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Soybean aphid, Aphis glycines Matsumura (Hemiptera: Aphididae), is a common pest of soybean, Glycine max (L.) Merrill (Fabales: Fabaceae), in North America requiring frequent scouting as part of an integrated pest management plan. Current scouting methods are time consuming and provide incomplete coverage of soybean. Unmanned aerial vehicles (UAVs) are capable of collecting high-resolution imagery that offer more detailed coverage in agricultural fields than traditional scouting methods. Recently, it was documented that changes to the spectral reflectance of soybean canopies caused by aphid-induced stress could be detected from ground-based sensors; however, it remained unknown whether these changes could also be detected from UAV-based sensors. Small-plot trials were conducted in 2017 and 2018 where cages were used to manipulate aphid populations. Additional open-field trials were conducted in 2018 where insecticides were used to create a gradient of aphid pressure. Whole-plant soybean aphid densities were recorded along with UAV-based multispectral imagery. Simple linear regressions were used to determine whether UAV-based multispectral reflectance was associated with aphid populations. Our findings indicate that near-infrared reflectance decreased with increasing soybean aphid populations in caged trials when cumulative aphid days surpassed the economic injury level, and in open-field trials when soybean aphid populations were above the economic threshold. These findings provide the first documentation of soybean aphid-induced stress being detected from UAV-based multispectral imagery and advance the use of UAVs for remote scouting of soybean aphid and other field crop pests.

Outbreaks of blastomycosis, caused by the fungus Blastomyces dermatitidis, occur in areas of endemicity in the United States and Canada, but the geographic range of blastomycosis is expanding. Previous studies inferred the location of B. dermatitidis through epidemiologic data associated with outbreaks because culture of B. dermatitidis from the environment is often unsuccessful. In this study, we used a culture-independent, PCR-based method to identify B. dermatitidis DNA in environmental samples using the BAD1 promoter region. We tested 250 environmental samples collected in Minnesota, either associated with blastomycosis outbreaks or environmental samples collected from regions of high and low endemicity to determine the basal prevalence of B. dermatitidis in the environment. We identified a fifth BAD1 promoter haplotype of B. dermatitidis prevalent in Minnesota. Ecological niche analysis identified latitude, longitude, elevation, and site classification as environmental parameters associated with the presence of B. dermatitidis. Using this analysis, a random forest model predicted the presence of B. dermatitidis in basal environmental samples with 75% accuracy. These data support the use of culture-independent, PCR-based environmental sampling to track spread into new regions and to characterize the unknown B. dermatitidis environmental niche. IMPORTANCE Upon inhalation of spores from the fungus Blastomyces dermatitidis from the environment, humans and animals can develop the disease blastomycosis. Based on disease epidemiology, B. dermatitidis is known to be endemic in the United States and Canada around the Great Lakes and in the Ohio and Mississippi River Valleys but is starting to emerge in other areas. B. dermatitidis is extremely difficult to culture from the environment, so little is known about the environmental reservoirs for this pathogen. We used a culture-independent PCR-based assay to identify the presence of B. dermatitidis DNA in soil samples from Minnesota. By combining molecular data with ecological niche modeling, we were able to predict the presence of B. dermatitidis in environmental samples with 75% accuracy and to define characteristics of the B. dermatitidis environmental niche. Importantly, we showed the effectiveness of using a PCR-based assay to identify B. dermatitidis in environmental samples.

Wetland managers, citizens and government leaders are observing rapid changes in coastal wetlands and associated habitats around the Great Lakes Basin due to human activity and climate variability. SAR and optical satellite sensors offer cost effective management tools that can be used to monitor wetlands over time, covering large areas like the Great Lakes and providing information to those making management and policy decisions. In this paper we describe ongoing efforts to monitor dynamic changes in wetland vegetation, surface water extent, and water level change. Included are assessments of simulated Radarsat Constellation Mission data to determine feasibility of continued monitoring into the future. Results show that integration of data from multiple sensors is most effective for monitoring coastal wetlands in the Great Lakes region. While products developed using methods described in this article provide valuable management tools, more effort is needed to reach the goal of establishing a dynamic, near-real-time, remote sensing-based monitoring program for the basin.

