Remote sensing of mangrove wetlands: Relating canopy spectra to site-specific data
No abstract available.
Florida↗
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Results for “Remote Sensing”
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Remotely sensed surface water storage shows distinct patterns from SWAT-simulated data
Quantifying and projecting the downstream benefits of water stored in lakes and wetlands (SW storage ) requires watershed hydrologic models, which often parameterize surface water storage in topographic depressions using static digital elevation model (DEM) data. Calibration and validation of modeled SW storage dynamics using external data sets is uncommon, particularly across major river basins, with model calibration typically focused on observed discharge. Here, we develop and assess a novel remote sensing-based (RS) SW storage data set (Sentinel-1 and Sentinel-2) for verifying simulated SW storage estimates from a Soil and Water Assessment Tool (SWAT) model of the Upper Mississippi River Basin (UMRB; ∼440,000 km 2 ). Our results suggest that static DEM-based parameterization as well as model calibration based solely on discharge do not adequately capture spatial and temporal SW storage dynamics in the UMRB. Mean SW storage as estimated by SWAT was 74% ± 122% (mean ± standard deviation) higher than RS SW storage , where SW storage in SWAT was underestimated in wetland-rich subbasins and overestimated in agricultural, tile-drained subbasins. Time series of SWAT SW storage and RS SW storage were positively correlated in only 38.8% of subbasins. As RS SW storage is also vulnerable to error, storage estimates were compared to bathymetric data in select small wetlands. While uncertainty remains in the conversion from extent to storage for RS SW storage , the method and data set presented here are a promising option for improved parameterization and calibration of SW storage processes in SWAT and other process-based hydrologic models. Further consideration of these storage processes can potentially improve the accuracy of simulated streamflow in wetland-rich model domains.
Illinois, Indiana, Iowa, Minnesota, Missouri, Sout↗
Enhanced algorithm performance for land cover classification from remotely sensed data using bagging and boosting
Two ensemble methods, bagging and boosting, were investigated for improving algorithm performance. Our results confirmed the theoretical explanation [1] that bagging improves unstable, but not stable, learning algorithms. While boosting enhanced accuracy of a weak learner, its behavior is subject to the characteristics of each learning algorithm.
IEEE Transactions on Geoscience and Remote Sensing↗
Possible fault detection in Cottonball Basin, California: an application of radar remote sensing.
An analysis of a 3-cm wavelength radar image of Cottonball Basin in Death Valley National Monument has revealed the existence of two suspect fault traces in evaporite deposits that are less than 2000-yr old. The traces are well defined on the image because the radar system was able to differentiate surface roughness variations at the centimeter scale. The features are not recognizable on data sets recorded in the visible and near-infrared spectral bands. - from Authors
Remote Sensing of Environment↗
The role of remotely sensed and other special data for predictive modeling: the Umatilla, Oregon example
Landsat data and 1:24 000-scale aerial photographs were initially used to map the expansion of irrigation from 1973 to 1979 and to identify crops under irrigation in 1979. The crop data were then used with historical water requirement figures and digital topographic and hydrographic data to estimate water and power use for the 1979 irrigation season. The final project task involved production of a composite map of land suitability for irrigation development based on land cover (from Landsat), landownership, soil irrigability, slope gradient, and potential energy costs.
