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At least 19 recordsLinked to original sources
USGS2020

Quantifying drought’s influence on moist soil seed vegetation in California’s Central Valley through time-series remote sensing

Californias Central Valley, USA is a critical component of the Pacific Flyway despite loss of more than 90% of its wetlands. Moist soil seed (MSS) wetland plants are now produced by mimicking seasonal flooding in managed wetlands to provide an essential food resource for waterfowl. Managers need MSS plant area and productivity estimates to support waterfowl conservation, yet this remains unknown at the landscape scale. Also the effects of recent drought on MSS plants have not been quantified. We generated Landsat-derived estimates of extents and productivity (seed yield or its proxy, the green chlorophyll index) of major MSS plants including watergrass (Echinochloa crusgalli) and smartweed (Polygonum spp.) (WGSW), and swamp timothy (Crypsis schoenoides) (ST) in all Central Valley managed wetlands from 20072017. We tested the effects of water year, land ownership and region on plant area and productivity with a multifactor nested analysis of variance. For the San Joaquin Valley we explored the association between water year and water supply, and we developed metrics to support management decisions. MSS plant area maps were based on a support vector machine classification of Landsat phenology metrics (2017 map overall accuracy: 89%). ST productivity maps were created with a linear regression model of seed yield (n=68, R2 = 0.53, normalized RMSE = 10.5%). The Central Valley-wide estimated area for ST in 2017 was 32,369 ha 2,524 ha (95% C.I.), and 13,012 ha 1,384 ha for WGSW. Mean ST seed yield ranged from 577 kg/ha in the Delta Basin to 365 kg/ha in the San Joaquin Basin. WGSW area and ST seed yield decreased while ST area increased in critical drought years compared to normal water years (Scheffes test, p<0.05). Greatest ST area increases occurred in the Sacramento Valley (~75%). Voluntary water deliveries increased in normal water years, and ST seed yield increased with water supply. Z-scores of ST seed yield can be used to evaluate wetland performance and aid resource allocation decisions. Updated maps will support habitat monitoring, conservation planning and water management in future years, which are likely to face greater uncertainty in water availability with climate change.

California↗
USGS2023

Toward consistent change detection across irregular remote sensing time series observations

The use of remote sensing in time series analysis enables wall-to-wall monitoring of the land surface and is critical for assessing and understanding land cover and land use change and for understanding the Earth system as a whole. However, variability in remote sensing observation frequency through time and across space presents challenges for producing consistent change detection results throughout the available satellite record using approaches such as the Continuous Change Detection and Classification (CCDC) change detection methodology. Here we investigate new modifications to this methodology with the goal of improving accuracy and consistency in results and increasing flexibility for operational usage and future development. The modified method (Band-First Probability, or CCD-BFP) change detection procedure works by calculating a test for each band through time before summarizing between bands. We evaluate the CCD-BFP method compared to an existing implementation of CCDC using a variety of approaches, including a validation dataset of human-interpreted locations, comparison with data from fire events, use of simulated remote sensing data, and qualitative inspection of areas of interest. We find CCD-BFP improves consistency across time and space compared to the existing implementation of CCDC, with more similarity in rates of change across Landsat swath boundaries and before and after the launch of Landsat 7. Also, we find that CCD-BFP detects more of the change events in the validation dataset while reducing the overall number of change detections, indicating that it is able to more accurately capture the most notable land surface change events.

Remote Sensing of Environment↗
USGS2018

Detecting geothermal anomalies and evaluating LST geothermal component by combining thermal remote sensing time series and land surface model data

This paper explores for the first time the possibilities to use two land surface temperature (LST) time series of different origins (geostationary Meteosat Second Generation satellite data and Noah land surface modelling, LSM), to detect geothermal anomalies and extract the geothermal component of LST, the LST gt . We hypothesize that in geothermal areas the LSM time series will underestimate the LST as compared to the remote sensing data, since the former does not account for the geothermal component in its model. In order to extract LST gt , two approaches of different nature (physical based and data mining) were developed and tested in an area of about 560 × 560 km 2 centered at the Kenyan Rift. Pre-dawn data in the study area during the first 45 days of 2012 were analyzed. The results show consistent spatial and temporal LST gt patterns between the two approaches, and systematic differences of about 2 K. A geothermal area map from surface studies was used to assess LST gt inside and outside the geothermal boundaries. Spatial means were found to be higher inside the geothermal limits, as well as the relative frequency of occurrence of high LST gt . Results further show that areas with strong topography can result in anomalously high LST gt values (false positives), which suggests the need for a slope and aspect correction in the inputs to achieve realistic results in those areas. The uncertainty analysis indicates that large uncertainties of the input parameters may limit detection of LST gt anomalies. To validate the approaches, higher spatial resolution images from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data over the Olkaria geothermal field were used. An established method to estimate radiant geothermal flux was applied providing values between 9 and 24 W/m 2 in the geothermal area, which coincides with the LST gt flux rates obtained with the proposed approaches. The proposed approaches are a first step in estimating LST gt at large spatial coverage from remote sensing and LSM data series, and provide an innovative framework for future improvements.

