Automated bird counting with deep learning for regional bird distribution mapping

A challenging problem in the field of avian ecology is deriving information on bird population movement trends. This necessitates the regular counting of birds which is usually not an easily-achievable task. A promising attempt towards solving the bird counting problem in a more consistent and fast way is to predict the number of birds in different regions from their photos. For this purpose, we exploit the ability of computers to learn from past data through deep learning which has been a leading sub-field of AI for image understanding. Our data source is a collection of on-ground photos taken during our long run of birding activity. We employ several state-of-the-art generic object-detection algorithms to learn to detect birds, each being a member of one of the 38 identified species, in natural scenes. The experiments revealed that computer-aided counting outperformed the manual counting with respect to both accuracy and time. As a real-world application of image-based bird counting, we prepared the spatial bird order distribution and species diversity maps of Turkey by utilizing the geographic information system (GIS) technology. Our results suggested that deep learning can assist humans in bird monitoring activities and increase citizen scientists’ participation in large-scale bird surveys.No sponso

Editorial for Special Issue: "Remote Sensing based Building Extraction II"

Accurate building extraction from remotely sensed images is essential for topographic mapping, urban planning, disaster management, navigation, and many other applications [...

Temperature Effects Explain Continental Scale Distribution of Cyanobacterial Toxins

Insight into how environmental change determines the production and distribution of cyanobacterial toxins is necessary for risk assessment. Management guidelines currently focus on hepatotoxins (microcystins). Increasing attention is given to other classes, such as neurotoxins (e.g., anatoxin-a) and cytotoxins (e.g., cylindrospermopsin) due to their potency. Most studies examine the relationship between individual toxin variants and environmental factors, such as nutrients, temperature and light. In summer 2015, we collected samples across Europe to investigate the effect of nutrient and temperature gradients on the variability of toxin production at a continental scale. Direct and indirect effects of temperature were the main drivers of the spatial distribution in the toxins produced by the cyanobacterial community, the toxin concentrations and toxin quota. Generalized linear models showed that a Toxin Diversity Index (TDI) increased with latitude, while it decreased with water stability. Increases in TDI were explained through a significant increase in toxin variants such as MC-YR, anatoxin and cylindrospermopsin, accompanied by a decreasing presence of MC-LR. While global warming continues, the direct and indirect effects of increased lake temperatures will drive changes in the distribution of cyanobacterial toxins in Europe, potentially promoting selection of a few highly toxic species or strains.Peer reviewe

VARIOUS METHODS TO DETECT BUILDINGS USING IMAGE AND LIDAR DATA

Four different variants of building detection are presented. Each variant has a different workflow and is capable of detecting buildings. The first variant of building detection is based on multispectral classification and DSM filtering. In the second variant, DSM blobs, mainly consisting of buildings and trees, are detected by subtraction of the DTM from the DSM. Then, trees are eliminated using NDVI data, derived from unsupervised ISODATA classification of the multispectral images, while small non-building objects are rejected based on size criteria. The third variant uses the planimetric density of raw LIDAR DTM data to detect the above-ground objects. The fourth variant is like the third one, but uses the vertical density of the raw LIDAR data (all points) to distinguish trees and buildings. To improve the results, a combination of the four variants using set intersections and unions is performed. The combination was empirical, with consideration of the datasets used in each variant and the advantages and disadvantages of each variant. In the evaluation, the combination of the four individual results yields 94% correct detections and an omission error of 12% for Zurich airport dataset

Various building detection methods with the use of image and LIDAR data

U ovom radu predstavljen je automatski pristup otkrivanja građevina uprabom zračne snimke i LIDAR podataka. Kombinirani pristup četiri metode postigao je najbolje rezultate, rabeći filtriranje DSM-a preko nagiba, i klasifikaciju multispektralnih snimaka, visinskih podataka i okomite gustoće LIDAR točaka. Prva varijanta otkrivanja građevina je utemeljena na multispektralnoj klasifikaciji i filtriranju DSM-a. U drugoj varijanti, objekti na DSM-u, a to su ponajprije građevine i drveće, otkriveni su pomoću računanja razlike između DTM-a i DSM-a. Treća varijanta rabi planimetrijsku gustoću neobrađenih LIDAR DTM podataka kako bi otkrila objekte iznad površine. Četvrta varijanta temelji se na trećoj varijanti, međutim rabi okomitu gustoću neobrađenih LIDAR podataka (sve točke) kako bi razlikovala drveće i građevine. Testiranje i ocjenjivanje postupka pokazalo je da kombinacija ove četiri metode vrši ispravno otkrivanje građevina u 94 % slučajeva uz grešku izostavljanja od 12 %.In this work, an automated approach for building detection using airborne images and LIDAR data is presented. A combined approach of four methods achieved the best results, using slope-based DSM filtering as well as classification of multispectral images, elevation data and vertical LIDAR point density. The first variant of building detection is based on multispectral classification and DSM filtering. In the second variant, DSM blobs, mainly consisting of buildings and trees, are detected by subtraction of the DTM from the DSM. The third variant uses the planimetric density of raw LIDAR DTM data to detect the above-ground objects. The fourth variant is like the third one, but uses the vertical density of the raw LIDAR data (all points) to distinguish trees and buildings. In the evaluation, the combination of the four methods yields 94 % correct detection at an omission error of 12 %