The ability to predict spatially explicit nitrogen uptake (NUP) in maize (Zea mays L) during the early development stages provides clear value for making in-season nitrogen fertilizer applications that can improve NUP efficiency and reduce the risk of nitrogen loss to the environment. Aerial hyperspectral imaging is an attractive agronomic research tool for its ability to capture spectral data over relatively large areas, enabling its use for predicting NUP at the field scale. The overarching goal of this work was to use supervised learning regression algorithms—Lasso, support vector regression (SVR), random forest, and partial least squares regression (PLSR)—to predict early season (i.e., V6–V14) maize NUP at three experimental sites in Minnesota using high-resolution hyperspectral imagery. In addition to the spectral features offered by hyperspectral imaging, the 10th percentile Modified Chlorophyll Absorption Ratio Index Improved (MCARI2) was made available to the learning models as an auxiliary feature to assess its ability to improve NUP prediction accuracy. The trained models demonstrated robustness by maintaining satisfactory prediction accuracy across locations, pixel sizes, development stages, and a broad range of NUP values (4.8 to 182 kg ha-1). Using the four most informative spectral features in addition to the auxiliary feature, the mean absolute error (MAE) of Lasso, SVR, and PLSR models (9.4, 9.7, and 9.5 kg ha-1, respectively) was lower than that of random forest (11.2 kg ha-1). The relative MAE for the Lasso, SVR, PLSR, and random forest models was 16.5%, 17.0%, 16.6%, and 19.6%, respectively. The inclusion of the auxiliary feature not only improved overall prediction accuracy by 1.6 kg ha-1 (14%) across all models, but it also reduced the number of input features required to reach optimal performance. The variance of predicted NUP increased as the measured NUP increased (MAE of the Lasso model increased from 4.0 to 12.1 kg ha-1 for measured NUP less than 25 kg ha-1 and greater than 100 kg ha-1, respectively). The most influential spectral features were oftentimes adjacent to each other (i.e., within approximately 6 nm), indicating the importance of both spectral precision and derivative spectra around key wavelengths for explaining NUP. Finally, several challenges and opportunities are discussed regarding the use of these results in the context of improving nitrogen fertilizer management.

Ash trees (Fraxinus spp.) are a prominent species in Minnesota forests, with an estimated 1.1 billion trees in the state, totaling approximately 8% of all trees. Ash trees are threatened by the invasive emerald ash borer (Agrilus planipennis Fairmaire), which typically results in close to 100% tree mortality within one to five years of infestation. A detailed, wall-to-wall map of ash presence is highly desirable for forest management and monitoring applications. We used Google Earth Engine to compile Landsat time series analysis, which provided unique information on phenologic patterns across the landscape to identify ash species. Topographic position information derived from lidar was added to improve spatial maps of ash abundance. These input data were combined to produce a classification map and identify the abundance of ash forests that exist in the state of Minnesota. Overall, 12,524 km2 of forestland was predicted to have greater than 10% probability of ash species present. The overall accuracy of the composite ash presence/absence map was 64% for all ash species and 72% for black ash, and classification accuracy increased with the length of the time series. Average height derived from lidar was the best model predictor for ash basal area (R2 = 0.40), which, on average, was estimated as 16.1 m2 ha−1. Information produced from this map will be useful for natural resource managers and planners in developing forest management strategies which account for the spatial distribution of ash on the landscape. The approach used in this analysis is easily transferable and broadly scalable to other regions threatened with forest health problems such as invasive insects.