Remote Sensing of Environment↗
USGS2021

The potential of satellite remote sensing time series to uncover wetland phenology under unique challenges of tidal setting

While growth history of vegetation within upland systems is well studied, plant phenology within coastal tidal systems is less understood. Landscape-scale, satellite-derived indicators of plant greenness may not adequately represent seasonality of vegetation biomass and productivity within tidal wetlands due to limitations of cloud cover, satellite temporal frequency and attenu-ation of plant signals by tidal flooding. However, understanding plant phenology is necessary to gain insight into aboveground biomass, photosynthetic activity, and carbon sequestration. In this study we use a modeling approach to estimate plant greenness throughout a year in tidal wet-lands located within the San Francisco Bay Area, USA. We used variables such as EVI history, temperature, and elevation to predict plant greenness on a 14-day timestep. We found this ap-proach accurately estimated plant greenness, with larger error observed within more dynamic restored wetlands, particularly at early post-restoration stages. We also found modeled EVI can be used as an input variable into greenhouse gas models, allowing for an estimate of carbon se-questration and gross primary production. Our strategy can be further developed in future re-search by assessing restoration and management effects on wetland phenological dynamics and through incorporating the entire Sentinel-2 time-series once it becomes available within Google Earth Engine.

California↗
USGS2022

Monitoring and characterizing multi-decadal variations of urban thermal condition using time-series thermal remote sensing and dynamic land cover data

Urban development and associated land cover and land use change alter the thermal, hydrological, and physical properties of the land surface. Assessments of surface urban heat island (UHI) usually focused on using remote sensing and land cover data to quantify UHI intensity and spatial distribution within a certain period. However, the mechanisms and complex interactions in landscape dynamics and land surface thermal features are still being assessed. In this study, we developed and implemented a novel approach to characterize landscape thermal conditions by focusing on UHI intensity and its spatiotemporal variation using the recently available time series of Landsat land surface temperature and land cover change products. We analyzed land surface temperature changes in urban and surrounding non-urban lands to quantify the UHI intensity and landscape thermal conditions in the Atlanta and Minneapolis metropolitan areas of the United States. Our results revealed that UHI intensities had averages of 3.4 °C and 3.3 °C in the Atlanta and Minneapolis metropolitan areas, respectively. The dominant land cover type in rural areas and urban imperviousness cover determines the UHI intensity. Increasing trends of 0.04 °C/year and 0.01 °C/year in UHI intensity between 1985 and 2018 were found in Atlanta and Minneapolis, respectively. The UHI intensity variations in 1985 and 2018 suggest that the magnitudes and temporal variations of UHI intensity averaged from all urban land cover classes are close to the UHI intensity estimated from the low intensity urban area only while the UHI intensities are more than 2 °C larger in medium to high and high intensity urban areas. The UHI intensities estimated from the maximum temperature that have statistically significant increasing trends suggest that the maximum temperature is a good element for measuring UHI effect. Urban land cover dynamics play an important role in controlling temporal variation of UHI and the UHI hotspots. Our findings support the scientific value of implementing the prototype approach as an objective framework to quantify and monitor UHI intensity at a large geographic extent.

Remote Sensing of Environment↗
USGS2021

Urban heat island and its regional impacts using remotely sensed thermal data – A review of recent developments and methodology

Many novel research algorithms have been developed to analyze urban heat island (UHI) and UHI regional impacts (UHIRIP) with remotely sensed thermal data tables. We present a comprehensive review of some important aspects of UHI and UHIRIP studies that use remotely sensed thermal data, including concepts, datasets, methodologies, and applications. We focus on reviewing progress on multi-sensor image selection, preprocessing, computing, gap filling, image fusion, deep learning, and developing new metrics. This literature review shows that new satellite sensors and valuable methods have been developed for calculating land surface temperature (LST) and UHI intensity, and for assessing UHIRIP. Additionally, some of the limitations of using remotely sensed data to analyze the LST, UHI, and UHI intensity are discussed. Finally, we review a variety of applications in UHI and UHIRIP analyses. The assimilation of time-series remotely sensed data with the application of data fusion, gap filling models, and deep learning using the Google Cloud platform and Google Earth Engine platform also has the potential to improve the estimation accuracy of change patterns of UHI and UHIRIP over long time periods.

