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Precision agriculture has emerged as a dominant force in the United States, with widespread adoption of advanced technologies and decision support systems (DSS) since the 1980s. Key tools such as variable rate application (VRA), autopilot systems, and remote sensing have become integral for U.S. farmers, offering invaluable insights from crop, soil, and weather information to optimize agricultural production while minimizing environmental impact. To synthesize and categorize the extensive research available on precision agriculture, a systematic review protocol has been designed. Our objective is to offer clear and authoritative insights into the nature, scope, and volume of this field. Implementing a rigorous search strategy, we utilized renowned databases such as Scopus® and Web of ScienceTM to gather relevant and significant materiality. The retrieval process involved the use of indexing terms and Boolean operators, with a focus on 'precision agriculture' and 'precision farming', striking a balance between specificity and comprehensiveness. To ensure the credibility of our findings, only peer-reviewed papers authored by individuals affiliated with U.S. institutions have been included. Expert reviewers with deep knowledge in the field independently assessed the selected papers, thoroughly evaluating titles, abstracts, keywords, methods, conclusions, and declarations. Consistency and eligibility were paramount in determining which papers met the criteria for inclusion. Any discrepancies or disagreements were resolved through rigorous consensus-building discussions among the reviewers. Through this comprehensive meta-review, we provide a scientific contribution that enhances our understanding of precision agriculture, highlighting focus areas for further research and development (R&D). By synthesizing and categorizing the existing literature, we offer authoritative insights into the research landscape, informing future investigations and fostering innovation. Focusing specifically on the U.S., we shed light on the unique aspects and pioneering advancements in precision agriculture within the country. Ultimately, our findings have the potential to drive progress, contributing to sustainable development, increased productivity, enhanced environmental sustainability, and responsible agricultural practices. [ABSTRACT FROM AUTHOR]

To date, no method utilizing satellite imagery exists for detailing the locations and functions of critical infrastructure across the United States, making response to natural disasters and other events challenging due to complex infrastructural interdependencies. This paper presents a repeatable, transferable, and explainable method for critical infrastructure analysis and implementation of a robust model for critical infrastructure detection in satellite imagery. This model consists of a DenseNet-161 convolutional neural network, pretrained with the ImageNet database. The model was provided additional training with a custom dataset, containing nine infrastructure classes. The resultant analysis achieved an overall accuracy of 90%, with the highest accuracy for airports (97%), hydroelectric dams (96%), solar farms (94%), substations (91%), potable water tanks (93%), and hospitals (93%). Critical infrastructure types with relatively low accuracy are likely influenced by data commonality between similar infrastructure components for petroleum terminals (86%), water treatment plants (78%), and natural gas generation (78%). Local interpretable model-agnostic explanations (LIME) was integrated into the overall modeling pipeline to establish trust for users in critical infrastructure applications. The results demonstrate the effectiveness of a convolutional neural network approach for critical infrastructure identification, with higher than 90% accuracy in identifying six of the critical infrastructure facility types. [ABSTRACT FROM AUTHOR]

During the summer of 2018, the upward-pointing Wyoming Cloud Lidar (WCL) was deployed on board the University of Wyoming King Air (UWKA) research aircraft for the Biomass Burning Flux Measurements of Trace Gases and Aerosols (BB-FLUX) field campaign. This paper describes the generation of calibrated attenuated backscatter coefficients and aerosol extinction coefficients from the WCL measurements. The retrieved aerosol extinction coefficients at the flight level strongly correlate (correlation coefficient, rr > 0.8) with in situ aerosol concentration and carbon monoxide (CO) concentration, providing a first-order estimate for converting WCL extinction coefficients into vertically resolved CO and aerosol concentration within wildfire smoke plumes. The integrated CO column concentrations from the WCL data in nonextinguished profiles also correlate (rr = 0.7) with column measurements by the University of Colorado Airborne Solar Occultation Flux instrument, indicating the validity of WCL-derived extinction coefficients. During BB-FLUX, the UWKA sampled smoke plumes from more than 20 wildfires during 35 flights over the western United States. Seventy percent of flight time was spent below 3 km above ground level (AGL) altitude, although the UWKA ascended up to 6 km AGL to sample the top of some deep smoke plumes. The upward-pointing WCL observed a nearly equal amount of thin and dense smoke below 2 km and above 5 km due to the flight purpose of targeted fresh fire smoke. Between 2 and 5 km, where most of the wildfire smoke resided, the WCL observed slightly more thin smoke than dense smoke due to smoke spreading. Extinction coefficients in dense smoke were 2–10 times stronger, and dense smoke tended to have larger depolarization ratio, associated with irregular aerosol particles. [ABSTRACT FROM AUTHOR]

