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USGS1980

The influences of land use and land cover on climate; an analysis of the Washington-Baltimore area that couples remote sensing with numerical simulation

The Sun drives the atmospheric heat engine by warming the terrestrial surface which in turn warms the atmosphere above. Climate, therefore, is significantly controlled by complex interaction of energy flows near and at the terrestrial surface. When man alters this delicate energy balance by his use of the land, he may alter his climatic environment as well. Land use climatology has emerged as a discipline in which these energy interactions are studied; first, by viewing the spatial distributions of their surface manifestations, and second, by analyzing the energy exchange processes involved. Two new tools for accomplishing this study are presented: one that can interpret surface energy exchange processes from space, and another that can simulate the complex of energy transfers by a numerical simulation model. Use of a satellite-borne multispectral scanner as an imaging radiometer was made feasible by devising a gray-window model that corrects measurements made in space for the effects of the atmosphere in the optical path. The simulation model is a combination of mathematical models of energy transfer processes at or near the surface. Integration of these two analytical approaches was applied to the Washington-Baltimore area to coincide with the August 5, 1973, Skylab 3 overpass which provided data for constructing maps of the energy characteristics of the Earth's surface. The use of the two techniques provides insights into the relationship of climate to land use and land cover and in predicting alterations of climate that may result from alterations of the land surface.

Professional Paper↗

USGS2020

Detecting cover crop end-of-season using VENµS and sentinel-2 satellite imagery

Cover crops are planted during the off-season to protect the soil and improve watershed management. The ability to map cover crop termination dates over agricultural landscapes is essential for quantifying conservation practice implementation, and enabling estimation of biomass accumulation during the active cover period. Remote sensing detection of end-of-season (termination) for cover crops has been limited by the lack of high spatial and temporal resolution observations and methods. In this paper, a new within-season termination (WIST) algorithm was developed to map cover crop termination dates using the Vegetation and Environment monitoring New Micro Satellite (VENµS) imagery (5 m, 2 days revisit). The WIST algorithm first detects the downward trend (senescent period) in the Normalized Difference Vegetation Index (NDVI) time-series and then refines the estimate to the two dates with the most rapid rate of decrease in NDVI during the senescent period. The WIST algorithm was assessed using farm operation records for experimental fields at the Beltsville Agricultural Research Center (BARC). The crop termination dates extracted from VENµS and Sentinel-2 time-series in 2019 and 2020 were compared to the recorded termination operation dates. The results show that the termination dates detected from the VENµS time-series (aggregated to 10 m) agree with the recorded harvest dates with a mean absolute difference of 2 days and uncertainty of 4 days. The operational Sentinel-2 time-series (10 m, 4–5 days revisit) also detected termination dates at BARC but had 7% missing and 10% false detections due to less frequent temporal observations. Near-real-time simulation using the VENµS time-series shows that the average lag times of termination detection are about 4 days for VENµS and 8 days for Sentinel-2, not including satellite data latency. The study demonstrates the potential for operational mapping of cover crop termination using high temporal and spatial resolution remote sensing data.

Remote Sensing↗

USGS2011

A working environment for digital planetary data processing and mapping using ISIS and GRASS GIS

Since the beginning of planetary exploration, mapping has been fundamental to summarize observations returned by scientific missions. Sensor-based mapping has been used to highlight specific features from the planetary surfaces by means of processing. Interpretative mapping makes use of instrumental observations to produce thematic maps that summarize observations of actual data into a specific theme. Geologic maps, for example, are thematic interpretative maps that focus on the representation of materials and processes and their relative timing. The advancements in technology of the last 30 years have allowed us to develop specialized systems where the mapping process can be made entirely in the digital domain. The spread of networked computers on a global scale allowed the rapid propagation of software and digital data such that every researcher can now access digital mapping facilities on his desktop. The efforts to maintain planetary missions data accessible to the scientific community have led to the creation of standardized digital archives that facilitate the access to different datasets by software capable of processing these data from the raw level to the map projected one. Geographic Information Systems (GIS) have been developed to optimize the storage, the analysis, and the retrieval of spatially referenced Earth based environmental geodata; since the last decade these computer programs have become popular among the planetary science community, and recent mission data start to be distributed in formats compatible with these systems. Among all the systems developed for the analysis of planetary and spatially referenced data, we have created a working environment combining two software suites that have similar characteristics in their modular design, their development history, their policy of distribution and their support system. The first, the Integrated Software for Imagers and Spectrometers (ISIS) developed by the United States Geological Survey, represents the state of the art for processing planetary remote sensing data, from the raw unprocessed state to the map projected product. The second, the Geographic Resources Analysis Support System (GRASS) is a Geographic Information System developed by an international team of developers, and one of the core projects promoted by the Open Source Geospatial Foundation (OSGeo). We have worked on enabling the combined use of these software systems throughout the set-up of a common user interface, the unification of the cartographic reference system nomenclature and the minimization of data conversion. Both software packages are distributed with free open source licenses, as well as the source code, scripts and configuration files hereafter presented. In this paper we describe our work done to merge these working environments into a common one, where the user benefits from functionalities of both systems without the need to switch or transfer data from one software suite to the other one. Thereafter we provide an example of its usage in the handling of planetary data and the crafting of a digital geologic map. ?? 2010 Elsevier Ltd. All rights reserved.

