Airborne lidar bathymetry: assessing quality assurance and quality control methods with Leica Chiroptera examples

Airborne lidar bathymetry (ALB) is an effective advanced technology for mapping and measuring water depth in shallow water coastal zones as well as inland freshwater bodies such as rivers and lakes. The ability of light beams to detect and traverse shallow water columns has provided valuable information about unmapped and often poorly understood coastal and inland waterbodies. ALB surveys require specific best practices and quality management procedures to provide the highest-quality end product. Implementing quality assurance procedures before the survey commences and frequent quality control checks during and after the survey are essential steps. In this article, we summarize overall ALB development history, discuss specific ALB requirements, and provide examples that reflect our experiences of the Leica Chiroptera ALB system. Supplemental surveys, in situ measurements, and developing in-house algorithms are all beneficial and have the means to increase confidence and versatility of lidar bathymetry.     

Integrating thermal and hyperspectral remote sensing for the detection of coastal springs and submarine groundwater discharges

This research focuses on the investigation of remote-sensing techniques for the detection of coastal sub-aerial springs and submarine groundwater discharges using airborne thermal and hyperspectral imagery. Very high spatial resolution thermal and hyperspectral images were acquired using Thermal Airborne Broadband Imager 320 (TABI-320) and Compact Airborne Spectrographic Imager 550 (CASI-550) sensors. Extensive in situ spectroradiometer and oceanographic measurements were carried out in parallel with thermal and hyperspectral image acquisitions. Experiments and analysis of the data show that the combined use of very high spatial resolution airborne thermal and hyperspectral sensors for the detection of relatively small sub-aerial coastal springs and submarine groundwater discharges proves to be a very efficient and operational method. Very high spatial resolution thermal data were able to detect even very small coastal sub-aerial springs. On the other hand, the hyperspectral data were the most appropriate for detecting relatively small submarine groundwater discharges, which were not detected on thermal imagery, due to the increase in turbidity that these discharges cause. This is confirmed by the strong correlations between the hyperspectral data and the in situ measured turbidity-related water inherent optical properties.     

Automatic system for operational traffic monitoring using very-high-resolution satellite imagery

Vehicle detection from very-high-resolution satellite imagery has received increasing interest during the last few years. In this article, we propose an automatic system for operational traffic monitoring using very-high-resolution optical satellite imagery (0.5–0.6 m resolution) of small highways with low traffic density and a range of different illumination conditions, including cloud-shadowed, hazy, and partially cloudy conditions. The proposed system includes cloud and cloud shadow detection, road detection, and vehicle detection, classification, and counting. The main part of the system is vehicle detection, which is constructed using an elliptical blob detection strategy followed by region growing and feature extraction steps. Vehicular objects are separated from non-vehicular objects using a K-nearest-neighbour classifier, with various classical features used for pattern recognition, as well as some proposed application-specific features, and are also classified according to vehicle size. The fully automatic processing chain has been validated on a selection of satellite scenes from different parts of Norway, including imagery with large amounts of cloud, fog, cloud shadows, and similar conditions that complicate image interpretation. The overall vehicle detection rate was 85.4% and the false detection rate was 9.2%. Overall, this demonstrates the potential of operational traffic monitoring using very-high-resolution satellites.     

Classification of shoreline vegetation in the Western Basin of Lake Erie using airborne hyperspectral imager HSI2, Pleiades and UAV data

