Applications of remote sensing, volume 3

The author has identified the following significant results. Of the four change detection techniques (post classification comparison, delta data, spectral/temporal, and layered spectral temporal), the post classification comparison was selected for further development. This was based upon test performances of the four change detection method, straightforwardness of the procedures, and the output products desired. A standardized modified, supervised classification procedure for analyzing the Texas coastal zone data was compiled. This procedure was developed in order that all quadrangles in the study are would be classified using similar analysis techniques to allow for meaningful comparisons and evaluations of the classifications

Towards the development of a smart flying sensor: illustration in the field of precision agriculture

Sensing is an important element to quantify productivity, product quality and to make decisions. Applications, such as mapping, surveillance, exploration and precision agriculture, require a reliable platform for remote sensing. This paper presents the first steps towards the development of a smart flying sensor based on an unmanned aerial vehicle (UAV). The concept of smart remote sensing is illustrated and its performance tested for the task of mapping the volume of grain inside a trailer during forage harvesting. Novelty lies in: (1) the development of a position-estimation method with time delay compensation based on inertial measurement unit (IMU) sensors and image processing; (2) a method to build a 3D map using information obtained from a regular camera; and (3) the design and implementation of a path-following control algorithm using model predictive control (MPC). Experimental results on a lab-scale system validate the effectiveness of the proposed methodology

Feasibility Study of Microwave Radiometric Remote Sensing. Volume 3 - Additional Plots and Printouts

Microwave radiometric remote sensing feasibility study - Vol. 3 - additional plots and printout

Holographic enhanced remote sensing system

The Holographic Enhanced Remote Sensing System (HERSS) consists of three primary subsystems: (1) an Image Acquisition System (IAS); (2) a Digital Image Processing System (DIPS); and (3) a Holographic Generation System (HGS) which multiply exposes a thermoplastic recording medium with sequential 2-D depth slices that are displayed on a Spatial Light Modulator (SLM). Full-parallax holograms were successfully generated by superimposing SLM images onto the thermoplastic and photopolymer. An improved HGS configuration utilizes the phase conjugate recording configuration, the 3-SLM-stacking technique, and the photopolymer. The holographic volume size is currently limited to the physical size of the SLM. A larger-format SLM is necessary to meet the desired 6 inch holographic volume. A photopolymer with an increased photospeed is required to ultimately meet a display update rate of less than 30 seconds. It is projected that the latter two technology developments will occur in the near future. While the IAS and DIPS subsystems were unable to meet NASA goals, an alternative technology is now available to perform the IAS/DIPS functions. Specifically, a laser range scanner can be utilized to build the HGS numerical database of the objects at the remote work site

The Huayruro Project: mapping the Calicanto Inca area buried by the A.D. 1600 Huaynaputina eruption, with geophysical imaging and remote sensing

We present geophysical and remote sensing observations near the Quinistaquillas town (southern Peru), in the framework of the HUAYRURO Project. This Inca zone was buried during the A.D. 1600 Huaynaputina eruption, the most important volcanic phenomenon of the last 400 years. The eruption had a global impact, due to the volume of emitted ash (2-3 times the one emitted by Vesuvius in A.D. 79). This lead to a 1.13°C cooling of the planet and caused a worlwide agricultural crisis. During the eruption, the Calicanto-Chimpapampa zone was covered by ashes and pyroclastic flows, with a thickness in the range [1 - 20] m. From 2015 to 2017, remote sensing and geophysical methods were deployed to map a ~ 1 km*2 km area, up to 3-m depth

Assessment of ecosystem integrity of lowland dipterocarp forest ecosystem using remote sensing