Land↗
USGS2018

Climate-related variation in plant peak biomass and growth phenology across Pacific Northwest tidal marshes

The interannual variability of tidal marsh plant phenology is largely unknown and may have important ecological consequences. Marsh plants are critical to the biogeomorphic feedback processes that build estuarine soils, maintain marsh elevation relative to sea level, and sequester carbon. We calculated Tasseled Cap Greenness, a metric of plant biomass, using remotely sensed data available in the Landsat archive to assess how recent climate variation has affected biomass production and plant phenology across three maritime tidal marshes in the Pacific Northwest of the United States. First, we used clipped vegetation plots at one of our sites to confirm that tasseled cap greenness provided a useful measure of aboveground biomass (r 2 = 0.72). We then used multiple measures of biomass each growing season over 20–25 years per study site and developed models to test how peak biomass and the date of peak biomass varied with 94 climate and sea-level metrics using generalized linear models and Akaike Information Criterion (AIC) model selection. Peak biomass was positively related to total annual precipitation, while the best predictor for date of peak biomass was average growing season temperature, with the peak 7.2 days earlier per degree C. Our study provides insight into how plants in maritime tidal marshes respond to interannual climate variation and demonstrates the utility of time-series remote sensing data to assess ecological responses to climate stressors.

Oregon, Washington↗
USGS2019

Thermal, deformation, and degassing remote sensing time-series (A.D. 2000-2017) at the 47 most active volcanoes in Latin America: Implications for volcanic systems

Volcanoes are hazardous to local and global populations, but only a fraction are continuously monitored by ground-based sensors. For example, in Latin America, more than 60% of Holocene volcanoes are unmonitored, meaning long-term multi-parameter datasets of volcanic activity are rare and sparse. We use satellite observations of degassing, thermal anomalies, and surface deformation spanning 17 years at 47 of the most active volcanoes in Latin America, and compare these datasets to ground-based observations archived by the Global Volcanism Program (GVP). This first comparison of multi-satellite time-series on a regional scale provides information regarding volcanic behavior during, non-, pre-, syn- and post-eruptive periods. For example, at Copahue volcano, deviations from background activity in all three types of satellite measurements were manifested months to years in advance of renewed eruptive activity in 2012. By quantifying the amount of degassing, thermal output, and deformation measured at each of these volcanoes, we test the classification of these volcanoes as open or closed volcanic systems. We find that ~28% of the volcanoes do not fall into either classification and the rest show elements of both, demonstrating a dynamic range of behavior that can change over time. Finally, we recommend how volcano monitoring could be improved through better coordination of available satellite-based capabilities and new instruments.

Journal of Geophysical Research↗
USGS2019

Investigating lake-area dynamics across a permafrost-thaw spectrum using airborne electromagnetic surveys and remote sensing time-series data in Yukon Flats, Alaska

Lakes in boreal lowlands cycle carbon and supply an important source of freshwater for wildlife and migratory waterfowl. The abundance and distribution of these lakes are supported, in part, by permafrost distribution, which is subject to change. Relationships between permafrost thaw and lake dynamics remain poorly known in most boreal regions. Here, new airborne electromagnetic (AEM) data collected during June 2010 and February 2016 were used to constrain deep permafrost distribution. AEM data were coupled with Landsat-derived lake surface-area data from 1979 through 2011 to inform temporal lake behavior changes in the 35 500- km 2 Yukon Flats ecoregion of Alaska. Together, over 1500 km of AEM data, and roughly 30 years of Landsat data were used to explore processes that drive lake dynamics across a variety of permafrost thaw states not possible in studies conducted with satellite imagery or field measurements alone. Clustered time-series data identified lakes with similar temporal dynamics. Clusters possessed similarities in lake permanence (i.e. ephemeral versus perennial), subsurface permafrost distribution, and proximity to rivers and streams. Of the clustered lakes, ~66% are inferred to have at least intermittent connectivity with other surface-water features, ~19% are inferred to have shallow subsurface connectivity to other surface water features that served as a low-pass filter for hydroclimatic fluctuations, and ~15% appear to be isolated by surrounding permafrost (i.e. no connectivity). Integrated analysis of AEM and Landsat data reveals a progression from relatively synchronous lake dynamics among disconnected lakes in the most spatially continuous, thick permafrost to quite high spatiotemporal heterogeneity in lake behavior among variably-connected lakes in regions with notably less continuous permafrost. Variability can be explained by the preferential development of thawed permeable gravel pathways for lateral water redistribution in this area. The general spatial progression in permafrost thaw state and lake area behavior may be extended to the temporal dimension. However, extensive permafrost thaw, beyond what is currently observed, is expected to promote ubiquitous subsurface connectivity, eventually evolving to a state of increased lake synchronicity.

