Object-based Machine Learning Research Articles

Quantification of active layer depth at multiple scales in Interior Alaska permafrost

Much of Interior Alaska is underlain by permafrost that has been thawing at an unprecedented rate. Top-down expansion of the seasonally thawed ‘active layer’ and development of thermokarst features are increasing across the landscape. This can be attributed primarily due to a warming climate and disturbances like wildfires which have accelerated summer season permafrost thaw. Quantification of active-layer thickness (ALT) is critical to understanding the response of permafrost terrains to these disturbances. ALT measurements are time consuming, and point based. As a result, there are large uncertainties in ALT estimates at regional/global scales (100 km2 or larger) using field scale (1 m2) measurements as direct inputs for calibrating/validating large scale process-based or statistical/empirical models. Here we developed a framework to link field scale ALT measurements with satellite observations to a regional scale (100 km2) via an intermediary upscaling of field scale ALT to the local scale (1 km2) with fine-resolution airborne hyperspectral and light detection and ranging data, thus leading to a characterization of ALT across space and time at multiple scales. We applied an object-based machine learning ensemble approach to upscale field scale (1 m2) measurements to the local (1 km2) and regional scale (100 km2) and achieved encouraging results across three permafrost experimental sites in Interior Alaska that represent a variety of terrain types. Our study demonstrates that generating local scale data products is an effective approach to bridge the gap with field scale measurements and regional scale estimations as it seeks to reduce upscaling uncertainty.

Forest Canopy Fuel Loads Mapping Using Unmanned Aerial Vehicle High-Resolution Red, Green, Blue and Multispectral Imagery

Canopy fuels determine the characteristics of the entire complex of forest fuels due to their constant changes triggered by the environment; therefore, the development of appropriate strategies for fire management and fire risk reduction requires an accurate description of canopy forest fuels. This paper presents a method for mapping the spatial distribution of canopy fuel loads (CFLs) in alignment with their natural variability and three-dimensional spatial distribution. The approach leverages an object-based machine learning framework with UAV multispectral data and photogrammetric point clouds. The proposed method was developed in the mixed forest of the natural protected area of “Sierra de Quila”, Jalisco, Mexico. Structural variables derived from photogrammetric point clouds, along with spectral information, were used in an object-based Random Forest model to accurately estimate CFLs, yielding R2 = 0.75, RMSE = 1.78 Mg, and an average Biasrel = 18.62%. Canopy volume was the most significant explanatory variable, achieving a mean decrease in impurity values greater than 80%, while the combination of texture and vegetation indices presented importance values close to 20%. Our modelling approach enables the accurate estimation of CFLs, accounting for the ecological context that governs their dynamics and spatial variability. The high precision achieved, at a relatively low cost, encourages constant updating of forest fuels maps to enable researchers and forest managers to streamline decision making on fuel and forest fire management.

Comparative evaluation of performances of algae indices, pixel- and object-based machine learning algorithms in mapping floating algal blooms using Sentinel-2 imagery

One of the main threats to freshwater resources is pollution from anthropogenic activities such as rapid urbanization and excessive agricultural nutrient runoff. Remote sensing technologies have been effectively used in monitoring and mapping rapid changes in the marine environment and assessing the overall health of freshwater ecosystems. The main goal of this study is to comparatively evaluate the performance of index-based and classification-based approaches in mapping dense floating algal blooms observed in Lake Burdur using Sentinel-2 imagery. For index-based mapping, algae-specific indices, namely the Floating Algae Index (FAI), Adjusted Floating Algae Index, Surface Algal Blooms Index (SABI), and Algal Blooms Detection Index (ABDI), were used. At the same time, pixel- and object-based Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and Long Short-Term Memory Network (LSTM) were utilized for classification-based algal mapping. For this purpose, seven Sentinel-2 images, selected through time series analysis performed on the Google Earth Engine platform, were used as the primary dataset in the application. The results show that high-density floating algae formations can be detected over 99% by both indices and classification-based approaches, whereas pixel-based classification is more successful in mapping low-density algal blooms. When two-class thematic maps representing water and floating algae classes were considered, the maps produced by index-based FAI using an appropriate threshold value and the classification-based RF algorithm reached an overall accuracy of over 99%. The highest algae density in the lake was observed on July 13, 2021, and was determined to be effective in ~ 45 km2 of the lake’s surface.

