Special Section Guest Editorial: Advances in Agro-Hydrological Remote Sensing for Water Resources Conservation

This special section focuses on the use of remote sensing tools in some of these areas, including monitoring the volume and turbidity in lake fresh water resources, retrieving soil organic matter from spectral information with particular attention to abandoned croplands and areas affected by wildfires, and identification and monitoring of natural and agricultural vegetation through emerging techniques such as shallow and deep learning algorithms. These data mining and analysis approaches are particularly promising and include convolutional neural network and the application of back propagation neural network algorithms for soil water content monitoring and the extraction of other canopy information

Introduction

This proceedings volume contains papers presented during the conference on Remote Sensing for Agriculture, Ecosystems, and Hydrology XV. The conference was part of the 20th International Symposium on Remote Sensing sponsored by SPIE—The International Society for Optical Engineering. The symposium was held at the Internationales Congress Center, Dresden, Germany, from 23rd to 26th of September 2013. The conference is dedicated to providing rapid dissemination of scientific and technical information, and attracted scientists and professionals from throughout Europe, Africa, Asia, and the Americas. Approximately 40 oral and 20 poster presentations were given, covering a broad range of topics in the field of remote sensing applications in environmental science. The program was organized according to major themes, with 11 sessions on Agriculture: Nitrogen and chlorophyll Assessment, Irrigation and soil water content, and Crop monitoring (3); Ecosystems: Estuaries, rivers, lakes; Forest monitoring; Classification and change detection; Land characterization, and, Environmental monitoring (5); Hydrology: Energy Balance and evapotranspiration and Hydrology (2). Finally, a Joint Session with SAR Image Analysis, Modelling and Techniques conference included selected papers concerning the subject “radar application in Agro-Hydrology”. The poster presentations also had good representation from the above-mentioned themes. The presentations described both fundamental and applications-based research activities including modelling, laboratory and field experiments, and operational applications. We extend our thanks to Goffredo La Loggia of Università degli Studi di Palermo for chairing two of the sessions, to the presenters for their efforts and to the participants for their insightful questions and discussions. Special thanks are also due to the host city for the excellent venue and to the SPIE organizational staff for their support prior to, during, and after the symposium. We look forward to an even more successful conference in 2014

Remote Sensing for Agriculture, Ecosystems, and Hydrology XIII

This proceedings volume contains papers presented during the conference on Remote Sensing for Agriculture, Ecosystems, and Hydrology XIII. The conference was part of the 18th International Symposium on Remote Sensing sponsored by SPIE—The International Society for Optical Engineering. The symposium was held at the Clarion Congress Hotel Prague, Prague, Czech Republic, from 19th to 21th of September 2011. The conference is dedicated to providing rapid dissemination of scientific and technical information, and attracted scientists and professionals from throughout Europe, Africa, Asia, and the Americas. Approximately 45 oral and 30 poster presentations were given, covering a broad range of topics in the field of remote sensing applications in environmental science. The program was organized according to major themes, with 10 sessions on Agriculture: Irrigation and Energy Balance and Agriculture (2); Ecosystems: Estuarine, Coastal and Inland Waters; Vegetation and Change detection; Hydrology: Snow and Hydrology (2). The poster presentations also had good representation from the abovementioned themes. The presentations described both fundamental and applications-based research activities from modelling, to laboratory and field experiments, to operational applications. The oral program also included three invited presentations: James L. Foster of NASA Goddard Space Flight Ctr. (USA) gave a presentation on the subject “Remote sensing of snow cover and snow water equivalent for the historic snowstorms in the Baltimore/Washington area during February 2010” within a Snow session; Heikki K. Saari of VTT Technical Research Ctr. of Finland (Finland) gave a presentation on the subject “Unmanned aerial vehicle (UAV) operated spectral camera system for forest and agriculture applications” within General Application session; Clement Atzberger of University of Natural Resources and Life Sciences (BOKU) Wien, (Austria) gave a presentation on the subject “Why confining to vegetation indices? Exploiting the potential of improved spectral observations” within a Vegetation session; Short reports pointing out the state of art and perspectives in the research fields of the invited talks are reported below. We extend our thanks to the co-editor Katja Richter of Ludwig-Maximilians-Univ. München, and Session chairs Francesco Vuolo of Univ. of Univ. für Bodenkultur Wien (Austria) and Goffredo La Loggia of Univ. degli Studi di Palermo, and to the presenters for their efforts and to the participants for their insightful questions and discussions. Special thanks are also due to the host city for the excellent venue and to all the SPIE organizational staff for their support prior to, during, and after the symposium. We look forward to an even more successful conference in 2012

A new methodology to assess the maximum irrigation rates at catchment scale using geostatistics and GIS

Soil hydraulic parameters are important for irrigation scheduling. In the domain of “precision irrigation”, knowledge of the spatial distribution of these parameters is useful in determining the maximum irrigation rate for each field in a catchment. This study focuses on the development of a new methodology to assess the spatial distribution of the maximum irrigation rate depending on the available soil water holding capacity (ASWHC). This methodology combines geostatistical techniques with geographical information system (GIS) tools. A pilot zone of 12 400 ha in a Spanish Mediterranean area was selected to develop this methodology. The linear coregionalization model (LMCR), considering the percentage of sand, carbonates, and ASWHC at others soil depths as covariates, was the best option to model the ASWHC. Other required soil parameters were also spatially modeled. The percent of coarse fragments was modeled by regression kriging considering the soil map as an auxiliary variable. The bulk density was spatially modeled by LMCR, and extended to the rooting depth by linear regression. The spatial distributions modeled were implemented in a GIS with other spatial information layers of irrigation management parameters, such as the maximum allowable depletion of soil water content, the percent of wetted soil and the irrigation depth. The combination of these layers in the GIS was used to estimate the maximum irrigation rates for each field. A propagation error analysis was performed to know the uncertainties in the maximum irrigation rate estimation. Based on this information, the irrigation managers could optimize the irrigation rates for each field

What is the value of a good map ? An example using high spatial resolution imagery to aid riparian restoration

Riparian areas contain structurally diverse habitats that are challenging to monitor routinely and accurately over broad areas. As the structural variability within riparian areas is often indiscernible using moderate-scale satellite imagery, new mapping techniques are needed. We used high spatial resolution satellite imagery from the QuickBird satellite to map harvested and intact forests in coastal British Columbia, Canada. We distinguished forest structural classes used in riparian restoration planning, each with different restoration costs. To assess the accuracy of high spatial resolution imagery relative to coarser imagery, we coarsened the pixel resolution of the image, repeated the classifications, and compared results. Accuracy assessments produced individual class accuracies ranging from 70 to 90% for most classes; whilst accuracies obtained using coarser scale imagery were lower. We also examined the implications of map error on riparian restoration budgets derived from our classified maps. To do so, we modified the confusion matrix to create a cost error matrix quantifying costs associated with misclassification. High spatial resolution satellite imagery can be useful for riparian mapping; however, errors in restoration budgets attributable to misclassification error can be significant, even when using highly accurate maps. As the spatial resolution of imagery increases, it will be used more routinely in ecosystem ecology. Thus, our ability to evaluate map accuracy in practical, meaningful ways must develop further. The cost error matrix is one method that can be adapted for conservation and planning decisions in many ecosystems