The increasing availability of high-quality remote sensing data and advanced technologies have spurred land cover mapping to characterize land change from local to global scales. However, most land change datasets either span multiple decades at a local scale or cover limited time over a larger geographic extent. Here, we present a new land cover and land surface change dataset created by the Land Change Monitoring, Assessment, and Projection (LCMAP) program over the conterminous United States (CONUS). The LCMAP land cover change dataset consists of annual land cover and land cover change products over the period 1985-2017 at 30-meter resolution using Landsat and other ancillary data via the Continuous Change Detection and Classification (CCDC) algorithm. In this paper, we describe our novel approach to implement the CCDC algorithm to produce the LCMAP product suite composed of five land cover and five land surface change related products. The LCMAP land cover products were validated using a collection of ~25,000 reference samples collected independently across CONUS. The overall agreement for all years of the LCMAP primary land cover product reached 82.5 %. The LCMAP products are produced through the LCMAP Information Warehouse and Data Store (IW+DS) and Shared Mesos Cluster systems that can process, store, and deliver all datasets for public access. To our knowledge, this is the first set of published 30 m annual land cover and land cover change datasets that span from the 1980s to the present for the United States. The LCMAP product suite provides useful information for land resource management and facilitates studies to improve the understanding of terrestrial ecosystems and the complex dynamics of the Earth system. The LCMAP system could be implemented to produce global land change products in the future. [ABSTRACT FROM AUTHOR]

Abstract In this paired study, we apply a recently developed high-resolution bathymetry estimation algorithm ("cBathy") to X-band marine radar observations at two nearshore field sites. The algorithm exploits observations of the spatial structure of wave phase to attain wavenumber estimates, inverts the linear water wave dispersion relation for depth, and then applies a Kalman filter to objectively update the bathymetry estimates. Previously, performance has only been tested using optical video observations. In this first of two papers, performance of the algorithm using X-band radar image time series is tested at two disparate barred beach environments: Duck, NC, USA, and Benson Beach, WA, USA. Each of the test beaches is either co-located with (Duck, NC) or geographically close to (Benson Beach, WA) those utilized in the original algorithm verification. Concurrent echosounder surveys are used as ground truth. The bulk performance of the radar-derived bathymetry estimate at Duck, NC, achieves 0.49 m root-mean-square error (RMSE) with 0.26 m bias deep. This compares well with the bulk performance of the concurrent estimate derived using optical video (0.44 m RMSE and 0.23 m bias deep). The radar-derived bathymetry estimate performance at Benson Beach, is similar (0.35 m RMSE and 0.11 m bias shallow), and is comparable to that of an optical video derived estimate at a similar Pacific Northwest beach (0.56 m RMSE and 0.41 m bias shallow). At both beaches, significantly higher performance is achieved at locations deeper than 2 m (offshore of the surfzone) than at locations shallower than 2 m (surfzone), where errors are often a large fraction of the total depth. Lastly, several weeks of observations are utilized to assess the sensitivity of algorithm quality control to environmental conditions. Thresholds based on the shoreward component of wind stress and offshore wave steepness are identified and shown to impact the areal coverage of radar-derived bathymetric estimates. Overall, these results demonstrate the viability of marine radar observations as input to the cBathy algorithm and delineate some environmental constraints on algorithm performance. In the companion paper, the algorithm is extended to areas where tidal currents are important, including an ebb tidal shoal and an estuary mouth. Highlights • Depth estimation algorithm, cBathy, is applied to X-band radar image time series. • Performance is assessed at two beaches (North Carolina and Washington, USA). • Accuracy via radar is comparable to video-based performance (reported previously). • Frequency of updates increases with wave height and when wind blows toward shore. [ABSTRACT FROM AUTHOR]

Urban tree canopy (UTC) assessment is essential for understanding the structure and function of urban forests and for devising management strategies. Geospatial techniques are routinely used for UTC assessment, yet their capabilities and limitations may not be apparent to urban forestry practitioners. This paper provides an overview of two primary methods of geospatial UTC assessment: photo interpretation (PI) and computerized image classification (IC). These methods were evaluated through a case study of an urbanized college campus in the eastern United States. The web-based application i-Tree Canopy is a PI method that uses statistical point sampling to estimate land cover. To examine the effect of point sample size on accuracy and certainty of the land cover estimates, we performed independently replicated assessments of our study area at various point sample sizes. We compared these findings with two IC methods: a proprietary analysis using high-spatial-resolution imagery and a low-spatial-resolution analysis using the web-based application i-Tree Landscape. With i-Tree Canopy, the estimate of UTC in our study area stabilized at a mean of 14.7% when point sample size reached 100 points, but it required more than 500 points to reach a tolerable standard error of less than 1.7%. By comparison, high-resolution imagery (considered the most robust form of assessment) estimated UTC in the study area at 16.1%, and i-Tree Landscape substantially underestimated UTC at 11.3%. Possible sources of variation in these estimates, along with practical considerations for choosing an appropriate UTC assessment method, are discussed. [ABSTRACT FROM AUTHOR]

