Use of Remote Sensing for Collection of Data Elements for Linear Referencing Systems, 2002

This report evaluates the use of remotely sensed images in implementing the Iowa DOT LRS that is currently in the stages of system architecture. The Iowa Department of Transportation is investing a significant amount of time and resources into creation of a linear referencing system (LRS). A significant portion of the effort in implementing the system will be creation of a datum, which includes geographically locating anchor points and then measuring anchor section distances between those anchor points. Currently, system architecture and evaluation of different data collection methods to establish the LRS datum is being performed for the DOT by an outside consulting team.

New developments in UAV remote sensing for pest management and the implications for cotton

Efficient crop monitoring and pest damage assessments are key to protecting the Australian agricultural industry and ensuring its leading position internationally. An important element in pest detection is gathering reliable crop data frequently and integrating analysis tools for decision making. Unmanned aerial systems are emerging as a cost-effective solution to a number of precision agriculture challenges. An important advantage of this technology is it provides a non-invasive aerial sensor platform to accurately monitor broad acre crops. In this presentation, we will give an overview on how unmanned aerial systems and machine learning can be combined to address crop protection challenges. A recent 2015 study on insect damage in sorghum will illustrate the effectiveness of this methodology. A UAV platform equipped with a high-resolution camera was deployed to autonomously perform a flight pattern over the target area. We describe the image processing pipeline implemented to create a georeferenced orthoimage and visualize the spatial distribution of the damage. An image analysis tool has been developed to minimize human input requirements. The computer program is based on a machine learning algorithm that automatically creates a meaningful partition of the image into clusters. Results show the algorithm delivers decision boundaries that accurately classify the field into crop health levels. The methodology presented in this paper represents a venue for further research towards automated crop protection assessments in the cotton industry, with applications in detecting, quantifying and monitoring the presence of mealybugs, mites and aphid pests.

Airborne remote sensing of ocean wave directional wavenumber spectra in the marginal ice zone

Interactions between surface waves and sea ice are thought to be an important, but poorly understood, physical process in the atmosphere-ice-ocean system. In this work, airborne scanning lidar was used to observe ocean waves propagating into the marginal ice zone (MIZ). These represent the first direct spatial measurements of the surface wave field in the polar MIZ. Data were compared against two attenuation models, one based on viscous dissipation and one based on scattering. Both models were capable of reproducing the measured wave energy. The observed wavenumber dependence of attenuation was found to be consistent with viscous processes, while the spectral spreading of higher wavenumbers suggested a scattering mechanism. Both models reproduced a change in peak direction due to preferential directional filtering. Floe sizes were recorded using co-located visible imagery, and their distribution was found to be consistent with ice breakup by the wave field.

The study of correlation between sea surface temperature (SST) by using the NOAA-AVHRR data and compare this data with temperature measurement in the Caspian Sea

The changes in time and location of surface temperature from a water body has an important effect on climate activities, marine biology, sea currents, salinity and other characteristics of the seas and lakes water. Traditional measurement of temperature is costly and time consumer due to its dispersion and instability. In recent years the use of satellite technology and remote sensing sciences for data acquiring and parameter and lysis of climatology and oceanography is well developed. In this research we used the NOAA’s Satellite images from its AVHRR system to compare the field surface temperature data with the satellite images information. Ten satellite images were used in this project. These images were calibrated with the field data at the exact time of satellite pass above the area. The result was a significant relation between surface temperatures from satellite data with the field work. As the relative error less than %40 between these two data is acceptable, therefore in our observation the maximum error is %21.2 that can be considered it as acceptable. In all stations the result of satellite measurements is usually less than field data that cores ponds with the global result too. As this sea has a vast latitude, therefore the different in the temperature is natural. But we know this factor is not the only cause for surface currents. The information of all satellites were images extracted by ERDAS software, and the “Surfer” software is used to plot the isotherm lines.

Agriculture, Environmental Incentive Payments, and Water Quality in the Chesapeake Bay

