Use of 21st century satellite remote sensing technology in natural hazard analysis
No abstract available.
Natural Hazards↗
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A practical model and online tool to predict cover crop nitrogen content and carbon-to-nitrogen ratio via remote sensing
Purpose: Estimations of cover crop (CC), nitrogen (N) content and carbon-to-nitrogen (C:N) ratio are crucial for guiding N fertilizer recommendations for subsequent cash crops, yet prediction tools that can be easily adopted by practitioners are lacking. This study aims to (1) develop models to predict CC N content and C:N ratio using Normalized Difference Vegetation Index (NDVI) and Normalized Difference Red Edge Index (NDRE) from Uncrewed Aerial Vehicles (UAV) and satellite imagery, combined with growing degree days (GDD, base 5 °C) and CC species; and (2) integrate the models developed into an online application. Methods: Over 260 CC biomass samples were collected from 10 Pennsylvania counties in the Chesapeake Bay Watershed representing diverse species (triticale, cereal rye, wheat, brassicas, legumes, and mixtures). Four models were evaluated: (1) UAV NDVI + GDD + species, (2) UAV NDRE + GDD + species, (3) satellite NDVI + GDD + species, and (4) satellite NDRE + GDD + species. Results: Random forest models (R² > 0.74 for N content and R² > 0.86 for C:N ratio) generally outperformed generalized additive models (R² > 0.70 for N content and R² > 0.85 for C:N ratio) based on 10-fold cross validation. GDD and vegetation indices were the strongest predictors for N content, while GDD and species group influenced C:N ratio the most. In the independent site-years validation, UAV-NDRE performed best for predicting CC N content (R² = 0.82), and both UAV-based models (R² = 0.52) outperformed the satellite models for C:N ratio prediction. Conclusion: Random forest models accurately predicted CC N content and C:N ratio and were integrated into an online application to support precision agriculture practices.
Pennsylvania↗
Classifying the hydrologic function of prairie potholes with remote sensing and GIS
A sequence of Landsat TM/ETM+ scenes capturing the substantial surface water variations exhibited by prairie pothole wetlands over a drought to deluge period were analyzed in an attempt to determine the general hydrologic function of individual wetlands (recharge, flow-through, and discharge). Multipixel objects (water bodies) were clustered according to their temporal changes in water extents. We found that wetlands receiving groundwater discharge responded differently over the time period than wetlands that did not. Also, wetlands located within topographically closed discharge basins could be distinguished from discharge basins with overland outlets. Field verification data showed that discharge wetlands with closed basins were most distinct and identifiable with reasonable accuracies (user’s accuracy = 97%, producer’s accuracy = 71%). The classification of other hydrologic function types had lower accuracies reducing the overall accuracy for the four hydrologic function classes to 51%. A simplified classification approach identifying only two hydrologic function classes was 82%. Although this technique has limited success for detecting small wetlands, Landsat-derived multipixel-object clustering can reliably differentiate wetlands receiving groundwater discharge and provides a new approach to quantify wetland dynamics in landscape scale investigations and models.
Wetlands↗
IUGS/UNESCO geologic applications of remote sensing (GARS) program
[No abstract available]
Advances in Space Research↗
Infrared remote sensing for monitoring rainfall
Evaluations of thermal infrared satellite data from TIROS-N and the Heat Capacity Mapping Mission (HCMM) showed that rainfall distribution patterns could be reliably detected on images acquired up to at least three days after the event. The temperature relationship decreased eight days after the event when soil variations influenced the signal. A time-series analysis reduced thermal variability normally observed over diverse landscapes and increased the sensitivity of the procedures. The method of repetitive low-resolution thermal observations could be operationally employed over large geographic regions with currently available satellite systems. The results would augment the existing rain gauge stations by increasing the spatial sensitivity and the reliability of detection and mapping individual rainfall events.