The main sources of global soil moisture information are remote sensing observations and land surface model estimates. Data assimilation (DA) aims at optimally combining these data sources through statistical merging. To properly parameterize such merging, one needs to obtain accurate knowledge of model and observation uncertainties, which is the crux of a successful DA system. In this paper, we propose a new Monte Carlo based adaptive Kalman filtering framework (MadKF) that estimates model and observation uncertainties (Q and R) and updates soil moisture forecasts simultaneously. Spatially distributed uncertainties are estimated by applying triple collocation analysis (TCA) to Monte Carlo simulations of the model open-loop, the model analysis, and the observation time series at each grid cell. Error cross-covariances, which are inevitable between these time series, are diagnosed from their ensembles and used to iteratively correct biases they cause in Q and R estimates, and hence, in the Kalman filter gain. The proposed MadKF is tested in a synthetic environment and by assimilating real satellite soil moisture retrievals from the Advanced SCATterometer (ASCAT) into the Antecedent Precipitation Index (API) model forced with daily aggregated satellite precipitation. Synthetic experiments indicate a good convergence of Q and R estimates. Internal DA diagnostics, i.e., the innovation auto-correlation (IAC) and the variance of the normalized innovations, asymptotically converge to their desired values, which indicates that the filter is operating near its optimum and reliably estimates analysis uncertainty. Real-data experiments assimilating ASCAT observations into the API model further indicate that the MadKF is robust against observation error auto-correlations, which typically cause problems in conventional IAC-tuning based adaptive filtering approaches. A performance evaluation over 264 in situ sites within the contiguous United States shows that the MadKF leads to a significant skill gain in surface soil moisture estimation. The MadKF is also potentially applicable when assimilating Level 1 backscatter or brightness temperature observations, or when updating/assimilating other geophysical variables. • A novel adaptive Kalman filtering framework is proposed. • Triple collocation analysis is used to estimate model and observation uncertainty. • Monte Carlo simulations are used to correct error cross-correlation related biases. [ABSTRACT FROM AUTHOR]

Synthetic Aperture Radar (SAR) remote sensing has been widely used for flood mapping and monitoring. Nevertheless, flood detection in urban areas still proves to be particularly challenging by using SAR. In this paper, we assess the roles of SAR intensity and interferometric coherence in urban flood detection using multi-temporal TerraSAR-X data. We further introduce an active self-learning convolution neural network (A-SL CNN) framework to alleviate the effect of a limited annotated training dataset. The proposed framework selects informative unlabeled samples based on a temporal-ensembling CNN model. These samples are subsequently pseudo-labeled by a multi-scale spatial filter. Consistency regularization is introduced to penalize incorrect labels caused by pseudo-labeling. We show results for a case study that is centered on flooded areas in Houston, USA, during hurricane Harvey in August 2017. Our experiments show that multi-temporal intensity (pre- and co-event) plays the most important role in urban flood detection. Adding multi-temporal coherence can increase the reliability of the inundation map considerably. Meanwhile, encouraging results are achieved by the proposed A-SL CNN framework: the к statistic is improved from 0.614 to 0.686 in comparison to its supervised counterpart. [ABSTRACT FROM AUTHOR]

Abstract Evaporation from open surface water is a critical and continuous process in the water cycle. Globally, evaporation losses from reservoirs are estimated to be greater than the combined consumption from industrial and domestic water uses. However, this large volume of water loss is only coarsely considered in modern water resources management practices due to the complexities involved with quantifying these losses. By fusing remote sensing and modeling approaches, this study developed a novel method to accurately estimate the evaporation losses from 721 reservoirs in the contiguous United States (CONUS). Reservoir surface areas were extracted and enhanced from the Landsat based Global Surface Water Dataset (GSWD) from March 1984 to October 2015. The evaporation rate was modeled using the Penman Equation in which the lake heat storage term was considered. Validation results using in situ observations suggest that this approach can significantly improve the accuracy of the simulated monthly reservoir evaporation rate. The evaporation losses were subsequently estimated as the product of the surface area and evaporation rate. This paper presents a first of its kind, comprehensively validated, locally practical, and continentally consistent reservoir evaporation dataset. The results suggest that the long term averaged annual evaporation volume from these 721 reservoirs is 33.73 × 109 m3, which is equivalent to 93% of the annual public water supply of the United States (in 2010). An increasing trend of the evaporation rate (0.0076 mm/d/year) and a slightly decreasing trend of the total surface area (−0.011 × 109 m2/year) were both detected during the study period. As a result, the total evaporation shows an insignificant trend, yet with significant spatial heterogeneity. This new reservoir evaporation dataset can help facilitate more efficient water management practices. Highlights • A long term, comprehensive evaporation dataset was developed for 721 US reservoirs. • The calculated evaporation rate was improved by including the heat storage effect. • Driven by global brightening, evaporation rate has increased over most reservoirs. • The long term annual evaporation amount from these reservoirs is 33.73 × 109 m3/year. [ABSTRACT FROM AUTHOR]

The accurate estimation of the terrestrial latent heat flux (LE) from satellite observations at high spatial and temporal scales plays an important role in the assessment of the water and heat exchange between the earth’s surface and the atmosphere. Although a variety of data fusion methods have been proposed to merge different LE products for more reliable estimates, most of them have ignored the spatiotemporal consistency of LE products across different resolutions. In this paper, we apply the multiresolution tree (MRT) method to improve the accuracy and reduce the inconsistency between the Moderate Resolution Imaging Spectroradiometer (MODIS) LE (MOD16) product and the Landsat-based LE product at different resolutions. Eddy covariance (EC) ground measurements at five sites, MODIS and Landsat images from January 2005 to December 2005 in the north central USA, are used to evaluate the performance of the MRT method. The results show that the MRT method can improve the accuracy of the original LE products (MOD16 and Landsat), and it has the potential to significantly reduce the uncertainty and inconsistency of these products. The bias decreased by 38.3% on average, and the root-mean-square error (RMSE) decreased by approximately 49.2% after the MRT was applied at each scale. Further studies are still required to make the MRT method more universal on a variety of land cover types for long-time periods. [ABSTRACT FROM AUTHOR]