Ecosystem Integrity Index (EII) is a concept to determine the quality or the health of an ecosystem. The EII development can assist forest managers and decision makers in the conservation effort and forest management in Malaysia through the development of a simple and easy-to-adopt index. The aim of this study is to assess and evaluate the EII through the development of forest structure empirical models from remotely sensed data for lowland dipterocarp forest in Malaysia. The objectives of this study are: (i) to assess the structure and composition of lowland dipterocarp forest in Malaysia, (ii) to develop empirical model for estimating stand structure from remotely sensed data, and (iii) to derive the ecosystem integrity index for lowland dipterocarp forest. Tree Basal Area (BA), aboveground biomass (AGB) and volume plot from plot data were used as dependent variables, while remote sensing data from Landsat, Pleiades and LiDAR were used as independent variables for model development. Tree plot census was carried out from 17 to 19 May 2016, while remote sensing data acquisition dates for Landsat, Pleiades and LiDAR were 13 March 2016, 24 January 2015 and April 2015 respectively. Forest Structure Modeling was carried out by means of a correlation analysis with the calibration of dependent and independent data to select the most significant and accurate remote sensing variables to derive empiric equation (model), fitting stage to select the best model with the highest coefficient of determination (R2) and the lowest root mean square error ( RMSE) validation of the final selected. The Ecosystem Integrity Index was developed by the average percentage of the predicted BA, AGB and model volume. The EII was categorised at five integrity levels as high (81–100%), medium high (61–80%), moderate (41–60%), medium low (21–40%) and low (0–20%). A total of 1035 trees with diameter at breast height (DBH) of 5.0 cm and above were recorded in 69.115 ha sampling areas. The total trees recorded represented 150 species from 87 genera and 34 families. Shorea macroptera (Dipterocarpaceae), S. leprosula (Dipterocarpaceae) and S. parviflora (Dipterocarpaceae) are three dominant species, with Species Important Value Index (SIVi) of 6.49%, 6.23% and 5.51%, respectively. Dipterocarpaceae is the most dominant with Family Important Value Index (FIVi) of 33.54%. The developed final model is robust and consistent with high R2 with range of 0.84 to 0.87. The final models constructed for AGB, BA and volume value of R2 are 0.85, 0.84 and 0.87 respectively. The RMSE of AGB, BA and volume model are 53.1 Mg/ha, 3.54 m2/ha and 46.4 m3/ha, respectively. The overall stand AGB, BA and volume for Sungai Menyala Forest Reserve is 282.29 Mg/ha, 17.68 m2/ha and 239.51 m3/ha. An Ecosystem Integrity Index (EII) assessment has been successfully demonstrated by this study with production of practical, multi-scaled, flexible, adjustable and policy-relevant index. The overall EII of Sungai Menyala Forest Reserve is in Category 3, which shows that the area is within the medium value

Estimation of forest structure and canopy fuel parameters from small-footprint full-waveform LiDAR data

Precise knowledge of fuel conditions is important for predicting fire hazards and simulating fire growth and intensity across the landscape. We present a methodology to retrieve and map forest canopy fuel and other forest structural parameters using small-footprint full-waveform airborne light detection and ranging (LiDAR) data. Full-waveform LiDAR sensors register the complete returned backscattered signal through time and can describe physical properties of the intercepted objects. This study was undertaken in a mixed forest dominated by Douglas-fir, occasionally mixed with other conifers, in north-west Oregon (United States). We extracted two sets of LiDAR metrics using pulse detection and waveform modelling and then constructed several predictive models using forward stepwise multiple linear regression. The resulting models explained ~80% of the variability for many of the canopy fuel and forest structure parameters: aboveground biomass (R2 = 0.84), quadratic mean diameter (R2 = 0.82), canopy height (R2 = 0.79), canopy base height (R2 = 0.78) and canopy fuel load (R2 = 0.79). The lowest performing models included basal area (R2 = 0.76), stand volume (R2 = 0.73), canopy bulk density (R2 = 0.67) and stand density index (R2 = 0.66). Our results indicate that full-waveform LiDAR systems show promise in systematically characterising the structure and canopy fuel loads of forests, which may enable accurate fire behaviour forecasting that in turn supports the development of prevention and planning policies.This paper was developed as a result of two mobility grants funded by the Erasmus Mundus Programme of the European Commission under the Transatlantic Partnership for Excellence in Engineering (TEE Project) and the Generalitat Valenciana (BEST/2012/235). The authors appreciate the financial support provided by the Spanish Ministry of Science and Innovation in the framework of the project CGL2010-19591/BTE. In addition, the authors thank the Panther Creek Remote Sensing and Research cooperative program for the data provided for this research, Jim Flewelling (Seattle Biometrics) and George McFadden (Bureau of Land Management) for their help in data availability and preparation.Hermosilla Gómez, T.; Ruiz Fernández, LÁ.; Kazakova, AN.; Coops, N.; Moskal, LM. (2014). Estimation of forest structure and canopy fuel parameters from small-footprint full-waveform LiDAR data. International Journal of Wildland Fire. 23(2):224-233. https://doi.org/10.1071/WF13086S224233232Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19(6), 716-723. doi:10.1109/tac.1974.1100705Andersen, H.-E., McGaughey, R. J., & Reutebuch, S. E. (2005). Estimating forest canopy fuel parameters using LIDAR data. 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Assessing canopy fuel stratum characteristics in crown fire prone fuel types of western North America. International Journal of Wildland Fire, 12(1), 39. doi:10.1071/wf02024Drake, J. B., Dubayah, R. O., Clark, D. B., Knox, R. G., Blair, J. B., Hofton, M. A., … Prince, S. (2002). Estimation of tropical forest structural characteristics using large-footprint lidar. Remote Sensing of Environment, 79(2-3), 305-319. doi:10.1016/s0034-4257(01)00281-4Erdody, T. L., & Moskal, L. M. (2010). Fusion of LiDAR and imagery for estimating forest canopy fuels. Remote Sensing of Environment, 114(4), 725-737. doi:10.1016/j.rse.2009.11.002Falkowski, M. J., Gessler, P. E., Morgan, P., Hudak, A. T., & Smith, A. M. S. (2005). Characterizing and mapping forest fire fuels using ASTER imagery and gradient modeling. Forest Ecology and Management, 217(2-3), 129-146. doi:10.1016/j.foreco.2005.06.013Flannigan, M. ., Stocks, B. ., & Wotton, B. . (2000). Climate change and forest fires. 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Reconstruction of cloud geometry using a scanning cloud radar

