Lidar Data Analysis in R

Question :

The project should follow the IMRAD style and all execution codes and packages should be written in the Appendix section of the assignment. the point cloud should be displayed in 3D. I need a well-detailed script for this work when completed. Well cited and referenced. should be plagiarism free and a short introduction, methodology, and discussion. Please use simple codes to execute the results. I have uploaded three data, one with the same name but with different formats and the other is the inventory dataset. So you can use the preferable one

Answer :

Introduction

LiDAR technology is one of the sources of geogra|phic inform|ation that allows coord|inate poi|nts inclu|ding the hei||ght to be obtained wi|th greater precision. One of the ma|in applic|ations that LiDAR has is in the fore|stry sector. LiDAR fosters the development of estimating forest resources. The study explores the us|e of LiD|AR da|ta for the estimation of For|est Volume using the Lidar Airborne data. Certain computer tools are used for management of analyzing the LiDAR data. Subsequently, a methodological procedure is established to obtain ground filtering algorithm that relates the normalized height data with the Forest Volume field variable (Barilotti, Turco, & Alberti, 2006). The ground filtering algorithm is formulated with the R studio representing the phenomenon, and its para|meters. 

Airborne Registration System that use lasers (LiDAR) are becoming the main instrument for collecting the cartographic information mainly due to the high point density, precision achieved and speed in obtaining digital models. However, it would be important to have algorithms that allow filtering the information, selecting those points measured in desired areas. When urban areas are measured, the most important are the buildings. Therefore, proposes a new algorithm that allows classifying and differentiate those points measured on buildings, extracting, as a result, the outer limit that define in such a way that the area could be calculated  built.

LiDAR“is an airborne laser sensor, which allows obtaining geo-referenced points of all the elements on the Earth's surface. The principle of operation is the calculation of the time it takes for a laser signal to go and return, which is sent to the surface, and the position of each point is adjusted in space thanks to Global Positioning Satellite Systems - GNSS, which are coupled to the sensor in aircraft and ground control stations.”Undoubtedly, one of the most developed applications with the use of LiDAR data has been in the forest field (Cavalli, Tarolli, Marchi, & Dalla Fontana, 2008). In countries where forest preservation and commercial exploitation are important, work has been carried out whose objective has been to find some relationship of the point cloud with three-dimensional coordinates provided by LiDAR and some forest variable. 

The main objective is the development of a methodology that allows estimating the Forest Volume of the Freidschane study area from the information provided by the LiDAR data. Processes or operations will be presented which should be applied to the LiDAR file for filtering points of interest that define the buildings. These processes they must separate those points given on the roofs of the buildings they were from measured on nearby woodland or own building facades. They will be used algorithms that directly use the information stored in a LiDAR file, without using digital grid models that they will always further distorts the measured reality.

Methodology

The methodology of the study is to perform ground filtering algorithm with complete data of the shapes of the LiDAR signals based on the cut technique of standard segmentation. The methodology consisted of decomposing the signal shape and obtaining the width and intensity of each signal pulse. The advantage and suitability of using LiDAR data for forest inventories and estimate forest parameters justifies that the comparison of traditional forest inventories with inventories supported by LiDAR data is evidential (Cavalli, et al, 2008). For the development of the methodology used to obtain the final result together with the objective of publicizing the advantages of using LiDAR data, an analysis of the computer tools that could facilitate its development was consulted and applied. After the analysis of the best computer tools, one of the methods applied by the various authors of studies with similar themes is chosen. The lidar analysis performed is relative to the forested test site of Freidschane data in the study. The initially analysis is performed with the available software to process the LiDAR data (Scheidl, Rickenmann, & Chiari, 2008). R studio is used for performing the analysis. 

The Software chosen to process the LiDAR data was R Studio as it is the most complete adequate tool for data treatment allowing not only to visualize it. The starting geometric condition for selecting a point will be based on the difference of dimension that it has on the points of its environment in such a way that if this difference exceeds a certain threshold you could consider or assume that the point may be selected (Bork, et al, 2007). The algorithm allows first to filter only the terrain points from a function that is executed for each point of the LiDAR data iteratively allowing the user to control which points will become part of the ground filtering algorithm. 

The foregoing leads to the behavior of the Forest Volume and its relationship with the average height and asymmetry being different depending on the type of species present, that is, trees with low height can present a very disparate Forest Volume due to the morphology of each type of species, on the other hand, high-altitude trees can present high and variable forest volumes, depending on the type of species and the density of the trees (Forlani, Nardinocchi, Scaioni, & Zingaretti, 2006). This analysis implies that the variance of the error is not constant in the sample and that the diversity of tree species makes the Forest Volume very uneven. 

Results 

In view of the results, it could claim that an algorithm has been presented that allows to find a high percentage of points belonging to buildings, discriminating other objects with a height similar to this type of objects. In addition, a possible solution is ascertained to find the limits. However, future lines of research will focus on various aspects where there is still room for improvement (Cavalli, et al, 2008). These improvements could come hand in hand with the use of additional information by radiometric information from photogrammetric images. Taking the three-dimensional model and photogrammetric image is more precise with greater precision. 

The inventory was verified in the field side supplied in the Freidschane forested site and the positioning with respect to the service. Selection of the sample areas are used to analyze the mean of the distance between trees. The results depict that the Total estimates of basal area, aboveground biomass, and total volume using linear and nonlinear regression are optimistic with respect to estimates using traditional inventory (Kreylos, Bawden, & Kellogg, 2008). In the case of the ratio estimator, the estimates are conservative for bio-mass and volume, while for the basal area they are slightly optimistic. 