Coastline and shoreline change assessment in sandy coasts based on machine learning models and high-resolution satellite images

Changes to the coastline or shoreline arise from the water's dynamic interaction with the land surface, which is triggered by ocean currents, waves, and winds. Various methods have been proposed to identify and monitor coastlines and shorelines, but their outcomes are uncertain. This study proposes indicators for identifying coastlines and shorelines in the fields and on the remote sensing data. Different pixel- and object-based machine learning (ML) models were built to automatically interpret coastlines and shorelines from high-resolution remote sensing images and monitor coastal erosion in Vietnam. Two pixel-based models using Random Forest and SVM structures and eight object-based models using U-Net, and U-Net3+ structures were trained. All models were trained using the high-resolution images gathered using Google Earth Pro as input data. The U-Net achieves the most remarkable performance of 98% with a loss function of 0.16 when utilizing an input-image size of 512×512.
 Object-based models have shown higher performance in analyzing coastlines and shorelines with linear and continuous structures than pixel-based models. Additionally, the coastline is appropriate to evaluate coastal erosion induced by the effect of sea-level rise during storms. At the same time, the shoreline is suited to observe seasonal tidal fluctuations or the instantaneous movements of current waves. Under the pressure of tourist development, the coasts in Danang and Quang Nam provinces have been eroded in the last 10 years. River and ocean currents also cause erosion in the southern Cua Dai estuary. In the future, the trained U-Net model can be used to monitor the changes in coastlines and shorelines worldwide.

Linking repeat lidar with Landsat products for large scale quantification of fire-induced permafrost thaw settlement in interior Alaska

The permafrost–fire–climate system has been a hotspot in research for decades under a warming climate scenario. Surface vegetation plays a dominant role in protecting permafrost from summer warmth, thus, any alteration of vegetation structure, particularly following severe wildfires, can cause dramatic top–down thaw. A challenge in understanding this is to quantify fire-induced thaw settlement at large scales (>1000 km2). In this study, we explored the potential of using Landsat products for a large-scale estimation of fire-induced thaw settlement across a well-studied area representative of ice-rich lowland permafrost in interior Alaska. Six large fires have affected ∼1250 km2 of the area since 2000. We first identified the linkage of fires, burn severity, and land cover response, and then developed an object-based machine learning ensemble approach to estimate fire-induced thaw settlement by relating airborne repeat lidar data to Landsat products. The model delineated thaw settlement patterns across the six fire scars and explained ∼65% of the variance in lidar-detected elevation change. Our results indicate a combined application of airborne repeat lidar and Landsat products is a valuable tool for large scale quantification of fire-induced thaw settlement.

Classifying vegetation communities karst wetland synergistic use of image fusion and object-based machine learning algorithm with Jilin-1 and UAV multispectral images

Fine classification of wetland vegetation communities using machine learning algorithm and high spatial resolution images have attracted increased attention. However, there exist several challenges in image fusion, data dimension reduction and algorithm tuning. To resolve these issues, this paper attempts to fuse Unmanned Aerial Vehicle (UAV) images with spaceborne Jilin-1 (JL101K) multispectral images for classifying vegetation communities of karst wetland using the optimized Random Forest (RF), Extreme gradient boosting (XGBoost) and Light Gradient Boosting (LightGBM) algorithms. This study also quantitatively evaluates image fusion quality from spatial detail and spectral fidelity, and explores the effects of different image feature combinations and classifiers on mapping vegetation communities by variable selection and dimensionality reduction. Finally, this paper further evaluates and quantifies the importance and contribution rate of feature variables for typical vegetation communities using Recursive feature elimination (RFE) algorithm. The results showed that: (1) the Gram-Schmidt (GS)algorithm produced the high-quality fusion image of JL101K and UAV, and the fusion image achieved higher overall accuracy (82.8%) than the original JL101K multispectral image; (2) UAV multispectral image and its derivatives (scheme 3) achieved the highest overall accuracy (87.8%) in all classification schemes; (3) The optimized object-based LightGBM algorithm outperformed XGBoost and RF algorithm, which provided an improvement of 0.6%∼3.5% in overall accuracy (OA). McNemar's test indicated that there existed significant differences in vegetation communities’ classification between the three algorithms. (4) The average accuracy (AA) of vegetation communities in karst wetlands was mainly ranged from 60% to 90%. The water hyacinth and herbaceous vegetation were sensitive to the Mean Digital Surface Model (DSM) and Standard RedEdge band.