Mapping land and aquatic vegetation of coastal areas using remote sensing for better management and conservation has been a long-standing interest in many parts of the world. Due to natural complexity and heterogeneity of vegetation cover, various remote sensing sensors and techniques are utilized for monitoring coastal ecosystems. In this study, two unsupervised and two supervised standard pixel-based classifiers were tested to evaluate the mapping performance of the second-generation airborne NASA Glenn Hyperspectral Imager (HSI2) over the narrow coastal area along the Western Lake Erie’s shoreline. Furthermore, the classification results of HSI2 (using the whole Visible-Near Infrared (VIS+ NIR) hyperspectral dataset, and also the spectral subset of Visible (VIS) spectral bands) were compared to multispectral Pleiades (VIS+ NIR) and Unmanned Aerial Vehicle (UAV) VIS classified images. The goal was to explore how different spectral ranges, and spatial and spectral resolutions impact the unsupervised and supervised classifiers. While the unsupervised classifiers depended more on the spectral range, spectral or spatial resolutions were important for the supervised classifiers. The Support Vector Machine (SVM) was found to perform better than other classification methods for the HSI2 images over all twenty-two study sites with the overall accuracy (OA) ranging from 82.6%–97.5% for VIS, and 81.5%–95.6 % for VIS + NIR. Considerably better performance of the supervised classifiers for the HSI2 VIS data over the Pleiades data (OA = 74.8–83.4%) suggested the importance of spectral resolution over spectral range (VIS vs. VIS+ NIR) for the supervised methods. The unsupervised classifiers exhibited low accuracy for both HSI2 VIS and UAV VIS imagery (OA< 30.0%) while the overall accuracy for the HSI2 VIS+ NIR and Pleiades data ranged from 60.4%–78.4 % and 42.1%–66.4%, respectively, suggesting the importance of spectral range for the unsupervised classifiers.     

Remote sensing of selected water-quality indicators with the hyperspectral imager for the coastal ocean (HICO) sensor

The Hyperspectral Imager for the Coastal Ocean (HICO) offers the coastal environmental monitoring community an unprecedented opportunity to observe changes in coastal and estuarine water quality across a range of spatial scales not feasible with traditional field-based monitoring or existing ocean colour satellites. HICO, an Office of Naval Research-sponsored programme, is the first space-based maritime hyperspectral imaging instrument designed specifically for the coastal ocean. HICO has been operating since September 2009 from the Japanese Experiment Module – Exposed Facility on the International Space Station (ISS). The high pixel resolution (approximately 95 m at nadir) and hyperspectral imaging capability offer a unique opportunity for characterizing a wide range of water colour constituents that could be used to assess environmental condition. In this study, we transform atmospherically corrected ISS/HICO hyperspectral imagery and derive environmental response variables routinely used for evaluating the environmental condition of coastal ecosystem resources. Using atmospherically corrected HICO imagery and a comprehensive field validation programme, three regionally specific algorithms were developed to estimate basic water-quality properties traditionally measured by monitoring agencies. Results indicated that a three-band chlorophyll a algorithm performed best (R² = 0.62) when compared with in situ measurement data collected 2–4 hours of HICO acquisitions. Coloured dissolved organic matter (CDOM) (R² = 0.93) and turbidity (R² = 0.67) were also highly correlated. The distributions of these water-quality indicators were mapped for four estuaries along the northwest coast of Florida from April 2010 to May 2012. However, before the HICO sensor can be transitioned from proof-of-concept to operational status and its data applied to benefit decisions made by coastal managers, problems with vicarious calibration of the sensor need to be resolved and standardized protocols are required for atmospheric correction. Ideally, the sensor should be placed on a polar orbiting platform for greater spatial and temporal coverage as well as for image synchronization with field validation efforts.     

Using remote-sensing data to detect habitat suitability for yellowfin tuna in the Western and Central Pacific Ocean

The empirical habitat suitability index (HSI) has been widely used to examine the habitat characteristics of terrestrial animals, though rarely used in highly migratory fish such as tuna. This study used the geographic information system technique to establish empirical models of HSI for yellowfin tuna (YFT) in the Western and Central Pacific Ocean (WCPO). Daily catch data from the Taiwanese purse seine fishery during 2003–2007 were aggregated monthly into sequential degrees before match processing the conducted data to obtain monthly remote-sensing data for multi-environmental factors, including sea surface temperature (SST), chlorophyll-a (chl-a), sea surface height (SSH) and sea surface salinity (SSS). According to the frequency distribution of each factor on which YFT were caught, this study transformed the values of the four factors into a suitability index (SI) ranging from low to high (0–1). These SI values were consequently combined into different empirical HSI models, and the optimum models were selected using the general linear model. The optimum empirical HSI for YFT in the study area was converted for SI (SST, SSH, chl-a and SSS) using the arithmetic mean model, of which the correct prediction rate was 71.9%. An agreement was present between the average HSI and total YFT catch. Furthermore, the high HSI area corresponds with the displacement of catch per unit effort (CPUE).     

