its unusual characteristics, challenges traditional swarm classification schemes and suggests that a reconsideration of the definition of seismic swarms as having the maximum magnitude event in the middle of the swarm is warranted.

Alaska↗
USGS2026

Strength of depensation not influenced by fish population productivity

A long-held assumption in the management of exploited fisheries is that fish populations will compensate with increased recruit survival to replenish the population when adult stock size is reduced through harvest. Observations of depensatory recruitment (reduced recruit survival at low adult stock size) and critical depensatory thresholds have challenged the compensation assumption. Post et al. (2002) postulated that critical depensatory thresholds were related to fish population productivity. Walleye Sander vitreus are a culturally, economically, and recreationally important sportfish whose persistence is being challenged by natural recruitment declines throughout much of its native range. Depensation, among other abiotic and biotic stressors, has been implicated in walleye natural recruitment declines. If walleye population productivity is related to critical depensatory thresholds, then population productivity benchmarks could be established to reduce the probability of crossing them. We used empirically-derived and model predicted depensation values ( q ) and empirical estimates of walleye population productivity to test for relationships between these variables in northern Wisconsin lakes. We found little evidence for a relationship between q and walleye population productivity across all lakes examined. Our finding failed to support the theoretical postulation of a relationship between these variables by Post et al. (2002) for walleye. Little evidence for a relationship between q and population productivity suggests that depensatory thresholds may differ among individual walleye populations and that walleye populations may transition abruptly between compensatory and depensatory states. Given our findings, conservation efforts for walleye that solely focus on low productivity populations may miss other trends because population productivity may not be considered a broad predictor of crossing a critical depensatory threshold.

Fisheries Research↗
USGS2009

Wildfire risk in the wildland-urban interface: A simulation study in northwestern Wisconsin

The rapid growth of housing in and near the wildland–urban interface (WUI) increases wildfire risk to lives and structures. To reduce fire risk, it is necessary to identify WUI housing areas that are more susceptible to wildfire. This is challenging, because wildfire patterns depend on fire behavior and spread, which in turn depend on ignition locations, weather conditions, the spatial arrangement of fuels, and topography. The goal of our study was to assess wildfire risk to a 60,000 ha WUI area in northwestern Wisconsin while accounting for all of these factors. We conducted 6000 simulations with two dynamic fire models: Fire Area Simulator (FARSITE) and Minimum Travel Time (MTT) in order to map the spatial pattern of burn probabilities. Simulations were run under normal and extreme weather conditions to assess the effect of weather on fire spread, burn probability, and risk to structures. The resulting burn probability maps were intersected with maps of structure locations and land cover types. The simulations revealed clear hotspots of wildfire activity and a large range of wildfire risk to structures in the study area. As expected, the extreme weather conditions yielded higher burn probabilities over the entire landscape, as well as to different land cover classes and individual structures. Moreover, the spatial pattern of risk was significantly different between extreme and normal weather conditions. The results highlight the fact that extreme weather conditions not only produce higher fire risk than normal weather conditions, but also change the fine-scale locations of high risk areas in the landscape, which is of great importance for fire management in WUI areas. In addition, the choice of weather data may limit the potential for comparisons of risk maps for different areas and for extrapolating risk maps to future scenarios where weather conditions are unknown. Our approach to modeling wildfire risk to structures can aid fire risk reduction management activities by identifying areas with elevated wildfire risk and those most vulnerable under extreme weather conditions.

Wisconsin↗
USGS2025

Practical application of time-lapse camera imagery to develop water-level data for three hydrologic monitoring sites in Wisconsin during water year 2020

Using camera imagery to measure water level (camera-stage) is a well-researched area of study. Previous camera-stage studies have shown promising results when implementing this technology with tight constraints on test conditions. However, there is a need for a more comprehensive evaluation of the extensibility of camera-stage to practical applications. Therefore, the aim of this study was to test a camera-stage method under a wide variety of test conditions to better understand the successes and challenges of using this technology in real-world scenarios. In this study, this approach was tested during Water Year 2020 at three existing U.S. Geological Study (USGS) stream gaging stations in south central Wisconsin that had existing USGS water-level instrumentation. The specific reference objects tested were white pipes and a concrete wall. Since successful application of camera-stage relies on use of suitable images, all captured images in this study were visually inspected to determine suitability for application of camera-stage. Camera-stage measurements were then computed only on images deemed suitable and the results were compared with ground-truth stage values to determine the accuracy. For the purposes of this study, camera-stage values within ±0.10 ft of the actual stage were considered acceptable. One major challenge highlighted was the potential difficulty in obtaining suitable imagery, with the proportion of suitable images varying greatly between the four trials from 38 % to 92 %. The results from applying camera-stage to suitable images were encouraging though, with 79 % to 99 % of evaluated camera-stage values qualifying as acceptable among the four test trials.

