Spectral Reflectance-Based Mangrove Species Mapping from WorldView-2 Imagery of Karimunjawa and Kemujan Island, Central Java Province, Indonesia

Mangrove mapping at the species level enables the creation of a detailed inventory of mangrove forest biodiversity and supports coastal ecosystem management. The Karimunjawa National Park in Central Java Province is one of Indonesia’s mangrove habitats with high biodiversity, namely, 44 species representing 25 true mangroves and 19 mangrove associates. This study aims to (1) classify and group mangrove species by their spectral reflectance characteristics, (2) map mangrove species by applying their spectral reflectance to WorldView-2 satellite imagery with the spectral angle mapper (SAM), spectral information divergence (SID), and spectral feature fitting (SFF) algorithms, and (3) assess the accuracy of the produced mangrove species mapping of the Karimunjawa and Kemujan Islands. The collected field data included (1) mangrove species identification, (2) coordinate locations of targeted mangrove species, and (3) the spectral reflectance of mangrove species measured with a field spectrometer. Dendrogram analysis was conducted with the Ward linkage method to classify mangrove species based on the distance between the closest clusters of spectral reflectance patterns. The dendrogram showed that the 24 mangrove species found in the field could be grouped into four levels. They consisted of two, four, and five species groups for Levels 1 to 3, respectively, and individual species for Level 4. The mapping results indicated that the SID algorithm had the highest overall accuracy (OA) at 49.72%, 22.60%, and 15.20% for Levels 1 to 3, respectively, while SFF produced the most accurate results for individual species mapping (Level 4) with an OA of 5.08%. The results suggest that the greater the number of classes to be mapped, the lower the mapping accuracy. The results can be used to model the spatial distribution of mangrove species or the composition of mangrove forests and update databases related to coastal management.

Machine Learning Classification of Endangered Tree Species in a Tropical Submontane Forest Using WorldView-2 Multispectral Satellite Imagery and Imbalanced Dataset

Accurate maps of the spatial distribution of tropical tree species provide valuable insights for ecologists and forest management. The discrimination of tree species for economic, ecological, and technical reasons is usually necessary for achieving promising results in tree species mapping. Most of the data used in tree species mapping normally have some degree of imbalance. This study aimed to assess the effects of imbalanced data in identifying and mapping trees species under threat in a selectively logged sub-montane heterogeneous tropical forest using random forest (RF) and support vector machine with radial basis function (RBF-SVM) kernel classifiers and WorldView-2 multispectral imagery. For comparison purposes, the original imbalanced dataset was standardized using three data sampling techniques: oversampling, undersampling, and combined oversampling and undersampling techniques in R. The combined oversampling and undersampling technique produced the best results: F1-scores of 68.56 ± 2.6% for RF and 64.64 ± 3.4% for SVM. The balanced dataset recorded improved classification accuracy compared to the original imbalanced dataset. This research observed that more separable classes recorded higher F1-scores. Among the species, Syzygium guineense and Zanthoxylum gilletii were the most accurately mapped whereas Newtonia buchananii was the least accurately mapped. The most important spectral bands with the ability to detect and distinguish between tree species as measured by random forest classifier, were the Red, Red Edge, Near Infrared 1, and Near Infrared 2.

Using Discrete-Point LiDAR to Classify Tree Species in the Riparian Pacific Northwest, USA

Practical methods for tree species identification are important for both land management and scientific inquiry. LiDAR has been widely used for species mapping due to its ability to characterize 3D structure, but in structurally complex Pacific Northwest forests, additional research is needed. To address this need and to determine the feasibility of species modeling in such forests, we compared six approaches using five algorithms available in R’s lidR package and Trimble’s eCognition software to determine which approach most consistently identified individual trees across a heterogenous riparian landscape. We then classified segments into Douglas fir (Pseudotsuga menziesii), black cottonwood (Populus balsamifera ssp. trichocarpa), and red alder (Alnus rubra). Classification accuracies based on the best-performing segmentation method were 91%, 92%, and 84%, respectively. To our knowledge, this is the first study to investigate tree species modeling from LiDAR in a natural Pacific Northwest forest, and the first to model Pacific Northwest species at the landscape scale. Our results suggest that LiDAR alone may provide enough information on tree species to be useful to land managers in limited applications, even under structurally challenging conditions. With slight changes to the modeling approach, even higher accuracies may be possible.

Is evolution faster at ecotones? A test using rates and tempo of diet transitions in Neotropical Sigmodontinae (Rodentia, Cricetidae)

We evaluated whether evolution is faster at ecotones as niche shifts may be needed to persist under unstable environment. We mapped diet evolution along the evolutionary history of 350 sigmodontine species. Mapping was used in three new tip-based metrics of trait evolution–Transition Rates, Stasis Time, and Last Transition Time–which were spatialized at the assemblage level (aTR, aST, aTL). Assemblages were obtained by superimposing range maps on points located at core and ecotone of the 91 South American ecoregions. Using Linear Mixed Models, we tested whether ecotones have species with more changes from the ancestral diet (higher aTR), have maintained the current diet for a shorter time (lower aST) and have more recent transitions to the current diet (lower aLT) than cores. We found higher aTR, aST and aLT at ecotones than at cores. Although ecotones are more heterogeneous, both environmentally and in relation to selection pressures they exert on organisms, ecotone species change little from the ancestral diet as generalist habits are necessary toward feeding in ephemeral environments. The need to incorporate phylogenetic uncertainty in tip-based metrics was evident from large uncertainty detected. Our study integrates ecology and evolution by analyzing how fast trait evolution is across space.