From the cloud to the ground: converting satellite data into conservation decisions

2021 ◽  
pp. 13-34
Author(s):  
Lilian Pintea
Keyword(s):  
Local Community ◽  
Multiple Scales ◽  
Land Use Changes ◽  
Geospatial Data ◽  
Conservation Strategies ◽  
Observation Data ◽  
Forest Reserves ◽  
Recent Developments ◽  
Earth Observation Data ◽  
Conservation Decisions

An estimated 65% of the world’s land and more than 80% of Earth’s biodiversity are under indigenous or local community customary ownership, care, and use. Recent developments in remote sensing, geographic information systems (GIS), mobile, and cloud computing provide the opportunity to systematically and cost-effectively monitor land-cover and land-use changes and threats at multiple scales. It is now possible, via satellite observations, to obtain a synoptic view of ecosystems at spatial and temporal resolutions that are more detailed, locally relevant, and consistent from village to global scales. However, to make geospatial data and technologies work for conservation, we still need to understand how data turn into actionable information and conservation decisions. This chapter uses Open Standards for the Practice of Conservation as a framework to discuss insights from 18 years of using geospatial technologies with the local communities, village and district governments, and other partners to monitor chimpanzee habitats and threats and inform chimpanzee conservation strategies and actions in Tanzania. It focuses on how Earth Observation data and associated technologies enabled and benefitted from the creation of research-implementation spaces in which stakeholders were able to collaborate and interact with geospatial data and results in a diversity of ways. This enabled development of geospatial applications and solutions ‘with’ and not ‘for’ local stakeholders, resulting in expansion of new protected areas managed by village and districts governments and restoration of habitats in some degraded village forest reserves.

scholarly journals An Overview of the Use of GML in Modern Spatial Data Infrastructures

2010 ◽  
Vol 42 (1) ◽  
pp. 60-67 ◽  
Author(s):  
Viktors Skoks ◽  
Christian Steurer
Keyword(s):  
Spatial Data ◽  
Earth Observation ◽  
Geospatial Data ◽  
Observation Data ◽  
Markup Language ◽  
Geography Markup Language ◽  
Data Infrastructures ◽  
Spatial Data Infrastructures ◽  
Earth Observation Data ◽  
Data Harmonisation

An Overview of the Use of GML in Modern Spatial Data InfrastructuresThis paper introduces an overview of the use of Geography Markup Language in modern Spatial Data Infrastructures. The goal of the paper was to indicate some of the main consequences of the use of Geography Markup Language in the important geospatial data harmonisation processes, both search and access, which are in current use. In order to show a practical example of the use of Geography Markup Language, the system for Earth observation data processing and distribution at the Institute for Applied Remote Sensing at EURAC, Bolzano was studied. The results of the paper set out how Geography Markup Language is used in modern Spatial Data Infrastructures, and the degree to which the Geography Markup Language standard is helpful in achieving data harmonisation and interoperability.

scholarly journals Linking the Recent Glacier Retreat and Depleting Streamflow Patterns with Land System Changes in Kashmir Himalaya, India

Water ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 1168 ◽  
Author(s):  
Irfan Rashid ◽  
Ulfat Majeed ◽  
Sheikh Aneaus ◽  
Mauri Pelto
Keyword(s):  
Land Use Changes ◽  
Rice Paddy ◽  
Observation Data ◽  
Kashmir Himalaya ◽  
Kashmir Valley ◽  
Equilibrium Line Altitude ◽  
Land System ◽  
System Changes ◽  
Earth Observation Data ◽  
Economic Considerations