Alaska↗
USGS2023

Monitoring long-term changes of urban surface temperature using time-series land cover and remote sensing data across 50 major cities in the United States

The increase of developed land changes the Earth’s ecosystems and, in doing so, impacts the natural environment and further affects the services it provides to humans. Urban growth and associated land cover transitions alter the thermal and physical properties of the land surface, resulting in surface temperature change in urban areas. In this study, we integrated both land cover and surface temperature information to characterize surface temperature spatiotemporal variations using the recently available time series of Landsat land surface temperature and annual land change products. We analyzed over thirty-year trends of land surface temperature (LST) in urban and surrounding non-urban lands. We found that the transitions of different land cover types to urban affected urban LST trends differently, and further impacted the temporal trend of urban heat island intensity.

continental United States↗
USGS2012

Radiometric calibration of the Landsat MSS sensor series

Multispectral remote sensing of the Earth using Landsat sensors was ushered on July 23, 1972, with the launch of Landsat-1. Following that success, four more Landsat satellites were launched, and each of these carried the Multispectral Scanner System (MSS). These five sensors provided the only consistent multispectral space-based imagery of the Earth's surface from 1972 to 1982. This work focuses on developing both a consistent and absolute radiometric calibration of this sensor system. Cross-calibration of the MSS was performed through the use of pseudoinvariant calibration sites (PICSs). Since these sites have been shown to be stable for long periods of time, changes in MSS observations of these sites were attributed to changes in the sensors themselves. In addition, simultaneous data collections were available for some MSS sensor pairs, and these were also used for cross-calibration. Results indicated substantial differences existed between instruments, up to 16%, and these were reduced to 5% or less across all MSS sensors and bands. Lastly, this paper takes the calibration through the final step and places the MSS sensors on an absolute radiometric scale. The methodology used to achieve this was based on simultaneous data collections by the Landsat-5 MSS and Thematic Mapper (TM) instruments. Through analysis of image data from a PICS location and through compensating for the spectral differences between the two instruments, the Landsat-5 MSS sensor was placed on an absolute radiometric scale based on the Landsat-5 TM sensor. Uncertainties associated with this calibration are considered to be less than 5%.

IEEE Transactions on Geoscience and Remote Sensing↗
USGS2026

Mapping winter cover crops using remote sensing of crop phenology

Winter cover crops (WCC) are an effective agricultural conservation practice for improving soil health and water quality. Several U.S. states have established incentive programs to promote planting WCC between cash crop rotations. In recent years, the area planted with WCC has steadily increased due to these efforts, although the planting area varies among states. Since these programs are largely led by individual counties or state programs, there is no centralized data repository that quantifies the total WCC planted annually or their locations. Therefore, there is a practical need to identify fields that have been planted with WCC, as well as to quantify the total planted area both during and after the growing season. Furthermore, incentive programs often require verification that WCC were planted to release payments to participating growers in a timely manner. Satellite remote sensing time series can provide information on WCC growth status. However, it remains unclear what kind of WCC the remote sensing data detects. For example, separating WCC from weeds and winter commodity crops remains a challenge. Additionally, a binary classification of WCC presence and absence cannot accurately describe the category and performance of the WCC. This paper presents a new remote sensing phenology-based approach to map WCC likelihood. We assessed model predictions of WCC likelihood using WCC planting records and ground observations over fields at the Beltsville Agricultural Research Center (BARC) from 2018 to 2024. Regional WCC maps were assessed using the Maryland Department of Agriculture's records of WCC fields enrolled in the 2019 and 2020 incentive programs. Our results show that the balanced overall accuracy for WCC and non-WCC ranges from 75% to 85% but could exceed 90%, with limited training samples. However, the high accuracy of WCC corresponds with low accuracy for non-WCC due to confusion with weeds and perennial grasses. Early-season detection yields promising results, with overall accuracies comparable to late-season detection. The after-season detection can differentiate between winter cereal grain cash crops and incentive WCC based on the ending date of the growing season. With a small set of WCC samples and management information, the phenology-based mapping approach offers a rapid and scalable solution for mapping WCC over a large region during or after each WCC growing season.

