Spatial Prediction of Landslide-Prone Areas Through k-Nearest Neighbor Algorithm and Logistic Regression Model Using High Resolution Airborne Laser Scanning Data

2017 ◽  
pp. 151-165 ◽  
Author(s):  
Biswajeet Pradhan ◽  
Mustafa Neamah Jebur
Keyword(s):  
Logistic Regression ◽  
High Resolution ◽  
Laser Scanning ◽  
Logistic Regression Model ◽  
Nearest Neighbor ◽  
Spatial Prediction ◽  
Airborne Laser Scanning ◽  
K Nearest Neighbor ◽  
Airborne Laser ◽  
K Nearest Neighbor Algorithm

scholarly journals The transferability of airborne laser scanning based tree-level models between different inventory areas

2019 ◽  
Vol 49 (3) ◽  
pp. 228-236 ◽  
Author(s):  
Tomi Karjalainen ◽  
Lauri Korhonen ◽  
Petteri Packalen ◽  
Matti Maltamo
Keyword(s):  
Laser Scanning ◽  
Nearest Neighbor ◽  
Airborne Laser Scanning ◽  
Tree Level ◽  
Stem Volume ◽  
K Nearest Neighbor ◽  
Individual Tree ◽  
Tree Detection ◽  
Airborne Laser ◽  
Crown Base Height

In this paper, we examine the transferability of airborne laser scanning (ALS) based models for individual-tree detection (ITD) from one ALS inventory area (A1) to two other areas (A2 and A3). All areas were located in eastern Finland less than 100 km from each other and were scanned using different ALS devices and parameters. The tree attributes of interest were diameter at breast height (Dbh), height (H), crown base height (Cbh), stem volume (V), and theoretical sawlog volume (Vlog) of Scots pine (Pinus sylvestris L.) with Dbh ≥ 16 cm. All trees were first segmented from the canopy height models, and various ALS metrics were derived for each segment. Then only the segments covering correctly detected pines were chosen for further inspection. The tree attributes were predicted using the k-nearest neighbor (k-NN) imputation. The results showed that the relative root mean square error (RMSE%) values increased for each attribute after the transfers. The RMSE% values were, for A1, A2, and A3, respectively: Dbh, 13.5%, 14.8%, and 18.1%; H, 3.2%, 5.9%, and 6.2%; Cbh, 13.3%, 15.3%, and 18.3%; V, 29.3%, 35.4%, and 39.1%; and Vlog, 38.2%, 54.4% and 51.8%. The observed values indicate that it may be possible to employ ALS-based tree-level k-NN models over different inventory areas without excessive reduction in accuracy, assuming that the tree species are known to be similar.

scholarly journals Estimating stand level stem diameter distribution utilizing harvester data and airborne laser scanning

Silva Fennica ◽  
2019 ◽  
Vol 53 (3) ◽  
Author(s):  
Matti Maltamo ◽  
Marius Hauglin ◽  
Erik Naesset ◽  
Terje Gobakken
Keyword(s):  
Norway Spruce ◽  
Laser Scanning ◽  
Nearest Neighbor ◽  
Airborne Laser Scanning ◽  
Stem Diameter ◽  
Training Data ◽  
Diameter Distribution ◽  
K Nearest Neighbor ◽  
Airborne Laser ◽  
Error Index

Accurately positioned single-tree data obtained from a cut-to-length harvester were used as training harvester plot data for k-nearest neighbor (k-nn) stem diameter distribution modelling applying airborne laser scanning (ALS) information as predictor variables. Part of the same harvester data were also used for stand-level validation where the validation units were stands including all the harvester plots on a systematic grid located within each individual stand. In the validation all harvester plots within a stand and also the neighboring stands located closer than 200 m were excluded from the training data when predicting for plots of a particular stand. We further compared different training harvester plot sizes, namely 200 m, 400 m, 900 m and 1600 m. Due to this setup the number of considered stands and the areas within the stands varied between the different harvester plot sizes. Our data were from final fellings in Akershus County in Norway and consisted of altogether 47 stands dominated by Norway spruce. We also had ALS data from the area. We concentrated on estimating characteristics of Norway spruce but due to the k-nn approach, species-wise estimates and stand totals as a sum over species were considered as well. The results showed that in the most accurate cases stand-level merchantable total volume could be estimated with RMSE values smaller than 9% of the mean. This value can be considered as highly accurate. Also the fit of the stem diameter distribution assessed by a variant of Reynold’s error index showed values smaller than 0.2 which are superior to those found in the previous studies. The differences between harvester plot sizes were generally small, showing most accurate results for the training harvester plot sizes 200 m and 400 m.222222

Behavior Analysis of Illegal Fishing in the Gulf of Mexico

2018 ◽  
Vol 15 (1) ◽  
Author(s):  
Ali Pala ◽  
Jing Zhang ◽  
Jun Zhuang ◽  
Nathan Allen
Keyword(s):  
Logistic Regression ◽  
Gulf Of Mexico ◽  
Wave Height ◽  
Nearest Neighbor ◽  
Moon Phase ◽  
K Nearest Neighbor ◽  
Nearest Neighbor Algorithm ◽  
Illegal Fishing ◽  
The Us ◽  
K Nearest Neighbor Algorithm

Abstract Illegal fishing activities in the Gulf of Mexico pose a threat to the US national security, as well as damage to the economy. The US Coast Guard (USCG) estimates over 1100 incursions by Mexican fisherman into US regulated waters in the Gulf of Mexico annually. Fishermen enter the water borders to catch red snapper, which is one of the Gulf of Mexico’s signature and most valuable fish. There are a number of academic contributions which have sought to improve the understanding of the problem of illegal fishing, and to try to generate better solutions. In this study, we investigate the relationship between illegal fishing activities and environmental factors with one-year of historical sight, weather, and moon phase data. Descriptive analysis provides some interesting insights such as sight patterns depending on wave height, moon phase, and hours of a day. Also, we develop logistic regression models that shows wave height is negatively correlated with sight occurrences for all sight types. In addition, we oversample the data and develop two pre diction models using logistic regression and k-nearest neighbor algorithm and compare prediction accuracies. The results show that k-nearest neighbor algorithm performs better in most of the cases.

scholarly journals Airborne laser scanning for high-resolution mapping of Antarctica

Eos ◽  
2005 ◽  
Vol 86 (25) ◽  
pp. 237 ◽  
Author(s):  
Bea Csatho ◽  
Toni Schenk ◽  
William Krabill ◽  
Terry Wilson ◽  
William Lyons ◽  
...  
Keyword(s):  
High Resolution ◽  
Laser Scanning ◽  
Airborne Laser Scanning ◽  
High Resolution Mapping ◽  
Airborne Laser ◽  
Resolution Mapping
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