Using an object-based machine learning ensemble approach to upscale evapotranspiration measured from eddy covariance towers in a subtropical wetland

Accurate prediction of evapotranspiration (ET) in wetlands is critical for understanding the coupling effects of water, carbon, and energy cycles in terrestrial ecosystems. Multiple years of eddy covariance (EC) tower ET measurements at five representative wetland ecosystems in the subtropical Big Cypress National Preserve (BCNP), Florida (USA) provide a unique opportunity to assess the performance of the Moderate Resolution Imaging Spectroradiometer (MODIS) ET operational product MOD16A2 and upscale tower measured ET to generate local/regional wetland ET maps. We developed an object-based machine learning ensemble approach to evaluate and map wetland ET by linking tower measured ET with key predictors from MODIS products and meteorological variables. The results showed MOD16A2 had poor performance in characterizing ET patterns and was unsatisfactory for estimating ET over four wetland communities where Nash-Sutcliffe model Efficiency (NSE) was less than 0.5. In contrast, the site-specific machine learning ensemble model had a high predictive power with a NSE larger than 0.75 across all EC sites. We mapped the ET rate for two distinctive seasons and quantified the prediction diversity to identify regions easier or more challenging to estimate from model-based analyses. An integration of MODIS products and other datasets through the machine learning upscaling paradigm is a promising tool for local wetland ET mapping to guide regional water resource management.

Combining Object-Based Machine Learning with Long-Term Time-Series Analysis for Informal Settlement Identification

Informal settlement mapping is essential for planning, as well as resource and utility management. Developing efficient ways of determining the properties of informal settlements (when, where, and who) is critical for upgrading services and planning. Remote sensing data are increasingly used to understand built environments. In this study, we combine two sources of data, very-high-resolution imagery and time-series Landsat data, to identify and describe informal settlements. The indicators characterising informal settlements were grouped into four different spatial and temporal levels: environment, settlement, object and time. These indicators were then used in an object-based machine learning (ML) workflow to identify informal settlements. The proposed method had a 95% overall accuracy at mapping informal settlements. Among the spatial and temporal levels examined, the contribution of the settlement level indicators was most significant in the ML model, followed by the object-level indicators. Whilst the temporal level did not contribute greatly to the classification of informal settlements, it provided a way of understanding when the settlements were formed. The adaptation of this method would allow the combination of a wide-ranging and diverse group of indicators in a comprehensive ML framework.

Object-based machine learning approach for soybean mapping using temporal sentinel-1/sentinel-2 data

Soybean mapping in Indian context is challenging owing to its short growing period coinciding with the monsoon clouds, inter-cropping and smallholders’ land. This study proposes an approach for mapping soybean by integrating object-based image analysis with machine learning (ML) based classification using temporal Sentinel-1 SAR (S-1) and Sentinel-2 optical (S-2) data. Field objects were delineated with scale-optimized multi-resolution segmentation using historical S-2 data. Object-based temporal VH-backscatter and NDVI were extracted for training, validation and testing of the three ML models. Validation results showed the outperformance of Extreme gradient boosting (OB-XGBoost) over Random Forest (OB-RF) and Support vector machine (OB-SVM) with an overall accuracy (OA) of 92.50, 91.08 and 90.1, respectively. Testing of OB-XGBoost model resulted in OA, kappa statistics, and F-score (soybean) of 86.12%, 0.82, and 87.23%, respectively. The soybean map produced by the proposed methodology has shown better representation in terms of homogeneity and uniformity than the pixel-based classification.

DEFORESTATION MAPPING USING SENTINEL-1 AND OBJECT-BASED RANDOM FOREST CLASSIFICATION ON GOOGLE EARTH ENGINE

Abstract. Deforestation can be defined as the conversion of forest land cover to another type. It is a process that has massively accelerated its rate and extent in the last several decades. Mainly due to human activities related to socio-economic processes as population growth, expansion of agricultural land, wood extraction, etc. In the meantime, there are great efforts by governments and agencies to reduce these deforestation processes by implementing regulations, which cannot always be properly monitored whether are followed or not. In this work is proposed an approach that can provide forest loss estimations for a short period of time, by using Synthetic Aperture Radar imagery for an area in the Brazilian Amazon. SAR are providing data with almost no alteration due to weather conditions, however they may present other limitations. To mitigate the speckle effect, here was applied the dry coefficient, which is the mean of image values under the first quartile while preserving the spatial resolution. While for obtaining land cover maps containing only forest and non-forest areas an object-based machine learning classification on the Google Earth Engine platform was applied. The preliminary tests were carried out in a bitemporal manner between 2015 and 2019, followed by applying the approach monthly for the year of 2020. The outputs yielded very satisfactory and accurate results, allowing to estimate the forest dynamics for the area under consideration for each month.

