Modelling, Characterizing, and Monitoring Boreal Forest Wetland Bird Habitat with RADARSAT-2 and Landsat-8 Data

Earth observation technologies have strong potential to help map and monitor wildlife habitats. Yellow Rail, a rare wetland obligate bird species, is a species of concern in Canada and provides an interesting case study for monitoring wetland habitat with Earth observation data. Yellow Rail has highly specific habitat requirements characterized by shallowly flooded graminoid vegetation, the availability of which varies seasonally and year-to-year. Polarimetric Synthetic Aperture Radar (SAR) in combination with optical data should, in theory, be a great resource for mapping and monitoring these habitats. This study evaluates the use of RADARSAT-2 data and Landsat-8 data to characterize, map, and monitor Yellow Rail habitat in a wetland area within the mineable oil sands region. Specifically, we investigate: (1) The relative importance of polarimetric SAR and Landsat-8 data for predicting Yellow Rail habitat; (2) characterization of wetland habitat with polarimetric SAR data; (3) yearly trends in available habitat; and (4) predictions of potentially suitable habitat across northeastern Alberta. Results show that polarimetric SAR using the Freeman–Durden decomposition and polarization ratios were the most important predictors when modeling the Yellow Rail habitat. These parameters also effectively characterize this habitat based on high congruence with existing descriptions of suitable habitat. Applying the prediction model across all wetland areas showed accurate predictions of occurrence (validated on field occurrence data), and high probability habitats were constrained to very specific wetland areas. Using the RADARSAT-2 data to monitor yearly changes to Yellow Rail habitat was inconclusive, likely due to the different image acquisition times of the 2014 and 2016 images, which may have captured seasonal, rather than inter-annual, wetland dynamics. Polarimetric SAR has proved to be very useful for capturing the specific hydrology and vegetation structure of the Yellow Rail habitat, which could be a powerful technology for monitoring and conserving wetland species habitat.

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Landslide Mapping and Susceptibility Assessment Using Geospatial Analysis and Earth Observation Data

Land ◽

10.3390/land9050133 ◽

2020 ◽

Vol 9 (5) ◽

pp. 133 ◽

Cited By ~ 5

Author(s):

Emmanouil Psomiadis ◽

Andreas Papazachariou ◽

Konstantinos Soulis ◽

Despoina-Simoni Alexiou ◽

Ioannis Charalampopoulos

Keyword(s):

Landslide Susceptibility ◽

Geospatial Analysis ◽

Earth Observation ◽

Anthropogenic Factors ◽

Landsat 8 ◽

Observation Data ◽

Crete Island ◽

Earth Observation Data ◽

Landslide Susceptibility Index ◽

The Impact

The western part of Crete Island has undergone serious landslide events in the past. The intense rainfalls that took place in the September 2018 to February 2019 period provoked extensive landslide events at the northern part of Chania prefecture, along the motorway A90. Geospatial analysis methods and earth observation data were utilized to investigate the impact of the various physical and anthropogenic factors on landslides and to evaluate landslide susceptibility. The landslide inventory map was created based on literature, aerial photo analysis, satellite images, and field surveys. A very high-resolution Digital Elevation Model (DEM) and land cover map was produced from a dense point cloud and Earth Observation data (Landsat 8), accordingly. Sentinel-2 data were used for the detection of the recent landslide events and offered suitable information for two of them. Eight triggering factors were selected and manipulated in a GIS-based environment. A semi-quantitative method of Analytical Hierarchy Process (AHP) and Weighted Linear Combination (WLC) was applied to evaluate the landslide susceptibility index (LSI) both for Chania prefecture and the motorway A90 in Chania. The validation of the two LSI maps provided accurate results and, in addition, several susceptible points with high landslide hazards along the motorway A90 were detected.

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Analysis of habitat suitability of birds family Accipitridae in the forest park of Pocut Meurah Intan

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/922/1/012056 ◽

2021 ◽

Vol 922 (1) ◽

pp. 012056

Author(s):

Y Gagarin ◽

A Abdullah ◽

K Khairil ◽

D Djufri ◽

S Supriatno

Keyword(s):

Bird Species ◽

Principal Component ◽

Distribution Area ◽

Survey Method ◽

Suitable Habitat ◽

Observation Data ◽

Forest Park ◽

Aceh Province ◽

The Look ◽

High Level

Abstract Birds are often used as indicators to describe the state of biodiversity, which means areas that are important for birds are also important for other flora and fauna. In addition, the distribution area of birds is well known, so that important regional livelihoods using birds as indicators can be carried out quickly and comprehensively. This research was conducted in February 2021, aiming to observe the habitat conditions of the Accipitridae family birds in Tahura Pocut Meurah Intan, Aceh Province. The method used in this research is an exploratory survey method. Data collection in the field by placing 15 observation points and 3 line transects is combined using the “look down method” and “upward observation”. Data analysis using principal component analysis (PCA) method. The results obtained that the area of suitable habitat (suitable) with good categories for bird species of the family Accipitridae of 4306, 106 ha or 69.23% and the medium category, which is available for food and accessible, but there is no reproductive location and has a high level of disturbance. of 1727, 294 ha (27.77 %). Birds of the Accipitridae family can carry out activities and find prey in three different forest types, namely primary, secondary and open forests.