Invasive plant species are an increasing worldwide threat both ecologically and financially. Knowing the location of these invasive plant infestations is the first step in their control. Surveying for invasive Phragmites australis is particularly challenging due to limited accessibility in wetland environments. Unoccupied aircraft systems (UAS) are a popular choice for invasive species management due to their ability to survey challenging environments and their high spatial and temporal resolution. This study tested the utility of three-band (i.e., red, green, and blue; RGB) UAS imagery for mapping Phragmites in the St. Louis River Estuary in Minnesota, U.S.A. and Saginaw Bay in Michigan, U.S.A. Iterative object-based image analysis techniques were used to identify two classes, Phragmites and Not Phragmites. Additionally, the effectiveness of canopy height models (CHMs) created from two data types, UAS imagery and commercial satellite stereo retrievals, and the RADARSAT-2 horizontal-horizontal (HH) polarization were tested for Phragmites identification. The highest overall classification accuracy of 90% was achieved when pairing the UAS imagery with a UAS-derived CHM. Producer’s accuracy for the Phragmites class ranged from 3 to 76%, and the user’s accuracies were above 90%. The Not Phragmites class had user’s and producer’s accuracies above 88%. Inclusion of the RADARSAT-2 HH polarization caused a slight reduction in classification accuracy. Commercial satellite stereo retrievals increased commission errors due to decreased spatial resolution and vertical accuracy. The lowest classification accuracy was seen when using only the RGB UAS imagery. UAS are promising for Phragmites identification, but the imagery should be used in conjunction with a CHM.

Tree management is becoming a big issue in a variety of societal domains. In recent years, historic wildfires and blackouts caused by failures in tree management have increased in both quantity and severity, resulting in many deaths and financial loses in the tens of billions of dollars. Many communities are also suffering from massive tree loss (e.g., in the millions) that affects the health and well-being of citizens. These problems are likely to worsen due to climate change, aging infrastructure and population growth. Tree management needs a revolution to deal with these urgent problems. This opens up new challenges and opportunities for the spatial community. This paper presents some of the open research problems from the perspectives of individual tree mapping and characterization as well as decision making and in-field intervention.

Given remote sensing datasets in a spatial domain, we aim to detect geospatial objects with minimum bounding rectangles (i.e., angle-aware) leveraging deep learning frameworks. Geospatial objects (e.g., buildings, vehicles, farms) provide meaningful information for a variety of societal applications, including urban planning, census, sustainable development, security surveillance, agricultural management, etc. The detection of these objects are challenging because their directions are often heavily mixed and not parallel to the orthogonal directions of an image frame due to topography, planning, etc. In addition, there is very limited training data with angle information for most types of objects. In related work, state-of-the-art deep learning frameworks detect objects using orthogonal bounding rectangles (i.e., sides are parallel to the sides of an input image), so they cannot identify the directions of objects and generate loose rectangular bounds on objects. We propose an Unsupervised Augmentation (UA) framework to detect geospatial objects with general minimum bounding rectangles (i.e., with angles). The UA framework contains two schemes, namely a ROtation-Vector (ROV) based scheme and a context-based scheme. The schemes completely avoid the need for: (1) additional ground-truth data with annotated angles; (2) restructuring of existing network architectures; and (3) re-training. Experimental results show that the UA framework can well approximate the angles of objects and generate much tighter bounding boxes on objects.

Long-term, large-scale underground coal mining activities in Huainan, China, have damaged the stability of the overlying rocks, resulting in large waterlogged areas. The water quality in these areas is compromised by agricultural pollutants and discharge of untreated waste. In this study, chlorophyll-a (Chl-a) concentrations were estimated in two types of waterlogged area, semi-closed (the Panji area [Pj]) and closed (the Guqiao area [Gq]), using information from water samples and in situ hyperspectral data collected in May and November 2013. The absorption coefficients of phytoplankton (aph(λ)), colored dissolved organic matter (aCDOM(λ)), and nonpigmented suspended matter (ad(λ)) were determined. Nonpigmented components (ad(λ) and aCDOM(λ)) had a significant influence on the waterlogged areas, more so in Pj than in Gq because of its connection to the Ni River.The NIR-red, three-band, and normalized difference chlorophyll index (NDCI) models were calibrated and validated to estimate Chl-a concentrations ...