基于HSI变换和QPSO变换的图像融合算法

提出了一种基于HSI和QPSO（即基于量子行为的粒子群优化算法）的图像融合方法,HSI变换方法对多光谱图像和全色图像进行融合会丢失较多的光谱信息。利用QPSO算法来求解HSI变换中光谱强度分量的最优变换问题。由于采用了QPSO算法,使最后变换后的图像与多光谱图像和全色图像都有很强的相关性。实验表明,此方法所得到的融合图像优于传统HSI变换。     

Extraction of landslide-related factors from ASTER imagery and its application to landslide susceptibility mapping

The aim of this study is to extract landslide-related factors from remote-sensing data, such as Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite imagery, and to examine their applicability to landslide susceptibility near Boun, Korea, using a geographic information system (GIS). Landslide was mapped from interpretation of aerial photographs and field surveying. Factors that influence landslide occurrence were extracted from ASTER imagery. The slope, aspect and curvature were calculated from the digital elevation model (DEM) with 25.77 m root mean square error (RMSE), which was derived from ASTER imagery. Lineaments, land-cover and normalized difference vegetation index (NDVI) layers were also estimated from ASTER imagery. Landslide-susceptible areas were analysed and mapped using the occurrence factors by a frequency ratio and logistic regression model. Validation results were 84.78% in frequency ratio and 84.20% in logistic regression prediction accuracy for the susceptibility map with respect to ground-truth data.     

Mapping wild pear trees (Pyrus bourgaeana) in Mediterranean forest using high-resolution QuickBird satellite imagery

Recent advances in spatial and spectral resolution of satellite imagery as well as in processing techniques are opening new possibilities of fine-scale vegetation analysis with interesting applications in natural resource management. Here we present the main results of a study carried out in Sierra Morena, Cordoba (southern Spain), aimed at assessing the potential of remote-sensing techniques to discriminate and map individual wild pear trees (Pyrus bourgaeana) in Mediterranean open woodland dominated by Quercus ilex. We used high spatial resolution (2.4 m multispectral/0.6 m panchromatic) QuickBird satellite imagery obtained during the summer of 2008. Given the size and features of wild pear tree crowns, we applied an atmospheric correction method, Fast Line-of-Sight Atmospheric Analysis of Spectral Hypercube (FLAASH), and six different fusion ‘pan-sharpening’ methods (wavelet ‘à trous’ weighted transform, colour normalized (CN), Gram–Schmidt (GS), hue–saturation–intensity (HSI) colour transformation, multidirection–multiresolution (MDMR), and principal component (PC)), to determine which procedure provides the best results. Finally, we assessed the potential of supervised classification techniques (maximum likelihood) to discriminate and map individual wild pear trees scattered over the Mediterranean open woodland.     

Distribution of hotspots of chub mackerel based on remote-sensing data in coastal waters of China

Sustainable use of fishery resources requires the effective monitoring and managing of fish stocks and fish habitats. Chub mackerel (Scomber japonicus), distributed in the East China Sea and Yellow Sea, are mainly caught by purse seine fishing fleets from China, Japan, and South Korea. This study used fishery data from Chinese large lighting–purse seine fleets and environmental data including sea surface height (SSH), sea surface temperature (SST) from remote sensing, and temperature gradient derived from SST (GSST) during 1998–2010 to develop habitat suitability index (HSI) models. The HSI models were then used to identify hotspots for chub mackerel for each month. HSI models were developed separately for each of the three distribution areas defined for chub mackerel. According to the frequency distribution of the fishing effort with respect to three environmental variables, suitability index (SI) values were calculated and SI models for each environmental variable were established. The three SI models were combined into two different empirical HSI models: the arithmetic mean model (AMM) and the geometric mean model (GMM). The results showed that the AMM was more suitable than the GMM to estimate the HSI for chub mackerel. The monthly latitudinal variation trend of hotspots was consistent with that of the gravity centres of fishing effort in almost all months. Hotspot maps based on the predicted HSI values were validated by fishery data in 2011. This result indicates that the HSI model based on the AMM can reliably predict hotspots for chub mackerel in the coastal waters of China.     