Agricultural Water Management↗
Summary of the Second International Planetary Dunes Workshop: Planetary Analogs - Integrating Models, Remote Sensing, and Field Data, Alamosa, Colorado, USA, May 18-21, 2010
The Second International Planetary Dunes Workshop took place in Alamosa, Colorado, USA from May 18-21, 2010. The workshop brought together researchers from diverse backgrounds to foster discussion and collaboration regarding terrestrial and extra-terrestrial dunes and dune systems. Two and a half days were spent on five oral sessions and one poster session, a full-day field trip to Great Sand Dunes National Park, with a great deal of time purposefully left open for discussion. On the last day of the workshop, participants assembled a list of thirteen priorities for future research on planetary dune systems. ?? 2010.
Conference Paper↗
Summary of the Third International Planetary Dunes Workshop: remote sensing and image analysis of planetary dunes
The Third International Planetary Dunes Workshop took place in Flagstaff, AZ, USA during June 12–15, 2012. This meeting brought together a diverse group of researchers to discuss recent advances in terrestrial and planetary research on aeolian bedforms. The workshop included two and a half days of oral and poster presentations, as well as one formal (and one informal) full-day field trip. Similar to its predecessors, the presented work provided new insight on the morphology, dynamics, composition, and origin of aeolian bedforms on Venus, Earth, Mars, and Titan, with some intriguing speculation about potential aeolian processes on Triton (a satellite of Neptune) and Pluto. Major advancements since the previous International Planetary Dunes Workshop include the introduction of several new data analysis and numerical tools and utilization of low-cost field instruments (most notably the time-lapse camera). Most presentations represented advancement towards research priorities identified in both of the prior two workshops, although some previously recommended research approaches were not discussed. In addition, this workshop provided a forum for participants to discuss the uncertain future of the Planetary Aeolian Laboratory; subsequent actions taken as a result of the decisions made during the workshop may lead to an expansion of funding opportunities to use the facilities, as well as other improvements. The interactions during this workshop contributed to the success of the Third International Planetary Dunes Workshop, further developing our understanding of aeolian processes on the aeolian worlds of the Solar System.
Aeolian Research↗
Merging remote sensing data and national agricultural statistics to model change in irrigated agriculture
Over 22 million hectares (ha) of U.S. croplands are irrigated. Irrigation is an intensified agricultural land use that increases crop yields and the practice affects water and energy cycles at, above, and below the land surface. Until recently, there has been a scarcity of geospatially detailed information about irrigation that is comprehensive, consistent, and timely to support studies tying agricultural land use change to aquifer water use and other factors. This study shows evidence for a recent overall net expansion of 522 thousand ha across the U.S. (2.33%) and 519 thousand ha (8.7%) in irrigated cropped area across the High Plains Aquifer (HPA) from 2002 to 2007. In fact, over 97% of the net national expansion in irrigated agriculture overlays the HPA. We employed a modeling approach implemented at two time intervals (2002 and 2007) for mapping irrigated agriculture across the conterminous U.S. (CONUS). We utilized U.S. Department of Agriculture (USDA) county statistics, satellite imagery, and a national land cover map in the model. The model output, called the Moderate Resolution Imaging Spectroradiometer (MODIS) Irrigated Agriculture Dataset for the U.S. (MIrAD-US), was then used to reveal relatively detailed spatial patterns of irrigation change across the nation and the HPA. Causes for the irrigation increase in the HPA are complex, but factors include crop commodity price increases, the corn ethanol industry, and government policies related to water use. Impacts of more irrigation may include shifts in local and regional climate, further groundwater depletion, and increasing crop yields and farm income.