The analysis on the intensity of land development and utilization is of great significance to improving the efficency of land development and utilization and promoting ecological and environmental protection work. Taking the Tampa Bay watershed of Florida, USA as the study area, based on the information of four TM/OLI remote sensing images of 1985, 1995, 2005 and 2015, this paper constructs a series of models of land- use dynamics, conversion matrix, land development and utilization intensity and land-use structure with GIS technology. Then, it conducts research on spatial and temporal pattern of land development and utilization intensity in Tampa Bay watershed. The results showed that: (1) Construction land and woodland are the dominant types of land use in the watershed. The area of construction land mainly converted from cultivated land increased most, with the ratio being 53.74%. (2) The relative change rates of all types of land use have obvious spatial differences and those of rivers and lakes have the greatest. The change speed of unused land is the largest, with the highest single dynamic degree of up to 1.11%. The average annual growth rate of construction land is steady at about 0.30%. The size with the highest dynamic degree was decreasing from the northern and southern polar nuclei outward, and the areas with higher dynamic degrees were gradually converted into the lower dynamic areas. (3) Information entropy slightly decreased, the land use structure heterogeneity constantly decreased. Area of the land-use composition is listed in the order of Construction Land>Wood>Cultivated Land and Pasture>Rivers and Lakes> Tidal Flat and Marsh> Unused Land. (4) The intensity of land development and utilization is the highest in the Coastal Pinellas county and Tampa city. The area of development is much larger compared with the adjustment period, and the intensity of land development and utilization was obviously strengthened. [ABSTRACT FROM AUTHOR]

• Current remote sensing applications in nut crops were reviewed comprehensively. • Available and suitable platforms (UAS, Aircraft, Satellite) and sensors were discussed thoroughly. • Current and anticipated analytic pipelines based on the increasing data size were presented. • Sensor calibration, feature extraction, and data fusion were identified as the most noticeable knowledge gaps in remote sensing applications for nut crops. Due to their high protein content, nuts (almond, walnut, and pistachio) are among the main substitutes for meat, with a growing share of the food basket in the United States. However, the rapidly growing acreage of these crops, new legislations, the necessity of minimizing the environmental footprint, and a cost-effective production demand certain managerial practices based on precision agriculture and remote sensing, which have shown promising results in food production. This paper presents a comprehensive review of remote sensing platforms, sensors, applications, and analytic pipelines with a focus on nut crops, even though the materials are applicable for other specialty crops. In this regard, the paper is divided into five main sections: First, the problems and potential solutions are elaborated in the introduction. Second, the available platforms: satellites, manned aircraft, and UASs are discussed. Then the sensors used for remote sensing, their working principle, and the pros and cons of each are presented. Next, practiced and suggested applications of remote sensing data are reviewed. Finally, data processing and analytics needed to produce and interpret reliable results are highlighted. Key findings are listed as: 1) The acreage of the nut orchards and the purpose of the studies determine the fitting sensor and platform. 2) Although various sensors are available and reported to have promising results in other crops, they have not been used for nut crops. 3) Accurate sensor calibration is crucial for repeatable results as well as temporal and inter-field comparisons. 4) Except for water management, most remote sensing applications are limitedly studied in nut orchards, creating some research opportunities. 5) Finally, increasing data size requires new machine learning techniques and data fusion frameworks to handle all variables and fill the knowledge gap. [ABSTRACT FROM AUTHOR]

Identifying and mapping tropical trees at the species level from space can support an improved assessment of forest composition, forest carbon uptake, tree species distribution and preferred habitat as well as a better understanding of the response of forests to climate change. In this study, the development of a validated data and image-processing schema demonstrated the capability of current metre-scale satellite technology (WorldView-3) to identify specific tree species within an unmanaged tropical forest. The experimental site, La Selva Biological Station in Costa Rica, provided access for field validation and spectral data acquisition of individual tree canopies from established canopy towers. It is also a representative biome of diverse lowland Atlantic tropical forests in Central America. The process defined in this paper calibrated and corrected field-acquired ASD field spectra for ten tree species and corrected WorldView-3 image data for viewing and illumination geometry. In addition, assessments of three current atmospheric compensation methods for correcting recent WorldView-3 satellite imagery established the most accurate compensation process for a tropical forest setting. Corrected reflectance in the satellite data matched the spectrometer data to ±0.25% for visible bands and ±0.5% for near-infrared bands. This study shows that spectral data from the satellite and field spectrometer data are nearly equivalent when applying the appropriate atmospheric compensation, band response emulation, and viewing correction processes established in this study. [ABSTRACT FROM AUTHOR]