Maryland↗
USGS2020

New operational national satellite burned area product

Introduction Lack of consistent spatial and temporal fire information with relevant spatial resolution hinders land management and broad-scale assessments of fire activity, especially in the eastern United States and the Great Plains where fi re is important ecologically and culturally. Remote sensing can be used to monitor fi re activity, augment existing fi re data, and fill information gaps. In particular, Landsat offers one of the most complete time series of remote sensing data sets as the Landsat satellites with spectral bands useful for mapping fi res and burn severity have been operational since 1984. Furthermore, Landsat satellite imagery collect data at a resolution useful for on-the-ground comparisons and management decisions. Methods A gradient-boosting regression model algorithm was used to predict burn probabilities (BP), indicating the likelihood that a pixel had burned in a fire. Then the algorithm translated the burn probability images to burn classification (BC) images using thresholding and region growing. Burned areas smaller than 5 acres were removed to reduce noise. The BP and BC products were generated for Landsat scenes collected from 1984 to present with <= 80% cloud cover and are available for download from USGSs EarthExplorer website (www.earthexplorer. usgs.gov). Annual summaries were also produced from the scene-level BC and BP products. These include: 1) the maximum burn probability value from all scenes in a year, (2) the burn classification count, or the number of times a pixel was classified as burned in a year, (3) the burn date, or the day of year of the first Landsat scene a pixel was classified as burned, and (4) a burn classification which removes burned areas still visible from previous years with a filter. Results The Landsat Burned Area Products identified 183% more burned area than the Monitoring Trends in Burn Severity and 56% more burned area than the MODIS MCD64A1.006 products2. From 1984 through 2018, annual burned area mapped by the Landsat Burned Area Products averaged 30,000 km2, ranged between 14,000 km2 in 1991 and 46,500 km2 in 2012, and increased over time at a rate of 356 km2/year. The Landsat Burned Area products consistently map burned areas 5 acres or larger over time while capturing unburned islands within fi res. Such characteristics are critical for understanding long-term impacts of fi res on human and natural systems. The products are being routinely produced as new Landsat data are collected and demonstrate the utility remote sensing offers for monitoring landscape change.

Report↗
USGS2023

Declining American Goshawk (Accipiter atricapillus) nest site habitat suitability in a timber production landscape: Effects of abiotic, biotic, and forest management factors

Conservation of the American Goshawk ( Accipiter atricapillus ; hereafter goshawk) has been contentious in relation to forest management. Higher quality goshawk nesting habitat is generally considered to consist of contiguous tracts of mature forest, due to goshawks' large home ranges, territoriality, and food requirements. The large trees of mature forest have the greatest economic value to timber companies. We used long-term (1965–2019) data from 281 goshawk nest site locations in the Black Hills National Forest (BHNF), South Dakota, and Wyoming, USA, to evaluate (1) abiotic and biotic factors associated with goshawk nest site habitat suitability (hereafter habitat suitability); (2) changes in habitat suitability over time; and (3) the effect of anthropogenic activities and natural disturbances on habitat suitability. We evaluated forest attributes across five spatial scales relevant to goshawks, used information-theoretic methods to rank and select models, and assessed the predictive capability of the best-approximating models using the concordance statistic. The best-approximating model had excellent predictive capability (concordance = 0.821). Forest attributes at the 12-ha scale were a better predictor of goshawk habitat suitability than covariates evaluated at the point or >12-ha scales, indicating the importance of managing goshawk habitat beyond the nest tree, but within the nest stand. Goshawk habitat suitability was positively related to mean percent canopy cover and median canopy base height, and negatively related to variability in canopy base height within 12 ha of the location. As mean percent canopy cover within 12 ha of a location increased, goshawk habitat suitability increased more slowly in burned compared to unburned areas. Commercial thinning treatments were more likely to occur in closed canopy forest that already had a higher likelihood of goshawk nesting, and we documented a positive relationship between habitat suitability and the interaction of canopy cover with commercial thinning. Goshawk habitat suitability was negatively related to slope and distance to drainage bottoms, and positively related to distance to ridges, which may be related to microclimatic factors. Our results indicate goshawk habitat suitability decreased across the BHNF over the past three decades and much high-quality nesting habitat was lost during this period due to a combination of unsustainable timber harvest and natural disturbances. Minimizing forest management activities that decrease canopy cover and canopy base height, and increase variability in canopy base height in areas of high- and medium-quality goshawk habitat are likely to slow the loss of higher-quality habitat and allow development of future nesting habitat. In addition to informing management, this study demonstrates the value of using existing long-term legacy datasets in conjunction with time series of remotely sensed habitat attributes to evaluate changes in habitat suitability for raptors in heavily managed landscapes with extensive natural disturbances.