Oregon↗
Spectral reflectance and emissivity features of broad leaf plants: Prospects for remote sensing in the thermal infrared (8.0-14.0 μm)
Field emissivity measurements were made of leaves collected from nine deciduous tree and agricultural plant species. The data show, for the first time, that it is possible to discriminate subtle spectral emissivity features of leaves from the natural background emission. Under conditions of controlled measurement geometry (leaves arranged to cover a flat surface), the field emissivity spectra agreed fairly well with emissivity values calculated from laboratory directional hemispherical reflectance measurements. Spectral features associated with a variety of leaf chemical constituents, including cellulose, cutin, xylan, silica, and oleanolic acid could be identified in the field emissivity data. Structural aspects of leaf surfaces also influenced spectral behavior, notably the abundance of trichomes, as well as wax thickness and texture. Field spectral measurements made at increasing distances from natural plant canopies showed progressive attenuation of the spectral emissivity features. This attenuation is ascribed to increased multiple scattering that superimposes an opposite-in-sign reflected component on the emittance, and to the increasing number of canopy voids within the instrument field of view. Errors associated with the removal of atmospheric features and with the non-isotropic thermal characteristics of canopies also contribute to the loss of spectral information at greater measurement distances. In contrast to visible and short-wave infrared data, thermal infrared spectra of broad leaf plants show considerable spectral diversity, suggesting that such data eventually could be utilized to map vegetation composition. However, remotely measuring the subtle emissivity features of leaves still presents major challenges. To be successful, sensors operating in the 8–14 μm atmospheric window must have high signal-to-noise and a small enough instantaneous field of view to allow measurements of only a few leaf surfaces. Methods for atmospheric compensation, temperature–emissivity separation, and spectral feature analysis also will need to be refined to allow the recognition, and perhaps, exploitation of leaf thermal infrared spectral properties.
Virginia↗
Remote sensing of ephemeral water bodies in western Niger
Research was undertaken to evaluate the feasibility of monitoring the small ephemeral water bodies of the Sahel with the 1.1 km resolution data of the National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR). Twenty-one lakes of western Niger with good ground observation records were selected for examination. Thematic Mapper images from 1988 were first analysed to determine surface areas and temperature differences between water and adjacent land. Six AVHRR scenes from the 1988-89 dry season were then studied. It was found that a lake can be monitored until its surface area drops below 10 ha, in most cases. Furthermore, with prior knowledge of the location and shape of a water body, its surface area can be estimated from AVHRR band 5 data to within about 10 ha. These results are explained by the sharp temperature contrast between water and land, on the order of 13?? C.
International Journal of Remote Sensing↗
Satellite remote sensing of crop water use across the Missouri River Basin for 1986–2018 period
Understanding historical crop water use (CWU) dynamics is important to improve land and water management. In this study, well-validated (coefficient of determination = 0.91, percent bias = 4%, and percent root mean square error = 11.8%) Landsat-based actual evapotranspiration (ETa) time-series estimations were used to (1) assess summer season CWU (CWU-Su) dynamics, (2) investigate CWU-Su trends over the study period (1986–2018; 33 years) at the regional- and pixel-scales, and (3) attribute CWU-Su driving factors across Missouri River Basin. Spatial variability of the ETa estimations along with the observed bimodal probability density distribution of ETa highlighted a strong relation between land cover and water uses across the basin. The bimodal distribution of ETa also indicated the presence of two major landcovers in the basin. The drier foothill regions in northwestern Missouri River Basin, dominated by grassland/shrubland, showed lower ETa (< 500 mm/year), whereas cropland dominated regions in lower semi-humid basin and forested subbasins exhibited higher ETa (> 600 mm/year). The CWU-Su anomalies revealed the vulnerability of the basin to year-to-year weather conditions. The CWU-Su trend analysis revealed a significant positive trend (p < 0.1) at the regional-scale affecting 30% of basin’s cropland pixels. The cropland pixels under positive CWU-Su trend were found to be clustered in the eastern and central Missouri River Basin as a result of the combined effect of increased crop production area, increased crop yields, crop practice shifts to higher biomass crops, and increased irrigated land. The effect of improved irrigation and water management practices on reducing CWU-Su was observed in western Missouri River Basin, which had a stable major crop throughout the study period. Overall, the study highlights the usefulness of Landsat imagery and remote sensing-based ETa modeling approaches in generating historical time-series ETa maps over a wide range of elevation, vegetation, and climate.
