Environmental applications of remote sensing

This article may also be accessed from the publisher\u27s website at http://www.svifsi.ch/revue/pages/issues/n004/no004.html Remote sensing is routinely used for understanding many aspects of the earth environment that are important to sustainability. Remote sensing is used in weather forecasting and global climate studies, natural hazard analysis, crop condition and yield prediction, and forestry applications, for example. The techniques and hardware used to obtain the remotely sensed data for these applications are as widely varying as the applications themselves. Remote imaging systems may collect spectral data of reflected sunlight, emitted thermal or microwave radiation, or reflected radar signals to provide the desired information on the current status of the environment. These data can be collected from the air or from space and may be useful in the form of numerical data or in the form of an image. The goal of this paper is to examine the current state of the art in transforming remotely sensed image data into more useful information by integration with predictive models

Determining improvements in Landsat spectral sampling for inland water quality monitoring

Inland waters are optically complex and provide an ongoing challenge to effective water quality monitoring through remote sensing. Imaging satellites with spectral sampling designed for this task often have coarse spatial resolutions, preventing any capture of information from small lakes. Medium resolution satellite systems such as Landsat 8 have the appropriate spatial resolution and sensitivity required to resolve these waterbodies, but the spectral sampling is not optimal. This work uses system simulation to explore potential changes to Landsat spectral sampling to determine if its ability to monitor inland waters could be improved. The HydroLight and MODTRAN radiative transfer models are used for simulation in a Look Up Table and spectrum matching approach to provide maximum flexibility intesting spectral sampling scenarios. To isolate the testing to the impacts of spectral sampling, all simulations were performed based on the known system noise characteristics of Landsat 8. Spectral sampling changes tested include the addition of yellow and red edge spectral bands as well as conversion to an imaging spectrometer. Simulated spectra of inland waters undergoing a cyanobacteria bloom, including atmospheric effects and sensor noise, were implemented with the Look-Up-Table retrieval process to extract estimated concentrations of waterbody components. The retrieval accuracy of each potential system is compared to that of a modeled Landsat 8 baseline. All potential systems show an increase of retrieval accuracy over the baseline. The best performing system design is an imaging spectrometer, followed by the addition of both a yellow and red edge band simultaneously, and the addition of either band individually. Testing also demonstrates that resampling an imaging spectrometer with 20 nm spectral resolution to the Landsat 8 band responses produces outputs matching those available from Landsat 8. Our results indicate that future Landsat missions should aim to add as much spectral sampling as is feasible, while maintaining at least the same sensitivity. The minimum change to improve water quality monitoring capability is the addition of a red edge spectral band

Glint Correction of Unmanned Aerial System Imagery

Glint in aquatic imagery captured by Unmanned Aerial Systems (UAS) is a limiting factor when performing spectral analysis. It cannot be corrected by methods developed for space-based imaging systems, meaning new approaches are required. Two processes using in-situ radiometric data were developed augmenting an established method for removing atmospheric effects from imagery, the Empirical Line Method (ELM), to remove glint from multispectral UAS imagery. The results of this correction showed good agreement with in-situ spectroradiometer measurements and similar accuracy to atmospherically compensated satellite measurements. The Root-Mean-Square Error of the UAS retrieved remote sensing reflectance was as low as 0.0004 sr -1 and outperformed the traditional ELM

Automated Extraction of Fire Line Parameters from Multispectral Infrared Images

Remotely sensed infrared images are often used to assess wildland ¯re conditions. Separately, ¯re propagation models are in use to forecast future conditions. In the Dynamic Data Driven Application System (DDDAS) concept, the ¯re propagation model will react to the image data, which should produce more accurate predictions of ¯re propagation. In this study we describe a series of image processing tools that can be used to extract ¯re propagation parameters from multispectral infrared images so that the parameters can be used to drive a ¯re propagation model built upon the DDDAS concept. The method is capable of automatically determining the ¯re perimeter, active ¯re line, and ¯re propagation direction. A multi-band image gradient calculation, the Normalized Di®erence Vegetation Index, and the Normalized Di®erence Burn Ratio along with several standard image processing techniques are used to identify and constrain the ¯re propagation parameters. These ¯re propagation parameters can potentially be used within the DDDAS modeling framework for model update and adjustment

An Automatic Statistical Segmentation Algorithm for Extraction of Fire and Smoke Regions

