Land use/cover change using Remote Sensing and Geographic Information Systems : Pic Macaya National Park, Haiti

A post classification change detection technique based on a hybrid classification approach (unsupervised and supervised) was applied to Landsat Thematic Mapper (TM), Landsat Enhanced Thematic Plus (ETM+), and ASTER images acquired in 1987, 2000 and 2004 respectively to map land use/cover changes in the Pic Macaya National Park in the southern region of Haiti. Each image was classified individually into six land use/cover classes: built-up, agriculture, herbaceous, open pine forest, mixed forest, and barren land using unsupervised ISODATA and maximum likelihood supervised classifiers with the aid of field collected ground truth data collected in the field. Ground truth information, collected in the field in December 2007, and including equalized stratified random points which were visual interpreted were used to assess the accuracy of the classification results. The overall accuracy of the land classification for each image was respectively: 1987 (82%), 2000 (82%), 2004 (87%). A post classification change detection technique was used to produce change images for 1987 to 2000, 1987 to 2004, and 2000 to 2004. It was found that significant changes in the land use/cover occurred over the 17- year period. The results showed increases in built up (from 10% to 17%) and herbaceous (from 5% to 14%) areas between 1987 and 2004. The increase of herbaceous was mostly caused by the abandonment of exhausted agriculture lands. At the same time, open pine forest and mixed forest areas lost (75%) and (83%) of their area to other land use/cover types. Open pine forest (from 20% to 14%) and mixed forest (from18 to 12%) were transformed into agriculture area or barren land. This study illustrated the continuing deforestation, land degradation and soil erosion in the region, which in turn is leading to decrease in vegetative cover. The study also showed the importance of Remote Sensing (RS) and Geographic Information System (GIS) technologies to estimate timely changes in the land use/cover, and to evaluate their causes in order to design an ecological based management plan for the park.

Remote sensing of volcanic plumes using the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER).

The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) has been used to quantify SO2 emissions from passively degassing volcanoes. This dissertation explores ASTER’s capability to detect SO2 with satellite validation, enhancement techniques and extensive processing of images at a variety of volcanoes. ASTER is compared to the Mini UV Spectrometer (MUSe), a ground based instrument, to determine if reasonable SO2 fluxes can be quantified from a plume emitted from Lascar, Chile. The two sensors were in good agreement with ASTER proving to be a reliable detector of SO2. ASTER illustrated the advantages of imaging a plume in 2D, with better temporal resolution than the MUSe. SO2 plumes in ASTER imagery are not always discernible in the raw TIR data. Principal Component Analysis (PCA) and Decorrelation Stretch (DCS) enhancement techniques were compared to determine how well they highlight a variety of volcanic plumes. DCS produced a consistent output and the composition of the plumes was easy to identify from explosive eruptions. As the plumes became smaller and lower in altitude they became harder to distinguish using DCS. PCA proved to be better at identifying smaller low altitude plumes. ASTER was used to investigate SO2 emissions at Lascar, Chile. Activity at Lascar has been characterized by cyclic behavior and persistent degassing (Matthews et al. 1997). Previous studies at Lascar have primarily focused on changes in thermal infrared anomalies, neglecting gas emissions. Using the SO2 data along with changes in thermal anomalies and visual observations it is evident that Lascar is at the end an eruptive cycle that began in 1993. Declining gas emissions and crater temperatures suggest that the conduit is sealing. ASTER and the Ozone Monitoring Instrument (OMI) were used to determine the annual contribution of SO2 to the troposphere from the Central and South American volcanic arcs between 2000 and 2011. Fluxes of 3.4 Tg/a for Central America and 3.7 Tg/a for South America were calculated. The detection limits of ASTER were explored. The results a proved to be interesting, with plumes from many of the high emitting volcanoes, such as Villarrica, Chile, not being detected by ASTER.