Missouri River Basin↗
Remote sensing of tamarisk biomass, insect herbivory, and defoliation: Novel methods in the Grand Canyon Region, Arizona
Tamarisk is an invasive, riparian shrub species in the southwestern USA. The northern tamarisk beetle (Diorhabda carinulata) has been introduced to several states to control tamarisk. We classified tamarisk distribution in the Glen Canyon National Recreation Area, Arizona using a 0.2 m resolution, airborne multispectral data and estimated tamarisk beetle effects (overall accuracy of 86 percent) leading to leaf defoliation in a 49,408 m2 area. We also estimated individual tamarisk tree biomass and their uncertainties using airbonre liday data (100 points/m2). On average, total above ground tamarisk biomass was 8.67 kg/m2 (SD=17.6). The tamarisk beetle defoliation resulted in a mean leaf biomass loss of 0.52 kg/m2 and an equivalent of 25,692 kg across the entire study area. Our defoliated tamarisk map and biomass estimates can help inform restoration treatments to reduce tamarisk. Continued monitoring of tamarisk and tamarisk beetle effects are recommended to understand the currently-unknown eventual equilibrium between the two species and the cascading effects on ecosystem processes.
Photogrammetric Engineering and Remote Sensing↗
Remote sensing change detection methods to track deforestation and growth in threatened rainforests in Madre de Dios, Peru
Two forestry-change detection methods are described, compared, and contrasted for estimating deforestation and growth in threatened forests in southern Peru from 2000 to 2010. The methods used in this study rely on freely available data, including atmospherically corrected Landsat 5 Thematic Mapper and Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation continuous fields (VCF). The two methods include a conventional supervised signature extraction method and a unique self-calibrating method called MODIS VCF guided forest/nonforest (FNF) masking. The process chain for each of these methods includes a threshold classification of MODIS VCF, training data or signature extraction, signature evaluation, k-nearest neighbor classification, analyst-guided reclassification, and postclassification image differencing to generate forest change maps. Comparisons of all methods were based on an accuracy assessment using 500 validation pixels. Results of this accuracy assessment indicate that FNF masking had a 5% higher overall accuracy and was superior to conventional supervised classification when estimating forest change. Both methods succeeded in classifying persistently forested and nonforested areas, and both had limitations when classifying forest change.
Madre de Dios↗
An earth remote sensing satellite- 1 Synthetic Aperture Radar Mosaic of the Tanana River Basin in Alaska
Because the pixel location in a line of Synthetic Aperture Radar (SAR) image data is directly related to the distance the pixel is from the radar, terrain elevations cause large displacement errors in the geo-referenced location of the pixel. This is especially true for radar systems with small angles between the nadir and look vectors. Thus, to geo-register a SAR image accurately, the terrain of the area must be taken into account. (Curlander et al., 1987; Kwok et al., 1987, Schreier et al., 1990; Wivell et al., 1992). As part of the 1992 National Aeronautics and Space Administration's Earth Observing System Version 0 activities, a prototype SAR geocod-. ing and terrain correction system was developed at the US. Geological Survey's (USGS) E~os Data Center (EDC) in Sioux Falls, South Dakota. Using this system with 3-arc-second digital elevation models (DEMs) mosaicked at the ED^ Alaska Field Office, 21 ERS-I s.4~ scenes acquired at the Alaska SAR Facility were automatically geocoded, terrain corrected, and mosaicked. The geo-registered scenes were mosaicked using a simple concatenation.
Photogrammetric Engineering and Remote Sensing↗
Terrestrial remote sensing science and algorithms planned for EOS/MODIS
The Moderate Resolution Imaging Spectroradiometer (MODIS) will be the primary daily global monitoring sensor on the NASA Earth Observing System (EOS) satellites, scheduled for launch on the EOS-AM platform in June 1998 and the EOS-PM platform in December 2000. MODIS is a 36 channel radiometer covering 0·415-14·235 μm wavelengths, with spatial resolution from 250 m to 1 km at nadir. MODIS will be the primary EOS sensor for providing data on terrestrial biospheric dynamics and process activity. This paper presents the suite of global land products currently planned for EOSDIS implementation, to be developed by the authors of this paper, the MODIS land team (MODLAND). These include spectral albedo, land cover, spectral vegetation indices, snow and ice cover, surface temperature and fire, and a number of biophysical variables that will allow computation of global carbon cycles, hydrologic balances and biogeochemistry of critical greenhouse gases. Additionally, the regular global coverage of these variables will allow accurate surface change detection, a fundamental determinant of global change.