Agricultural Systems↗
Satellite remotely-sensed land surface parameters and their climatic effects for three metropolitan regions
By using both high-resolution orthoimagery and medium-resolution Landsat satellite imagery with other geospatial information, several land surface parameters including impervious surfaces and land surface temperatures for three geographically distinct urban areas in the United States – Seattle, Washington, Tampa Bay, Florida, and Las Vegas, Nevada, are obtained. Percent impervious surface is used to quantitatively define the spatial extent and development density of urban land use. Land surface temperatures were retrieved by using a single band algorithm that processes both thermal infrared satellite data and total atmospheric water vapor content. Land surface temperatures were analyzed for different land use and land cover categories in the three regions. The heterogeneity of urban land surface and associated spatial extents were shown to influence surface thermal conditions because of the removal of vegetative cover, the introduction of non-transpiring surfaces, and the reduction in evaporation over urban impervious surfaces. Fifty years of in situ climate data were integrated to assess regional climatic conditions. The spatial structure of surface heating influenced by landscape characteristics has a profound influence on regional climate conditions, especially through urban heat island effects.
Advances in Space Research↗
Multi-sensor proximal remote sensing for cover crop biomass estimation at high and moderate spatial resolutions
Cover crops play a critical role in providing agroecological services such as improving soil health, reducing erosion and nitrogen loss, and suppressing weeds, which are closely tied to their performance such as accumulated biomass. This study evaluated the Active Canopy Sensor (ACS) -214, an active proximal sensing device equipped with its own light-emitting red and near-infrared spectral reflectance sensors, a time-of-flight laser, and an ultrasonic sensor, for estimating winter cover crop biomass across 13 U.S. states from 2020 to 2024. We assessed 11 species from three functional groups – grasses ( n = 797), legumes ( n = 264), and brassicas ( n = 181) – using Random Forest (RF) models and four cross-validation strategies. The ACS-214 showed moderate to strong prediction accuracy for grasses ( R 2 = 0.51 – 0.64) and legumes ( R 2 = 0.44 – 0.76), though performance declined in leave-one-region-out analyses ( R 2 = 0.06 – 0.46), indicating limited spatial generalizability. Brassica models had low prediction accuracy for all models ( R 2 < 0.30), likely due to flowering and patchy growth. Biomass prediction breakpoints were observed at ∼3000 kg ha −1 for legumes and ∼4000 kg ha −1 for grasses. We also evaluated the effectiveness of using ACS-214 data to train Sentinel-2 satellite imagery for estimating grass cover crop biomass using withheld, out of bag data from 2023 to 2024. Sentinel-2 RF models trained with ACS-214 data showed good agreement with field-sampled ( R 2 = 0.58 – 0.61) and ACS-214-estimated biomass ( R 2 = 0.70). While Sentinel-2 offers scalability, the ACS-214 enables finer-resolution biomass mapping and better accounts for within-field variability, making it an effective tool for localized management and monitoring. These findings support the integration of proximal and satellite sensing approaches to enhance cover crop biomass estimation and agroecological assessment.
Alabama, Florida, Indiana, Iowa, Kansas, Maryland,↗
Evapotranspiration by remote sensing: An analysis of the Colorado River Delta before and after the Minute 319 pulse flow to Mexico
The unique hydrologic conditions characterizing riparian ecosystems in dryland (arid and semi-arid) areas help maintain high biodiversity and support high levels of primary productivity compared to associated uplands. In western North America, many riparian ecosystems have been damaged by altered flow regimes (e.g., impoundments and diversions) and over utilization of water resources (e.g., groundwater pumping for agriculture and human consumption). This has led some state and national governments to provide occasional environmental flows to address the declining condition of such riparian systems. In a historic agreement between the United States and Mexico, 130 million cubic meters (mcm) of water was released to the lower Colorado River Delta in Mexico, with the intent to evaluate the hydrological and biological response of the ecosystem. We used the Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) to estimate long term (2000–2014) and short term (pre- and post-pulse; 2013 and 2014) evapotranspiration (ET; used herein as an indicator of plant health) of the delta’s riparian corridor. We found the pulse flow helped reverse a decline in ET from 2011 to 2013, with a small, but statistically significant increase in 2014 (P < 0.05). ET was greater than 100 mcm in all years analyzed (even in years without surface flows) and exceeded surface flows in all years except 2000 (result of excess flows following an El Niño cycle in 1997) and 2014 (year of the pulse flow). Based on groundwater salinities and MODIS ET estimates, we estimated groundwater flow into the delta to be ∼103 mcm. Shallow groundwater salinities in the riparian zone increased from 1.30 g L −1 in the most upstream reach to 2.77 g L −1 in the most downstream reach we measured, partly due to uptake of water by riparian vegetation and partly to intrusion of saline agricultural return flows. The disparity between surface flows and ET can likely be explained by the predominantly phreatophytic plants characterizing the area, which draw water from the aquifer. These results also suggest that the deteriorated condition of vegetation within the riparian zone might not be reversed by a single pulse event and could instead require subsequent pulse flows as a long term strategy to restore vegetation in this riparian ecosystem.