Mapping CO2 fluxes of cypress swamp and marshes in the Greater Everglades using eddy covariance measurements and Landsat data

Wetlands play a critical role in the global carbon (C) budget by sequestering carbon dioxide (CO2) and storing significant amounts of C, but climate change is altering C dynamics in these ecosystems. Therefore, predicting and mapping net ecosystem exchange (NEE) of CO2 in wetlands has emerged as a focus of research to better understand where, how, and when biogeochemical cycles will be modified by human and natural disturbances like climate change. To address this, we developed an object-based machine learning ensemble approach to upscale eddy covariance (EC) CO2 flux measurements across two major wetland ecosystems (cypress swamp and freshwater marshes) in the Greater Everglades of south Florida. We linked 30-m Landsat, water depth, and air temperature data with EC flux measurements using the temporally composite concept for model development, and mapped fluxes within Big Cypress National Preserve (BCNP) and Everglades National Park (ENP) to characterize two distinct seasonal flux patterns. Using an ensemble analysis of three machine learning model outputs (Artificial Neural Network, Support Vector Machine, and Random Forest), our models of NEE at BCNP had a high predictive power with a R2 greater than 0.8, while NEE was predicted at ENP with a R2 greater than 0.5. We also generated an uncertainty map to quantify the prediction diversity caused by application of multiple models in upscaling. This uncertainty map identifies regions that are either easy or challenging to predict from model based analyses. We conclude that integration of Landsat, water depth, and air temperature data is valuable for quantifying CO2 exchange between the atmosphere and Everglades ecosystems, and the developed paradigm is promising for upscaling EC flux measurements in wetlands to study and mitigate disturbances to wetland ecosystem services such as hurricanes, fires, water management and/or climate change.

Pixel and Object-Based Machine Learning Classification Schemes for Lithological Mapping Enhancement of Semi-Arid Regions Using Sentinel-2A Imagery: A Case Study of the Southern Moroccan Meseta

Mapping lithological units of an area using remote sensing data can be broadly grouped into pixel-based (PBIA), sub-pixel based (SPBIA) and object-based (GEOBIA) image analysis approaches. Since it is not only the datasets adequacy but also the correct classification selection that influences the lithological mapping. This research is intended to analyze and evaluate the efficiency of these three approaches for lithological mapping in semi-arid areas, by using Sentinel-2A data and many algorithms for image enhancement and spectral analysis, in particular two specialized Band Ratio (BR) and the Independent component analysis (ICA), for that reason the Paleozoic Massif of Skhour Rehamna, situated in the western Moroccan Meseta was chosen. In this study, the support vector machine (SVM) that is theoretically more efficient machine learning algorithm (MLA) in geological mapping is used in PBIA and GEOBIA approaches. The evaluation and comparison of the performance of these different methods showed that SVM-GEOBIA approach gives the highest overall classification accuracy (OA $\approx ~93$ %) and kappa coefficient (K) of 0, 89, while SPBIA classification showed OA of approximately 89% and kappa coefficient of 0, 84, whereas the lithological maps resulted from SVM-PBIA method exhibit salt and pepper noise, with a lower OA of 87% and kappa coefficient of 0, 80 comparing them with the other classification approaches. From the results of this comparative study, we can conclude that the SVM-GEOBIA classification approach is the most suitable technique for lithological mapping in semi-arid regions, where outcrops are often inaccessible, which complicates classic cartographic work.

Evaluation and enhancement of unmanned aircraft system photogrammetric data quality for coastal wetlands

ABSTRACT Understanding the impacts of flight configuration and post-mission data processing techniques on unmanned aircraft system (UAS) photogrammetric data quality is essential for employing this popular technique in coastal wetland ecosystems. In this study, we systematically evaluated the effects of flight configuration (flying altitude, image overlap, and lighting conditions) on UAS photogrammetric level 1 products: orthoimagery and point clouds, and level 2 products: digital terrain models (DTM) and canopy height models (CHM). We also developed an object-based machine learning approach to correct UAS DTMs to mitigate data uncertainties caused by flight configuration and dense vegetation. Flying altitude was identified as the leading parameter in the quality of level 1 products, while image overlap was the most influential determinant for the quality of level 2 products. The correction approach effectively reduced the vertical error of DTMs for two study sites. This study informs UAS photogrammetric survey design and data enhancement for applications in coastal wetlands.