Conference Paper↗

USGS2011

LiDAR: Providing structure

Since the days of MacArthur, three-dimensional (3-D) structural information on the environment has fundamentally transformed scientific understanding of ecological phenomena (MacArthur and MacArthur 1961). Early data on ecosystem structure were painstakingly laborious to collect. However, as reviewed and reported in recent volumes of Frontiers(eg Vierling et al. 2008; Asner et al.2011), advances in light detection and ranging (LiDAR) remote-sensing technology provide quantitative and repeatable measurements of 3-D ecosystem structure that enable novel ecological insights at scales ranging from the plot, to the landscape, to the globe. Indeed, annual publication of studies using LiDAR to interpret ecological phenomena increased 17-fold during the past decade, with over 180 new studies appearing in 2010 (ISI Web of Science search conducted on 23 Mar 2011: [{lidar AND ecol*} OR {lidar AND fores*} OR {lidar AND plant*}]).

Frontiers in Ecology and the Environment↗

USGS2001

Geologic studies in Alaska by the U.S. Geological Survey, 1999

The collection of nine papers that follow continue the series of U.S. Geological Survey (USGS) investigative reports in Alaska under the broad umbrella of the geologic sciences. The series presents new and sometimes preliminary findings that are of interest to earth scientists in academia, government, and industry; to land and resource managers; and to the general public. Reports presented in Geologic Studies in Alaska cover a broad spectrum of topics from various parts of the State (fig. 1), serving to emphasize the diversity of USGS efforts to meet the Nation's needs for earth-science information in Alaska. The papers in this volume are organized under the topics: Hazards, Geologic Framework, Environment and Climate, and Resources. This organization is intended to reflect the scope and objectives of USGS geologic programs currently active in Alaska. The two Hazards studies discuss volcano-related topics in the seismically active southcentral Alaska region. The first paper revisits the eruptive events of Redoubt Volcano that occurred more than a decade ago and the subsequent development of the Alaska Volcano Observatory (AVO). This treatise documents the historic impact of this eruption and briefly summarizes the state of our knowledge of the other Cook Inlet, Alaska Peninsula, and Aleutian Island volcanoes. Finally, it discusses the recent role that AVO has had in seismic station installation and hazard assessment at volcanically active sites throughout the world. The second paper discusses the eruptive history of Snowy Mountain in the upper Alaska Peninsula. Because subsets of its 25-30 lava flows erupted as packages in short episodes, calculation of the volcano's lifetime average volumetric eruption rate is problematic. A portion of the cone was hydrothermally weakened and collapsed in the late Holocene producing a 22-km2 debris avalanche. Geologic Framework studies provide background information that is the scientific basis for present and future earth science investigations. The first paper compares and contrasts the Insular-Intermontane suture zone (IISZ) of southeast Alaska with the Adria-Europe suture zone (AESZ) of Switzerland and Hungary. The study develops the hypothesis that the zones have distinct differences as well as similarities and neither is a simple lithotectonic terrane boundary. The second paper discusses the relation among volcanic, glacial, and tectonic activity in the Cold Bay and False Pass 1 :250,000-scale quadrangles on the Alaska Peninsula. During Pleistocene time, continental-shelf glaciations and two massive volcanic centers were the dominant controls over landscape development. The third paper gives detailed geologic information for Paleozoic rocks within the Taylor Mountains D-1 quadrangle portion of the Holitna Lowland of southwestern Alaska. Because of the excellent preservation of megafossils, these Silurian and Ordovician strata lend themselves to detailed statigraphic investigations. Further, low thermal alteration indices of this area have made them a potential target of petroleum exploration. The final report in this section discusses the development of a new spectral enhancement approach for interpreting Multispectral Scanner (MSS) and Thematic Mapper (TM) satellite images. This technique enhances the use of remote sensing data in identifying geologic units in areas that have been poorly investigated. This study used this technique to better define the distribution of a JMtu (mafic, ultramafic, and sedimentary) unit and a PzZrqs (pelitic and quartzitic schist) unit. Environment and climate studies are the emphasis of two papers. One presents the first radiocarbon-dated postglacial vegetation history of the Kenai Mountains of southcentral Alaska. This reconstruction is the result of the analysis of pollen assemblages and peat from sediments collected in Tern Lake and presents a minimum age for deglaciation of these interior valleys at 9,31 0±200 yr B .P. Current vegetation, however, developed within the past ca. 2,500 years. A second study discusses the cycling of arsenic and cadmium in sub-arctic boreal forest ecosystems typical of interior Alaska and defines the importance of various natural (geogenic) sources. The transport and uptake into vegetation of these elements from soils developed from loess as well as soils developed from the major rock units is presented. The bioaccumulation of cadmium in willow (Salix sp.) and its potential consequence to the health of browsing animals is discussed. Papers related to resource issues comprise the topic of the final report. This paper presents a brief statistical summary of the geochemistry of rock samples collected in the east-central portion of the Eagle 1 :250,000-scale quadrangle. This study helps define the rock unit source of both resource- and environmental-based chemical elements of interest in the Fortymile mining district. Two bibliographies at the end of the volume list reports covering Alaska earth science topics in USGS publications during 1999 and reports about Alaska by USGS authors in non-USGS publications during the same period.