Ecological Engineering↗
Remote sensing of tamarisk beetle (Diorhabda carinulata) impacts along 412 km of the Colorado River in the Grand Canyon, Arizona, USA
Tamarisk ( Tamarix spp.) is an invasive plant species that is rapidly expanding along arid and semi-arid rivers in the western United States. A biocontrol agent, tamarisk beetle ( Diorhabda carinulata ), was released in 2001 in California, Colorado, Utah, and Texas. In 2009, the tamarisk beetle was found further south than anticipated in the Colorado River ecosystem within the Grand Canyon National Park and Glen Canyon National Recreation Area. Our objectives were to classify tamarisk stands along 412 km of the Colorado River from the Glen Canyon Dam through the Grand Canyon National Park using 2009 aerial, high spatial resolution multispectral imagery, and then quantify tamarisk beetle impacts by comparing the pre-beetle images from 2009 with 2013 post-beetle images. We classified tamarisk presence in 2009 using the Mahalanobis Distance method with a total of 2500 training samples, and assessed the classification accuracy with an independent set of 7858 samples across 49 image quads. A total of 214 ha of tamarisk were detected in 2009 along the Colorado River, where each image quad, on average, included an 8.4 km segment of the river. Tamarisk detection accuracies varied across the 49 image quads, but the combined overall accuracy across the entire study region was 74%. Using the Normalized Difference Vegetation Index (NDVI) from 2009 and 2013 with a region-specific ratio of >1.5 decline between the two image dates (2009NDVI/2013NDVI), we detected tamarisk defoliation due to beetle herbivory. The total beetle-impacted tamarisk area was 32 ha across the study region, where tamarisk defoliation ranged 1–86% at the local levels. Our tamarisk classification can aid long-term efforts to monitor the spread and impact of the beetle along the river and the eventual mortality of tamarisk due to beetle impacts. Identifying areas of tamarisk defoliation is a useful ecological indicator for managers to plan restoration and tamarisk removal efforts.
Arizona↗
Using remote sensing to quantify ecosystem site potential community structure and deviation in the Great Basin, United States
The semi-arid Great Basin region in the Northwest U.S. is impacted by a suite of change agents including fire, grazing, and climate variability to which native vegetation can have low resilience and resistance. Assessing ecosystem condition in relation to these change agents is difficult due to a lack of a consistent and objective Site Potential (SP) information of the conditions biophysically possible at each site. Our objectives were to assess and quantify patterns in ecosystem condition, based on actual fractional component cover and a SP map and to evaluate drivers of change. We used long-term 90th percentile Landsat NDVI(Normalized Difference Vegetation Index) and biophysical variables to produce a map of SP. Ecosystem condition was assessed using two methods, first we integrated fractional components into an index which was regressed against SP. Regression confidence intervals were used to segment the study area into normal, over-, and under-performing relative to SP. Next, the relationships between SP and fractional component cover produced SP expected component cover, from which we mapped the actual cover deviation. Much of the study area is within the range of conditions expected by the SP model, but degraded conditions are more common than those above SP expectations. We found that shrub cover deviation is more positive at higher elevation, while herbaceous cover deviation has the opposite pattern, supporting the hypothesis that more resistant and resilient sites are less likely to change from the shrub dominated legacy. Another key finding was that regions with significant annual herbaceous invasions tend to have lower than expected bare ground and shrub cover.
