Remote Sens.2014, 6(12), 11810-11828; doi:10.3390/rs61211810 - published 27 November 2014 Show/Hide Abstract
Abstract: Thermal Infrared (TIR) remote sensing images of urban environments are increasingly available from airborne and satellite platforms. However, limited access to high-spatial resolution (H-res: ~1 m) TIR satellite images requires the use of TIR airborne sensors for mapping large complex urban surfaces, especially at micro-scales. A critical limitation of such H-res mapping is the need to acquire a large scene composed of multiple flight lines and mosaic them together. This results in the same scene components (e.g., roads, buildings, green space and water) exhibiting different temperatures in different flight lines. To mitigate these effects, linear relative radiometric normalization (RRN) techniques are often applied. However, the Earth’s surface is composed of features whose thermal behaviour is characterized by complexity and non-linearity. Therefore, we hypothesize that non-linear RRN techniques should demonstrate increased radiometric agreement over similar linear techniques. To test this hypothesis, this paper evaluates four (linear and non-linear) RRN techniques, including: (i) histogram matching (HM); (ii) pseudo-invariant feature-based polynomial regression (PIF_Poly); (iii) no-change stratified random sample-based linear regression (NCSRS_Lin); and (iv) no-change stratified random sample-based polynomial regression (NCSRS_Poly); two of which (ii and iv) are newly proposed non-linear techniques. When applied over two adjacent flight lines (~70 km2) of TABI-1800 airborne data, visual and statistical results show that both new non-linear techniques improved radiometric agreement over the previously evaluated linear techniques, with the new fully-automated method, NCSRS-based polynomial regression, providing the highest improvement in radiometric agreement between the master and the slave images, at ~56%. This is ~5% higher than the best previously evaluated linear technique (NCSRS-based linear regression).
Remote Sens.2014, 6(12), 11791-11809; doi:10.3390/rs61211791 - published 27 November 2014 Show/Hide Abstract
Abstract: Daily, or more frequent, maps of surface water have important applications in environmental and water resource management. In particular, surface water maps derived from remote sensing imagery play a useful role in the derivation of spatial inundation patterns over time. MODIS data provide the most realistic means to achieve this since they are daily, although they are often limited by cloud cover during flooding events, and their spatial resolutions (250–1000 m pixel) are not always suited to small river catchments. This paper tests the suitability of the MODIS sensor for identifying flood events through comparison with streamflow and rainfall measurements at a number of sites during the wet season in Northern Australia. This is done using the MODIS Open Water Likelihood (OWL) algorithm which estimates the water fraction within a pixel. On a temporal scale, cloud cover often inhibits the use of MODIS imagery at the start and lead-up to the peak of a flood event, but there are usually more cloud-free data to monitor the flood’s recession. Particularly for smaller flood events, the MODIS view angle, especially when the view angle is towards the sun, has a strong influence on total estimated flood extent. Our results showed that removing pixels containing less than 6% water can eliminate most commission errors when mapping surface water. The exception to this rule was for some spectrally dark pixels occurring along the edge of the MODIS swath where the relative azimuth angle (i.e., angle between the MODIS’ and sun’s azimuth angle) was low. Using only MODIS OWL pixels with a low view angle, or a range distance of less than 1000 km, also improves the results and minimizes multi-temporal errors in flood identification and extent. Given these limitations, MODIS OWL surface water maps are sensitive to the dynamics of water movement when compared to streamflow data and does appear to be a suitable product for the identification and mapping of inundation extent at large regional/basin scales.
Remote Sens.2014, 6(12), 11770-11790; doi:10.3390/rs61211770 - published 27 November 2014 Show/Hide Abstract
Abstract: Water authorities are required to have a survey of large woody debris (LWD) in river channels and to manage this aspect of the stream habitat, making decisions on removing, positioning or leaving LWD in a natural state. The main objective of this study is to develop a new methodology that assists in decision making for sustainable management of river channels by using generated low-cost, geomatic products to detect LWD. The use of low-cost photogrammetry based on the use of economical, conventional, non-metric digital cameras mounted on low-cost aircrafts, together with the use of the latest computational vision techniques and open-source geomatic tools, provides useful geomatic products. The proposed methodology, compared with conventional photogrammetry or other traditional methods, led to a cost savings of up to 45%. This work presents several contributions for the area of free and open source software related to Geographic Information System (FOSSGIS) applications to LWD management in streams, while developing a QGIS  plugin that characterizes the risk from the automatic calculation of geometrical parameters.