Alaska↗

USGS2025

Comparisons of shoreline positions from satellite-derived and traditional field- and remote-sensing techniques

Satellite-derived shorelines (SDS) have the potential to help researchers answer critical coastal science questions and support work to predict coastal change by filling in the spatial and temporal gaps present in current field-based and remote-sensing data collection methods. The U.S. Geological Survey conducted comparison analyses of traditionally sourced shorelines and SDS in diverse coastal landscapes to determine how SDS could be used in ongoing and future work across varied coastal environments and provided some initial findings that could be used for implementation. Using CoastSeg, a browser-based program for SDS detection and mapping, SDS for the period 1984–2023 for multiple locations across the United States were compared to shoreline positions from traditionally sourced shoreline data. In this report, the authors present these comparisons alongside lessons learned and challenges encountered when building SDS workflows in different coastal locations. Results show that individual SDS have larger uncertainty and yet produced similar linear trends to sparser, traditionally sourced shoreline data; because SDS methods provide orders of magnitude more data than traditional shoreline-detection methods, they can be used to evaluate shoreline behaviors. Refining average scalar slopes used in tidal corrections did not result in substantial decreases in uncertainty. Using lessons from this work to outline needs for regional implementation, initial setup time would be considerable, being on the order of weeks. However, once complete, shoreline detections and analyses are fast (on the order of minutes to hours) and achievable using a desktop computer.

Alaska, Florida, Massachusetts, Washington↗

USGS2022

Constructing a large-scale landslide database across heterogeneous environments using task-specific model updates

Preparation and mitigation efforts for widespread landslide hazards can be aided by a large-scale, well-labeled landslide inventory with high location accuracy. Recent smallscale studies for pixel-wise labeling of potential landslide areas in remotely-sensed images using deep learning (DL) showed potential but were based on data from very small, homogeneous regions with unproven model transferability. In this paper we consider a more realistic and practical setting for large-scale heterogeneous landslide data collection and DL-based labeling. In this setting, remotely sensed images are collected sequentially in temporal batches, where each batch focuses on images from a particular ecoregion, but different batches can focus on different ecoregions with distinct landscape characteristics. For such a scenario, we study the following questions: (1) How well do DL models trained in homogeneous regions perform when they are transferred to different ecoregions, (2) Does increasing the spatial coverage in the data improve model performance in a given ecoregion (even when the extra data do not come from the ecoregion), and (3) Can a landslide pixel labeling model be incrementally updated with new data, but without access to the old data and without losing performance on the old data (so that researchers can share models obtained from proprietary datasets)' We address these questions by extending the Learning without Forgetting framework, which is used for incremental training of image classification models, to the setting of incremental training of semantic segmentation models (e.g., identifying all landslide pixels in an image). We call the resulting extension Task-Specific Model Updates (TSMU). TSMU semantic segmentation framework consists of an encoder shared by all ecoregions to capture the similarities between them, and ecoregion-specific decoders to capture the nuances of each ecoregion. This framework is continually updated using a threestage training procedure for each new addition of an ecoregion without having to revisit data from old ecoregions and without losing performance on them.