Nonpoint sources (NPS) pollution from agriculture is the leading source of water quality impairment in U.S. rivers and streams, and a major contributor to lakes, wetlands, estuaries and coastal waters (U.S. EPA 2016). Using data from a survey of farmers in Maryland, this dissertation examines the effects of a cost sharing policy designed to encourage adoption of conservation practices that reduce NPS pollution in the Chesapeake Bay watershed. This watershed is the site of the largest Total Maximum Daily Load (TMDL) implemented to date, making it an important setting in the U.S. for water quality policy. I study two main questions related to the reduction of NPS pollution from agriculture. First, I examine the issue of additionality of cost sharing payments by estimating the direct effect of cover crop cost sharing on the acres of cover crops, and the indirect effect of cover crop cost sharing on the acres of two other practices: conservation tillage and contour/strip cropping. A two-stage simultaneous equation approach is used to correct for voluntary self-selection into cost sharing programs and account for substitution effects among conservation practices. Quasi-random Halton sequences are employed to solve the system of equations for conservation practice acreage and to minimize the computational burden involved. By considering patterns of agronomic complementarity or substitution among conservation practices (Blum et al., 1997; USDA SARE, 2012), this analysis estimates water quality impacts of the crowding-in or crowding-out of private investment in conservation due to public incentive payments. Second, I connect the econometric behavioral results with model parameters from the EPA’s Chesapeake Bay Program to conduct a policy simulation on water quality effects. I expand the econometric model to also consider the potential loss of vegetative cover due to cropland incentive payments, or slippage (Lichtenberg and Smith-Ramirez, 2011). Econometric results are linked with the Chesapeake Bay Program watershed model to estimate the change in abatement levels and costs for nitrogen, phosphorus and sediment under various behavioral scenarios. Finally, I use inverse sampling weights to derive statewide abatement quantities and costs for each of these pollutants, comparing these with TMDL targets for agriculture in Maryland.

A COMPARISON OF PIXEL-BASED VERSUS OBJECT ORIENTED ANALYSIS OF LANDSLIDES USING HISTORICAL REMOTE SENSING DATA

With recent advances in remote sensing processing technology, it has become more feasible to begin analysis of the enormous historic archive of remotely sensed data. This historical data provides valuable information on a wide variety of topics which can influence the lives of millions of people if processed correctly and in a timely manner. One such field of benefit is that of landslide mapping and inventory. This data provides a historical reference to those who live near high risk areas so future disasters may be avoided. In order to properly map landslides remotely, an optimum method must first be determined. Historically, mapping has been attempted using pixel based methods such as unsupervised and supervised classification. These methods are limited by their ability to only characterize an image spectrally based on single pixel values. This creates a result prone to false positives and often without meaningful objects created. Recently, several reliable methods of Object Oriented Analysis (OOA) have been developed which utilize a full range of spectral, spatial, textural, and contextual parameters to delineate regions of interest. A comparison of these two methods on a historical dataset of the landslide affected city of San Juan La Laguna, Guatemala has proven the benefits of OOA methods over those of unsupervised classification. Overall accuracies of 96.5% and 94.3% and F-score of 84.3% and 77.9% were achieved for OOA and unsupervised classification methods respectively. The greater difference in F-score is a result of the low precision values of unsupervised classification caused by poor false positive removal, the greatest shortcoming of this method.

Assessing the impact of agricultural drought on maize prices in Kenya with the approach of the SPOT-VEGETATION NDVI remote sensing

The high cost of maize in Kenya is basically driven by East African regional commodity demand forces and agricultural drought. The production of maize, which is a common staple food in Kenya, is greatly affected by agricultural drought. However, calculations of drought risk and impact on maize production in Kenya is limited by the scarcity of reliable rainfall data. The objective of this study was to apply a novel hyperspectral remote sensing method to modelling temporal fluctuations of maize production and prices in five markets in Kenya. SPOT-VEGETATION NDVI time series were corrected for seasonal effects by computing the standardized NDVI anomalies. The maize residual price time series was further related to the NDVI seasonal anomalies using a multiple linear regression modelling approach. The result shows a moderately strong positive relationship (0.67) between residual price series and global maize prices. Maize prices were high during drought periods (i.e. negative NDVI anomalies) and low during wet seasons (i.e. positive NDVI anomalies). This study concludes that NDVI is a good index for monitoring the evolution of maize prices and food security emergency planning in Kenya. To obtain a very strong correlation for the relationship between the wholesale maize price and the global maize price, future research could consider adding other price-driving factors into the regression models.

Analysis of spatial variation of tree heights in an urban area using LiDAR data.

Estimating with greater precision and accuracy the height of plants has been a challenge for the scientific community. The objective this study is to evaluate the spatial variation of tree heights at different spatial scales in areas of the city of Recife, Brazil, using LiDAR remote sensing data. The LiDAR data were processed in the QT Modeler (Quick Terrain Modeler v. 8.0.2) software from Applied Imagery. The TreeVaW software was utilized to estimate the heights and crown diameters of trees. The results obtained for tree height were consistent with field measurements.

Detecting outliers and asserting consistency in agriculture ground truth information by using temporal VI data from modis.