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Our goal is to establish the earth observation data in the business world Unser Ziel ist es, die Erdbeobachtungsdaten in der Geschäftswelt zu etablieren

GIS Business ◽

10.26643/gis.v12i3.5187 ◽

2019 ◽

Vol 12 (3) ◽

pp. 12-14

Author(s):

Eicher, A

Keyword(s):

Earth Observation ◽

Observation Data ◽

Business World ◽

The Earth ◽

Earth Observation Data

Our goal is to establish the earth observation data in the business world Unser Ziel ist es, die Erdbeobachtungsdaten in der Geschäftswelt zu etablieren

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Mapping slums and model population density using earth observation data and open source solutions

2019 Joint Urban Remote Sensing Event (JURSE) ◽

10.1109/jurse.2019.8808934 ◽

2019 ◽

Author(s):

Tais Grippa ◽

Stefanos Georganos ◽

Sabine Vanhuysse ◽

Moritz Lennert ◽

Nicholus Mboga ◽

...

Keyword(s):

Population Density ◽

Open Source ◽

Earth Observation ◽

Observation Data ◽

Model Population ◽

Earth Observation Data

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Examining the Economic and Environmental Impacts of COVID-19 Using Earth Observation Data

Remote Sensing ◽

10.3390/rs13010005 ◽

2020 ◽

Vol 13 (1) ◽

pp. 5

Author(s):

William Straka ◽

Shobha Kondragunta ◽

Zigang Wei ◽

Hai Zhang ◽

Steven D. Miller ◽

...

Keyword(s):

Air Quality ◽

Los Angeles ◽

Environmental Impacts ◽

Demographic Data ◽

The United States ◽

Earth Observation ◽

Observation Data ◽

Washington Dc ◽

Mobility Data ◽

Earth Observation Data

The COVID-19 pandemic has infected almost 73 million people and is responsible for over 1.63 million fatalities worldwide since early December 2019, when it was first reported in Wuhan, China. In the early stages of the pandemic, social distancing measures, such as lockdown restrictions, were applied in a non-uniform way across the world to reduce the spread of the virus. While such restrictions contributed to flattening the curve in places like Italy, Germany, and South Korea, it plunged the economy in the United States to a level of recession not seen since WWII, while also improving air quality due to the reduced mobility. Using daily Earth observation data (Day/Night Band (DNB) from the National Oceanic and Atmospheric Administration Suomi-NPP and NO2 measurements from the TROPOspheric Monitoring Instrument TROPOMI) along with monthly averaged cell phone derived mobility data, we examined the economic and environmental impacts of lockdowns in Los Angeles, California; Chicago, Illinois; Washington DC from February to April 2020—encompassing the most profound shutdown measures taken in the U.S. The preliminary analysis revealed that the reduction in mobility involved two major observable impacts: (i) improved air quality (a reduction in NO2 and PM2.5 concentration), but (ii) reduced economic activity (a decrease in energy consumption as measured by the radiance from the DNB data) that impacted on gross domestic product, poverty levels, and the unemployment rate. With the continuing rise of COVID-19 cases and declining economic conditions, such knowledge can be combined with unemployment and demographic data to develop policies and strategies for the safe reopening of the economy while preserving our environment and protecting vulnerable populations susceptible to COVID-19 infection.

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2nd Edition of Instrumenting Smart City Applications with Big Sensing and Earth Observatory Data: Tools, Methods and Techniques

Remote Sensing ◽

10.3390/rs13071310 ◽

2021 ◽

Vol 13 (7) ◽

pp. 1310

Author(s):

Gabriele Bitelli ◽

Emanuele Mandanici

Keyword(s):

High Resolution ◽

Exponential Growth ◽

Smart City ◽

Earth Observation ◽

Urban Environments ◽

Observation Data ◽

High Resolution Imagery ◽

Observatory Data ◽

Methods And Techniques ◽

Earth Observation Data

The exponential growth in the volume of Earth observation data and the increasing quality and availability of high-resolution imagery are increasingly making more applications possible in urban environments [...]

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Quantifying Tree Cover Loss in Urban Forests within Nairobi City Metropolitan Area from Earth Observation Data

Environmental Sciences Proceedings ◽

10.3390/iecf2020-07952 ◽

2020 ◽

Vol 3 (1) ◽

pp. 78

Author(s):

Francis Oloo ◽

Godwin Murithi ◽

Charlynne Jepkosgei

Keyword(s):

Land Cover ◽

Metropolitan Area ◽

Land Surface ◽

Urban Forest ◽

Urban Forests ◽

Earth Observation ◽

Tree Cover ◽

Observation Data ◽

Vegetation Health ◽

Earth Observation Data

Urban forests contribute significantly to the ecological integrity of urban areas and the quality of life of urban dwellers through air quality control, energy conservation, improving urban hydrology, and regulation of land surface temperatures (LST). However, urban forests are under threat due to human activities, natural calamities, and bioinvasion continually decimating forest cover. Few studies have used fine-scaled Earth observation data to understand the dynamics of tree cover loss in urban forests and the sustainability of such forests in the face of increasing urban population. The aim of this work was to quantify the spatial and temporal changes in urban forest characteristics and to assess the potential drivers of such changes. We used data on tree cover, normalized difference vegetation index (NDVI), and land cover change to quantify tree cover loss and changes in vegetation health in urban forests within the Nairobi metropolitan area in Kenya. We also used land cover data to visualize the potential link between tree cover loss and changes in land use characteristics. From approximately 6600 hectares (ha) of forest land, 720 ha have been lost between 2000 and 2019, representing about 11% loss in 20 years. In six of the urban forests, the trend of loss was positive, indicating a continuing disturbance of urban forests around Nairobi. Conversely, there was a negative trend in the annual mean NDVI values for each of the forests, indicating a potential deterioration of the vegetation health in the forests. A preliminary, visual inspection of high-resolution imagery in sample areas of tree cover loss showed that the main drivers of loss are the conversion of forest lands to residential areas and farmlands, implementation of big infrastructure projects that pass through the forests, and extraction of timber and other resources to support urban developments. The outcome of this study reveals the value of Earth observation data in monitoring urban forest resources.

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