South Dakota, Wyoming↗
USGS2026

Characterizing the influence of remotely sensed wetland and lake water storage on discharge using LSTM models

Globally, many wetlands and lakes are at risk for further loss, which can amplify downstream consequences of flood and drought events. We derived remotely sensed based time series of surface water storage (SW storage ) to determine when and where accounting for SW storage dynamics improves predictions of river discharge. We trained four long short-term memory (LSTM) models, that differed in their inclusion of storage data and catchment characteristics, to simulate daily river discharge (2016–2023) for select watersheds across the conterminous United States. Adding SW storage to a meteorology-only or meteorology-and-catchment characteristics model improved upon model Nash-Sutcliffe efficiency (NSE) in 80.6% of the watersheds. Residuals during low-flow (Q70) events decreased by 47.6% when adding storage to meteorological data. Improvements were most consistent in ecoregions with a greater abundance of non-floodplain lakes and wetlands. This effort represents the first exploration to train a multi-watershed LSTM on landscape-scale remotely sensed time series of SW storage .

conterminous United States↗
USGS2020

Quantifying western U.S. rangelands as fractional components with multi-resolution remote sensing and in situ data

Quantifying western U.S. rangelands as a series of fractional components with remote sensing provides a new way to understand these changing ecosystems. Nine rangeland ecosystem components, including percent shrub, sagebrush ( Artemisia ), big sagebrush, herbaceous, annual herbaceous, litter, and bare ground cover, along with sagebrush and shrub heights, were quantified at 30 m resolution. Extensive ground measurements, two scales of remote sensing data from commercial high-resolution satellites and Landsat 8, and regression tree models were used to create component predictions. In the mapped area (2,993,655 km²), bare ground averaged 45.5%, shrub 15.2%, sagebrush 4.3%, big sagebrush 2.9%, herbaceous 23.0%, annual herbaceous 4.2%, and litter 15.8%. Component accuracies using independent validation across all components averaged R 2 values of 0.46 and an root mean squared error (RMSE) of 10.37, and cross-validation averaged R 2 values of 0.72 and an RMSE of 5.09. Component composition strongly varies by Environmental Protection Agency (EPA) level III ecoregions ( n = 32): 17 are bare ground dominant, 11 herbaceous dominant, and four shrub dominant. Sagebrush physically covers 90,950 km², or 4.3%, of our study area, but is present in 883,449 km², or 41.5%, of the mapped portion of our study area.

Arizona, California, Colorado, Idaho, Montana, Nev↗
USGS2020

Transitioning from change detection to monitoring with remote sensing: A paradigm shift

The use of time series analysis with moderate resolution satellite imagery is increasingly common, particularly since the advent of freely available Landsat data. Dense time series analysis is providing new information on the timing of landscape changes, as well as improving the quality and accuracy of information being derived from remote sensing. Perhaps most importantly, time series analysis is expanding the kinds of land surface change that can be monitored using remote sensing. In particular, more subtle changes in ecosystem health and condition and related to land use dynamics are being monitored. The result is a paradigm shift away from change detection, typically using two points in time, to monitoring, or an attempt to track change continuously in time. This trend holds many benefits, including the promise of near real-time monitoring. Anticipated future trends include more use of multiple sensors in monitoring activities, increased focus on the temporal accuracy of results, applications over larger areas and operational usage of time series analysis.

Remote Sensing of Environment↗
USGS2011

Fuel loads and fuel type mapping

Correct description of fuel properties is critical to improve fire danger assessment and fire behaviour modeling, since they guide both fire ignition and fire propagation. This chapter deals with properties of fuel that can be considered static in short periods of time: biomass loads, plant geometry, compactness, etc. Mapping these properties require a detail knowledge of vegetation vertical and horizontal structure. Several systems to classify the great diversity of vegetation characteristics in few fuel types are described, as well as methods for mapping them with special emphasis on those based on remote sensing images.

Series in Remote Sensing↗