IEEE Journal of Selected Topics in Applied Earth O↗

USGS2023

High resolution lidar data shed light on inter-island translocation of endangered bird species in the Hawaiian Islands

Translocation, often a management solution reserved for at-risk species, is a highly time-sensitive intervention in the face of a rapidly changing climate. The definition of abiotic and biotic habitat requirements is essential to the selection of appropriate release sites in novel environments. However, field-based approaches to gathering this information are often too time intensive, especially in areas of complex topography where common, coarse-scale climate models lack essential details. We apply a fine-scale remote sensing-based approach to study the 'akikiki ( Oreomystis bairdi ) and 'akeke'e ( Loxops caeruleirostris ), Hawaiian honeycreepers endemic to Kaua'i that are experiencing large-scale population declines due to warming-induced spread of invasive disease. We use habitat suitability modeling based on fine-scale light detection and ranging (lidar)-derived habitat structure metrics to refine coarse climate ranges for these species in candidate translocation areas on Maui. We found that canopy density was consistently the most important variable in defining habitat suitability for the two Kaua'i species. Our models also corroborated known habitat preferences and behavioral information for these species that are essential for informing translocation. We estimated a nesting habitat that will persist under future climate conditions on east Maui of 23.43 km 2 for 'akikiki, compared to the current Kaua'i range of 13.09 km 2 . In contrast, the novel nesting range for 'akeke'e in east Maui was smaller than its current range on Kaua'i (26.29 vs. 38.48 km 2 , respectively). We were also able to assess detailed novel competitive interactions at a fine scale using models of three endemic Maui species of conservation concern: 'ākohekohe ( Palmeria dolei ), Maui 'alauahio ( Paroreomyza montana ), and kiwikiu ( Pseudonestor xanthophrys ). Weighted overlap areas between the species from both islands were moderate (<12 km 2 ), and correlations between Maui and Kaua'i bird habitat were generally low, indicating limited potential for competition. Results indicate that translocation to east Maui could be a viable option for 'akikiki but would be more uncertain for 'akeke'e. Our novel multifaceted approach allows for the timely analysis of both climate and vegetation structure at informative scales for the effective selection of appropriate translocation sites for at-risk species.

Hawaii↗

USGS1978

A selected bibliography: Remote sensing applications for tropical and subtropical vegetation analysis

This bibliography contains 425 citations of selected technical reports, journal articles, and other publications covering the general subject of tropical and subtropical vegetation analysis. Functionally related topics that include vegetation analysis are included for completeness, and citations have been organized under the following subheadings for ease of reference: remote sensing application overviews, vegetation (general), forestry, grasslands/savannah/shrublands, agriculture, land use/thematic mapping, and integrated surveys/multiple resource analysis/land systems. The terms "tropics and subtropics" are used in the widest context to include applications related to a broad range of equatorial environments. The bibliography contains selected citations published between 1924 and 1978. Many foreign language and non U.S.- source items are included.

