International Journal of Remote Sensing

Liaghat, Shohreh; Ehsani, Reza; Mansor, Shattri; Shafri, Helmi Z.M.; Meon, Sariah; Sankaran, Sindhuja; Azam, Siti H.M.N.

doi: 10.1080/01431161.2014.903353pmid: N/A

Basal stem rot (BSR) is a fatal fungal (Ganoderma) disease of oil palm plantations and has a significant impact on the production of palm oil in Malaysia. Because there is no effective treatment to control this disease, early detection of BSR is vital for sustainable disease management. The limitations of visual detection have led to an interest in the development of spectroscopically based detection techniques for rapid diagnosis of this disease. The aim of this work was to develop a procedure for early and accurate detection and differentiation of Ganoderma disease with different severities, based on spectral analysis and statistical models. Reflectance spectroscopy analysis ranging from the visible to near infrared region (325–1075 nm) was applied to analyse oil palm leaf samples of 47 healthy (G0), 55 slightly damaged (G1), 48 moderately damaged (G2), and 40 heavily damaged (G3) trees in order to detect and quantify Ganoderma disease at different levels of severity. Reflectance spectra were pre-processed, and principal component analysis (PCA) was performed on different pre-processed datasets including the raw dataset, first derivative, and second derivative datasets. The classification models: linear and quadratic discrimination analysis, k-nearest neighbour (kNN), and Naïve–Bayes were applied to PC scores for classifying four levels of stress in BSR-infected oil palm trees. The analysis showed that the kNN-based model predicted the disease with a high average overall classification accuracy of 97% with the second derivative dataset. Results confirmed the usefulness and efficiency of the spectrally based classification approach in rapid screening of BSR in oil palm.

Adam, Elhadi; Mutanga, Onisimo; Odindi, John; Abdel-Rahman, Elfatih M.

doi: 10.1080/01431161.2014.903435pmid: N/A

Mapping of patterns and spatial distribution of land-use/cover (LULC) has long been based on remotely sensed data. In the recent past, efforts to improve the reliability of LULC maps have seen a proliferation of image classification techniques. Despite these efforts, derived LULC maps are still often judged to be of insufficient quality for operational applications, due to disagreement between generated maps and reference data. In this study we sought to pursue two objectives: first, to test the new-generation multispectral RapidEye imagery classification output using machine-learning random forest (RF) and support vector machines (SVM) classifiers in a heterogeneous coastal landscape; and second, to determine the importance of different RapidEye bands on classification output. Accuracy of the derived thematic maps was assessed by computing confusion matrices of the classifiers’ cover maps with respective independent validation data sets. An overall classification accuracy of 93.07% with a kappa value of 0.92, and 91.80 with a kappa value of 0.92 was achieved using RF and SVM, respectively. In this study, RF and SVM classifiers performed comparatively similarly as demonstrated by the results of McNemer’s test (Z = 1.15). An evaluation of different RapidEye bands using the two classifiers showed that incorporation of the red-edge band has a significant effect on the overall classification accuracy in vegetation cover types. Consequently, pursuit of high classification accuracy using high-spatial resolution imagery on complex landscapes remains paramount.

Hisaki, Yukiharu

doi: 10.1080/01431161.2014.904971pmid: N/A

Wave data near coasts estimated from high-frequency (HF) radar are inter-compared with in situ and model-predicted wave data. The comparisons are useful since the in situ wave data are limited to the main wave parameters and the observation is limited to a single point. The agreement between in situ and model-predicted wave heights is reasonable, considering the accuracy of the model input wind. The agreement between radar-estimated and model-predicted wave energy in the intermediate frequency band, which is the most energetic frequency band, is better than that in the low- or high-frequency bands. On the other hand, the agreement between the radar-estimated and model-predicted wave direction is best in the high-frequency band, which is the band closest to the Bragg frequency. The spatial distribution of the radar-estimated wave heights during the observation period is similar to that of the model-predicted wave height in the limited area.

Aptoula, E.

doi: 10.1080/01431161.2014.905729pmid: N/A

Quasi-fl at zones are powerful morphological image simplification tools capable of producing unique image partitions at multiple scales. In this paper we investigate their impact on the morphological description of hyperspectral images and, specifically, whether they can improve classification performance when used for preprocessing. In particular, we employ them in order to group spatially adjacent pixels according to spectral criteria, prior to the computation of extended morphological profiles. Moreover, we explore both marginal quasi-flat zones and recent multivariate extensions using well-known vector orderings. The method studied is tested with multiple hyperspectral images, where it leads consistently to improvements in classification performance.

