ALBERT

Description: Especially in the remote sensing context, thematic classification is a desired product for coral reef surveys. This study presents a novel statistical-based image classification approach, namely Partial Least Square Discriminant Analysis (PLS-DA), capable of doing so. Three classification models were built and implemented for the images while the fourth was a combination of spectra from all three images together. The classification was optimised by using pre-processing transformations (PPTs) and post-classification low-pass filtering. Despite the fact that the images were acquired under different conditions and quality, the best classification model was achieved by combining spectral training samples from three images (accuracy 0.63 for all classes). PPTs improved the classification accuracy by 5%–15% and post-classification treatments further increased the final accuracy by 10%–20%. The fourth classification model was the most accurate one, suggesting that combining spectra from differ conditions improves thematic classification. Despite some limitations, available aerial sensors already provide an opportunity to implement the described classification and mark the next investigation step. Nonetheless, the findings of this study are relevant both to the field of remote sensing in general and to the niche of coral reef spectroscopy.

Description: Temporal changes and spatial patterns are often studied by analyzing land-cover changes (LCCs) using spaceborne images. LCC is an important factor, affecting runoff within watersheds. The objective was to estimate the effects of 20 years of LCCs on rainfall-runoff relations in an extreme rainfall event. A 1989 Landsat TM-derived classification map was used as input for a Kinematic Runoff and Erosion (KINEROS) hydrological model along with the precipitation data of an extreme rainfall event. Model calibration was performed using measured runoff volume data. Validation of the model performance was conducted by comparing the model results to measured data. A similar procedure was used with a 2009 land-cover classification map as an input to the KINEROS model, along with similar precipitation data and calibration parameters, in order to understand the possible outcomes of a rainfall event of such a magnitude and duration after 20 years of LCCs. The results show an increase in runoff volume and peak discharge between the time periods as a result of LCCs. A strong relationship was detected between vegetation cover and the runoff volume. The LCCs with most pronounced effects on runoff volumes were related to urbanization and vegetation removal.

Description: The successful launch of the Landsat 8 satellite with two thermal infrared bands on February 11, 2013, for continuous Earth observation provided another opportunity for remote sensing of land surface temperature (LST). However, calibration notices issued by the United States Geological Survey (USGS) indicated that data from the Landsat 8 Thermal Infrared Sensor (TIRS) Band 11 have large uncertainty and suggested using TIRS Band 10 data as a single spectral band for LST estimation. In this study, we presented an improved mono-window (IMW) algorithm for LST retrieval from the Landsat 8 TIRS Band 10 data. Three essential parameters (ground emissivity, atmospheric transmittance and effective mean atmospheric temperature) were required for the IMW algorithm to retrieve LST. A new method was proposed to estimate the parameter of effective mean atmospheric temperature from local meteorological data. The other two essential parameters could be both estimated through the so-called land cover approach. Sensitivity analysis conducted for the IMW algorithm revealed that the possible error in estimating the required atmospheric water vapor content has the most significant impact on the probable LST estimation error. Under moderate errors in both water vapor content and ground emissivity, the algorithm had an accuracy of ~1.4 K for LST retrieval. Validation of the IMW algorithm using the simulated datasets for various situations indicated that the LST difference between the retrieved and the simulated ones was 0.67 K on average, with an RMSE of 0.43 K. Comparison of our IMW algorithm with the single-channel (SC) algorithm for three main atmosphere profiles indicated that the average error and RMSE of the IMW algorithm were −0.05 K and 0.84 K, respectively, which were less than the −2.86 K and 1.05 K of the SC algorithm. Application of the IMW algorithm to Nanjing and its vicinity in east China resulted in a reasonable LST estimation for the region. Spatial variation of the extremely hot weather, a frequently-occurring phenomenon of an abnormal heat flux process in summer along the Yangtze River Basin, had been thoroughly analyzed. This successful application suggested that the IMW algorithm presented in the study could be used as an efficient method for LST retrieval from the Landsat 8 TIRS Band 10 data.

Description: Soil quality (SQ) assessment has numerous applications for managing sustainable soil function. Airborne imaging spectroscopy (IS) is an advanced tool for studying natural and artificial materials, in general, and soil properties, in particular. The primary goal of this research was to prove and demonstrate the ability of IS to evaluate soil properties and quality across anthropogenically induced land-use changes. This aim was fulfilled by developing and implementing a spectral soil quality index (SSQI) using IS obtained by a laboratory and field spectrometer (point scale) as well as by airborne hyperspectral imaging (local scale), in two experimental sites located in Israel and Germany. In this regard, 13 soil physical, biological, and chemical properties and their derived soil quality index (SQI) were measured. Several mathematical/statistical procedures, consisting of a series of operations, including a principal component analysis (PCA), a partial least squares-regression (PLS-R), and a partial least squares-discriminate analysis (PLS-DA), were used. Correlations between the laboratory spectral values and the calculated SQI coefficient of determination (R2) and ratio of performance to deviation (RPD) were R2 = 0.84; RPD = 2.43 and R2 = 0.78; RPD = 2.10 in the Israeli and the German study sites, respectively. The PLS-DA model that was used to develop the SSQI showed high classification accuracy in both sites (from laboratory, field, and imaging spectroscopy). The correlations between the SSQI and the SQI were R2 = 0.71 and R2 = 0.7, in the Israeli and the German study sites, respectively. It is concluded that soil quality can be effectively monitored using the spectral-spatial information provided by the IS technology. IS-based classification of soils can provide the basis for a spatially explicit and quantitative approach for monitoring SQ and function at a local scale.