Can we infer plant facilitation from remote sensing? a test across global drylands

Facilitation is a major force shaping the structure and diversity of plant communities in terrestrial ecosystems. Detecting positive plant–plant interactions relies on the combination of field experimentation and the demonstration of spatial association between neighboring plants. This has often restricted the study of facilitation to particular sites, limiting the development of systematic assessments of facilitation over regional and global scales. Here we explore whether the frequency of plant spatial associations detected from high-resolution remotely sensed images can be used to infer plant facilitation at the community level in drylands around the globe. We correlated the information from remotely sensed images freely available through Google Earth with detailed field assessments, and used a simple individual-based model to generate patch-size distributions using different assumptions about the type and strength of plant–plant interactions. Most of the patterns found from the remotely sensed images were more right skewed than the patterns from the null model simulating a random distribution. This suggests that the plants in the studied drylands show stronger spatial clustering than expected by chance. We found that positive plant co-occurrence, as measured in the field, was significantly related to the skewness of vegetation patch-size distribution measured using Google Earth images. Our findings suggest that the relative frequency of facilitation may be inferred from spatial pattern signals measured from remotely sensed images, since facilitation often determines positive co-occurrence among neighboring plants. They pave the road for a systematic global assessment of the role of facilitation in terrestrial ecosystems. Read More: http://www.esajournals.org/doi/10.1890/14-2358.1

Radiance, irradiance, and remote sensing reflectance during the North Sea Coast Harmful Algal Bloom (NORCOHAB II) HEINCKE cruise HE302

In this study four data quality flags are presented for automated and unmanned above-water hyperspectral optical measurements collected underway in the North Sea, The Minch, Irish Sea and Celtic Sea in April/May 2009. Coincident to these optical measurements a DualDome D12 (Mobotix, Germany) camera system was used to capture sea surface and sky images. The first three flags are based on meteorological conditions, to select erroneous incoming solar irradiance (ES) taken during dusk, dawn, before significant incoming solar radiation could be detected or under rainfall. Furthermore, the relative azimuthal angle of the optical sensors to the sun is used to identify possible sunglint free sea surface zones. A total of 629 spectra remained after applying the meteorological masks (first three flags). Based on this dataset, a fourth flag for sunglint was generated by analysing and evaluating water leaving radiance (LW) and remote sensing reflectance (RRS) spectral behaviour in the presence and absence of sunglint salient in the simultaneously available sea surface images. Spectra conditions satisfying "mean LW (700-950 nm) < 2 mW/m**2/nm/Sr" or alternatively "minimum RRS (700-950 nm) < 0.010/Sr", mask the most measurements affected by sunglint, providing efficient flagging of sunglint in automated quality control. It is confirmed that valid optical measurements can be performed 0° <= theta <= 360° although 90° <= theta <= 135° is recommended.

Shipborne ocean color remote sensing

In this paper, modernized shipborne procedures are presented to collect and process above-water radiometry for remote sensing applications. A setup of five radiometers and a bidirectional camera system, which provides panoramic sea surface and sky images, is proposed for the collection of high-resolution radiometric quantities. Images from the camera system can be used to determine sky state and potential glint, whitecaps, or foam contamination. A peak in the observed remote sensing reflectance RRS spectra between 750-780 nm was typically found in spectra with relatively high surface reflected glint (SRG), which suggests this waveband could be a useful SRG indicator. Simplified steps for computing uncertainties in SRG corrected RRS are proposed and discussed. The potential of utilizing "unweighted multimodel averaging," which is the average of four or more common SRG correction models, is examined to determine the best approximation RRS. This best approximation RRS provides an estimate of RRS based on various SRG correction models established using radiative transfer simulations and field investigations. Applying the average RRS provides a measure of the inherent uncertainties or biases that result from a user subjectively choosing any one SRG correction model. Comparisons between inherent and apparent optical property derived observations were used to assess the robustness of the SRG multimodel averaging ap- proach. Correlations among the standard SRG models were completed to determine the degree of association or similarities between the SRG models. Results suggest that the choice of glint models strongly affects derived RRS values and can also influence the blue to green band ratios used for modeling biogeochemical parameters such as for chlorophyll a. The objective here is to present a uniform and traceable methodology for determining ship- borne RRS measurements and its associated errors due to glint correction and to ensure the direct comparability of these measurements in future investigations. We encourage the ocean color community to publish radiometric field measurements with matching and complete metadata in open access repositories.