Given learning samples from a raster data set, spatial decision tree learning aims to find a decision tree classifier that minimizes classification errors as well as salt-and-pepper noise. The problem has important societal applications such as land cover classification for natural resource management. However, the problem is challenging due to the fact that learning samples show spatial autocorrelation in class labels, instead of being independently identically distributed. Related work relies on local tests (i.e., testing feature information of a location) and cannot adequately model the spatial autocorrelation effect, resulting in salt-and-pepper noise. In contrast, we recently proposed a focal-test-based spatial decision tree (FTSDT), in which the tree traversal direction of a sample is based on both local and focal (neighborhood) information. Preliminary results showed that FTSDT reduces classification errors and salt-and-pepper noise. This paper extends our recent work by introducing a new focal test approach with adaptive neighborhoods that avoids over-smoothing in wedge-shaped areas. We also conduct computational refinement on the FTSDT training algorithm by reusing focal values across candidate thresholds. Theoretical analysis shows that the refined training algorithm is correct and more scalable. Experiment results on real world data sets show that new FTSDT with adaptive neighborhoods improves classification accuracy, and that our computational refinement significantly reduces training time.

Modern climate change in Alaska has resulted in widespread thawing of permafrost, increased fire activity, and extensive changes in vegetation characteristics that have significant consequences for socioecological systems. Despite observations of the heightened sensitivity of these systems to change, there has not been a comprehensive assessment of factors that drive ecosystem changes throughout Alaska. Here we present research that improves our understanding of the main drivers of the spatiotemporal patterns of carbon dynamics using in situ observations, remote sensing data, and an array of modeling techniques. In the last 60 yr, Alaska has seen a large increase in mean annual air temperature (1.7°C), with the greatest warming occurring over winter and spring. Warming trends are projected to continue throughout the 21st century and will likely result in landscape-level changes to ecosystem structure and function. Wetlands, mainly bogs and fens, which are currently estimated to cover 12.5% of the landscape, strongly influence exchange of methane between Alaska's ecosystems and the atmosphere and are expected to be affected by thawing permafrost and shifts in hydrology. Simulations suggest the current proportion of near-surface (within 1 m) and deep (within 5 m) permafrost extent will be reduced by 9-74% and 33-55% by the end of the 21st century, respectively. Since 2000, an average of 678 595 ha/yr was burned, more than twice the annual average during 1950-1999. The largest increase in fire activity is projected for the boreal forest, which could result in a reduction in late-successional spruce forest (8-44%) and an increase in early-successional deciduous forest (25-113%) that would mediate future fire activity and weaken permafrost stability in the region. Climate warming will also affect vegetation communities across arctic regions, where the coverage of deciduous forest could increase (223-620%), shrub tundra may increase (4-21%), and graminoid tundra might decrease (10-24%). This study sheds light on the sensitivity of Alaska's ecosystems to change that has the potential to significantly affect local and regional carbon balance, but more research is needed to improve estimates of land-surface and subsurface properties, and to better account for ecosystem dynamics affected by a myriad of biophysical factors and interactions.