Land cover classification provides valuable information for prioritizing management and conservation operations across large landscapes. Current regional scale land cover geospatial products within the United States have a spatial resolution that is too coarse to provide the necessary information for operations at the local and project scales. This paper describes a methodology that uses recent advances in spatial analysis software to create a land cover classification over a large region in the southeastern United States at a fine (1 m) spatial resolution. This methodology used image texture metrics and principle components derived from National Agriculture Imagery Program (NAIP) aerial photographic imagery, visually classified locations and a softmax neural network model. The model efficiently produced classification surfaces at 1mresolution across roughly 11.6 million hectares (28.8 million acres) with less than 10% average error in modeled probability. The classification surfaces consist of probability estimates of 13 visually distinct classes for each 1 m cell across the study area. This methodology and the tools used in this study constitute a highly flexible fine resolution land cover classification that can be applied across large extents using standard computer hardware, common and open source software and publicly available imagery. [ABSTRACT FROM AUTHOR]

The International Charter ‘Space and Major Disasters’ (“Charter”) is a collaboration among Earth observation satellite owners and operators to provide remotely sensed (RS) data following natural and man-made disasters. Charter members provided post-flood and pre-flood or archived RS data after the April 2011 floods in the Midwestern US. These multispectral and RADAR images provided valuable information to end users and first responders in the flood affected states for their planning and relief efforts. This effort was successful because of the process established by the Charter for soliciting requests, and acquiring and distributing data through a network of international and national agencies, and data vendors. This paper describes the a) role played by the Charter to acquire and distribute RS data through six of its members, b) value of archived satellite data, c) need for image processing experts to process these near real-time data, and transfer them in a timely manner, and d) data acquisition and distribution policies of the Charter. Lessons learned from this response along with the policy recommendations will be helpful in future activations for maximizing the value of RS data and services provided by the Charter. [ABSTRACT FROM AUTHOR]

In this paper, we present methods to assess the homogeneity of the settlement landscape and to identify settlement clusters in terms of location and size. Due to various input data, methodologies, and spatial concepts, there are only a few international comparative research studies with quantitative, spatial approaches to date. Existing studies are mostly qualitative and empirically hardly replicable. Here, we introduce two novel methodological approaches to describe urban land patterns in a numerically stable, consistent and globally comparable manner. The first method evaluates the multi-scale homogeneity of the settlements and the respective density distributions in a purely non-parametric approach. The second method transfers the settlement patterns into a Gaussian Mixture Model via hierarchical multi-scale clustering in order to describe each cluster by the parameters of a 2D Gaussian distribution. In order to proof the robustness of both approaches, two different reference units are considered for the interpretation of the results: OECD functional urban areas and standardized subsets of 200 km × 200 km around the same central cities. We show that German urban areas are characterized by a higher heterogeneity within a smaller neighbourhood with an almost symmetric density distribution in comparison to urban areas in the U.S. Furthermore, German urban areas possess an increasing homogeneity with increasing size, i.e., the larger the urban agglomeration, the more uniform it appears. It is the contrary in the U.S. These findings are substantiated by the analysis of individual city centres. In Germany, many clusters of similar size define the urban region, whereas in the U.S. one large dominating cluster exceeds most neighbouring settlements significantly. The study is performed on the Global Urban Footprint Density, which assigns a density value to each urban pixel of about 30 m by 30 m. In this way, the approach is a blueprint to be extended to urban patterns of any spatial unit worldwide. • Quantitative assessment of heterogeneity, number and size of urban agglomerations • Two data-driven multi-scale approaches for the characterization for urban patterns • Study covers 68 metropolitan areas in Germany and 162 cities in the United States • Interpretation based on both functional areas and fixed subsets as reference units • First numerical comparison of urbanization in Germany and in the United States [ABSTRACT FROM AUTHOR]

Probabilistic flood mapping offers flood managers, decision makers, insurance agencies, and humanitarian relief organizations a useful characterization of uncertainty in flood mapping delineation. Probabilistic flood maps are also of high interest for data assimilation into numerical models. The direct assimilation of probabilistic flood maps into hydrodynamic models would be beneficial because it would eliminate the intermediate step of having to extract water levels first. This paper introduces a probabilistic flood mapping procedure based on synthetic aperture radar (SAR) data. Given a SAR image of backscatter values, we construct a total histogram of backscatter values and decompose this histogram into probability distribution functions of backscatter values associated with flooded (open water) and non-flooded pixels, respectively. These distributions are then used to estimate, for each pixel, its probability of being flooded. The new approach improves on binary SAR-based flood mapping procedures, which do not inform on the uncertainty in the pixel state. The proposed approach is tested using four SAR images from two floodplains, i.e., the Severn River (U.K.) and the Red River (U.S.). In all four test cases, reliability diagrams, with error values ranging from 0.04 to 0.23, indicate a good agreement between the SAR-derived probabilistic flood map and an independently available validation map, which is obtained from aerial photography. [ABSTRACT FROM PUBLISHER]

With this brief summary, we provide an overview of the 10th Southern Forestry and Natural Resource Management GIS Conference (SOFOR GIS), and a Special Section of this journal related to it. The conference was held in Athens, Georgia (USA) on December 7-8, 2015 at the University of Georgia Center for Continuing Education. Several papers from this conference were submitted for the publication in the journal; and some are still undergoing peer review. We anticipate that future issues of Mathematical and Computational Forestry & Natural-Resource Sciences will include other papers from the conference, while this Special Section contains the first two articles of the collection that already have passed the peer review process. [ABSTRACT FROM AUTHOR]