International Journal of Remote Sensing↗
Estimation of forest fuel load from radar remote sensing
Understanding fire behavior characteristics and planning for fire management require maps showing the distribution of wildfire fuel loads at medium to fine spatial resolution across large landscapes. Radar sensors from airborne or spaceborne platforms have the potential of providing quantitative information about the forest structure and biomass components that can be readily translated to meaningful fuel load estimates for fire management. In this paper, we used multifrequency polarimetric synthetic aperture radar (SAR) imagery acquired over a large area of the Yellowstone National Park by the Airborne SAR sensor to estimate the distribution of forest biomass and canopy fuel loads. Semiempirical algorithms were developed to estimate crown and stem biomass and three major fuel load parameters, namely: 1) canopy fuel weight; 2) canopy bulk density; and 3) foliage moisture content. These estimates, when compared directly to measurements made at plot and stand levels, provided more than 70% accuracy and, when partitioned into fuel load classes, provided more than 85% accuracy. Specifically, the radar-generated fuel parameters were in good agreement with the field-based fuel measurements, resulting in coefficients of determination of R2 = 85 for the canopy fuel weight, R 2 = 0.84 for canopy bulk density, and R2 =0.78 for the foliage biomass. ?? 2007 IEEE.
IEEE Transactions on Geoscience and Remote Sensing↗
Novel approach for computing photosynthetically active radiation for productivity modeling using remotely sensed images in the Great Plains, United States
Gross primary production (GPP) is a key indicator of ecosystem performance, and helps in many decision-making processes related to environment. We used the Eddy covariancelight use efficiency (EC-LUE) model for estimating GPP in the Great Plains, United States in order to evaluate the performance of this model. We developed a novel algorithm for computing the photosynthetically active radiation (PAR) based on net radiation. A strong correlation ( R 2 =0.94, N =24) was found between daily PAR and Landsat-based mid-day instantaneous net radiation. Though the Moderate Resolution Spectroradiometer (MODIS) based instantaneous net radiation was in better agreement ( R 2 =0.98, N =24) with the daily measured PAR, there was no statistical significant difference between Landsat based PAR and MODIS based PAR. The EC-LUE model validation also confirms the need to consider biological attributes (C 3 versus C 4 plants) for potential light use efficiency. A universal potential light use efficiency is unable to capture the spatial variation of GPP. It is necessary to use C 3 versus C 4 based land use/land cover map for using EC-LUE model for estimating spatiotemporal distribution of GPP.
Journal of Applied Remote Sensing↗
Remote sensing of tidal chlorophyll-a variations in estuaries
Simultaneous acquisition of surface chlorophyll-a concentrations for 39 samples from boats and Daedalus 1260 Multispectral Scanner data from a U-2 aircraft was conducted in the northern reaches of San Francisco Bay on 28 August 1980. These data were used to develop regression models for predicting surface chlorophyll-a concentrations over the study area for ebb-tide (8.40 a.m. P.D.T. (Pacific Daylight Time)) and flood-tide (3.10 p.m. P.D.T.) conditions. After selection of a single ‘best fitting’ model for both morning and afternoon data sets, the chlorophyll-a concentration was predicted for ebb and flood tide for the entire study area at approximately 40m × 40m resolution. The predicted spatial display of chlorophyll-a revealed a localized area of high phytoplankton biomass that has been inferred from field surveys and appears to be a common summer phenomenon. Knowledge of the distribution of phytoplankton and the location of this zone of high biomass is valuable in establishing management policies for this ecologically important estuary. Furthermore, the techniques used here may provide an alternative cost-effective method for assessing water-quality conditions and they may prove useful for studying spatial variations (patchiness) and seasonal variations in phytoplankton biomass in other estuaries and coastal waters.
