Bark Classification of Trees Using K-Nearest Neighbor & Nearest Neighbor Algorithms

Pakistan is an agricultural country and less than 4 % of area secured with forests. Tree automatic classification based on computer science and it is the developing trend of classification. In this paper we examine how we can done bark classification of trees using k-nearest neighbor and nearest neighbor algorithms. There we discuss how these algorithms can be used to automatically classify trees from images of bark. We get the images of five kinds of different trees names suppose as A, B, C, D and E through using digital camera. We take ten different images of each kind of trees. The capability and information of inspectors are essential to perfectly achieve this process. The all the process will be done in computer vision image processing tool. In this tool we use the Histogram Features, Texture Features, and Pattern Classification. We achieved the final results of five kinds of different trees using nearest neighbor on distance two 82% average and on k-nearest neighbor when k=2 then the average result 82%, when k=3 the average result 82%, when k=4 then the average result 76% and when k=5 the average percentage 72% the result shows the maximum correct result and classifies the trees. These are the best percentage results using these algorithms for classification. In this way we can easily classify the different trees and also these methods provide opportunity to farmer and other people for identify and select the different better different trees for getting more benefit. Keywords: CVIP Tool, Histogram Features, Texture Features, Pattern Classification, Classification Algorithm

Unmanned Aerial Vehicles (UAVs) in environmental biology: A Review

Acquiring information about the environment is a key step during each study in the field of environmental biology at different levels, from an individual species to community and biome. However, obtaining information about the environment is frequently difficult because of, for example, the phenological timing, spatial distribution of a species or limited accessibility of a particular area for the field survey. Moreover, remote sensing technology, which enables the observation of the Earth’s surface and is currently very common in environmental research, has many limitations such as insufficient spatial, spectral and temporal resolution and a high cost of data acquisition. Since the 1990s, researchers have been exploring the potential of different types of unmanned aerial vehicles (UAVs) for monitoring Earth’s surface. The present study reviews recent scientific literature dealing with the use of UAV in environmental biology. Amongst numerous papers, short communications and conference abstracts, we selected 110 original studies of how UAVs can be used in environmental biology and which organisms can be studied in this manner. Most of these studies concerned the use of UAV to measure the vegetation parameters such as crown height, volume, number of individuals (14 studies) and quantification of the spatio-temporal dynamics of vegetation changes (12 studies). UAVs were also frequently applied to count birds and mammals, especially those living in the water. Generally, the analytical part of the present study was divided into following sections: (1) detecting, assessing and predicting threats on vegetation, (2) measuring the biophysical parameters of vegetation, (3) quantifying the dynamics of changes in plants and habitats and (4) population and behaviour studies of animals. At the end, we also synthesised all the information showing, amongst others, the advances in environmental biology because of UAV application. Considering that 33% of studies found and included in this review were published in 2017 and 2018, it is expected that the number and variety of applications of UAVs in environmental biology will increase in the future

Assessing a novel modelling approach with high resolution UAV imagery for monitoring health status in priority riparian forests

ResearchBackground: Black alder (Alnus glutinosa) forests are in severe decline across their area of distribution due to a disease caused by the soil-borne pathogenic Phytophthora alni species complex (class Oomycetes), “alder Phytopththora”. Mapping of the different types of damages caused by the disease is challenging in high density ecosystems in which spectral variability is high due to canopy heterogeneity. Data obtained by unmanned aerial vehicles (UAVs) may be particularly useful for such tasks due to the high resolution, flexibility of acquisition and cost efficiency of this type of data. In this study, A. glutinosa decline was assessed by considering four categories of tree health status in the field: asymptomatic, dead and defoliation above and below a 50% threshold. A combination of multispectral Parrot Sequoia and UAV unmanned aerial vehicles -red green blue (RGB) data were analysed using classical random forest (RF) and a simple and robust three-step logistic modelling approaches to identify the most important forest health indicators while adhering to the principle of parsimony. A total of 34 remote sensing variables were considered, including a set of vegetation indices, texture features from the normalized difference vegetation index (NDVI) and a digital surface model (DSM), topographic and digital aerial photogrammetry-derived structural data from the DSM at crown level. Results: The four categories identified by the RF yielded an overall accuracy of 67%, while aggregation of the legend to three classes (asymptomatic, defoliated, dead) and to two classes (alive, dead) improved the overall accuracy to 72% and 91% respectively. On the other hand, the confusion matrix, computed from the three logistic models by using the leave-out cross-validation method yielded overall accuracies of 75%, 80% and 94% for four-, three- and two-level classifications, respectively. Discussion: The study findings provide forest managers with an alternative robust classification method for the rapid, effective assessment of areas affected and non-affected by the disease, thus enabling them to identify hotspots for conservation and plan control and restoration measures aimed at preserving black alder forestsinfo:eu-repo/semantics/publishedVersio