Unmanned Aerial Vehicle (UAV) Remote sensing (RS) has unique advantages over traditional satellite RS, including convenience, high resolution, affordability and fast acquisition speed, making it widely used in many fields. To provide an overview of the development of UAV RS applications during the past decade, we screened related publications from the Web of Science core database from 2010 to 2021, built co-author networks, a discipline interaction network, a keywords timeline view, a co-citation cluster, and detected burst citations using bibliometrics and social network analysis. Our results show that: (1) The number of UAV RS publications had an increasing trend, with explosive growth in the past five years. The number of papers published by China and the United States (US) is far ahead in this field; (2) The US has currently the greatest influence in this field through the largest number of international cooperations. Cooperation is mainly concentrated in countries and institutions with a large number of publications but is not widely distributed. (3) The application of UAV RS involves multiple interdisciplinary subjects, among which "Environmental Science and Ecology" ranks first; (4) Future research trends of UAV RS are expected to be related to artificial intelligence (e.g., artificial neural networks-based research). This paper provides a scientific basis and guidance for future developments of UAV RS applications, which can help the research community to better grasp the developments of this field. [ABSTRACT FROM AUTHOR]

The National Oceanic and Atmospheric Administration (NOAA) Geostationary Operational Environmental Satellite series R (GOES-R) will greatly expand the ability to observe the earth from geostationary orbit compared to the current-generation GOES, with more than 3 times as many spectral bands and a 75% reduction in footprint size. These enhanced capabilities are beneficial to rainfall rate estimation since they provide sensitivity to cloud-top properties such as phase and particle size that cannot be achieved using the limited channel selection of current GOES. The GOES-R rainfall rate algorithm, which is an infrared-based algorithm calibrated in real time against passive microwave rain rates, has been previously described in an algorithm theoretical basis document (ATBD); this paper describes modifications since the release of the ATBD, including a correction for evaporation of precipitation in dry regions and improved calibration updates. These improvements have been evaluated using a simplified version applicable to current-generation GOES to take advantage of the high-resolution ground validation data routinely available over the conterminous United States. Correcting for subcloud evaporation using relative humidity from a numerical model reduced false alarm rainfall by half and reduced the overall error by 35% for hourly accumulations validated against the National Centers for Environmental Prediction stage IV radar-gauge field; however, the number of missed events did increase slightly. Reducing the size of the calibration regions and increasing the training data requirements improved the consistency of the retrieved rates in time and space and reduced the overall error by an additional 4%. [ABSTRACT FROM AUTHOR]

The use of irrigation scheduling tools to produce cotton under-surface irrigation in the arid southwestern USA is minimal. In the State of Arizona, where traditional irrigation scheduling is the norm, producers use an average of 1460 mm annually to grow a cotton crop. The purpose of this paper was to determine whether or not the use of ET-based irrigation scheduling methods could improve lint yield and irrigation water use productivity over traditional cotton border irrigation scheduling practices in the region. A field study with four irrigation scheduling treatments replicated in 4 blocks was conducted for two cotton seasons (2009 and 2011) in 16, 12-m × 168-m cotton borders at the Maricopa Agricultural Center (MAC), in Arizona, USA. Remotely-sensed vegetation indices (VI) were used to estimate basal crop coefficients (Kcb) at 40, 4-m × 8-m zones within borders for two treatments, denoted as VI_A and VI_B, whereas a single Kcb curve was applied to all zones in borders for a third treatment (FAO). Daily ETc for these three treatments was estimated using FAO-56 dual crop coefficient procedures with local weather data and irrigation scheduling for the three treatments were based on soil water balance predictions of soil water depletion (SWD). For the VI_A and FAO treatments, irrigations were given when predicted SWD of all 160 zones in the treatment averaged 45% of total available water (TAW). For the VI_B treatment, irrigations were given when 5% of the 160 zones in the treatment were predicted to be at 65% SWD. A fourth treatment (MAC) represented the traditional irrigation scheduling treatment and was scheduled solely by the MAC farm irrigation manager using only experience as a guide. The study showed that the lint yields attained under the MAC farm manager’s irrigation scheduling equaled or exceeded the yields for the three ET-based irrigation scheduling treatments. Although the MAC irrigation scheduling resulted in somewhat higher irrigation input than for the other treatments, the MAC treatment maintained or exceeded the irrigation water productivity attained for other treatments that had lower irrigation inputs. A major conclusion of the study was that present-day irrigation water use for cotton in surface-irrigated fields could be substantially reduced. When compared to Arizona state cotton averages, any of the four treatments presented in the study could potentially offer methods to significantly reduce cotton irrigation water use while maintaining or increasing current lint yields levels. [ABSTRACT FROM AUTHOR]