Measurement of water colour using AVIRIS imagery to assess the potential for an operational monitoring capability in the Pamlico Sound Estuary,USA

The monitoring of water colour parameters can provide an important diagnostic tool for the assessment of aquatic ecosystem condition. Remote sensing has long been used to effectively monitor chlorophyll concentrations in open ocean systems; however, operational monitoring in coastal and estuarine areas has been limited because of the inherent complexities of coastal systems, and the coarse spectral and spatial resolutions of available satellite systems. Data were collected using the National Aeronautics and Space Administration (NASA) Advanced Visible–Infrared Imaging Spectrometer (AVIRIS) flown at an altitude of approximately 20 000 m to provide hyperspectral imagery and simulate both MEdium Resolution Imaging Spectrometer (MERIS) and Moderate Resolution Imaging Spectrometer (MODIS) data. AVIRIS data were atmospherically corrected using a radiative transfer modelling approach and analysed using band ratio and linear regression models. Regression analysis was performed with simultaneous field measurements data in the Neuse River Estuary (NRE) and Pamlico Sound on 15 May 2002. Chlorophyll a (Chl a) concentrations were optimally estimated using AVIRIS bands (9.5 nm) centred at 673.6 and 692.7 nm, resulting in a coefficient of determination (R ²) of 0.98. Concentrations of Chromophoric Dissolved Organic Matter (CDOM), Total Suspended Solids (TSS) and Fixed Suspended Solids (FSS) were also estimated, resulting in coefficients of determination of R ² = 0.90, 0.59 and 0.64, respectively. Ratios of AVIRIS bands centred at or near those corresponding to the MERIS and MODIS sensors indicated that relatively good satellite‐based estimates could potentially be derived for water colour constituents at a spatial resolution of 300 and 500 m, respectively.     

Robustness in organizational-learning oriented classifier system

 An organizational-learning oriented classifier system (OCS) is an extension of learning classifier systems (LCSs) to multiagent environments, where the system introduces the concepts of organizational learning (OL) in organization and management science. To investigate the capabilities of OCS as a new multiagent-based LCS architecture, this paper specifically focuses on the robustness of OCS in multiagent environments and explores its capability in space shuttle crew task scheduling as one of real-world applications. Intensive simulations on a complex domain problem revealed that OCS has robustness capability in the given problem. Concretely, we found that OCS derives the following implications on robustness: (1) OCS finds good solutions at small computational costs even after anomaly situations occur; and (2) this advantage becomes stronger as the number of anomalies increases.     

HYDROPT: A fast and flexible method to retrieve chlorophyll-a from multispectral satellite observations of optically complex coastal waters

We present a generic innovative algorithm for remote sensing of coastal waters that can deal with a large range of concentrations of chlorophyll-a, SPM and CDOM and their inherent optical properties. The algorithm is based on the exact solutions of the HYDROLIGHT numerical radiative transfer model to support retrieval in optically complex waters with varying sensor wide swath viewing geometry. The algorithm estimates the concentrations by minimizing the difference between observed and modeled reflectance spectra. The use of a look-up table and polynomial interpolation greatly reduces computation time, allowing operational and near-real time processing of large sets of satellite imagery. Because the remote sensing reflectance was tabulated as a function of in-water light absorption and scattering, rather than actual constituents concentrations, the algorithm can be applied with any definition of the specific inherent optical properties of CHL, SPM and CDOM. A statistical measure for the goodness-of-fit and the formal standard errors in the fitted concentrations are provided, thus producing error maps with each thematic chlorophyll image, often lacking in most applications of innovative algorithms. The performance of the algorithm is demonstrated for multispectral observations of the North Sea, a shallow coastal sea with large concentration gradients in SPM (due to resuspension) and CDOM (from riverine influx). The standard errors of estimated chlorophyll-a concentrations ranged between 0.5 and 3 (mg m^− 3) for mean concentrations between 2 and 20 (mg m^− 3), quite acceptable results for these optically complex waters.     