Sequential Feature Selection Using Hybridized Differential Evolution Algorithm and Haar Cascade for Object Detection Framework

Intelligent systems an aspect of artificial intelligence have been developed to improve satellite image interpretation with several foci on object-based machine learning methods but lack an optimal feature selection technique. Existing techniques applied to satellite images for feature selection and object detection have been reported to be ineffective in detecting objects. In this paper, differential Evolution (DE) algorithm has been introduced as a technique for selecting and mapping features to Haarcascade machine learning classifier for optimal detection of satellite image was acquired, pre-processed and features engineering was carried out and mapped using adopted DE algorithm. The selected feature was trained using Haarcascade machine learning algorithm. The result shows that the proposed technique has performance Accuracy of 86.2%, sensitivity 89.7%, and Specificity 82.2% respectively.

Object-based random forest classification for informal settlements identification in the Middle East: Jeddah a case study

ABSTRACTThe identification of informal settlements in urban areas is an important step in developing and implementing pro-poor urban policies. Understanding when, where and who lives inside informal settlements is critical in efforts to improve their resilience. This study aims to analyse the capability of machine-learning (ML) methods to map informal settlement areas in Jeddah, Saudi Arabia, using very-high-resolution (VHR) imagery and terrain data. Fourteen indicators of settlement characteristics were derived and mapped using an object-based ML approach and VHR imagery. These indicators were categorized according to three different spatial levels: environ, settlement and object. The most useful indicators for prediction were found to be density and texture measures, (with random forest (RF) relative importance measures of over 25% and 23% respectively). The success of this approach was evaluated using a small, fully independent validation dataset. Informal areas were mapped with an overall accuracy of 91%. Object-based ML as a processing chain approach performed better (8%) than object-based image analysis alone due to its ability to encompass all available geospatial levels.

Mapping Landslides on EO Data: Performance of Deep Learning Models vs. Traditional Machine Learning Models

Mapping landslides using automated methods is a challenging task, which is still largely done using human efforts. Today, the availability of high-resolution EO data products is increasing exponentially, and one of the targets is to exploit this data source for the rapid generation of landslide inventory. Conventional methods like pixel-based and object-based machine learning strategies have been studied extensively in the last decade. In addition, recent advances in CNN (convolutional neural network), a type of deep-learning method, has been widely successful in extracting information from images and have outperformed other conventional learning methods. In the last few years, there have been only a few attempts to adapt CNN for landslide mapping. In this study, we introduce a modified U-Net model for semantic segmentation of landslides at a regional scale from EO data using ResNet34 blocks for feature extraction. We also compare this with conventional pixel-based and object-based methods. The experiment was done in Douglas County, a study area selected in the south of Portland in Oregon, USA, and landslide inventory extracted from SLIDO (Statewide Landslide Information Database of Oregon) was considered as the ground truth. Landslide mapping is an imbalanced learning problem with very limited availability of training data. Our network was trained on a combination of focal Tversky loss and cross-entropy loss functions using augmented image tiles sampled from a selected training area. The deep-learning method was observed to have a better performance than the conventional methods with an MCC (Matthews correlation coefficient) score of 0.495 and a POD (probability of detection) rate of 0.72 .

Object-based correction of LiDAR DEMs using RTK-GPS data and machine learning modeling in the coastal Everglades

Light Detection and Ranging (LiDAR) Digital Elevation Models (DEMs) are frequently applied in modeling coastal environments. We present an object-based correction approach for accurate and precise DEMs by integrating LiDAR point data, aerial imagery, and Real Time Kinematic-Global Positioning Systems. Four machine learning techniques (Random Forest, Support Vector Machine, k-Nearest Neighbor, and Artificial Neural Network) were compared with the commonly used bias-correction method. The Random Forest object-based model produced best predictions for two study areas: Nine Mile (Mean Bias Error (MBE) reduced 0.18 to −0.02 m, Root Mean Square Error (RMSE) reduced 0.22 to 0.08 m) and Flamingo (MBE reduced 0.17 to 0.02 m, RMSE reduced 0.24 to 0.10 m). A Monte Carlo model was developed to combine errors into the object-based machine learning corrected DEMs, and uncertainty maps spatially revealed the likelihood of error. The object-based correction approach provides an attractive alternative to the bias-correction method.