This paper investigates the angular effects causing spectral distortions in multi-angle remote sensing imagery. We study two WorldView-2 multispectral in-track sequences acquired over the cities of Atlanta, USA, and Rio de Janeiro, Brazil, consisting of 13 and 20 co-located images, respectively. The sequences possess off-nadir acquisition angles up to 47.5° and bear markedly different sun-satellite configurations with respect to each other. Both scenes comprise classic urban structures such as buildings of different size, road networks, and parks. First, we quantify the degree of distortion affecting the sequences by means of a non-linear measure of distance between probability distributions, the Maximum Mean Discrepancy. Second, we assess the ability of a classification model trained on an image acquired at a certain view angle to predict the land-cover of all the other images in the sequence. The portability across the sequence is investigated for supervised classifiers of different nature by analyzing the evolution of the classification accuracy with respect to the off-nadir look angle. For both datasets, the effectiveness of physically- and statistically-based normalization methods in obtaining angle-invariant data spaces is compared and synergies are discussed. The empirical results indicate that, after a suitable normalization (histogram matching, atmospheric compensation), the loss in classification accuracy when using a model trained on the near-nadir image to classify the most off-nadir acquisitions can be reduced to as little as 0.06 (Atlanta) or 0.03 (Rio de Janeiro) Kappa points when using a SVM classifier. [ABSTRACT FROM AUTHOR]

Wildland fires produce smoke plumes that impact air quality and human health. To understand the effects of wildland fire smoke on humans, the amount and composition of the smoke plume must be quantified. Using a fire emissions inventory is one way to determine the emissions rate and composition of smoke plumes from individual fires. There are multiple fire emissions inventories, and each uses a different method to estimate emissions. This paper presents a comparison of four emissions inventories and their products: Fire INventory from NCAR (FINN version 1.5), Global Fire Emissions Database (GFED version 4s), Missoula Fire Labs Emissions Inventory (MFLEI (250 m) and MFLEI (10 km) products), and Wildland Fire Emissions Inventory System (WFEIS (MODIS) and WFEIS (MTBS) products). The outputs from these inventories are compared directly. Because there are no validation datasets for fire emissions, the outlying points from the Bayesian models developed for each inventory were compared with visible images and fire radiative power (FRP) data from satellite remote sensing. This comparison provides a framework to check fire emissions inventory data against additional data by providing a set of days to investigate closely. Results indicate that FINN and GFED likely underestimate emissions, while the MFLEI products likely overestimate emissions. No fire emissions inventory matched the temporal distribution of emissions from an external FRP dataset. A discussion of the differences impacting the emissions estimates from the four fire emissions inventories is provided, including a qualitative comparison of the methods and inputs used by each inventory and the associated strengths and limitations. [ABSTRACT FROM AUTHOR]

The objective of this paper is to investigate long-term inter-annual, seasonal, and diurnal rainfall characteristics in the Southeastern United States. In order to capture precipitation features at high resolution, we use precipitation estimates from the Tropical Rainfall Measuring Mission (TRMM); the TRMM Precipitation Radar (TPR 2A25: 0.05°×0.05°/daily) and the TRMM Multi-satellite Precipitation Analysis (TMPA 3B42: 0.25°×0.25°/3-h) datasets to create a 13-year rainfall climatology. The higher resolution climatology (2A25) displays a greater ability to capture more localized landform precipitation features when compared with 3B42. On an annual basis, the Southeastern US is characterized by a succession of cold and warm precipitation regimes. The cold season is characterized by higher rain intensity West of 82°W (roughly Atlanta, GA) and the warm season is characterized by higher rain intensity over the coastal areas. The cold/warm rainfall regime duality is modulated by local topographic characteristics that prevail along a W–E direction. During the cold season, the diurnal cycle of precipitation is characterized by a quasi-constant repartition of rain events throughout the day and an absence of land/ocean contrast. On the contrary for summertime there is a strong land/ocean signature with a predominance of late morning/early afternoon (12:00–15:00LST) rainfall over ocean and afternoon/early evening (15:00–18:00LST) precipitation events over land that account for more than 25% of the daily events along the coasts. Differences are observed for the Florida peninsula, where the diurnal cycle displays an afternoon maximum of variable intensity due to sea breeze effects regardless of the season. [ABSTRACT FROM AUTHOR]

This paper proposes an automated active fire detection framework using Sentinel-2 imagery. The framework is made up of three basic parts including data collection and preprocessing, deep-learning-based active fire detection, and final product generation modules. The active fire detection module is developed on a specifically designed dual-domain channel-position attention (DCPA)+HRNetV2 model and a dataset with semi-manually annotated active fire samples is constructed over wildfires that commenced on the east coast of Australia and the west coast of the United States in 2019–2020 for the training process. This dataset can be used as a benchmark for other deep-learning-based algorithms to improve active fire detection accuracy. The performance of active fire detection is evaluated regarding the detection accuracy of deep-learning-based models and the processing efficiency of the whole framework. Results indicate that the DCPA and HRNetV2 combination surpasses DeepLabV3 and HRNetV2 models for active fire detection. In addition, the automated framework can deliver active fire detection results of Sentinel-2 inputs with coverage of about 12,000 km2 (including data download) in less than 6 min, where average intersections over union (IoUs) of 70.4% and 71.9% were achieved in tests over Australia and the United States, respectively. Concepts in this framework can be further applied to other remote sensing sensors with data acquisitions in SWIR-NIR-Red ranges and can serve as a powerful tool to deal with large volumes of high-resolution data used in future fire monitoring systems and as a cost-efficient resource in support of governments and fire service agencies that need timely, optimized firefighting plans. [ABSTRACT FROM AUTHOR]