Offshore remote sensing of the ocean by stereo vision systems

In recent years, remote sensing imaging systems for the measurement of oceanic sea states have attracted renovated attention. Imaging technology is economical, non-invasive and enables a better understanding of the space-time dynamics of ocean waves over an area rather than at selected point locations of previous monitoring methods (buoys, wave gauges, etc.). We present recent progress in space-time measurement of ocean waves using stereo vision systems on offshore platforms, which focus on sea states with wavelengths in the range of 0.01 m to 1 m. Both traditional disparity-based systems and modern elevation-based ones are presented in a variational optimization framework: the main idea is to pose the stereoscopic reconstruction problem of the surface of the ocean in a variational setting and design an energy functional whose minimizer is the desired temporal sequence of wave heights. The functional combines photometric observations as well as spatial and temporal smoothness priors. Disparity methods estimate the disparity between images as an intermediate step toward retrieving the depth of the waves with respect to the cameras, whereas elevation methods estimate the ocean surface displacements directly in 3-D space. Both techniques are used to measure ocean waves from real data collected at offshore platforms in the Black Sea (Crimean Peninsula, Ukraine) and the Northern Adriatic Sea (Venice coast, Italy). Then, the statistical and spectral properties of the resulting observed waves are analyzed. We show the advantages and disadvantages of the presented stereo vision systems and discuss future lines of research to improve their performance in critical issues such as the robustness of the camera calibration in spite of undesired variations of the camera parameters or the processing time that it takes to retrieve ocean wave measurements from the stereo videos, which are very large datasets that need to be processed efficiently to be of practical usage. Multiresolution and short-time approaches would improve efficiency and scalability of the techniques so that wave displacements are obtained in feasible times.

Application of remote sensing and GIS in mineral resource mapping - An overview

Remote sensing, as a direct adjunct to field, lithologic and structural mapping, and more recently, GIS have played an important role in the study of mineralized areas. A review on the application of remote sensing in mineral resource mapping is attempted here. It involves understanding the application of remote sensing in lithologic, structural and alteration mapping. Remote sensing becomes an important tool for locating mineral deposits, in its own right, when the primary and secondary processes of mineralization result in the formation of spectral anomalies. Reconnaissance lithologic mapping is usually the first step of mineral resource mapping. This is complimented with structural mapping, as mineral deposits usually occur along or adjacent to geologic structures, and alteration mapping, as mineral deposits are commonly associated with hydrothermal alteration of the surrounding rocks. In addition to these, understanding the use of hyperspectral remote sensing is crucial as hyperspectral data can help identify and thematically map regions of exploration interest by using the distinct absorption features of most minerals. Finally coming to the exploration stage, GIS forms the perfect tool in integrating and analyzing various georeferenced geoscience data in selecting the best sites of mineral deposits or rather good candidates for further exploration.

Mud, algae or seagrass: An assessment of remote sensing techniques for mapping inter-tidal seagrass cover and composition in turbid tropical environments

Large areas of tropical sub- and inter-tidal seagrass beds occur in highly turbid environments and cannot be mapped through the water column. The purpose of this project was to determine if and how airborne and satellite imaging systems could be used to map inter-tidal seagrass properties along the wet-tropics coast in north Queensland, Australia. The work aimed to: (1) identify the minimum level of seagrass foliage cover that could be detected from airborne and satellite imagery; and (2) define the minimum detectable differences in seagrass foliage cover in exposed intertidal seagrass beds. High resolution spectral-reflectance data (2040 bands, 350 – 2500nm) were collected over 40cm diameter plots from 240 sites on Magnetic Island, Pallarenda Beach and Green Island in North Queensland at spring low tides in April 2006. The seagrass species sampled were: Thalassia hemprechii, Halophila ovalis, Halodule uninerivs; Syringodium isoetifolium, Cymodocea serrulata, and Cymodoea rotundata. Digital photos were captured for each plot and used to derive estimates of seagrass species cover, epiphytic growth, micro- and macro-algal cover, and substrate colour. Sediment samples were also collected and analysed to measure the concentration of Chlorophyll-a associated with benthic micro-algae. The field reflectance spectra were analysed in combination with their corresponding seagrass species foliage cover levels to establish the minimum foliage projective cover required for each seagrass to be significantly different from bare substrate and substrate with algal cover. This analysis was repeated with reflectance spectra resampled to the bandpass functions of Quickbird, Ikonos, SPOT 5 and Landsat 7 ETM. Preliminary results indicate that conservative minimum detectable seagrass cover levels across most the species sampled were between 30%- 35% on dark substrates. Further analysis of these results will be conducted to determine their separability and satellite images and to assess the effects epiphytes and algal cover.

