Space oddity: estimating Earth biodiversity from a satellite

2020 ◽  
Author(s):  
Duccio Rocchini
Keyword(s):  
Remote Sensing ◽  
Functional Diversity ◽  
Repeated Measures ◽  
Satellite Remote Sensing ◽  
Sampling Design ◽  
Cost Effective ◽  
Taxonomic Diversity ◽  
Practical Reasons ◽  
Diversity Measures ◽  
Short Period

<p><span>Assessing biodiversity from field-based data is difficult for a number of practical reasons: (i) establishing the total number of sampling units to be investigated and the sampling design (e.g. systematic, random, stratified) can be difficult; (ii) the choice of the sampling design can affect the results; and (iii) defining the focal population of interest can be challenging. Satellite remote sensing is one of the most cost-effective and comprehensive approaches to identify biodiversity hotspots and predict changes in species composition. This is because, in contrast to field-based methods, it allows for complete spatial coverages of the Earth's surface under study over a short period of time. Furthermore, satellite remote sensing provides repeated measures, thus making it possible to study temporal changes in biodiversity. While taxonomic diversity measures have long been established, problems arising from abundance related measures have not been yet disentangled. Moreover, little has been done to account for functional diversity besides taxonomic diversity measures. The aim of this talk is to propose robust measures of remotely sensed heterogeneity to perform exploratory analysis for the detection of hotspots of taxonomic and functional diversity of plant species.</span></p>

Evaluation of Sea fog Detection Accuracy Based on Geostationary Satellite Image Using Machine Learning

2020 ◽  
Author(s):  
NaKyeong Kim ◽  
Suho Bak ◽  
Minji Jeong ◽  
Hongjoo Yoon
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Satellite Remote Sensing ◽  
Geostationary Satellite ◽  
Machine Learning Algorithms ◽  
Detection Accuracy ◽  
Sea Fog ◽  
Short Period ◽  
Wide Range ◽  
Fog Detection

<p><span>A sea fog is a fog caused by the cooling of the air near the ocean-atmosphere boundary layer when the warm sea surface air moves to a cold sea level. Sea fog affects a variety of aspects, including maritime and coastal transportation, military activities and fishing activities. In particular, it is important to detect sea fog as they can lead to ship accidents due to reduced visibility. Due to the wide range of sea fog events and the lack of constant occurrence, it is generally detected through satellite remote sensing. Because sea fog travels in a short period of time, it uses geostationary satellites with higher time resolution than polar satellites to detect fog. A method for detecting fog by using the difference between 11 μm channel and 3.7 μm channel was widely used when detecting fog by satellite remote sensing, but this is difficult to distinguish between lower clouds and fog. Traditional algorithms are difficult to find accurate thresholds for fog and cloud. However, machine learning algorithms can be used as a useful tool to determine this. In this study, based on geostationary satellite imaging data, a comparative analysis of sea fog detection accuracy was conducted through various methods of machine learning, such as Random Forest, Multi-Layer Perceptron, and Convolutional Neural Networks.</span></p>

Satellite remote sensing and the management of protected areas

2019 ◽  
pp. 79-94
Author(s):  
Nathalie Pettorelli
Keyword(s):  
Remote Sensing ◽  
Protected Areas ◽  
Satellite Data ◽  
Satellite Remote Sensing ◽  
Management Strategies ◽  
Cost Effective ◽  
Global Scale ◽  
Climatic Conditions ◽  
The Third ◽  
Cost Effective Method

This chapter explores how satellite-based approaches can be used as a cost-effective method to support monitoring efforts of protected areas, offering a cheap, verifiable way to identify areas of concern at a global scale, and to support managers in their effort to design and apply adaptive management strategies. Because protected areas can differ in terms of management needs and landscape/seascape access, the chapter starts with a quick introduction to categories of protected areas. Where to set new protected areas is one of the key questions faced by decision makers in need of meeting current biodiversity targets, and the second part of this work explores how satellite remote sensing can inform such a choice. Climatic conditions can significantly impact protected areas’ biodiversity, and the third section of this chapter briefly assesses common ways to derive information about local climatic anomalies from satellite data. The last sections of this chapter discuss the use of satellite data to assess effectiveness, and introduce the Digital Observatory of Protected Areas.

scholarly journals Systematic Review of t he Study of Flood Risks u sing Remote Sensing

2021 ◽  
Vol 11 (2) ◽  
pp. 96-103
Author(s):  
Ing. Sócrates P. Muñoz Pérez ◽  
◽  
Kristell E. Bonilla Bances ◽  
Lesly J. Torres Zavaleta ◽  
Heber Ivan Mejía Cabrera ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Flood Risk ◽  
Satellite Images ◽  
Cost Effective ◽  
High Flow ◽  
Large Area ◽  
Remote Sensing Technology ◽  
Sensing Technology ◽  
Short Period ◽  
Record Data

Floods are one of the most devastating natural disasters that cause various losses by having an excess of rainfall in a short period of time, they cause a high flow in rivers, subsequently damaging crops and infrastructure. They also cause sedimentation of reservoirs and therefore limit the ability of existing dams to control floods. In other words, the purpose of assessing the risk of a flood is to identify the areas of a plan that are at risk of flooding based on the factors that are relevant to the risks of flooding. Therefore, it is important to create a flood map that is easy to read and quickly accessible. Maps provide a stronger and more direct impression of the spatial distribution of flood risk, like diagrams and verbal descriptions. On the other hand, the repeated taking of satellite images in periods of time of a few days makes it possible to know the evolution of the floods, helping the authorities to access the affected population, as well as to define safety areas. The current work aims to systematically evaluate the study of flood risk through remote sensing. A qualitative analysis was carried out through which 80 articles indexed between 2017 and 2021 were reviewed, distributed as follows: 49 articles are from Scopus, 10 from Ebsco and 21 from ScienceDirect; It is concluded that geographic information system together with remote sensing technology are the key tools for flood monitoring, as it is a very cost-effective way to reliably deliver the required data over a large area, as well as record data under extreme conditions to overcome the limitations of ground stations

Monitoring land use change in the Lake Taupo catchment between 1975, 1990 and 2002 using satellite remote sensing data

2007 ◽  
pp. 17-22
Author(s):  
H. Lilienthal ◽  
A. Brauer ◽  
K. Betteridge ◽  
E. Schnug
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Land Use ◽  
Land Use Change ◽  
Satellite Remote Sensing ◽  
Remote Sensing Data ◽  
Native Vegetation ◽  
Lake Water Quality ◽  
Livestock Farming ◽  
Slow Decline

Conversion of native vegetation into farmed grassland in the Lake Taupo catchment commenced in the late 1950s. The lake's iconic value is being threatened by the slow decline in lake water quality that has become apparent since the 1970s. Keywords: satellite remote sensing, nitrate leaching, land use change, livestock farming, land management

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