Great Basin↗
Remote sensing of particle backscattering in Chesapeake Bay: a 6-year SeaWiFS retrospective view
Traditional field techniques to monitor water quality in large estuaries, such as boat-based surveys and autonomous moored sensors, generally provide limited spatial coverage. Satellite imagery potentially can be used to address both of these limitations. Here, we show that satellite-based observations are useful for inferring total-suspended-solids (TSS) concentrations in estuarine areas. A spectra-matching optimization algorithm was used to estimate the particle backscattering coefficient at 400 nm, b bp (400), in Chesapeake Bay from Sea-viewing Wide-Field-of-view Sensor (SeaWiFS) satellite imagery. These estimated values of b bp (400) were compared to in situ measurements of TSS for the study period of September 1997–December 2003. Contemporaneous SeaWiFS b bp (400) values and TSS concentrations were positively correlated (N = 340, r 2 = 0.4, P < 0.0005), and the satellite-derived b bp (400) values served as a reasonable first-order approximation for synoptically mapping TSS. Overall, large-scale patterns of SeaWiFS b bp (400) appeared to be consistent with expectations based on field observations and historical reports of TSS. Monthly averages indicated that SeaWiFS b bp (400) was typically largest in winter (>0.049 m −1 , November–February) and smallest in summer (<0.031 m −1 , June–August), regardless of the amount of riverine discharge to the bay. The study period also included Hurricanes Floyd and Isabel, which caused large-scale turbidity events and changes in the water quality of the bay. These results demonstrate that this technique can provide frequent synoptic assessments of suspended solids concentrations in Chesapeake Bay and other coastal regions.
Chesapeake Bay↗
A remote sensing approach for estimating the location and rate of urban irrigation in semi-arid climates
Urban irrigation is an important component of the hydrologic cycle in many areas of the arid and semiarid western United States. This paper describes a new approach that uses readily available datasets to estimate the location and rate of urban irrigation. The approach provides a repeatable methodology at 1/3 km 2 resolution across a large urbanized area (500 km 2 ). For this study, Landsat Thematic Mapper satellite imagery, air photos, climatic records, and a land-use map were used to: (1) identify the fraction of irrigated landscaping in urban areas, and (2) estimate the monthly rate of irrigation being applied to those areas. The area chosen for this study was the San Fernando Valley in Southern California. Identifying irrigated areas involved the use of 29 satellite images, air photos, and a land-use map. The fraction of a pixel that consists of irrigated landscaping (F irr ) was estimated using a linear-mixture model of two land-cover endmembers (selected pixels within a satellite image that represent a targeted land-cover). The two endmembers were impervious and fully-irrigated landscaping. In the San Fernando Valley, we used airport buildings, runways, and pavement to represent the impervious endmember; golf courses and parks were used to represent the fully irrigated endmember. The average F irr using all 29 satellite scenes was 44%. F irr calculated from hand-digitizing using air photos for 13 randomly selected single-family-residential neighborhoods showed similar results (42%). Estimating the rate of irrigation required identification of a third endmember: areas that consisted of urban vegetation but were not irrigated. This "nonirrigated" endmember was used to compute a Normalized Difference Vegetation Index (NDVI) surplus, defined as the difference between the NDVI signals of the irrigated and nonirrigated endmembers. The NDVI signals from irrigated areas remains relatively constant throughout the year, whereas the signal from nonirrigated areas rises and falls seasonally due to precipitation. The areas between airport runways were chosen to represent the nonirrigated endmember. Water-delivery records from 65 spatially-distributed single-family neighborhoods, consisting of nearly 1800 homes, were correlated with the NDVI surplus. The results show a strong exponential correlation ( r 2 = 0.94). In the absence of water-delivery records, which can be difficult to obtain, a surrogate was identified: the landscape evapotranspiration rate (ET L ). ET L was used to scale NDVI surplus (which is dimensionless) to irrigation rates using an exponential scaling function. The monthly irrigation rates calculated from satellite and climatic data compared well with irrigation rates calculated from actual water-delivery data using a paired Wilcoxan signed-rank test ( p = 0.0063). Identification of F irr at the pixel scale, along with identification of the irrigation rate for a fully-irrigated pixel, allows for mapping of urban irrigation over large areas. Maps showing the location and rate of monthly irrigation for the San Fernando study area were computed for January and August 1997.