Clouds are one of the main reasons of uncertainties in the forecasts of weather and climate. In part, this is due to limitations of remote sensing of cloud microphysics. Present approaches often use passive spectral measurements for the remote sensing of cloud microphysical parameters. Large uncertainties are introduced by three-dimensional (3-D) radiative transfer effects and cloud inhomogeneities. Such effects are largely caused by unknown orientation of cloud sides or by shadowed areas on the cloud. Passive ground-based remote sensing of cloud properties at high spatial resolution could be crucially improved with this kind of additional knowledge of cloud geometry. To this end, a method for the accurate reconstruction of 3-D cloud geometry from cloud radar measurements is developed in this work. Using a radar simulator and simulated passive measurements of model clouds based on a large eddy simulation (LES),the effects of different radar scan resolutions and varying interpolation methods are evaluated. In reality, a trade-off between scan resolution and scan duration has to be found as clouds change quickly. A reasonable choice is a scan resolution of 1 to 2\degree. The most suitable interpolation procedure identified is the barycentric interpolation method. The 3-D reconstruction method is demonstrated using radar scans of convective cloud cases with the Munich miraMACS, a 35 GHz scanning cloud radar. As a successful proof of concept, camera imagery collected at the radar location is reproduced for the observed cloud cases via 3-D volume reconstruction and 3-D radiative transfer simulation. Data sets provided by the presented reconstruction method will aid passive spectral ground-based measurements of cloud sides to retrieve microphysical parameters

Reconstruction of cloud geometry using a scanning cloud radar

Clouds are one of the main reasons of uncertainties in the forecasts of weather and climate. In part, this is due to limitations of remote sensing of cloud microphysics. Present approaches often use passive spectral measurements for the remote sensing of cloud microphysical parameters. Large uncertainties are introduced by three-dimensional (3-D) radiative transfer effects and cloud inhomogeneities. Such effects are largely caused by unknown orientation of cloud sides or by shadowed areas on the cloud. Passive ground-based remote sensing of cloud properties at high spatial resolution could be crucially improved with this kind of additional knowledge of cloud geometry. To this end, a method for the accurate reconstruction of 3-D cloud geometry from cloud radar measurements is developed in this work. Using a radar simulator and simulated passive measurements of model clouds based on a large eddy simulation (LES),the effects of different radar scan resolutions and varying interpolation methods are evaluated. In reality, a trade-off between scan resolution and scan duration has to be found as clouds change quickly. A reasonable choice is a scan resolution of 1 to 2\degree. The most suitable interpolation procedure identified is the barycentric interpolation method. The 3-D reconstruction method is demonstrated using radar scans of convective cloud cases with the Munich miraMACS, a 35 GHz scanning cloud radar. As a successful proof of concept, camera imagery collected at the radar location is reproduced for the observed cloud cases via 3-D volume reconstruction and 3-D radiative transfer simulation. Data sets provided by the presented reconstruction method will aid passive spectral ground-based measurements of cloud sides to retrieve microphysical parameters