Collecting ground truth data is an important step to be accomplished before performing a supervised classification. However, its quality depends on human, financial and time ressources. It is then important to apply a validation process to assess the reliability of the acquired data. In this study, agricultural infomation was collected in the Brazilian Amazonian State of Mato Grosso in order to map crop expansion based on MODIS EVI temporal profiles. The field work was carried out through interviews for the years 2005-2006 and 2006-2007. This work presents a methodology to validate the training data quality and determine the optimal sample to be used according to the classifier employed. The technique is based on the detection of outlier pixels for each class and is carried out by computing Mahalanobis distances for each pixel. The higher the distance, the further the pixel is from the class centre. Preliminary observations through variation coefficent validate the efficiency of the technique to detect outliers. Then, various subsamples are defined by applying different thresholds to exclude outlier pixels from the classification process. The classification results prove the robustness of the Maximum Likelihood and Spectral Angle Mapper classifiers. Indeed, those classifiers were insensitive to outlier exclusion. On the contrary, the decision tree classifier showed better results when deleting 7.5% of pixels in the training data. The technique managed to detect outliers for all classes. In this study, few outliers were present in the training data, so that the classification quality was not deeply affected by the outliers.

Comprehensive characterization of snowfall in terms of microphysical features, quantitative precipitation estimation and scavenging properties by in situ and remote sensing observations at an Antarctic coastal site

Snow plays a crucial role in the Earth's hydrological cycle and energy budget, making its monitoring necessary. In this context, ground-based radars and in situ instruments are essential thanks to their spatial coverage, resolution, and temporal sampling. Deep understanding and reliable measurements of snow properties are crucial over Antarctica to assess potential future changes of the surface mass balance (SMB) and define the contribution of the Antarctic ice sheet on sea-level rise. However, despite its key role, Antarctic precipitation is poorly investigated due to the continent's inaccessibility and extreme environment. In this framework, this Thesis aims to contribute to filling this gap by in-depth characterization of Antarctic precipitation at the Mario Zucchelli station from different points of view: microphysical features, quantitative precipitation estimation (QPE), vertical structure of precipitation, and scavenging properties. For this purpose, a K-band vertically pointing radar collocated with a laser disdrometer and an optical particle counter (OPC) were used. The radar probed the lowest atmospheric layers with high vertical resolution, allowing the first trusted measurement at only 105 m height. Disdrometer and OPC provided information on the particle size distribution and aerosol concentrations. An innovative snow classification methodology was designed by comparing the radar reflectivity (Ze) and disdrometer-derived reflectivity by means of DDA simulations. Results of classification were exploited in QPE through appropriate Ze-snow rate relationships. The accuracy of the resulting QPE was benchmarked against a collocated weighing gauge. Vertical radar profiles were also investigated to highlight hydrometeors' sublimation and growth processes. Finally, OPC and disdrometer data allowed providing the first-ever estimates of scavenging properties of Antarctic snowfall. Results presented in this Thesis give rise to advances in knowledge of the characteristics of snowfall in Antarctica, contributing to a better assessment of the SMB of the Antarctic ice sheet, the major player in the global sea-level rise.

Rural and Urban Streams: Anthropogenic Influences and Impacts on Water and Sediment Quality

The Pirapo river watershed (Parana State, Brazil) compounds a relatively industrialized and urbanized region, undergoing great pressure from the discharge of industrial, agricultural and domestic wastes. We evaluated the environmental quality of ten streams belonging to this watershed in April and June 2008 by performing acute and chronic toxicity tests with Daphnia similis and Ceriodaphnia silvestrii from water and sediment samples. We tested the hypothesis that the streams located in urban areas are more exposed to the influence of pollutants, than those outside the city limits. In addition, we obtained the measures of physical and chemical parameters, and identified the main polluted sources. Contrary to what was expected, the rural streams were more toxic than those located in urban area. These results demonstrate that the water bodies located in rural areas are being affected by the pollution of aquatic ecosystems as far as those found in urban areas, requiring the same attention of environmental managers in relation to its monitoring.

Regional investment strategies (RIS) and coastal catchment initiative (CCI) project alignmnet to Great Barrier Reef Water Quality Protection Plan

The Great Barrier Reef Water Quality Protection Plan (the Reef Plan) is a joint initiative of the Australian and Queensland Governments. The Reef Plan aims to progress an integrated approach to natural resource management planning by building on the existing partnerships between the different levels of government, industry groups, the community and research providers within the Reef catchments, principally through partnerships with the regional natural resource management (NRM) bodies.