Forest fuel treatment detection using multi-temporal airborne lidar data and high-resolution aerial imagery: a case study in the Sierra Nevada Mountains,California

Treatments to reduce forest fuels are often performed in forests to enhance forest health, regulate stand density, and reduce the risk of wildfires. Although commonly employed, there are concerns that these forest fuel treatments (FTs) may have negative impacts on certain wildlife species. Often FTs are planned across large landscapes, but the actual treatment extents can differ from the planned extents due to operational constraints and protection of resources (e.g. perennial streams, cultural resources, wildlife habitats). Identifying the actual extent of the treated areas is of primary importance to understand the environmental influence of FTs. Light detection and ranging (lidar) is a powerful remote-sensing tool that can provide accurate measurements of forest structures and has great potential for monitoring forest changes. This study used the canopy height model (CHM) and canopy cover (CC) products derived from multi-temporal airborne laser scanning (ALS) data to monitor forest changes following the implementation of landscape-scale FT projects. Our approach involved the combination of a pixel-wise thresholding method and an object-of-interest (OBI) segmentation method. We also investigated forest change using normalized difference vegetation index (NDVI) and standardized principal component analysis from multi-temporal high-resolution aerial imagery. The same FT detection routine was then applied to compare the capability of ALS data and aerial imagery for FT detection. Our results demonstrate that the FT detection using ALS-derived CC products produced both the highest total accuracy (93.5%) and kappa coefficient (κ) (0.70), and was more robust in identifying areas with light FTs. The accuracy using ALS-derived CHM products (the total accuracy was 91.6%, and the κ was 0.59) was significantly lower than that using ALS-derived CC, but was still higher than using aerial imagery. Moreover, we also developed and tested a method to recognize the intensity of FTs directly from pre- and post-treatment ALS point clouds.     

Estimation of chlorophyll-a concentration in coastal waters with HJ-1A HSI data using a three-band bio-optical model and validation

Accurate assessment of phytoplankton chlorophyll-a (chl-a) concentration in turbid waters by means of remote sensing is challenging because of the optical complexity of case 2 waters. We applied a bio-optical model of the form [R^–1(λ₁) – R^–1(λ₂)](λ₃), where R(λ_i) is the remote-sensing reflectance at wavelength λ_i, to estimate chl-a concentration in coastal waters. The objectives of this article are (1) to validate the three-band bio-optical model using a data set collected in coastal waters, (2) to evaluate the extent to which the three-band bio-optical model could be applied to the spectral radiometer (SR) ISI921VF-512T data and the hyperspectral imager (HSI) data on board the Chinese HJ-1A satellite, (3) to evaluate the application prospects of HJ-1A HSI data in case 2 waters chl-a concentration mapping. The three-band model was calibrated using three SR spectral bands (λ₁ = 664.9 nm, λ₂ = 706.54 nm, and λ₃ = 737.33 nm) and three HJ-1A HSI spectral bands (λ₁ = 637.725 nm, λ₂ = 711.495 nm, and λ₃ = 753.750 nm). We assessed the accuracy of chl-a prediction with 21 in situ sample plots. Chl-a predicted by SR data was strongly correlated with observed chl-a (R² = 0.93, root mean square error (RMSE) = 0.48 mg m^–3, coefficient of variation (CV) (RMSE/mean(chl-a_mea)) = 3.72%). Chl-a predicted by HJ-1A HSI data was also closely correlated with observed chl-a (R² = 0.78, RMSE = 0.45 mg m^–3, CV (RMSE/mean(chl-a_mea)) = 7.51%). These findings demonstrate that the HJ-1A HSI data are promising for quantitative monitoring of chl-a in coastal case-2 waters.     

Mapping changes in coastline geomorphic features using Landsat TM and ETM+ imagery: examples in southeastern Brazil

Multitemporal Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) imagery was used to assess coastline morphological changes in southeastern Brazil. A spectral linear mixing approach (SLMA) was used to estimate fraction imagery representing amounts of vegetation, clean water (a proxy for shade) and soil. Fraction abundances were related to erosive and depositional features. Shoreline, sandy banks (including emerged and submerged banks) and sand spits were highlighted mainly by clean water and soil fraction imagery. To evaluate changes in the coastline geomorphic features, the fraction imagery generated for each data set was classified in a contextual approach using a segmentation technique and ISOSEG, an unsupervised classification. Evaluation of the classifications was performed visually and by an error matrix relating ground-truth data to classification results. Comparison of the classification results revealed an intense transformation in the coastline, and that erosive and depositional features are extremely dynamic and subject to change in short periods of time.     