California↗
Developing in situ non-destructive estimates of crop biomass to address issues of scale in remote sensing
Ground-based estimates of aboveground wet (fresh) biomass (AWB) are an important input for crop growth models. In this study, we developed empirical equations of AWB for rice, maize, cotton, and alfalfa, by combining several in situ non-spectral and spectral predictors. The non-spectral predictors included: crop height (H), fraction of absorbed photosynthetically active radiation (F APAR ), leaf area index (LAI), and fraction of vegetation cover (FVC). The spectral predictors included 196 hyperspectral narrowbands (HNBs) from 350 to 2500 nm. The models for rice, maize, cotton, and alfalfa included H and HNBs in the near infrared (NIR); H, F APAR , and HNBs in the NIR; H and HNBs in the visible and NIR; and FVC and HNBs in the visible; respectively. In each case, the non-spectral predictors were the most important, while the HNBs explained additional and statistically significant predictors, but with lower variance. The final models selected for validation yielded an R 2 of 0.84, 0.59, 0.91, and 0.86 for rice, maize, cotton, and alfalfa, which when compared to models using HNBs alone from a previous study using the same spectral data, explained an additional 12%, 29%, 14%, and 6% in AWB variance. These integrated models will be used in an up-coming study to extrapolate AWB over 60 × 60 m transects to evaluate spaceborne multispectral broad bands and hyperspectral narrowbands.
Remote Sensing↗
Assessing long-term variations in sagebrush habitat: characterization of spatial extents and distribution patterns using multi-temporal satellite remote-sensing data
An approach that can generate sagebrush habitat change estimates for monitoring large-area sagebrush ecosystems has been developed and tested in southwestern Wyoming, USA. This prototype method uses a satellite-based image change detection algorithm and regression models to estimate sub-pixel percentage cover for five sagebrush habitat components: bare ground, herbaceous, litter, sagebrush and shrub. Landsat images from three different months in 1988, 1996 and 2006 were selected to identify potential landscape change during these time periods using change vector (CV) analysis incorporated with an image normalization algorithm. Regression tree (RT) models were used to estimate percentage cover for five components on all change areas identified in 1988 and 1996, using unchanged 2006 baseline data as training for both estimates. Over the entire study area (24 950 km 2 ), a net increase of 98.83 km 2 , or 0.7%, for bare ground was measured between 1988 and 2006. Over the same period, the other four components had net losses of 20.17 km 2 , or 0.6%, for herbaceous vegetation; 30.16 km 2 , or 0.7%, for litter; 32.81 km 2 , or 1.5%, for sagebrush; and 33.34 km 2 , or 1.2%, for shrubs. The overall accuracy for shrub vegetation change between 1988 and 2006 was 89.56%. Change patterns within sagebrush habitat components differ spatially and quantitatively from each other, potentially indicating unique responses by these components to disturbances imposed upon them.
International Journal of Remote Sensing↗
Spatio-temporal analysis of gyres in oriented lakes on the Arctic Coastal Plain of northern Alaska based on remotely sensed images
The formation of oriented thermokarst lakes on the Arctic Coastal Plain of northern Alaska has been the subject of debate for more than half a century. The striking elongation of the lakes perpendicular to the prevailing wind direction has led to the development of a preferred wind-generated gyre hypothesis, while other hypotheses include a combination of sun angle, topographic aspect, and/or antecedent conditions. A spatio-temporal analysis of oriented thermokarst lake gyres with recent (Landsat 8) and historical (Landsat 4, 5, 7 and ASTER) satellite imagery of the Arctic Coastal Plain of northern Alaska indicates that wind-generated gyres are both frequent and regionally extensive. Gyres are most common in lakes located near the Arctic coast after several days of sustained winds from a single direction, typically the northeast, and decrease in number landward with decreasing wind energy. This analysis indicates that the conditions necessary for the Carson and Hussey (1962) wind-generated gyre for oriented thermokarst lake formation are common temporally and regionally and correspond spatially with the geographic distribution of oriented lakes on the Arctic Coastal Plain. Given an increase in the ice-free season for lakes as well as strengthening of the wind regime, the frequency and distribution of lake gyres may increase. This increase has implications for changes in northern high latitude aquatic ecosystems, particularly if wind-generated gyres promote permafrost degradation and thermokarst lake expansion.
Alaska↗