Operationalization of Remote Sensing Solutions for Sustainable Forest Management

The great potential of remote sensing technologies for operational use in sustainable forest management is addressed in this book, which is the reprint of papers published in the Remote Sensing Special Issue “Operationalization of Remote Sensing Solutions for Sustainable Forest Management”. The studies come from three continents and cover multiple remote sensing systems (including terrestrial mobile laser scanning, unmanned aerial vehicles, airborne laser scanning, and satellite data acquisition) and a diversity of data processing algorithms, with a focus on machine learning approaches. The focus of the studies ranges from identification and characterization of individual trees to deriving national- or even continental-level forest attributes and maps. There are studies carefully describing exercises on the case study level, and there are also studies introducing new methodologies for transdisciplinary remote sensing applications. Even though most of the authors look forward to continuing their research, nearly all studies introduced are ready for operational use or have already been implemented in practical forestry

Deep-learning feature descriptor for tree bark re-identification

L’habilité de visuellement ré-identifier des objets est une capacité fondamentale des systèmes de vision. Souvent, ces systèmes s’appuient sur une collection de signatures visuelles basées sur des descripteurs comme SIFT ou SURF. Cependant, ces descripteurs traditionnels ont été conçus pour un certain domaine d’aspects et de géométries de surface (relief limité). Par conséquent, les surfaces très texturées telles que l’écorce des arbres leur posent un défi. Alors, cela rend plus difficile l’utilisation des arbres comme points de repère identifiables à des fins de navigation (robotique) ou le suivi du bois abattu le long d’une chaîne logistique (logistique). Nous proposons donc d’utiliser des descripteurs basés sur les données, qui une fois entraîné avec des images d’écorce, permettront la ré-identification de surfaces d’arbres. À cet effet, nous avons collecté un grand ensemble de données contenant 2 400 images d’écorce présentant de forts changements d’éclairage, annotées par surface et avec la possibilité d’être alignées au pixels près. Nous avons utilisé cet ensemble de données pour échantillonner parmis plus de 2 millions de parcelle d’image de 64x64 pixels afin d’entraîner nos nouveaux descripteurs locaux DeepBark et SqueezeBark. Notre méthode DeepBark a montré un net avantage par rapport aux descripteurs fabriqués à la main SIFT et SURF. Par exemple, nous avons démontré que DeepBark peut atteindre une mAP de 87.2% lorsqu’il doit retrouver 11 images d’écorce pertinentes, i.e correspondant à la même surface physique, à une image requête parmis 7,900 images. Notre travail suggère donc qu’il est possible de ré-identifier la surfaces des arbres dans un contexte difficile, tout en rendant public un nouvel ensemble de données.The ability to visually re-identify objects is a fundamental capability in vision systems. Oftentimes,it relies on collections of visual signatures based on descriptors, such as SIFT orSURF. However, these traditional descriptors were designed for a certain domain of surface appearances and geometries (limited relief). Consequently, highly-textured surfaces such as tree bark pose a challenge to them. In turn, this makes it more difficult to use trees as identifiable landmarks for navigational purposes (robotics) or to track felled lumber along a supply chain (logistics). We thus propose to use data-driven descriptors trained on bark images for tree surface re-identification. To this effect, we collected a large dataset containing 2,400 bark images with strong illumination changes, annotated by surface and with the ability to pixel align them. We used this dataset to sample from more than 2 million 64 64 pixel patches to train our novel local descriptors DeepBark and SqueezeBark. Our DeepBark method has shown a clear advantage against the hand-crafted descriptors SIFT and SURF. For instance, we demonstrated that DeepBark can reach a mAP of 87.2% when retrieving 11 relevant barkimages, i.e. corresponding to the same physical surface, to a bark query against 7,900 images. ur work thus suggests that re-identifying tree surfaces in a challenging illuminations contextis possible. We also make public our dataset, which can be used to benchmark surfacere-identification techniques

Single-picture reconstruction and rendering of trees for plausible vegetation synthesis

State-of-the-art approaches for tree reconstruction either put limiting constraints on the input side (requiring multiple photographs, a scanned point cloud or intensive user input) or provide a representation only suitable for front views of the tree. In this paper we present a complete pipeline for synthesizing and rendering detailed trees from a single photograph with minimal user effort. Since the overall shape and appearance of each tree is recovered from a single photograph of the tree crown, artists can benefit from georeferenced images to populate landscapes with native tree species. A key element of our approach is a compact representation of dense tree crowns through a radial distance map. Our first contribution is an automatic algorithm for generating such representations from a single exemplar image of a tree. We create a rough estimate of the crown shape by solving a thin-plate energy minimization problem, and then add detail through a simplified shape-from-shading approach. The use of seamless texture synthesis results in an image-based representation that can be rendered from arbitrary view directions at different levels of detail. Distant trees benefit from an output-sensitive algorithm inspired on relief mapping. For close-up trees we use a billboard cloud where leaflets are distributed inside the crown shape through a space colonization algorithm. In both cases our representation ensures efficient preservation of the crown shape. Major benefits of our approach include: it recovers the overall shape from a single tree image, involves no tree modeling knowledge and minimal authoring effort, and the associated image-based representation is easy to compress and thus suitable for network streaming.Peer ReviewedPostprint (author's final draft