Remote sensing applied to slope stability in mountainous roads in Iran

The Alborz Mountain range separates the northern part of Iran from the southern part. It also isolates a narrow coastal strip to the south of the Caspian Sea from the Central Iran plateau. Communication between the south and north until the 1950's was via two roads and one rail link. In 1963 work was completed on a major access road via the Haraz Valley (the most physically hostile area in the region). From the beginning the road was plagued by accidents resulting from unstable slopes on either side of the valley. Heavy casualties persuaded the government to undertake major engineering works to eliminate ''black spots" and make the road safe. However, despite substantial and prolonged expenditure the problems were not solved and casualties increased steadily due to the increase in traffic using the road. Another road was built to bypass the Haraz road and opened to traffic in 1983. But closure of the Haraz road was still impossible because of the growth of settlements along the route and the need for access to other installations such as the Lar Dam. The aim of this research was to explore the possibility of applying Landsat MSS imagery to locating black spots along the road and the instability problems. Landsat data had not previously been applied to highway engineering problems in the study area. Aerial photographs are better in general than satellite images for detailed mapping, but Landsat images are superior for reconnaissance and adequate for mapping at the 1 :250,000 scale. The broad overview and lack of distortion in the Landsat imagery make the images ideal for structural interpretation. The results of Landsat digital image analysis showed that certain rock types and structural features can be delineated and mapped. The most unstable areas comprising steep slopes, free of vegetation cover can be identified using image processing techniques. Structural lineaments revealed from the image analysis led to improved results (delineation of unstable features). Damavand Quaternary volcanics were found to be the dominant rock type along a 40 km stretch of the road. These rock types are inherently unstable and partly responsible for the difficulties along the road. For more detailed geological and morphological interpretation a sample of small subscenes was selected and analysed. A special developed image analysis package was designed at Aston for use on a non specialized computing system. Using this package a new and unique method for image classification was developed, allowing accurate delineation of the critical features of the study area.

Lithological mapping of Northwest Argentina with remote sensing data using tonal, textural and contextual features

Tonal, textural and contextual properties are used in manual photointerpretation of remotely sensed data. This study has used these three attributes to produce a lithological map of semi arid northwest Argentina by semi automatic computer classification procedures of remotely sensed data. Three different types of satellite data were investigated, these were LANDSAT MSS, TM and SIR-A imagery. Supervised classification procedures using tonal features only produced poor classification results. LANDSAT MSS produced classification accuracies in the range of 40 to 60%, while accuracies of 50 to 70% were achieved using LANDSAT TM data. The addition of SIR-A data produced increases in the classification accuracy. The increased classification accuracy of TM over the MSS is because of the better discrimination of geological materials afforded by the middle infra red bands of the TM sensor. The maximum likelihood classifier consistently produced classification accuracies 10 to 15% higher than either the minimum distance to means or decision tree classifier, this improved accuracy was obtained at the cost of greatly increased processing time. A new type of classifier the spectral shape classifier, which is computationally as fast as a minimum distance to means classifier is described. However, the results for this classifier were disappointing, being lower in most cases than the minimum distance or decision tree procedures. The classification results using only tonal features were felt to be unacceptably poor, therefore textural attributes were investigated. Texture is an important attribute used by photogeologists to discriminate lithology. In the case of TM data, texture measures were found to increase the classification accuracy by up to 15%. However, in the case of the LANDSAT MSS data the use of texture measures did not provide any significant increase in the accuracy of classification. For TM data, it was found that second order texture, especially the SGLDM based measures, produced highest classification accuracy. Contextual post processing was found to increase classification accuracy and improve the visual appearance of classified output by removing isolated misclassified pixels which tend to clutter classified images. Simple contextual features, such as mode filters were found to out perform more complex features such as gravitational filter or minimal area replacement methods. Generally the larger the size of the filter, the greater the increase in the accuracy. Production rules were used to build a knowledge based system which used tonal and textural features to identify sedimentary lithologies in each of the two test sites. The knowledge based system was able to identify six out of ten lithologies correctly.