California↗
The ghost plume phenomenon and its impact on zenith-facing remote sensing measurements of volcanic SO2 emission rates
A large source of error in SO 2 emission rates derived from mobile Differential Optical Absorption Spectroscopy (DOAS) of volcanic gas plumes is the uncertainty in atmospheric light paths between the sun and the instrument, particularly under non-ideal atmospheric conditions, such as the presence of low clouds. DOAS instruments measure the SO 2 column density along the effective light path, so changes to that pathway directly affect the measured SO 2 signal. Due to complex radiative transfer mechanisms when a cloud is between the DOAS viewing position and a volcanic plume, measured plumes can appear spatially offset from their true location, a phenomenon informally referred to as “ghost plumes.” In addition to the appearance of ghost plumes, DOAS measurements recorded in non-ideal conditions have poorly characterized errors and are often discarded, limiting the data available to characterize volcanic degassing. In this study we simulate the radiative transfer associated with zenith-facing mobile DOAS traverses using the McArtim radiative transfer model for scenarios when there is a cloud layer between the instrument and the volcanic plume. In total, 217 permutations of atmospheric optical conditions are considered with varying cloud opacities (AOD = 0, 1, 2, 4, 8, 20), plume opacities (AOD = 0, 1, 2, 4, 8), solar zenith angles (SZA = 1°, 30°, 60°), and cloud thicknesses (200, 400, 800 m). We first develop objective criteria for selecting SO 2 baseline absorption levels and plume spatial extents. The simulated plume traverses are then integrated to obtain the SO 2 cross-sectional burdens which, after multiplication with the wind speed, yield SO 2 emission rates. We find large modification in the shape of the modeled cross-sectional burdens even under translucent (low AOD) cloud conditions in our modeled scenarios. Despite modification of the plume shape, the presence of a low cloud layer is typically not a large source of error in the SO 2 cross-sectional burden or emission rate obtained from zenith-facing DOAS traverses. We find that all measured cross-sectional burdens simulated using an aerosol-free plume in the above conditions and SZA ≤ 30° are within ±25% of the true value.
Journal of Volcanology and Goethermal Research↗
Integration of a numerical model and remotely sensed data to study urban/rural land surface climate processes
Simulation of urban/rural land surface climate processes using boundary layer climate models requires accurate input data with regard to surface thermal and radiative properties. The research reported here resulted in development of a procedure to integrate the satellite-derived surface biophysical parameters with a boundary layer climate model for simulating spatial surface energy exchange. The procedure was tested through spatial surface energy balance simulation of an urban/rural landscape in eastern Nebraska. The modeled surface temperature and net radiation were compared to those derived from the concurrent satellite data. The errors of the modeled surface temperature were small, and were mainly attributed to uncertainties in the estimation of surface moisture availability and satellite-derived surface radiant temperature. Modeled net radiation was also in agreement with the values calculated from satellite data. Modeled turbulent heat fluxes were in general agreement as compared to those reported in the literature, but the model tended to overestimate the latent heat flux for most rural land cover types. It was concluded that by incorporation of satellite-derived surface physical parameters into a boundary layer model, simulation of spatial land surface climate processes was much improved. The method and procedures developed from this study can be utilized in other boundary layer climate models.
Nebraska↗