Tropical Cyclone Center Determination Algorithm by Texture and Gradient of Infrared Satellite Image

A novel algorithm for tropical cyclone (TC) center determination is presented by using texture and gradient of infrared satellite image from geostationary satellite. Except those latter disappearing TC satellite images that are little valuable to a TC center determination, generally other periods of TC, all have an inner core. And the centers are generally determined in the inner core. Based on this, an efficient TC center determination algorithm is designed. First, the inner core of a TC is obtained. Then, according to the texture and gradient information of the inner core, the center location of the TC is determined. The effectiveness of the proposed TC center determination algorithm is verified by using Chinese FY-2C stationary infrared satellite image. And the location result is compared with that of the “tropical cyclone yearbook,” which was compiled by Shanghai Typhoon Institute of China Meteorological Administration. Experimental results show that the proposed algorithm can provide a new technique that can automatically determine the center location for a TC based on infrared satellite image

Detection of Fir Trees (Abies sibirica) Damaged by the Bark Beetle in Unmanned Aerial Vehicle Images with Deep Learning

We are very grateful to the reviewers for their valuable comments that helped to improve the paper. We appreciate the support of a vice-director of the “Stolby” State Nature Reserve, Anastasia Knorre. We also thank two Ph.D. students Egor Trukhanov and Anton Perunov from Siberian Federal University for their help in data acquisition (aerial photography from UAV) on two research plots in 2016 and raw imagery processing.Invasion of the Polygraphus proximus Blandford bark beetle causes catastrophic damage to forests with firs (Abies sibirica Ledeb) in Russia, especially in Central Siberia. Determining tree damage stage based on the shape, texture and colour of tree crown in unmanned aerial vehicle (UAV) images could help to assess forest health in a faster and cheaper way. However, this task is challenging since (i) fir trees at different damage stages coexist and overlap in the canopy, (ii) the distribution of fir trees in nature is irregular and hence distinguishing between different crowns is hard, even for the human eye. Motivated by the latest advances in computer vision and machine learning, this work proposes a two-stage solution: In a first stage, we built a detection strategy that finds the regions of the input UAV image that are more likely to contain a crown, in the second stage, we developed a new convolutional neural network (CNN) architecture that predicts the fir tree damage stage in each candidate region. Our experiments show that the proposed approach shows satisfactory results on UAV Red, Green, Blue (RGB) images of forest areas in the state nature reserve “Stolby” (Krasnoyarsk, Russia).A.S. was supported by the grant of the Russian Science Foundation No. 16-11-00007. S.T. was supported by the Ramón y Cajal Programme (No. RYC-2015-18136). S.T. and F.H. received funding from the Spanish Ministry of Science and Technology under the project TIN2017-89517-P. D.A.-S. received support from project ECOPOTENTIAL, which received funding from the European Union Horizon 2020 Research and Innovation Programme under grant agreement No. 641762, from the European LIFE Project ADAPTAMED LIFE14 CCA/ES/000612 and from project 80NSSC18K0446 of the NASA’s Group on Earth Observations Work Programme 2016. A.R. was supported by the grant of the Russian Science Foundation No. 18-74-10048. Y. M. was supported by the grant of Russian Foundation for Basic Research No. 18-47-242002, Government of Krasnoyarsk Territory and Krasnoyarsk Regional Fund of Science

The Application of Barnes Filter to Positioning the Center of Landed Tropical Cyclone in Numerical Models

After a tropical cyclone (TC) making landfall, the numerical model output sea level pressure (SLP) presents many small-scale perturbations which significantly influence the positioning of the TC center. To fix the problem, Barnes filter with weighting parameters C=2500 and G=0.35 is used to remove these perturbations. A case study of TC Fung-Wong which landed China in 2008 shows that Barnes filter not only cleanly removes these perturbations, but also well preserves the TC signals. Meanwhile, the centers (track) obtained from SLP processed with Barnes filter are much closer to the observations than that from SLP without Barnes filter. Based on the distance difference (DD) between the TC center determined by SLP with/without Barnes filter and observation, statistics analysis of 12 TCs which landed China during 2005–2015 shows that in most cases (about 85%) the DDs are small (between −30 km and 30 km), while in a few cases (about 15%) the DDs are large (greater than 30 km even 70 km). This further verifies that the TC centers identified from SLP with Barnes filter are more accurate compared to that directly obtained from model output SLP. Moreover, the TC track identified with Barnes filter is much smoother than that without Barnes filter

Earth resources: A continuing bibliography with indexes (issue 62)

This bibliography lists 544 reports, articles, and other documents introduced into the NASA scientific and technical information system between April 1 and June 30, 1989. Emphasis is placed on the use of remote sensing and geophysical instrumentation in spacecraft and aircraft to survey and inventory natural resources and urban areas. Subject matter is grouped according to agriculture and forestry, environmental changes and cultural resources, geodesy and cartography, geology and mineral resources, hydrology and water management, data processing and distribution systems, instrumentation and sensors, and economic analysis

UAVs for the Environmental Sciences

This book gives an overview of the usage of UAVs in environmental sciences covering technical basics, data acquisition with different sensors, data processing schemes and illustrating various examples of application

Habitats of the World

Today it is not easy to talk about habitats and to think about the various threats facing them. We are living in an age in which we are poised between having everything immediately, and maintaining good living conditions on Earth. Unfortunately, this is almost impossible!For this reason it is important that everyone understands the importance of the habitats of the world and the inhabitants: including humans!This book aims to describe some of the world's habitats, their characteristics, and their daily threats. This is done in the hope that our children will see all of this tomorrow. Enjoy reading

Avion 2006-02-07

https://commons.erau.edu/avion/2039/thumbnail.jp