Discussion

The estimation of the variables such as the basal area, total aerial biomass, tree cover and total timber volume, is generated from Freidschane LiDAR data with good precision and with the advantage of creating maps that expose the spatial variability for each one of the variables. The total estimates obtained using the LiDAR data analysis method is considered very adequate. Also, the values are within the estimates using the methodology associated with a traditional forest inventory (Forlani, et al, 2006). This algorithm uses an active shape model to represent a three-dimensional contour, which works as a network to eliminate non-ground points. A horizontal network is computer and placed just below all points in the cloud. Subsequently, this surface will rise until it meets the terrain formed by the points. It is considered that the terrain will be easily reached, but the roofs of houses and trees will present more difficulty to be found. Thus, buildings and vegetation were discarded. 

Its main objective will be to compare the elevations of the points and those estimated from various interpolation methods. An iterative process is applied based on the calculation of an average surface using all the LiDAR data. The points belonging to the topographic surface will have negative residues, while those belonging to the vegetation present very small or positive residues. These residuals are used as weights for each point, defining the weight function with values between 1 and 0. At first, few points are taken and approximation of the terrain is calcaulted. Weights are assigned to the points according to their vertical distance to the approximate surface: the points above will be given a low weight, and those below the surface will be given a high weight. The surface is recalculated with a linear interpolation function and using the assigned weights. This process will be repeated until there are no significant changes between the iterations.

Information generated from LiDAR data used for monitoring forest resources on a small spatial scale and in a short period of time. It is used to measure and quantify the state and development of forests, as well as the quantity of wood and existing biomass. As more data from active sensors such as LiDAR becomes available, it will be possible to generate and improve the estimates of the most common variables. It is also used to supporting ground filtering algorithm information to form appropriate imagery i.e. DTM (Digital Terrain Model) to further improve accuracy (Mallet, Soergel, & Bretar, 2008). The results obtained provide valuable information that can be used in the development of new models, with improved precision for estimating the forest parameters of interest on a regional scale.

It is still necessary to make use of field data to validate the models, however, this stage no longer has to be so exhaustive since now there is three-dimensional information. It allows monitoring the entire area of interest which allows minimizing time and cost that is invested in the measurement of variables in the field. The infor|mation provi|ded by the LiD|AR da|ta also yie|lds additi|onal attrib|utes su|ch as tr|ee hei|ght, cro|wn si|ze an|d cro|wn ba|se hei|ght. The bene|fits of deri|ving featu|res fro|m geom|etric perspe|ctive ar|e th|e clo|se relatio|nship betw|een the|se featu|res to tr|ee for|ms whi|ch ca|n be relat|ed to th|e bioph|ysical interpr|etation. It al|so yie|lds bet|ter vis|ual represe|ntations (Reitberger, Krzystek, & Stilla, 2008). Th|e tw|o approa|ches ha|ve its advan|tage an|d limita|tions and the cur|rent stu|dy sug|gest th|at th|ey cou|ld be comb|ined and prov|ide supple|mentary inform|ation to fo|rm a stro|nger class|ifier, deliv|ering a bet|ter accu|racy of the” res|ults.

The gaps in the dominant canopy allow for some of the LiDAR pulses to reach the lower vegetation which has also a low probability to return from the top of it, generating lower heights. As these points can mainly be gather in the gaps, no high angles can be used as they the pulses need to be as vertical as possible to actually receive any return which also reduces the possibilities of top understory points to be form in the final cloud. In any event, we can hypothesize that the point cloud density, from underneath the main canopy layer in a given small area. Given the morphological similarity between an inverted canopy and a terrain model, this same process can be used to outline tree crowns (Tarsha-Kurdi, Landes, Grussenmeyer, & Koehl, 2007). However, a potential problem is the issue of over segmentation, bumps and other spurious treetops are given their own segments. This source of error can be mitigated by using a variant of the algorithm known as marker-controlled segmentation.

References;

Barilotti, A., Turco, S., & Alberti, G. (2006, February). LAI determination in forestry ecosystem by LiDAR data analysis. In Proceedings of Workshop 3D Remote Sensing in Forestry, Vienna, Austria (Vol. 1415).

Bork, E. W., & Su, J. G. (2007). Integrating LIDAR data and multispectral imagery for enhanced classification of rangeland vegetation: A meta-analysis. Remote Sensing of Environment, 111(1), 11-24. 

Cavalli, M., Tarolli, P., Marchi, L., & Dalla Fontana, G. (2008). The effectiveness of airborne LiDAR data in the recognition of channel-bed morphology. Catena, 73(3), 249-260.

Forlani, G., Nardinocchi, C., Scaioni, M., & Zingaretti, P. (2006). Complete classification of raw LIDAR data and 3D reconstruction of buildings. Pattern analysis and applications, 8(4), 357-374.

Kreylos, O., Bawden, G. W., & Kellogg, L. H. (2008, December). Immersive visualization and analysis of LiDAR data. In International Symposium on Visual Computing (pp. 846-855). Springer, Berlin, Heidelberg.

Mallet, C., Soergel, U., & Bretar, F. (2008, July). Analysis of full-waveform lidar data for classification of urban areas.

Reitberger, J., Krzystek, P., & Stilla, U. (2008). Analysis of full waveform LIDAR data for the classification of deciduous and coniferous trees. International journal of remote sensing, 29(5), 1407-1431. 

Scheidl, C., Rickenmann, D., & Chiari, M. (2008). The use of airborne LiDAR data for the analysis of debris flow events in Switzerland. Natural Hazards and Earth System Sciences, 8(5), 1113-1127.

Tarsha-Kurdi, F., Landes, T., Grussenmeyer, P., & Koehl, M. (2007, September). Model-driven and data-driven approaches using LIDAR data: Analysis and comparison.