A new synergistic approach for monitoring wetlands using Sentinels -1 and 2 data with object-based machine learning algorithms

In this work the synergistic use of Sentinel-1 and 2 combined with the System for Automated Geoscientific Analyses (SAGA) Wetness Index in the content of land use/cover (LULC) mapping with emphasis in wetlands is evaluated. A further objective has been to develop a new Object-based Image Analysis (OBIA) approach for mapping wetland areas using Sentinel-1 and 2 data, where the latter is also tested against two popular machine learning algorithms (Support Vector Machines - SVMs and Random Forests - RFs). The highly vulnerable iSimangaliso Wetland Park was used as the study site. Results showed that two-part image segmentation could efficiently create object features across the study area. For both classification algorithms, an increase in overall accuracy was observed when the full synergistic combination of available datasets. A statistically significant difference in classification accuracy at all levels between SVMs and RFs was also reported, with the latter being up to 2.4% higher. SAGA wetness index showed promising ability to distinguish wetland environments, and in combination with Sentinel-1 and 2 synergies can successfully produce a land use and land cover classification in a location where both wetland and non-wetland classes exist.

Wetland classification using Radarsat-2 SAR quad-polarization and Landsat-8 OLI spectral response data: a case study in the Hudson Bay Lowlands Ecoregion

ABSTRACTCircumboreal Canadian bogs and fens distinguished by differences in soils, hydrology, vegetation and morphological features were classified using combinations of Radarsat-2 synthetic aperture radar (SAR) quad-polarization data and Landsat-8 Operational Land Imager (OLI) spectral response patterns. Separate classifications were conducted using a traditional pixel-based maximum likelihood classifer and a machine learning algorithm following an object-based image analysis (OBIA). This study focused on two wetland classes with extensive coverage in the area (bog and fen). In the pixel-based maximum likelihood classification, accuracy increased from approximately 69% user’s accuracy and 79% producer’s accuracy using Radarsat-2 SAR data alone to approximately 80% user’s accuracy and 87% producer’s accuracy using Landsat-8 OLI data alone. Use of the Radarsat-2 SAR and Landsat-8 OLI data following principal components analysis (PCA) data fusion did not result in higher pixel-based maximum likelihood classification accuracy. In the object-based machine learning classification, higher bog and fen class accuracies were obtained when using Radarsat-2 and Landsat OLI data individually compared to the equivalent pixel-based classification. Subsequently, a PCA-data fusion product outperformed the individual bands of the Radarsat-2 and Landsat-8 imagery in object-based classification. Greater than 90% producer’s accuracy was obtained. The margin of error (MOE) was less than 5% in all classifications reported here. Further research will examine alternative data fusion techniques and the addition of Radarsat-2 SAR interferometric digital elevation model (DEM)-based geomorphometrics in object-based classification of different morphological types of bogs and fens.

Object-based habitat mapping using very high spatial resolution multispectral and hyperspectral imagery with LiDAR data

This study investigated the combined use of multispectral/hyperspectral imagery and LiDAR data for habitat mapping across parts of south Cumbria, North West England. The methodology adopted in this study integrated spectral information contained in pansharp QuickBird multispectral/AISA Eagle hyperspectral imagery and LiDAR-derived measures with object-based machine learning classifiers and ensemble analysis techniques. Using the LiDAR point cloud data, elevation models (such as the Digital Surface Model and Digital Terrain Model raster) and intensity features were extracted directly. The LiDAR-derived measures exploited in this study included Canopy Height Model, intensity and topographic information (i.e. mean, maximum and standard deviation). These three LiDAR measures were combined with spectral information contained in the pansharp QuickBird and Eagle MNF transformed imagery for image classification experiments. A fusion of pansharp QuickBird multispectral and Eagle MNF hyperspectral imagery with all LiDAR-derived measures generated the best classification accuracies, 89.8 and 92.6% respectively. These results were generated with the Support Vector Machine and Random Forest machine learning algorithms respectively. The ensemble analysis of all three learning machine classifiers for the pansharp QuickBird and Eagle MNF fused data outputs did not significantly increase the overall classification accuracy. Results of the study demonstrate the potential of combining either very high spatial resolution multispectral or hyperspectral imagery with LiDAR data for habitat mapping.