Estimation of the extent and spread of wildland fires is an important application of high spatial resolution multispectral images. This work addresses a fuzzy segmentation algorithm to map fire extent, active fire front, hot burn scar, and smoke regions based on a statistical model. The fuzzy results are useful data sources for integrated fire behavior and propagation models built using Dynamic Data Driven Applications Systems (DDDAS) concepts that use data assimilation techniques which require error estimates or probabilities for the data parameters. The Hidden Markov Random Field (HMRF) model has been used widely in image segmentation, but it is assumed that each pixel has a particular class label belonging to a prescribed finite set. The mixed pixel problem can be addressed by modeling the fuzzy membership process as a continuous Multivariate Gaussian Markov Random Field. Techniques for estimating the class membership and model parameters are discussed. Experimental results obtained by applying this technique to two Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) images show that the proposed methodology is robust with regard to noise and variation in fire characteristics as well as background. The segmentation results of our algorithm are compared with the results of a K-means algorithm, an Expectation Maximization (EM) algorithm (which is very similar to the Fuzzy C-Means Clustering algorithm with entropy regularization), and an MRF-MAP algorithm. Our fuzzy algorithm achieves more consistent segmentation results than the comparison algorithms for these test images with the added advantage of simultaneously providing a proportion or error map needed for the data assimilation problem

Simulation of Internet of Things Water Management for Efficient Rice Irrigation in Rwanda

The central role of water access for agriculture is a clear challenge anywhere in the world and particularly in areas with significant seasonal variation in rainfall such as in Eastern and Central Africa. The combination of modern sensor technologies, the Internet, and advanced irrigation equipment combined in an Internet of Things (IoT) approach allow a relatively precise control of agricultural irrigation and creating the opportunity for high efficiency of water use for agricultural demands. This IoT approach can thereby increase the resilience of agricultural systems in the face of complex demands for water use. Most previous works on agricultural IoT systems are in the context of countries with higher levels of economic development. However, in Rwanda, with a low level of economic development, the advantages of efficient water use from the application of IoT technology requires overcoming constraints such as lack of irrigation control for individual farmers, lack of access to equipment, and low reliability of power and Internet access. In this work, we describe an approach for adapting previous studies to the Rwandan context for rice (Oryza sativa) farming with irrigation. The proposed low cost system would automatically provide irrigation control according to seasonal and daily irrigational needs when the system sensors and communications are operating correctly. In cases of system component failure, the system switches to an alternative prediction mode and messages farmers with information about the faults and realistic irrigation options until the failure is corrected. We use simulations to demonstrate, for the Muvumba Rice Irrigation Project in Northeast Rwanda, how the system would respond to growth stage, effective rainfall, and evapotranspiration for both correct operation and failure scenarios

Towards a Real-Time Data Driven Wildland Fire Model

A wildland fire model based on semi-empirical relations for the spread rate of a surface fire and post-frontal heat release is coupled with the Weather Research and Forecasting atmospheric model (WRF). The propagation of the fire front is implemented by a level set method. Data is assimilated by a morphing ensemble Kalman filter, which provides amplitude as well as position corrections. Thermal images of a fire will provide the observations and will be compared to a synthetic image from the model state.Comment: 5 pages, 4 figure

Autonomous Field-Deployable Wildland Fire Sensors

An Autonomous Fire Detector (AFD) is a miniature electronic package combining position location capability [using the Global Positioning System (GPS)], communications (packet or voice-synthesized radio), and fire detection capability (thermal, gas, smoke detector) into an inexpensive, deployable package. The AFD can report fire-related parameters, like temperature, carbon monoxide concentration, or smoke levels via a radio link to firefighters located on the ground. These systems are designed to be inserted into the fire by spotter planes at a fire site or positioned by firefighters already on the ground. AFDs can also be used as early warning devices near critical assets in the urban–wildland interface. AFDs can now be made with commercial off-the-shelf components. Using modern micro-electronics, an AFD can operate for the duration of even the longest fire (weeks) using a simple dry battery pack, and can be designed to have a transmitting range of up to several kilometers with current low power radio communication technology. A receiver to capture the data stream from the AFD can be made as light, inexpensive and portable as the AFD itself. Inexpensive portable repeaters can be used to extend the range of the AFD and to coordinate many probes into an autonomous fire monitoring network

Building capacity in remote sensing for conservation: present and future challenges

Remote sensing (RS) has made significant contributions to conservation and ecology; however, direct use of RS-based information for conservation decision making is currently very limited. In this paper, we discuss the reasons and challenges associated with using RS technology by conservationists and suggest how training in RS for conservationists can be improved. We present the results from a survey organized by the Conservation Remote Sensing Network to understand the RS expertise and training needs of various categories of professionals involved in conservation research and implementation. The results of the survey highlight the main gaps and priorities in the current RS data and technology among conservation practitioners from academia, institutions, NGOs and industry. We suggest training to be focused around conservation questions that can be addressed using RS-derived information rather than training pure RS methods which are beyond the interest of conservation practitioners. We highlight the importance of developing essential biodiversity variables (EBVs) and how this can be achieved by increasing the RS capacity of the conservation community. Moreover, we suggest that open-source software is adopted more widely in the training modules to facilitate access to RS data and products in developing countries, and that online platforms providing mapping tools should also be more widely distributed. We believe that improved RS capacity among conservation scientists will be essential to improve conservation efforts on the ground and will make the conservation community a key player in the definition of future RS-based products that serve conservation and ecological needs