Evaluating remote sensing datasets and machine learning algorithms for mapping plantations and successional forests in Phnom Kulen National Park of Cambodia

International audienceThis study develops a modelling framework by utilizing multi-sensor imagery for classifying different forest and land use types in the Phnom Kulen National Park (PKNP) in Cambodia. Three remote sensing datasets (Landsat optical data, ALOS L-band data and LiDAR derived Canopy Height Model (CHM)) were used in conjunction with three different machine learning (ML) regression techniques (Support Vector Machines (SVM), Random Forests (RF) and Artificial Neural Networks (ANN)). These ML methods were implemented on (a) Landsat spectral data, (b) Landsat spectral band & ALOS backscatter data, and (c) Landsat spectral band, ALOS backscatter data, & LiDAR CHM data. The Landsat-ALOS combination produced more accurate classification results (95% overall accuracy with SVM) compared to Landsat-only bands for all ML models. Inclusion of LiDAR CHM (which is a proxy for vertical canopy heights) improved the overall accuracy to 98%. The research establishes that majority of PKNP is dominated by cashew plantations and the nearly intact forests are concentrated in the more inaccessible parts of the park. The findings demonstrate how different RS datasets can be used in conjunction with different ML models to map forests that had undergone varying levels of degradation and plantations

Mapping and Characterizing Selected Canopy Tree Species at the Angkor World Heritage Site in Cambodia Using Aerial Data

<div><p>At present, there is very limited information on the ecology, distribution, and structure of Cambodia’s tree species to warrant suitable conservation measures. The aim of this study was to assess various methods of analysis of aerial imagery for characterization of the forest mensuration variables (i.e., tree height and crown width) of selected tree species found in the forested region around the temples of Angkor Thom, Cambodia. Object-based image analysis (OBIA) was used (using multiresolution segmentation) to delineate individual tree crowns from very-high-resolution (VHR) aerial imagery and light detection and ranging (LiDAR) data. Crown width and tree height values that were extracted using multiresolution segmentation showed a high level of congruence with field-measured values of the trees (Spearman’s rho 0.782 and 0.589, respectively). Individual tree crowns that were delineated from aerial imagery using multiresolution segmentation had a high level of segmentation accuracy (69.22%), whereas tree crowns delineated using watershed segmentation underestimated the field-measured tree crown widths. Both spectral angle mapper (SAM) and maximum likelihood (ML) classifications were applied to the aerial imagery for mapping of selected tree species. The latter was found to be more suitable for tree species classification. Individual tree species were identified with high accuracy. Inclusion of textural information further improved species identification, albeit marginally. Our findings suggest that VHR aerial imagery, in conjunction with OBIA-based segmentation methods (such as multiresolution segmentation) and supervised classification techniques are useful for tree species mapping and for studies of the forest mensuration variables.</p></div

Applying GLCM Texture Analysis to Aerial Imagery.

<p>Applying GLCM Texture Analysis to Aerial Imagery.</p

An Overview of the Study Area.

<p>Top: A map of Cambodia, shaded relief courtesy NASA/SRTM. Top Inset: An archaeological map of central Angkor, courtesy of the Greater Angkor Project (GAP). Bottom: The study area within the walled city of Angkor Thom, shown against a background of LiDAR intensity data, courtesy of the Khmer Archaeology LiDAR Consortium (KALC), and archaeological data, courtesy of GAP. All remote sensing data for the study were provided by Damian Evans.</p

Oversegmentation versus Undersegmentation.

<p>The green polygons are the manually digitized polygons that were overlaid on multiresolution segmentation polygons (shown in pink).</p

Comparison of Producer’s and User’s Accuracy Levels for Three-band Maximum Likelihood Classification.

<p>CHAM: <i>Cynometra ramiflora</i> CHH: <i>Dipterocarpus alatus;</i> KOKI: <i>Hopea odorata;</i> SPUNG: <i>Tetrameles nudiflora;</i> SVAY: <i>Anacardiac mangifera</i> and SRL: <i>Lagerstroemia calyculata</i>.</p><p>Comparison of Producer’s and User’s Accuracy Levels for Three-band Maximum Likelihood Classification.</p

Applying Classifiers to Aerial Imagery.

<p>Applying Classifiers to Aerial Imagery.</p

A Classification Map of Tree Species from Three-band Aerial Imagery.

<p>CHH: <i>Dipterocarpus alatus;</i> KOKI: <i>Hopea odorata;</i> SPUNG: <i>Tetrameles nudiflora;</i> SRALAO: <i>Lagerstroemia calyculata;</i> CHAM: <i>Cynometra ramiflora</i>; and SRL: <i>Lagerstroemia calyculata</i>.</p