- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Estimation of 1-Km All-Weather Land Surface Temperature Over the Tibetan Plateau
摘要： Land surface temperature (LST) immensely affects the energy balance and water cycle on the earth’s surface. Merging thermal infrared (TIR) and passive microwave (MW) remote sensing provides the possibility to obtain all-weather LST with moderate resolutions. However, due to difficulties in downscaling MW LST, current methods merging TIR LST and MW LST into such an all-weather LST are limited over large areas with very complicated land surfaces (e.g. the Tibetan Plateau). By fully considering the influence of the topography on estimation of merged LSTs, this study revises the recently-developed physical method for generating the 1-km all-weather LST and applies it over the Tibetan Plateau to merge MODIS (1 km) and AMSR2 (10 km) observations. Results show that the merged LST has accuracy of 0.99 K-3.22 K when validated against in-situ LSTs from five ground stations with various land cover types. This study would be beneficial for continuously monitoring LST and improving spatio-temporal resolutions for associated land surface process studies requiring high-quality all-weather LST over large scales.
关键词： MODIS,Spatial correlations,AMSR2,Land surface temperature (LST),Passive microwave remote sensing
更新于2025-09-23 15:21:01
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Estimating above Ground Biomass and Carbon Stock in the Lake Hawassa Watershed, Ethiopia by Integrating Remote Sensing and Allometric Equations
摘要： With the increasing concentration of carbon dioxide in the Earth’s atmosphere as the result of deforestation, there is a pressing need to estimate biomass and carbon pools in tropical forests. This is, particularly, essential in Africa where reliable biomass data is lacking. The present study was aimed at classifying land use land cover, estimating above ground biomass using remote sensing data and allometric equations, and determining the importance value of species in Lake Hawassa Watershed. Pantropic allometric equations were used that relate tree variables obtained by non-destructive measurements to the oven dry biomass. Local species specific biomass equations were also used to compare the results. The results indicated that the natural forest had lower mean above ground biomass (200.9 Mg/ha) than the plantation forest (223.6 Mg/ha). The pantropic allometric equations overestimated the above ground biomass by about 13.0% and 20.5% for natural and plantation forests, respectively, compared to the local equations. This variation is likely to be the main source of uncertainty for biomass computed using generalized equations. The species sampled ranged from 1 to 22 per plot and the overall mean stand density was 785 stems/ha. Cupressus lucitanica (60.09%), Grevillea robusta (28.65%), and Eucalyptus citriodora (20.87%) were the species with the highest importance value. The majority of tree species belonged to the diameter at breast height class of 5–25 cm accounting for 79.1% and 73.3% in plantation and natural forests, respectively. The total above ground biomass of the forest in the study area in 2011 was estimated at 1.72 Megatons. Although using generalized allometric equations demonstrated variations in above ground biomass estimates compared to the local species specific equations, results from this research effort can be used in absence of area specific models.
关键词： Allometric equations,Remote sensing,Importance value index,Above ground biomass,Forest inventory
更新于2025-09-23 15:21:01
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[IEEE 2018 IEEE 31st Canadian Conference on Electrical & Computer Engineering (CCECE) - Quebec City, QC (2018.5.13-2018.5.16)] 2018 IEEE Canadian Conference on Electrical & Computer Engineering (CCECE) - Solar Forecasting Using Remote Solar Monitoring Stations and Artificial Neural Networks
摘要： The need to accurately forecast available solar irradiance is a significant issue for the power industry and poses special challenges for utilities who serve customers in isolated regions where weather forecast data may not be abundant. This paper proposes a method to forecast two hour ahead solar irradiance levels at a site in Northwestern Alberta, Canada using real-time solar irradiance measured both locally and at remote monitoring stations. This paper makes use of an Artificial Neural Network (ANN) to forecast the solar irradiance levels and uses the genetic algorithm to determine the optimal array size and positioning of solar monitoring stations to obtain the most accurate forecast from the ANN. The findings of this paper are that it is possible to use as few as five remote monitoring stations to obtain a near-peak forecasting accuracy from the algorithm and that providing adequate geospatial separation of the remote monitoring sites around the target site is more desirable than clustering the sites in the strictly upwind directions.