Report↗

USGS2000

Techniques for identifying dust devils in mars pathfinder images

Image processing methods used to identify and enhance dust devil features imaged by IMP (Imager for Mars Pathfinder) are reviewed. Spectral differences, visible red minus visible blue, were used for initial dust devil searches, driven by the observation that Martian dust has high red and low blue reflectance. The Martian sky proved to be more heavily dust-laden than pre-Pathfinder predictions, based on analysis of images from the Hubble Space Telescope. As a result, these initial spectral difference methods failed to contrast dust devils with background dust haze. Imager artifacts (dust motes on the camera lens, flat-field effects caused by imperfections in the CCD, and projection onto a flat sensor plane by a convex lens) further impeded the ability to resolve subtle dust devil features. Consequently, reference images containing sky with a minimal horizon were first subtracted from each spectral filter image to remove camera artifacts and reduce the background dust haze signal. Once the sky-flat preprocessing step was completed, the red-minus-blue spectral difference scheme was attempted again. Dust devils then were successfully identified as bright plumes. False-color ratios using calibrated IMP images were found useful for visualizing dust plumes, verifying initial discoveries as vortex-like features. Enhancement of monochromatic (especially blue filter) images revealed dust devils as silhouettes against brighter background sky. Experiments with principal components transformation identified dust devils in raw, uncalibrated IMP images and further showed relative movement of dust devils across the Martian surface. A variety of methods therefore served qualitative and quantitative goals for dust plume identification and analysis in an environment where such features are obscure.

IEEE Transactions on Geoscience and Remote Sensing↗

USGS2012

Crop classification modelling using remote sensing and environmental data in the Greater Platte River Basin, USA

With an ever expanding population, potential climate variability and an increasing demand for agriculture-based alternative fuels, accurate agricultural land-cover classification for specific crops and their spatial distributions are becoming critical to researchers, policymakers, land managers and farmers. It is important to ensure the sustainability of these and other land uses and to quantify the net impacts that certain management practices have on the environment. Although other quality crop classification products are often available, temporal and spatial coverage gaps can create complications for certain regional or time-specific applications. Our goal was to develop a model capable of classifying major crops in the Greater Platte River Basin (GPRB) for the post-2000 era to supplement existing crop classification products. This study identifies annual spatial distributions and area totals of corn, soybeans, wheat and other crops across the GPRB from 2000 to 2009. We developed a regression tree classification model based on 2.5 million training data points derived from the National Agricultural Statistics Service (NASS) Cropland Data Layer (CDL) in relation to a variety of other relevant input environmental variables. The primary input variables included the weekly 250 m US Geological Survey Earth Observing System Moderate Resolution Imaging Spectroradiometer normalized differential vegetation index, average long-term growing season temperature, average long-term growing season precipitation and yearly start of growing season. An overall model accuracy rating of 78% was achieved for a test sample of roughly 215 000 independent points that were withheld from model training. Ten 250 m resolution annual crop classification maps were produced and evaluated for the GPRB region, one for each year from 2000 to 2009. In addition to the model accuracy assessment, our validation focused on spatial distribution and county-level crop area totals in comparison with the NASS CDL and county statistics from the US Department of Agriculture (USDA) Census of Agriculture. The results showed that our model produced crop classification maps that closely resembled the spatial distribution trends observed in the NASS CDL and exhibited a close linear agreement with county-by-county crop area totals from USDA census data ( R 2 = 0.90).

Greater Platte River Basin↗

USGS2017

Camera system considerations for geomorphic applications of SfM photogrammetry

The availability of high-resolution, multi-temporal, remotely sensed topographic data is revolutionizing geomorphic analysis. Three-dimensional topographic point measurements acquired from structure-from-motion (SfM) photogrammetry have been shown to be highly accurate and cost-effective compared to laser-based alternatives in some environments. Use of consumer-grade digital cameras to generate terrain models and derivatives is becoming prevalent within the geomorphic community despite the details of these instruments being largely overlooked in current SfM literature. This article is protected by copyright. All rights reserved. A practical discussion of camera system selection, configuration, and image acquisition is presented. The hypothesis that optimizing source imagery can increase digital terrain model (DTM) accuracy is tested by evaluating accuracies of four SfM datasets conducted over multiple years of a gravel bed river floodplain using independent ground check points with the purpose of comparing morphological sediment budgets computed from SfM- and lidar-derived DTMs. Case study results are compared to existing SfM validation studies in an attempt to deconstruct the principle components of an SfM error budget. This article is protected by copyright. All rights reserved. Greater information capacity of source imagery was found to increase pixel matching quality, which produced 8 times greater point density and 6 times greater accuracy. When propagated through volumetric change analysis, individual DTM accuracy (6–37 cm) was sufficient to detect moderate geomorphic change (order 100,000 m 3 ) on an unvegetated fluvial surface; change detection determined from repeat lidar and SfM surveys differed by about 10%. Simple camera selection criteria increased accuracy by 64%; configuration settings or image post-processing techniques increased point density by 5–25% and decreased processing time by 10–30%. This article is protected by copyright. All rights reserved. Regression analysis of 67 reviewed datasets revealed that the best explanatory variable to predict accuracy of SfM data is photographic scale. Despite the prevalent use of object distance ratios to describe scale, nominal ground sample distance is shown to be a superior metric, explaining 68% of the variability in mean absolute vertical error.