Remote Sens.2014, 6(12), 11753-11769; doi:10.3390/rs61211753 - published 27 November 2014 Show/Hide Abstract
Abstract: The Thermal Infrared Sensor (TIRS) requirements for noise, stability, and uniformity were designed to ensure the radiometric integrity of the data products. Since the launch of Landsat 8 in February 2013, many of these evaluations have been based on routine measurements of the onboard calibration sources, which include a variable-temperature blackbody and a deep space view port. The noise equivalent change in temperature (NEdT) of TIRS data is approximately 0.05 K @ 300 K in both bands, exceeding requirements by about a factor of 8 and Landsat 7 ETM+ performance by a factor of 3. Coherent noise is not readily apparent in TIRS data. No apparent change in the detector linearization has been observed. The radiometric stability of the TIRS instrument over the period between radiometric calibrations (about 40 min) is less than one count of dark current and the variation in terms of radiance is less than 0.015 \(W/m^2/sr/\mu m\) (or 0.13 K) at 300 K, easily meeting the short term stability requirements. Long term stability analysis has indicated a degradation of about 0.2% or less per year. The operational calibration is only updated using the biases taken every orbit, due to the fundamental stability of the instrument. By combining the data from two active detector rows per band, 100% detector operability is maintained for the instrument. No trends in the noise, operability, or short term radiometric stability are apparent over the mission life. The uniformity performance is more difficult to evaluate as scene-varying banding artifacts have been observed in Earth imagery. Analyses have shown that stray light is affecting the recorded signal from the Earth and inducing the banding depending on the content of the surrounding Earth surface. As the stray light effects are stronger in the longer wavelength TIRS band11 (12.0 \(\mu m\)), the uniformity is better in the shorter wavelength band10 (10.9 \(\mu m\)). Both bands have exceptional noise and stability performance and band10 has generally adequate uniformity performance and should currently be used in preference to band11. The product uniformity will improve with the stray light corrections being developed.
Remote Sens.2014, 6(12), 11731-11752; doi:10.3390/rs61211731 - published 25 November 2014 Show/Hide Abstract
Abstract: Knowledge of impervious surface areas (ISA) and on their changes in magnitude, location, geometry and morphology over time is significant for a range of practical applications and research alike from local to global scales. Despite this, use of Earth Observation (EO) technology in mapping ISAs within some European Union (EU) countries, such as the United Kingdom (UK), is to some extent scarce. In the present study, a combination of methods is proposed for mapping ISA based on freely distributed EO imagery from Landsat TM/ETM+ sensors. The proposed technique combines a traditional classifier and a linear spectral mixture analysis (LSMA) with a series of Landsat TM/ETM+ images to extract ISA. Selected sites located in Wales, UK, are used for demonstrating the capability of the proposed method. The Welsh study areas provided a unique setting in detecting largely dispersed urban morphology within an urban-rural frontier context. In addition, an innovative method for detecting clouds and cloud shadow layers for the full area estimation of ISA is also presented herein. The removal and replacement of clouds and cloud shadows, with underlying materials is further explained. Aerial photography with a spatial resolution of 0.4 m, acquired over the summer period in 2005 was used for validation purposes. Validation of the derived products indicated an overall ISA detection accuracy in the order of ~97%. The latter was considered as very satisfactory and at least comparative, if not somehow better, to existing ISA products provided on a national level. The hybrid method for ISA extraction proposed here is important on a local scale in terms of moving forward into a biennial program for the Welsh Government. It offers a much less subjectively static and more objectively dynamic estimation of ISA cover in comparison to existing operational products already available, improving the current estimations of international urbanization and soil sealing. Findings of our study provide important assistance towards the development of relevant EO-based products not only inaugurate to Wales alone, but potentially allowing a cost-effective and consistent long term monitoring of ISA at different scales based on EO technology.
Remote Sens.2014, 6(12), 11708-11730; doi:10.3390/rs61211708 - published 25 November 2014 Show/Hide Abstract
Abstract: Cultivated land resources are an important basis of regional sustainability; thus, it is important to determine the distribution of the cultivated land in the Northeast Asia trans-boundary area of China, Russia and Mongolia, which has a continuous geographic and ecological environment and an uneven population distribution. Extracting information about the cultivated land and determining the spatial and temporal distribution of its features in this large trans-boundary area is a challenge. In this study, we derived information about the cultivated land of the North-South Transect in Northeast Asia by Linear Spectral Mixing Model, using time series data with MODerate resolution Imaging Spectroradiometer (MODIS) in 2000 and 2010. The validation showed more than 98% pixels with a root mean square error less than 0.05. The overall accuracy and spatial consistency coefficients were 81.63% and 0.78 in 2000 and 72.81% and 0.75 in 2010, respectively. The transect analyses indicate the presence of a greater amount of cultivated land in the south and less in the north. China owns most of the cultivated land in the transect area, followed by Mongolia and then Russia. A gradient analysis revealed a decrease of 34.16% of the cultivated land between 2000 and 2010. The amount of cultivated land decreased 22.37%, 58.93%, and 64.73% in China, Russia, and Mongolia, respectively. An analysis shows that the amount of cultivated land is primarily influenced by the various land development and protection policies in the different counties in this trans-boundary area.