Using Penalized Linear Discriminant Analysis and Normalized Difference Indices Derived from Landsat 8 Images to Classify Fruit-tree Crops in the Aconcagua Valley, Chile

Accurate crop type maps are critical for yield estimation and agricultural practices in modern agriculture. A new approach is proposed in this thesis to improve the crop type classification accuracy, by creating a new feature set containing new spectral indices in addition to basic bands. Two types of penalized linear discriminant analysis classifiers are adopted to do the classification, and the cross-validated classification accuracies on the two different feature sets are compared to see whether the new feature set can improve the crop identification. The result shows with new indices in the feature set the classification mean error rates were decreased substantially for both classifiers (21.6% and 25.2%). Through analyzing the coefficients retrieved from the best model, the variable importance was assessed. The coefficients are summarized by different bands and images, and the result suggest that red and shortwave infrared are the two bands highly related to the fruit-trees type identification in the study area in Aconcagua valley, Chile. Also late winter to early spring may be the best time to do crop type mapping for these crop types

Large-area inventory of species composition using airborne laser scanning and hyperspectral data

5openInternationalInternational coauthor/editorTree species composition is an essential attribute in stand-level forest management inventories and remotely sensed data might be useful for its estimation. Previous studies on this topic have had several operational drawbacks, e.g., performance studied at a small scale and at a single tree-level with large fieldwork costs. The current study presents the results from a large-area inventory providing species composition following an operational area-based approach. The study utilizes a combination of airborne laser scanning and hyperspectral data and 97 field sample plots of 250 m2 collected over 350 km2 of productive forest in Norway. The results show that, with the availability of hyperspectral data, species-specific volume proportions can be provided in operational forest management inventories with acceptable results in 90% of the cases at the plot level. Dominant species were classified with an overall accuracy of 91% and a kappa-value of 0.73. Species-specific volumes were estimated with relative root mean square differences of 34%, 87%, and 102% for Norway spruce (Picea abies (L.) Karst.), Scots pine (Pinus sylvestris L.), and deciduous species, respectively. A novel tree-based approach for selecting pixels improved the results compared to a traditional approach based on the normalized difference vegetation index.openØrka, Hans Ole; Hansen, Endre Hofstad; Dalponte, Michele; Gobakken, Terje; Næsset, ErikØrka, H.O.; Hansen, E.H.; Dalponte, M.; Gobakken, T.; Næsset, E

High spatial resolution and hyperspectral remote sensing for mapping vegetation species in tropical rainforest

The focus of this study is on vegetation species mapping using high spatial resolution IKONOS-2 and digital Color Infrared (CIR) Aerial Photos (spatial resolution 4 m for IKONOS-2 and 20 cm for CIR) and Hyperion Hyperspectral data (spectral resolution 10 nm) in Pasoh Forest Reserve, Negeri Sembilan. Spatial and spectral separability in distinguishing vegetation species were investigated prior to vegetation species mapping to provide optimal vegetation species discrimination. A total of 88 selected vegetation species and common timber groups of the dominant family Dipterocarpaceae with diameter at breast height more than 30 cm were used in this study, where trees spectra were collected by both in situ and laboratory measurements of foliar samples. The trees spectra were analysed using first and second order derivative analysis together with scatter matrix plot based on multiobjective optimization algorithm to identify the best separability and sensitive wavelength portions for vegetation species mapping. In high spatial resolution data mapping, both IKONOS-2 and CIR data were classified by supervised classification approach using maximum likelihood and neural network classifiers, while the Hyperion data was classified by spectral angle mapper and linear mixture modeling. Results of this study indicate that only a total of ten common timber group of dominant Dipterocarpaceae genus were able to be recognized at significant divergence. Both high spatial resolution data (IKONOS-2 and CIR) gave very good classification accuracy of more than 83%. The classified hyperspectral data at 30 m spatial resolution gave a classification accuracy of 65%, hence confirming that spatial resolution is more sensitive in identification of tree genus. However, for species mapping, both high spatial and spectral remotely sensed data used are marginally less sensitive than at genus level

Detection of European Aspen (Populus tremula L.) Based on an Unmanned Aerial Vehicle Approach in Boreal Forests

European aspen (Populus tremula L.) is a keystone species for biodiversity of boreal forests.Large-diameter aspens maintain the diversity of hundreds of species, many of which are threatened in Fennoscandia. Due to a low economic value and relatively sparse and scattered occurrence of aspen in boreal forests, there is a lack of information of the spatial and temporal distribution of aspen, which hampers efficient planning and implementation of sustainable forest management practices and conservation efforts. Our objective was to assess identification of European aspen at the individual tree level in a southern boreal forest using high-resolution photogrammetric point cloud (PPC) and multispectral (MSP) orthomosaics acquired with an unmanned aerial vehicle (UAV). The structure-from-motion approach was applied to generate RGB imagery-based PPC to be used for individual tree-crown delineation. Multispectral data were collected using two UAV cameras:Parrot Sequoia and MicaSense RedEdge-M. Tree-crown outlines were obtained from watershed segmentation of PPC data and intersected with multispectral mosaics to extract and calculate spectral metrics for individual trees. We assessed the role of spectral data features extracted from PPC and multispectral mosaics and a combination of it, using a machine learning classifier—Support Vector Machine (SVM) to perform two different classifications: discrimination of aspen from the other species combined into one class and classification of all four species (aspen, birch, pine, spruce) simultaneously. In the first scenario, the highest classification accuracy of 84% (F1-score) for aspen and overall accuracy of 90.1% was achieved using only RGB features from PPC, whereas in the second scenario, the highest classification accuracy of 86 % (F1-score) for aspen and overall accuracy of 83.3% was achieved using the combination of RGB and MSP features. The proposed method provides a new possibility for the rapid assessment of aspen occurrence to enable more efficient forest management as well as contribute to biodiversity monitoring and conservation efforts in boreal forests.peerReviewe

An Aerial Perspective: Using Unmanned Aerial Systems to Predict Presence of Lesser Earless Lizards (Holbrookia Maculata)

Implementation of unmanned aerial system (UAS) in conservation biology has allowed researchers to extend their surveying range for monitoring wildlife. Wildlife biologists have started using UAS technology for detecting large species (i.e. elk, manatees) within their surveying range and monitoring changes and disturbance in the landscape. Despite this technological advancement, there are few studies that target smaller species (reptiles, rodents, amphibians) for UAS surveys. The primary reason for this is that these organisms are simply too small for detection for aerial surveying. However, certain species are restricted in their range because they have specific environmental requirements, and the target for UAS survey could change focus from detection of species to detection of their habitat. The Lesser Earless lizard (Holbrookia maculata) is smaller species of lizard that inhabits arid, rocky regions in the southwest United States, which is known to occupy areas of sparse vegetation and rocky or loamy soils. Although this species would be difficult to detect in aerial surveys, their habitat can easily be distinguished in aerial imagery. For this project, aerial surveys performed by UAS technology and ground surveying of H. maculata were analyzed in combination to generate a predictive model of H. maculata presence within a landscape. Three survey areas were assigned for this project: one to generate the predictive model from data collected from ground and aerial surveys, and two were assigned to assess the accuracy of the predictive model based off ground and aerial surveys