Wisconsin↗
USGS2018

A 30-m landsat-derived cropland extent product of Australia and China using random forest machine learning algorithm on Google Earth Engine cloud computing platform

Mapping high resolution (30-m or better) cropland extent over very large areas such as continents or large countries or regions accurately, precisely, repeatedly, and rapidly is of great importance for addressing the global food and water security challenges. Such cropland extent products capture individual farm fields, small or large, and are crucial for developing accurate higher-level cropland products such as cropping intensities, crop types, crop watering methods (irrigated or rainfed), crop productivity, and crop water productivity. It also brings many challenges that include handling massively large data volumes, computing power, and collecting resource intensive reference training and validation data over complex geographic and political boundaries. Thereby, this study developed a precise and accurate Landsat 30-m derived cropland extent product for two very important, distinct, diverse, and large countries: Australia and China. The study used of eight bands (blue, green, red, NIR, SWIR1, SWIR2, TIR1, and NDVI) of Landsat-8 every 16-day Operational Land Imager (OLI) data for the years 2013–2015. The classification was performed by using a pixel-based supervised random forest (RF) machine learning algorithm (MLA) executed on the Google Earth Engine (GEE) cloud computing platform. Each band was time-composited over 4–6 time-periods over a year using median value for various agro-ecological zones (AEZs) of Australia and China. This resulted in a 32–48-layer mega-file data-cube (MFDC) for each of the AEZs. Reference training and validation data were gathered from: (a) field visits, (b) sub-meter to 5-m very high spatial resolution imagery (VHRI) data, and (c) ancillary sources such as from the National agriculture bureaus. Croplands versus non-croplands knowledge base for training the RF algorithm were derived from MFDC using 958 reference-training samples for Australia and 2130 reference-training samples for China. The resulting 30-m cropland extent product was assessed for accuracies using independent validation samples: 900 for Australia and 1972 for China. The 30-m cropland extent product of Australia showed an overall accuracy of 97.6% with a producer’s accuracy of 98.8% (errors of omissions = 1.2%), and user’s accuracy of 79% (errors of commissions = 21%) for the cropland class. For China, overall accuracies were 94% with a producer’s accuracy of 80% (errors of omissions = 20%), and user’s accuracy of 84.2% (errors of commissions = 15.8%) for cropland class. Total cropland areas of Australia were estimated as 35.1 million hectares and 165.2 million hectares for China. These estimates were higher by 8.6% for Australia and 3.9% for China when compared with the traditionally derived national statistics. The cropland extent product further demonstrated the ability to estimate sub-national cropland areas accurately by providing an R 2 value of 0.85 when compared with province-wise cropland areas of China. The study provides a paradigm-shift on how cropland maps are produced using multi-date remote sensing. These products can be browsed at www.croplands.org and made available for download at NASA’s Land Processes Distributed Active Archive Center (LP DAAC) https://www.lpdaac.usgs.gov/node/1282 .

ISPRS Journal of Photogrammetry and Remote Sensing↗
USGS2011

Adaptive management for a turbulent future

The challenges that face humanity today differ from the past because as the scale of human influence has increased, our biggest challenges have become global in nature, and formerly local problems that could be addressed by shifting populations or switching resources, now aggregate (i.e., "scale up") limiting potential management options. Adaptive management is an approach to natural resource management that emphasizes learning through management based on the philosophy that knowledge is incomplete and much of what we think we know is actually wrong. Adaptive management has explicit structure, including careful elucidation of goals, identification of alternative management objectives and hypotheses of causation, and procedures for the collection of data followed by evaluation and reiteration. It is evident that adaptive management has matured, but it has also reached a crossroads. Practitioners and scientists have developed adaptive management and structured decision making techniques, and mathematicians have developed methods to reduce the uncertainties encountered in resource management, yet there continues to be misapplication of the method and misunderstanding of its purpose. Ironically, the confusion over the term "adaptive management" may stem from the flexibility inherent in the approach, which has resulted in multiple interpretations of "adaptive management" that fall along a continuum of complexity and a priori design. Adaptive management is not a panacea for the navigation of 'wicked problems' as it does not produce easy answers, and is only appropriate in a subset of natural resource management problems where both uncertainty and controllability are high. Nonetheless, the conceptual underpinnings of adaptive management are simple; there will always be inherent uncertainty and unpredictability in the dynamics and behavior of complex social-ecological systems, but management decisions must still be made, and whenever possible, we should incorporate learning into management. ?? 2010 .

Journal of Environmental Management↗