关键词： GHI,remote sensing,solar,PV,isolated generation,forecasting,irradiance,artificial neural network
更新于2025-09-23 15:21:01
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Long-Term Agroecosystem Research in the Central Mississippi River Basin: Hyperspectral Remote Sensing of Reservoir Water Quality
摘要： In situ methods for estimating water quality parameters would facilitate efforts in spatial and temporal monitoring, and optical reflectance sensing has shown potential in this regard, particularly for chlorophyll, suspended sediment, and turbidity. The objective of this research was to develop and evaluate relationships between hyperspectral remote sensing and lake water quality parameters—chlorophyll, turbidity, and N and P species. Proximal hyperspectral water reflectance data were obtained on seven sampling dates for multiple arms of Mark Twain Lake, a large man-made reservoir in northeastern Missouri. Aerial hyperspectral data were also obtained on two dates. Water samples were collected and analyzed in the laboratory for chlorophyll, nutrients, and turbidity. Previously reported reflectance indices and full-spectrum (i.e., partial least squares regression) methods were used to develop relationships between spectral and water quality data. With the exception of dissolved NH3, all measured water quality parameters were strongly related (R2 ≥ 0.7) to proximal reflectance across all measurement dates. Aerial hyperspectral sensing was somewhat less accurate than proximal sensing for the two measurement dates where both were obtained. Although full-spectrum calibrations were more accurate for chlorophyll and turbidity than results from previously reported models, those previous models performed better for an independent test set. Because extrapolation of estimation models to dates other than those used to calibrate the model greatly increased estimation error for some parameters, collection of calibration samples at each sensing date would be required for the most accurate remote sensing estimates of water quality.
关键词： water quality,Mark Twain Lake,partial least squares regression,chlorophyll,hyperspectral remote sensing,nutrients,turbidity
更新于2025-09-23 15:21:01
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Spectral Image Fusion from Compressive Measurements
摘要： Compressive spectral imagers reduce the number of sampled pixels by coding and combining the spectral information. However, sampling compressed information with simultaneous high spatial and high spectral resolution demands expensive high-resolution sensors. This work introduces a model allowing data from high spatial/low spectral and low spatial/high spectral resolution compressive sensors to be fused. Based on this model, the compressive fusion process is formulated as an inverse problem that minimizes an objective function defined as the sum of a quadratic data fidelity term and smoothness and sparsity regularization penalties. The parameters of the different sensors are optimized and the choice of an appropriate regularization is studied in order to improve the quality of the high resolution reconstructed images. Simulation results conducted on synthetic and real data, with different CS imagers, allow the quality of the proposed fusion method to be appreciated.
关键词： Spectral imaging,data fusion,remote sensing,compressive sampling
更新于2025-09-23 15:21:01
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Cross Validation of GPM and Ground-Based Radar in Latin America and the Caribbean
摘要： A comparison between GPM Dual-frequency Precipitation Radar and ground-based radars located in South America and the Caribbean is presented. The analysis compares radar variables from both system during overpasses of GPM over ground-based radars in the region of interest. Attenuation and bias correction is performed to ground radar data. The results show the potential of GPM to calibrate and monitor weather radars and subsequently using them for ground validation in Latin America and the Caribbean.
关键词： Ground-based weather Radar,Precipitation Measurement,Remote Sensing,GPM,DPR,Ground Validation
更新于2025-09-23 15:21:01
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[American Society of Agricultural and Biological Engineers 2018 Detroit, Michigan July 29 - August 1, 2018 - ()] 2018 Detroit, Michigan July 29 - August 1, 2018 - <i>Cotton Yield Estimation based on Plant Height From UAV-based Imagery Data </i>
摘要： Accurate estimation of crop yield before harvest, especially in early growth stages, is important for farmers and researchers to optimize field management and evaluate crop performance. However, conventional methods of using ground sensing to estimate crop yield are not efficient. The goal of this research was to evaluate the potential of using a UAV-based remote sensing system with a low-cost RGB camera to estimate yield of cotton within season. The UAV system took images at 50 m above ground level over a cotton field at the growth stage of first flower. Waypoints and flight speed were selected to allow > 70% image overlap in both forward and side directions. Images were processed to develop a geo-referenced orthomosaic image and a digital elevation model (DEM) of the field, which was then used to map plant height by calculating the difference in elevation between the crop canopy and the bare soil surface. Twelve ground control points (calibration objects) with known GPS coordinates and height were deployed in the field and were used as check points for geo-referencing and height calibration. Geo-referenced yield data were registered with the plant height map row-by-row. Correlation analysis between yield and plant height was conducted row-by-row with row registration and without row registration respectively. Pearson correlation coefficients between yield and plant height for all individual rows were in the range of 66% to 96%, higher than those without row registration (54% to 95%). A non-parametric regression used for building a yield estimation model based on image-derived plant height was able to estimate yield with less than 10% error (root mean square error of 360.4 kg ha-1 and mean absolute error of 180.9 kg ha-1). The results indicated that the UAV-based remote sensing system equipped with a low-cost digital camera was able to estimate cotton yield with acceptable errors.