Earth Surface Processes and Landforms↗

USGS2013

CEOS visualization environment (COVE) tool for intercalibration of satellite instruments

Increasingly, data from multiple instruments are used to gain a more complete understanding of land surface processes at a variety of scales. Intercalibration, comparison, and coordination of satellite instrument coverage areas is a critical effort of international and domestic space agencies and organizations. The Committee on Earth Observation Satellites Visualization Environment (COVE) is a suite of browser-based applications that leverage Google Earth to display past, present, and future satellite instrument coverage areas and coincident calibration opportunities. This forecasting and ground coverage analysis and visualization capability greatly benefits the remote sensing calibration community in preparation for multisatellite ground calibration campaigns or individual satellite calibration studies. COVE has been developed for use by a broad international community to improve the efficiency and efficacy of such calibration planning efforts, whether those efforts require past, present, or future predictions. This paper provides a brief overview of the COVE tool, its validation, accuracies, and limitations with emphasis on the applicability of this visualization tool for supporting ground field campaigns and intercalibration of satellite instruments.

IEEE Transactions on Geoscience and Remote Sensing↗

USGS2012

State of the Earth’s cryosphere at the beginning of the 21st century: Glaciers, global snow cover, floating ice, and permafrost and periglacial environments

This chapter is the tenth in a series of 11 book-length chapters, collectively referred to as “this volume,” in the series U.S. Geological Survey Professional Paper 1386, Satellite Image Atlas of Glaciers of the World. In the other 10 chapters, each of which concerns a specific glacierized region of Earth, the authors used remotely sensed images, primarily from the Landsat 1, 2, and 3 series of spacecraft, in order to analyze that glacierized region and to monitor changes in its glaciers. Landsat images, acquired primarily during the period 1972 through 1981, were used by an international team of glaciologists and other scientists to study the various glacierized regions and (or) to discuss related glaciological topics. In each glacierized region, the present distribution of glaciers within its geographic area is compared, wherever possible, with historical information about their past areal extent. The atlas provides an accurate regional inventory of the areal extent of glacier ice on our planet during the 1970s as part of an expanding international scientific effort to measure global environmental change on the Earth’s surface. However, this chapter differs from the other 10 in its discussion of observed changes in all four elements of the Earth’s cryosphere (glaciers, snow cover, floating ice, and permafrost) in the context of documented changes in all components of the Earth System. Human impact on the planet at the beginning of the 21st century is pervasive. The focus of Chapter A is on changes in the cryosphere and the importance of long-term monitoring by a variety of sensors carried on Earth-orbiting satellites or by a ground-based network of observatories in the case of permafrost. The chapter consists of five parts. The first part provides an introduction to the Earth System, including the interrelationships of the geosphere (cryosphere, hydrosphere, lithosphere, and atmosphere), the biosphere, climate processes, biogeochemical cycles, and the critically important hydrologic cycle, in which glacier ice is the second largest reservoir of water after the oceans. The second part assesses the state of glaciers in all of the glacierized regions of the planet, primarily as drawn in the other 10 chapters. It includes sections on ice cores and the climate record they contain, volumetric changes in glaciers, harnessing spaceborne sensors to measure changes in glaciers, and related topics. The third part summarizes trends in global snow cover. The fourth part summarizes long-term changes in area and thickness of floating ice, including polar sea ice and freshwater (lake and river) ice. The fifth part assesses the loss of permafrost and changes in periglacial environments at high latitudes and high altitudes.

Professional Paper↗

USGS2013

Mapping monkeypox transmission risk through time and space in the Congo Basin

Monkeypox is a major public health concern in the Congo Basin area, with changing patterns of human case occurrences reported in recent years. Whether this trend results from better surveillance and detection methods, reduced proportions of vaccinated vs. non-vaccinated human populations, or changing environmental conditions remains unclear. Our objective is to examine potential correlations between environment and transmission of monkeypox events in the Congo Basin. We created ecological niche models based on human cases reported in the Congo Basin by the World Health Organization at the end of the smallpox eradication campaign, in relation to remotely-sensed Normalized Difference Vegetation Index datasets from the same time period. These models predicted independent spatial subsets of monkeypox occurrences with high confidence; models were then projected onto parallel environmental datasets for the 2000s to create present-day monkeypox suitability maps. Recent trends in human monkeypox infection are associated with broad environmental changes across the Congo Basin. Our results demonstrate that ecological niche models provide useful tools for identification of areas suitable for transmission, even for poorly-known diseases like monkeypox.