Use of Image and Laser Scanning Data for Building Detection

In this work, we focus on the mainly detection of buildings.. As input data, we use LIDAR data and multispectral aerial images of two different test sites. One is from Zurich airport and the other one is from Vaihingen region close to Stuttgart. Quality assessment has been performed by comparing our results with existing reference data which are generated using commercial photogrammetric software and manual stereo measurement.ISSN:1682-1750ISSN:2194-9034ISSN:1682-177

Object Extraction at Airport Sites Using DTMs/DSMs and Multipectral Image Analysis

The automatic detection and 3D modeling of objects at airports is an important issue for the EU FP6 project PEGASE. PEGASE is a feasibility study of a new 24/7 navigation system, which could either replace or complement existing systems and would allow a three dimensional truly autonomous aircraft landing and take-off primarily for airplanes and secondary for helicopters. This new navigation system relies on three key technologies: • The specification and acquisition of a reliable geospatial reference database of the airports. • Various sensors onboard the aircraft that perform a real-time extraction of relevant features from the data. • Innovative correlation techniques between the above features and the onboard reference database to determine the location of the aircraft and plan its future trajectory for safe landing or take-off. In this work, we focus on the first topic. Since often existing data for airports have not the necessary accuracy, resolution and/or currency, we need to develop automated methods for generating them.ISSN:1682-1750ISSN:2194-9034ISSN:1682-177

Detection of Urban Buildings by Using Multispectral Gokturk-2 and Sentinel 1A Synthetic Aperture Radar Images

Urban areas are important for city planning, security, traffic purposes, decision makers etc. Remotely sensed data are useful to detect urban areas either with active or passive systems. Each system has advantages and disadvantages. Passive images are mainly multispectral images and have rich information with their rich spectral resolution. In addition, they are affected by the atmospheric conditions, so there should not be clouds over the sensed region during data acquisition. On the other hand, SAR (Synthetic Aperture Radar) systems are not affected by the atmospheric conditions, but their spectral resolution is low, with mainly one-channel SAR systems. Also, the structure of passive images is completely different from that of multispectral images. Moreover, the geometrical and electrical properties of objects play an important role in the pixel values. In this study, a multispectral GOKTURK-2 MS (Multispectral) image and a SENTINEL 1A SAR image were used to detect urban buildings, using the advantages of both datasets. Firstly, the SVM (Support Vector Machines) method was applied to detect the buildings in the GOKTURK image. Then, the buildings were detected from the SAR image with the fuzzy logic approach. Finally, the buildings were detected by intersecting the results from both methods. The results from the SAR image could eliminate the false negative results from the GOKTURK-2 image. The study area was selected in Antalya province, Kepez district. The detected urban area was 288.353 m2 in the selected study area

2021 Turkey mega forest Fires: Biodiversity measurements of the IUCN Red List wildlife mammals in Sentinel-2 based burned areas

Mega wildfires are one of the environmental disasters worldwide. This study evaluates the pre-fire species diversity and the indirect effects, including habitat loss for one of the largest wildfires in Manavgat (Antalya-Turkey) in 2021, with a two-step methodology. Here, (1) burnt areas in the Manavgat district (2021) were detected with remote sensing data from Sentinel-2A by delta Normalized Burn Ratio calculation for a selected area in Google Earth Engine, and (2) mammals' habitat vector data of International Union for Conservation of Nature (IUCN) Red List were integrated into Habitat and Biodiversity modelling of Terrset to analyze the alpha, beta, gamma diversity and Range Restriction Index for the wildfire region. In the total 4210 km2 study area, 696 km2 of the area was damaged by different fire severity; also, there were 56 mammal species' habitats here. These species include bats (i.e. Nyctalus leisleri), felids (i.e. Felis chaus), rodents (i.e. Rattus norvegicus) and large mammals (i.e. Ursus arctos). 88 % of these species are in IUCN's Least Concern category. The remaining classes are Near Threatened (3.7 %) and Vulnerable (7.4 %). This study also indicated that the burnt area's species rich-ness does not totally consist of endemic species. Therefore, pre-fire species richness analyses of this study can be a base for further studies about the species' post-fire activity and occupancy.Furthermore, the 2021 mega wildfires show us the necessity of wildfire monitoring and risk studies in the entire Mediterranean ecosys-tem to evaluate the risks to the Sustainable Development Goals. Therefore, post-fire spatial data, fire migration monitorization, and recording of the species' activities should be performed temporally. In this way, the ability of wildlife's recovering, and the direct and indirect effects of the fire will be examined for ecosystems in the long term.(c) 2023 COSPAR. Published by Elsevier B.V. All rights reserved

Analysis of the Cosmic Ray Effects on Sentinel-1 SAR Satellite Data

Ionizing radiation sources such as Solar Energetic Particles and Galactic Cosmic Radiation may cause unexpected errors in imaging and communication systems of satellites in the Space environment, as reported in the previous literature. In this study, the temporal variation of the speckle values on Sentinel 1 satellite images were compared with the cosmic ray intensity/count data, to analyze the effects which may occur in the electromagnetic wave signals or electronic system. Sentinel 1 Synthetic Aperture Radar (SAR) images nearby to the cosmic ray stations and acquired between January 2015 and December 2019 were processed. The median values of the differences between speckle filtered and original image were calculated on Google Earth Engine Platform per month. The monthly median “noise” values were compared with the cosmic ray intensity/count data acquired from the stations. Eight selected stations’ data show that there are significant correlations between cosmic ray intensities and the speckle amounts. The Pearson correlation values vary between 0.62 and 0.78 for the relevant stations