Migliaccio, M.; Nunziata, F.

doi: 10.1080/01431161.2014.905730pmid: N/A

A polarimetric scattering model is proposed to exploit quad-polarimetric synthetic aperture radar (SAR) data to both observe surfactants at sea and provide the first information on the spatial variability of their damping properties. The model is based on the departure from the clean sea surface Bragg/tilted Bragg scattering mechanism. This departure is shown to be a function of the surfactant’s characteristics, and therefore, it is exploited to map them. Case studies of polarimetric SAR data collected during the Deepwater Horizon oil spill in Gulf of Mexico are examined. The approach is robust enough to successfully exploit both L-band airborne and C-band satellite SAR data. This is of paramount importance, even operationally, since it makes this physical approach cross-sensors and, therefore, suitable to exploit all the operational polarimetric missions, thus allowing a denser spatial/temporal coverage.

Nagai, Shin; Inoue, Tomoharu; Ohtsuka, Toshiyuki; Kobayashi, Hideki; Kurumado, Kenji; Muraoka, Hiroyuki; Nasahara, Kenlo Nishida

doi: 10.1080/01431161.2014.907937pmid: N/A

We examined the relationship between the spatio-temporal distribution of leaf litter for each species and the seasonal patterns of in situ and satellite-observed daily vegetation indices in a cool-temperate deciduous broad-leaved forest. The timing and distribution of leaf-fall revealed spatio-temporal relationships with species and topography. Values of the normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and green–red vegetation index (GRVI), measured both in situ and by satellite, and those of the in situ-measured leaf area index (LAI), rapidly declined at the peak of leaf-fall. At the late stage of leaf-fall, in situ-measured values of NDVI, EVI, and LAI declined but those of GRVI changed from decreasing to increasing. The peak timing of leaf-fall, when 50–73% of the leaf litter had fallen, corresponds to LAI = 1.80–0.81, NDVI = 0.61–0.54, EVI = 0.29–0.25, and GRVI = 0.01 ∼ ‒0.07. Although the distribution of leaf litter among species displayed spatial characteristics at the peak of leaf-fall, spatial heterogeneity of amount of leaf litter at the peak timing of leaf-fall was less than that at the beginning and end. These facts suggest that the criterion for determining the timing of leaf-fall from vegetation indices should be a value corresponding to the peak of leaf-fall rather than its end. In a high-biodiversity forest, such as this study forest, the effect of spatial heterogeneity on the timing and patterns of leaf-fall on vegetation indices can be reduced by observing only the seasonal variation in colour on the canopy surface by using GRVI, which consists of visible reflectance bands, rather than that of both leaf area and colour of the canopy surface by using NDVI and EVI, which consist of visible and near-infrared reflectance bands.

Shi, Xian-Zhong; Aspandiar, Mehrooz; Oldmeadow, David

doi: 10.1080/01431161.2014.907938pmid: N/A

Acid sulphate soils (ASS) are widely spread worldwide and are detrimental to the environment. South Yunderup is one of the coastal areas of Western Australia heavily affected by ASS. Conventional investigation is costly and time-consuming, and thus there is an urgent need to rapidly characterize and identify this type of soil. This paper aims to characterize these soils using reflectance spectra, which may be one of the most significant steps in effectively identifying them and mapping their extent by remote sensing. The ASS from the study area were divided into several groups and subtypes according to both pH measurements and mineral composition as identified by X-ray diffraction analysis. Each group and subtype was then characterized by its spectral reflectance features. We found that the spectral characteristics of ASS are governed by the spectral features of its compositional minerals. In particular, some secondary iron-bearing minerals produced by the formation of ASS, together with surrounding minerals such as carbonate, play vitally important roles in influencing the spectral characterization of ASS. These iron-bearing minerals, including iron oxides, hydroxides/oxyhydroxides (e.g. haematite, goethite, and ferrihydrite), and iron sulphates (e.g. jarosite and copiapite), have diagnostic spectral features and are therefore detectable in the reflectance range. Moreover, these secondary iron-bearing minerals could be indicators suggesting the pH conditions in which they formed. The results of this study include the overall mineral distribution of the study area, the spectral characterization of different groups and subtypes of ASS, and the linkages between spectral features and pH ranges.