Comparisons of algorithms to estimate water turbidity in the coastal areas of China

Turbidity is an important indicator of water environments and water-quality conditions. Ocean colour remote sensing has proved to be an efficient way of monitoring water turbidity because of its wide synoptic coverage and repeated regular sampling. However, operational tasks are still challenging in high-turbidity waters, especially in estuaries and the coastal regions of China. In these areas, the existing algorithms derived from remote-sensing reflectance (R_rs) are usually invalid because it is difficult to correctly estimate the reflectance R_rs from satellite data such as Moderate Resolution Imaging Spectroradiometer (MODIS) data. A new algorithm that uses Rayleigh-corrected reflectance (R_rc) instead of R_rs has been recently introduced and was used to estimate water turbidity in Zhejiang (ZJ) coastal areas from Geostationary Ocean Color Imager (GOCI) data. The R_rc algorithm has previously shown a capability to estimate water turbidity. However, its performance still requires careful evaluation. In this article, we compared the new R_rc algorithm with two other existing algorithms. Differences among the three algorithms were assessed by comparing the results from using R_rc data and R_rs reflectance data derived from both GOCI and MODIS imagery data. The capability of the new R_rc algorithm to estimate water turbidity in larger areas and extended seasons in the coastal seas of China was also estimated. The results showed that the new R_rc algorithm is suitable for the coastal waters of China, especially for highly turbid waters.     

Retrieval of aerosol type and optical thickness over the Mediterranean from SeaWiFS images using an automatic neural classification method

We present an automatic classification method based on topological neural network algorithms to retrieve aerosol optical properties from multi-spectral ocean-color satellite imagery. The first step of the method consisted in an unsupervised classification of a large set of clear-sky top of the atmosphere reflectance spectra measured by the sensor. We used the so-called Kohonen map which aggregates similar spectra into a reduced set of pertinent groups. The second step consisted in labeling these groups by clustering them with synthetic TOA reflectance spectra whose optical properties (i.e., aerosol type or optical thickness) are known. These synthetic spectra have been computed using a radiative transfer model. In the present study, we dealt with five aerosol types (maritime, coastal, tropospheric, oceanic and mineral) and several aerosol optical thickness values ranging from 0.05 to 0.8. These simulated spectra were then projected onto the Kohonen map to label each group of the map. The last step consisted in applying this method to the SeaWiFS imagery of the Mediterranean region for the years 1999 and 2000. The Kohonen map was “educated” from pixels randomly extracted during the year 1999 in this region. We accounted for the viewing geometry of the sensor by clustering the simulated spectra into ten groups of similar geometries, as defined by both scattering and sun zenith angles. The analysis of SeaWiFS images was performed pixel-by-pixel by selecting the suitable labeling (in terms of viewing geometry), then by identifying the closest spectrum in the Kohonen map, which finally gives the aerosol optical properties. This method led to accurate and coherent results, as shown by the comparison with in situ aerosol measurements provided by the AERONET station at Lampedusa and by the study of two aerosol events over the Mediterranean. One of the major advantages of this method is that it enables us to automatically identify the aerosol type and to retrieve the aerosol optical properties with a better accuracy than classical methods such as those used by SeaWifs. It gives accurate results for optical thickness values larger than 0.35 and is able to retrieve dust aerosols such as African dust aerosol (absorbing aerosol). These should ensure a more precise inversion of ocean-color imagery where the knowledge of atmospheric optical parameters is essential. Moreover the method is able to give probabilities for the estimate values of aerosol properties.     

深度学习在高光谱图像分类领域的研究现状与展望

高光谱图像（Hyperspectral imagery,HSI）分类是高光谱遥感对地观测技术的一项重要内容,在军事及民用领域都有着重要的应用.然而,高光谱图像的高维特性、波段间高度相关性、光谱混合等使得高光谱图像分类面临巨大挑战.近年来,随着深度学习新技术的出现,基于深度学习的高光谱图像分类在方法和性能上得到了突破性的进展,为其研究提供了新的契机.本文首先介绍了高光谱图像分类的背景、研究现状及几个常用的数据集,并简要概述了几种典型的深度学习模型,最后详细介绍了当前的一些基于深度学习的高光谱图像分类方法,总结了深度学习在高光谱图像分类领域中的主要作用和存在的问题,并对未来的研究方向进行了展望.     

Land-use/cover classification in a heterogeneous coastal landscape using RapidEye imagery: evaluating the performance of random forest and support vector machines classifiers

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.