The Southern Ocean is one of the most rapidly changing ecosystems on the planet due to the effects of climate change and commercial fishing for ecologically important krill and fish. Because sea ice loss is expected to be accompanied by declines in krill and fish predators, decoupling the effects of climate and anthropogenic changes on these predator populations is crucial for ecosystem-based management of the Southern Ocean. We reviewed research published from 2007 to 2014 that incorporated very high-resolution satellite imagery to assess distribution, abundance, and effects of climate and other anthropogenic changes on populations of predators in polar regions. Very high-resolution imagery has been used to study 7 species of polar animals in 13 papers, many of which provide methods through which further research can be conducted. Use of very high-resolution imagery in the Southern Ocean can provide a broader understanding of climate and anthropogenic forces on populations and inform management and conservation recommendations. We recommend that conservation biologists continue to integrate high-resolution remote sensing into broad-scale biodiversity and population studies in remote areas, where it can provide much needed detail. Aplicaciones de Imagenes de Muy Alta Resolucion en el Estudio y Conservacion de Grandes Depredadores en el Oceano Antartico

Mapping impervious surfaces over regional or continental scale study areas with high spatial resolution imagery is difficult due to the cost and time involved in processing the large number of images required. This study investigated the benefits of using the coarse spatial resolution, high temporal resolution MODIS sensor to produce impervious surface maps. MODIS NDVI data for multiple years were analyzed with two multi-temporal image analysis methods: the Sequential Maximum Angle Convex Cone and Linear Spectral Unmixing. Impervious surface maps were generated and compared with a set of reference data and a Landsat-derived impervious cover map. The mapping accuracies for the algorithms used were generally good, particularly for the LSU approach, which was able to identify areas with 50-60% impervious cover at 77% accuracy and areas with a cumulative impervious cover of 50% or greater at 80% accuracy. The methods presented in this paper have potential for mapping impervious cover over large areas where the use of higher spatial resolution data is impracticable.

Currently available land cover data sets for large geographic regions are produced on an intermittent basis and are often dated. Ideally, annually updated data would be available to support environ...

Land-cover (LC) maps derived from remotely sensed data are often presented using a minimum mapping unit (MMU) to characterize a particular landscape theme of interest. The choice of an MMU that is appropriate for the projected use of a classification is an important consideration. The objective of this experiment was to determine the effect of MMU on a LC classification of the Neuse River Basin (NRB) in North Carolina. The results of this work indicate that MMU size had a significant effect on accuracy estimates only when the MMU was changed by relatively large amounts. Typically, an MMU is selected as close as possible to the original data resolution so as to reduce the loss of specificity introduced in the resampling process. Since only large MMU changes resulted in significant differences in the accuracy estimates, an analyst may have the flexibility to select from a range of MMUs that are appropriate for a given application.

The monitoring of water colour parameters can provide an important diagnostic tool for the assessment of aquatic ecosystem condition. Remote sensing has long been used to effectively monitor chlorophyll concentrations in open ocean systems; however, operational monitoring in coastal and estuarine areas has been limited because of the inherent complexities of coastal systems, and the coarse spectral and spatial resolutions of available satellite systems. Data were collected using the National Aeronautics and Space Administration (NASA) Advanced Visible-Infrared Imaging Spectrometer (AVIRIS) flown at an altitude of approximately 20 000 m to provide hyperspectral imagery and simulate both MEdium Resolution Imaging Spectrometer (MERIS) and Moderate Resolution Imaging Spectrometer (MODIS) data. AVIRIS data were atmospherically corrected using a radiative transfer modelling approach and analysed using band ratio and linear regression models. Regression analysis was performed with simultaneous field measurements data in the Neuse River Estuary (NRE) and Pamlico Sound on 15 May 2002. Chlorophyll a (Chl a) concentrations were optimally estimated using AVIRIS bands (9.5 nm) centred at 673.6 and 692.7 nm, resulting in a coefficient of determination (R 2) of 0.98. Concentrations of Chromophoric Dissolved Organic Matter (CDOM), Total Suspended Solids (TSS) and Fixed Suspended Solids (FSS) were also estimated, resulting in coefficients of determination of R 2 = 0.90, 0.59 and 0.64, respectively. Ratios of AVIRIS bands centred at or near those corresponding to the MERIS and MODIS sensors indicated that relatively good satellite-based estimates could potentially be derived for water colour constituents at a spatial resolution of 300 and 500 m, respectively.