Sekovski, Ivan; Stecchi, Francesco; Mancini, Francesco; Del Rio, Laura

doi: 10.1080/01431161.2014.907939pmid: N/A

Comprehension of vulnerability to coastal erosion in dynamic coastal environments strongly depends on accurate and frequent detection of shoreline position. The monitoring of such environments could benefit from the semi-automatic shoreline delineation method, especially in terms of time, cost, and labour-intensiveness. This article explores the potential of using a semi-automatic approach in delineating a proxy-based shoreline by processing high-resolution multispectral WorldView-2 satellite imagery. We studied the potential and differences of basic and easily accessible standard classification methods for shoreline detection. In particular we explored the use of high spatial and spectral resolution satellite imagery for shoreline extraction. The case study was carried out on a 40 km coastal stretch facing the Northern Adriatic Sea (Italy) and belonging to the Municipality of Ravenna. In this area a frequent monitoring of shoreline position is required because of the extreme vulnerability to erosion phenomena that have resulted in a general trend of coastal retreat over recent decades. The wet/dry shorelines were delineated between the classes of wet and dry sand, resulting from different supervised (Parallelepiped, Gaussian Maximum Likelihood, Minimum-Distance-to-Means, and Mahalanobis distance) image classification techniques and the unsupervised Iterative Self-Organizing Data Analysis Technique (ISODATA). In order to assign reliability to outcomes, the extrapolated shorelines were compared to reference shorelines visually identified by an expert, by assessing the average mean distance between them. In addition, the correlation between offset rates and different types of coast was investigated to examine the influence of specific coastal features on shoreline extraction capability. The results highlighted a high level of compatibility. The average median distance between reference shorelines and those resulting from the classification methods was less than 5.6 m (Maximum likelihood), whereas a valuable distance of just 2.2 m was detected from ISODATA and Mahalanobis. Heterogeneous coastal stretches exhibited a larger offset between extracted and reference shorelines than the homogeneous ones. To finally evaluate the coastal evolution of the area, results from Mahalanobis classification were compared to a shoreline derived from airborne light detection and ranging (lidar) data. The fine spatial resolution provided by both methodologies allowed a detailed Digital Shoreline Analysis System (DSAS) comparison, detecting an erosive trend within a wide portion of the study area.

Araújo, Suzana R.; Demattê, José A. M.; Vicente, Simone

doi: 10.1080/01431161.2014.907940pmid: N/A

Soil contamination is an ever-growing concern and demands efficient methods for diagnosis of areas under suspected contamination. Spectroscopy reflectance vis-NIR has been shown to be a reliable and environmentally friendly method for the rapid detection and monitoring of soil properties. Despite the use of vis-NIR it is necessary to test the effectiveness of other wavelengths (mid-IR 4000–400 cm−1). We aim with this study to (1) evaluate the contamination of Cr applied by tannery sludge and CrCl3·6H2O in tropical soils through sequential extraction procedures and spectroscopy techniques; (2) identify parameters of soil spectral variation (vis-NIR-mid) associated with Cr and explore their viability in the evaluation of contaminated soils; and (3) investigate the feasibility of using soil spectral data and chemometrics methods to predict Cr in soils. Results indicate that metal adsorption to soil constituents caused expressive changes in soil spectral curves, showing differentiation between highly contaminated soils and those that are relatively contaminant-free. Cr content can be predicted by spectroscopy reflectance in vis-NIR-mid data. The mid-IR models of Cr outperformed vis-NIR. Organic matter played a more important role in determining soil spectral signatures than the mineralogical characteristics of soils, especially in those with high organic carbon content.

Bulgin, C.E.; Sembhi, H.; Ghent, D.; Remedios, J.J.; Merchant, C.J.

doi: 10.1080/01431161.2014.907941pmid: N/A

We present five new cloud detection algorithms over land based on dynamic threshold or Bayesian techniques, applicable to the Advanced Along-Track Scanning Radiometer (AATSR) instrument and compare these to the standard threshold-based SADIST cloud detection scheme. We use a manually classified dataset as a reference to assess algorithm performance and quantify the impact of each cloud detection scheme on land-surface temperature (LST) retrieval. The use of probabilistic Bayesian cloud detection methods improves algorithm true skill scores by 8–9% over SADIST (maximum score of 77.93% compared with 69.27%). We present an assessment of the impact of imperfect cloud masking, in relation to the reference cloud mask, on the retrieved AATSR LST imposing a 2 K tolerance over a 3 × 3 pixel domain. We find an increase of 5–7% in the observations falling within this tolerance when using Bayesian methods (maximum of 92.02% compared with 85.69%). We also demonstrate that the use of dynamic thresholds in the tests employed by SADIST can significantly improve performance, applicable to cloud-test data to be provided by the Sea and Land Surface Temperature Radiometer (SLSTR) due to be launched on the Sentinel 3 mission (estimated 2014).