Phytoplankton blooms have caused many serious public safety incidents and eco-environmental problems worldwide and became a focus issue for research. Accurate and rapid monitoring of phytoplankton blooms is critical for forecasting, treating, and management. With the advantages of large spatial coverage and high temporal resolution, remote sensing has been widely used to monitor phytoplankton blooms. Numerous advances have been made in the remote sensing of phytoplankton blooms, biomass, and phenology over the past several decades. To fully understand the development history, research hotspots, and future trends of remote-sensing technology in the study of phytoplankton blooms, we conducted a comprehensive review to systematically analyze the research trends in the remote sensing of phytoplankton blooms through bibliometrics. Our findings showed that research on the use of remote-sensing technology in this field increased substantially in the past 30 years. "Oceanography," "Environmental Sciences," and "Remote Sensing" are the most popular subject categories. Remote Sensing of Environment, Journal of Geophysical Research: Oceans, and International Journal of Remote Sensing were the journals with the most published articles. The results of the analysis of international influence and cooperation showed that the United States had the greatest influence in this field and that the cooperation between China and the United States was the closest. The Chinese Academy of Sciences published the largest number of papers, reaching 542 articles. Keyword and topic analysis results showed that "phytoplankton," "chlorophyll," and "ocean" were the most frequently occurring keywords, while "eutrophication management and monitoring," "climate change," "lakes," and "remote-sensing algorithms" were the most popular research topics in recent years. Researchers are now paying increasing attention to the phenological response of phytoplankton under the conditions of climate change and the application of new remote-sensing methods. With the development of new remote-sensing technology and the expansion of phytoplankton research, future research should focus on (1) accurate observation of phytoplankton blooms; (2) the traits of phytoplankton blooms; and (3) the drivers, early warning, and management of phytoplankton blooms. In addition, we discuss the future challenges and opportunities in the use of remote sensing in phytoplankton blooms. Our review will promote a deeper and wider understanding of the field. [ABSTRACT FROM AUTHOR]

Given the recent advances in remote sensing analytics, cloud computing, and machine learning, it is imperative to evaluate capabilities of remote sensing for water quality monitoring in the context of water resources management and decision-making. The objectives of this review were to analyze recent advances in water quality remote sensing and determine limitations of current systems, estimation methods, and suggest future improvements. To that end, we collected over 200 sets of water quality data including blue-green algae phycocyanin (BGA-PC), chlorophyll-a (Chl-a), dissolved oxygen (DO), specific conductivity (SC), fluorescent dissolved organic matter (fDOM), turbidity, and pollution-sediments from 2016 to 2018. The water quality data, generated from laboratory analysis of grab samples and in-situ real-time monitoring sensors distributed in eight lakes and rivers in Midwestern United States, were paired with synchronous proximal spectra, tripod-mounted hyperspectral imagery, and satellite data. The results showed that both proximal and satellite-based sensors have great potential to provide accurate estimate of optically active parameters, and remote sensing of non-optically active parameters may be indirectly estimated but still remains a challenge. Data-driven empirical approaches, i.e., deep learning outperformed the other competing methods, providing promising possibility for operational use of remote sensing in water quality monitoring and decision-making. As the first-time review of deep neural networks for water quality estimation, the paper concludes that anomaly detection utilizing multi-sensor data fusion and virtual constellation in cloud-computing is the most promising means for predicting impending water pollution outbreaks such as algal blooms. [ABSTRACT FROM AUTHOR]

Forest ecosystems provide critical ecosystem goods and services, and any disturbance-induced changes can have cascading impacts on natural processes and human socioeconomic systems. Forest disturbance frequency, intensity, and spatial and temporal scale can be altered by changes in climate and human activity, but without baseline forest disturbance data, it is impossible to quantify the magnitude and extent of these changes. Methodologies for quantifying forest cover change have been developed at the regional-to-global scale via several approaches that utilize data from high (e.g., IKONOS, Quickbird), moderate (e.g., Landsat) and coarse (e.g., Moderate Resolution Imaging Spectroradiometer (MODIS)) spatial resolution satellite imagery. While detection and quantification of forest cover change is an important first step, attribution of disturbance type is critical missing information for establishing baseline data and effective land management policy. The objective here was to prototype and test a semi-automated methodology for characterizing high-magnitude (>50% forest cover loss) forest disturbance agents (stress, fire, stem removal) across the conterminous United States (CONUS) from 2003–2011 using the existing University of Maryland Landsat-based Global Forest Change Product and Web-Enabled Landsat Data (WELD). The Forest Cover Change maps were segmented into objects based on temporal and spatial adjacency, and object-level spectral metrics were calculated based on WELD reflectance time series. A training set of objects with known disturbance type was developed via high-resolution imagery and expert interpretation, ingested into a Random Forest classifier, which was then used to attribute disturbance type to all 15,179,430 forest loss objects across CONUS. Accuracy assessments of the resulting classification was conducted with an independent dataset consisting of 4156 forest loss objects. Overall accuracy was 88.1%, with the highest omission and commission errors observed for fire (32.8%) and stress (31.9%) disturbances, respectively. Of the total 172,686 km2 of forest loss, 83.75% was attributed to stem removal, 10.92% to fire and 5.33% to stress. The semi-automated approach described in this paper provides a promising framework for the systematic characterization and monitoring of forest disturbance regimes. [ABSTRACT FROM AUTHOR]