关键词： yield estimation,UAV-based remote sensing,geo-registration,plant height,Cotton
更新于2025-09-23 15:21:01
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An introduction to the FY3 GNOS instrument and mountain-top tests
摘要： The FY3 (Feng-Yun-3) GNOS (GNSS Occultation Sounder) mission is a GNSS (Global Navigation Satellite System) radio occultation mission of China for remote sensing of Earth’s neutral atmosphere and the ionosphere. GNOS will use both the global positioning system (GPS) and the Beidou navigation satellite systems on the China Feng-Yun-3 (FY3) series satellites. The ?rst FY3-C was launched at 03:07 UTC on 23 September 2013. GNOS was developed by the Center for Space Science and Applied Research, Chinese Academy of Sciences (CSSAR). It will provide vertical pro?les of atmospheric temperature, pressure, and humidity, as well as ionospheric electron density pro?les on a global basis. These data will be used for numerical weather prediction, climate research, and ionospheric research and space weather. This paper describes the FY3 GNOS mission and the GNOS instrument characteristics. It presents simulation results of the number and distribution of GNOS occultation events with the regional Beidou constellation and the full GPS constellation, under the limitation of the GNOS instrument occultation channel number. This paper presents the instrument performance as derived from analysis of measurement data in laboratory and mountain-based occultation validation experiments at Mt. Wuling in Hebei Province. The mountain-based GNSS occultation validation tests show that GNOS can acquire or track low-elevation radio signal for rising or setting occultation events. The refractivity pro?les of GNOS obtained during the mountain-based experiment were compared with those from radiosondes. The results show that the refractivity pro?les obtained by GNOS are consistent with those from the radiosonde. The rms of the differences between the GNOS and radiosonde refractivities is less than 3 %.
关键词： GNSS Occultation Sounder,FY3 GNOS,Beidou,atmospheric remote sensing,ionospheric research,GPS
更新于2025-09-23 15:21:01
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Comprehensive Remote Sensing || Accuracy and Area Estimation
摘要： A key strength of remote sensing, and one of the main reason for its usage, is the provision of spatially exhaustive (wall-to-wall) coverage of a region of interest. Classification and interpretation of the remote sensing data allow for thematic mapping of features present in the region. However, mapping complex and often spatially continuous surface conditions into a set of discrete map categories is bound to result in some of the map units being erroneous. The magnitude of errors will determine the reliability, usage, and interpretation of the map, which is why map users and producers have a direct interest in communicating and understanding the quality of maps. This is the primary reason for the tradition within the remote sensing community of conducting map accuracy assessments (terms in italic are explained in the Terminology section). The basis of an accuracy assessment is the comparison of the map and a sample of observations of reference conditions at certain selected locations. The sample is selected by probability sampling if the locations of the sampling units are selected such that the likelihood of a unit (a pixel for example) being included in the sample is known and greater than zero (Stehman, 2001). A probability sample allows for inference of the accuracy of the map for the entire population, which in this case is the collection of map units from which the sample is selected. For example, consider the following common scenario: a land-cover map has been constructed over specific region and a set of units have been selected by simple random sampling. If identifying land-cover reference conditions at each location in the sample, the overall accuracy of the map can easily be computed as the ratio of correctly classified units to the total number of units in the sample. The overall accuracy is a measure of the probability that a random map unit is correctly classified—not just a random map unit in the sample, but of all units of the map. This holds true because the sample was selected by probability sampling. As explained in section “Design-Based Inference”, accuracy measures specific to the individual map categories are also easily computed.
关键词： thematic mapping,probability sampling,remote sensing,accuracy assessment,error matrix
更新于2025-09-23 15:21:01
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Vine Signal Extraction – an Application of Remote Sensing in Precision Viticulture
摘要： This paper presents a study of precision agriculture in the wine industry. While precision viticulture mostly aims to maximise yields by delivering the right inputs to appropriate places on a farm in the correct doses and at the right time, the objective of this study was rather to assess vine biomass differences. The solution proposed in this paper uses aerial imagery as the primary source of data for vine analysis. The first objective to be achieved by the solution is to automatically identify vineyards blocks, vine rows, and individual vines within rows. This is made possible through a series of enhancements and hierarchical segmentations of the aerial images. The second objective is to determine the correlation of image data with the biophysical data (yield and pruning mass) of each vine. A multispectral aerial image is used to compute vegetation indices, which serve as indicators of biophysical measures. The results of the automatic detection are compared against a test field, to verify both vine location and vegetation index correlation with relevant vine parameters. The advantage of this technique is that it functions in environments where active cover crop growth between vines is evident and where variable vine canopy conditions are present within a vineyard block.
关键词： precision viticulture,remote sensing,segmentation,GIS,Precision agriculture,classification
更新于2025-09-23 15:21:01