PLoS ONE↗

USGS2023

A river basin spatial model to quantitively advance understanding of riverine tree response dynamics to water availability and hydrological management

Ecological condition continues to decline in arid and semi-arid river basins globally due to hydrological over-abstraction combined with changing climatic conditions. Whilst provision of water for the environment has been a primary approach to alleviate ecological decline, how to accurately monitor changes in riverine trees at fine spatial and temporal scales, remains a substantial challenge. This is further complicated by constantly changing water availability across expansive river basins with varying climatic zones. Within, we combine rare, fine-scale, high frequency temporal in-situ field collected data with machine learning and remote sensing, to provide a robust model that enables broadscale monitoring of physiological tree water stress response to environmental changes via actual evapotranspiration (ET). Physiological variation of Eucalyptus camaldulensis (River Red Gum) and E. largiflorens (Black Box) trees across 10 study locations in the southern Murray-Darling Basin, Australia, was captured instantaneously using sap flow sensors, substantially reducing tree response lags encountered by monitoring visual canopy changes. Actual ET measurement of both species was used to bias correct a national spatial ET product where a Random Forest model was trained using continuous timeseries of in-situ data of up to four years. Precise monthly AMLETT ( A ustralia-wide M achine L earning ET for T rees) ET outputs in 30 m pixel resolution from 2012 to 2021, were derived by incorporating additional remote sensing layers such as soil moisture, land surface temperature, radiation and EVI and NDVI in the Random Forest model. Landsat and Sentinal-2 correlation results between in-situ ET and AMLETT ET returned R 2 of 0.94 (RMSE 6.63 mm period −1 ) and 0.92 (RMSE 6.89 mm period −1 ), respectively. In comparison, correlation between in-situ ET and a national ET product returned R 2 of 0.44 (RMSE 34.08 mm period −1 ) highlighting the need for bias correction to generate accurate absolute ET values. The AMLETT method presented here, enhances environmental management in river basins worldwide. Such robust broadscale monitoring can inform water accounting and importantly, assist decisions on where to prioritize water for the environment to restore and protect key ecological assets and preserve floodplain and riparian ecological function.

The Journal of Environmental Management↗

USGS1997

Estimating millet production for famine early warning: An application of crop simulation modelling using satellite and ground-based data in Burkina Faso

Early warning of impending poor crop harvests in highly variable environments can allow policy makers the time they need to take appropriate action to ameliorate the effects of regional food shortages on vulnerable rural and urban populations. Crop production estimates for the current season can be obtained using crop simulation models and remotely sensed estimates of rainfall in real time, embedded in a geographic information system that allows simple analysis of simulation results. A prototype yield estimation system was developed for the thirty provinces of Burkina Faso. It is based on CERES-Millet, a crop simulation model of the growth and development of millet ( Pennisetum spp.). The prototype was used to estimate millet production in contrasting seasons and to derive production anomaly estimates for the 1986 season. Provincial yields simulated halfway through the growing season were generally within 15% of their final (end-of-season) values. Although more work is required to produce an operational early warning system of reasonable credibility, the methodology has considerable potential for providing timely estimates of regional production of the major food crops in countries of sub-Saharan Africa.

Agricultural and Forest Meteorology↗

USGS2012

Storm surge modeling and applications in coastal areas

This chapter introduces the reader to a wide spectrum of storm surge modeling systems used to assess the impact of tropical cyclones, covering a range of numerical methods, model domains, forcing and boundary conditions, and purposes. New technologies to obtain data such as deployment of temporary sensors and remote sensing practices to support modeling are also presented. Extensive storm surge modeling applications have been made with existing modeling systems and some of them are described in this chapter. The authors recognize the importance of evaluating river-ocean interactions in coastal environments during tropical cyclones. Therefore, the coupling of hydraulic (riverine) and storm surge models is discussed. In addition, results from studies performed in the coast of India are shown which generated maps to help emergency managers and reduce risk due to coastal inundation.

Book chapter↗