This study reports the changes in glacier extent and streamflow similar to many Himalayan studies, but takes the unusual step of also linking these to downstream land use changes in Kashmir Valley. This study assessed changes in the area, snout, and equilibrium line altitude (ELA) of four parts of the Kolahoi Glacier using earth observation data from 1962 to 2018. Changes in the discharge of the two streams flowing out from Kolahoi Glacier into the Jhelum basin were also assessed between 1972 and 2018. Additionally, satellite data was used to track the downstream land system changes concerning agriculture, orchards, and built-up areas between 1980 and 2018. This analysis suggested a cumulative deglaciation of 23.6% at a rate of 0.42% per year from 1962 to 2018. The snout of two larger glaciers, G1 and G2, retreated at a rate of 18.3 m a−1 and 16.4 m a−1, respectively, from 1962 to 2018, although the rate of recession accelerated after 2000. Our analysis also suggested the upward shift of ELA by ≈120 m. The streamflows measured at five sites showed statistically significant depleting trends that have been a factor in forcing extensive land system changes downstream. Although the area under agriculture in Lidder watershed shrunk by 39%, there was a massive expansion of 176% and 476% in orchards and built-up areas, respectively, from 1980 to 2018. The conversion of irrigation-intensive agriculture lands (rice paddy) to less water-intensive orchards is attributed to economic considerations and depleting streamflow.

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 ◽  
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

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

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

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

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.

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

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 [...]

scholarly journals Semantics-Driven Remote Sensing Scene Understanding Framework for Grounded Spatio-Contextual Scene Descriptions

2021 ◽  
Vol 10 (1) ◽  
pp. 32
Author(s):  
Abhishek V. Potnis ◽  
Surya S. Durbha ◽  
Rajat C. Shinde
Keyword(s):  
Remote Sensing ◽  
Domain Knowledge ◽  
Situational Awareness ◽  
Scene Understanding ◽  
Graph Representation ◽  
Observation Data ◽  
Test Scenario ◽  
Semantic Web Technologies ◽  
Earth Observation Data ◽  
Contextual Domain

Earth Observation data possess tremendous potential in understanding the dynamics of our planet. We propose the Semantics-driven Remote Sensing Scene Understanding (Sem-RSSU) framework for rendering comprehensive grounded spatio-contextual scene descriptions for enhanced situational awareness. To minimize the semantic gap for remote-sensing-scene understanding, the framework puts forward the transformation of scenes by using semantic-web technologies to Remote Sensing Scene Knowledge Graphs (RSS-KGs). The knowledge-graph representation of scenes has been formalized through the development of a Remote Sensing Scene Ontology (RSSO)—a core ontology for an inclusive remote-sensing-scene data product. The RSS-KGs are enriched both spatially and contextually, using a deductive reasoner, by mining for implicit spatio-contextual relationships between land-cover classes in the scenes. The Sem-RSSU, at its core, constitutes novel Ontology-driven Spatio-Contextual Triple Aggregation and realization algorithms to transform KGs to render grounded natural language scene descriptions. Considering the significance of scene understanding for informed decision-making from remote sensing scenes during a flood, we selected it as a test scenario, to demonstrate the utility of this framework. In that regard, a contextual domain knowledge encompassing Flood Scene Ontology (FSO) has been developed. Extensive experimental evaluations show promising results, further validating the efficacy of this framework.

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

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.

scholarly journals A Possible Role of Copernicus Sentinel-2 Data to Support Common Agricultural Policy Controls in Agriculture

Agronomy ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 110
Author(s):  
Filippo Sarvia ◽  
Elena Xausa ◽  
Samuele De Petris ◽  
Gianluca Cantamessa ◽  
Enrico Borgogno-Mondino
Keyword(s):  
Agricultural Policy ◽  
Common Agricultural Policy ◽  
Observation Data ◽  
Key Factor ◽  
Ndvi Time Series ◽  
Earth Observation Data ◽  
Four Levels ◽  
Nw Italy ◽  
Sentinel 2

Farmers that intend to access Common Agricultural Policy (CAP) contributions must submit an application to the territorially competent Paying Agencies (PA). Agencies are called to verify consistency of CAP contributions requirements through ground campaigns. Recently, EU regulation (N. 746/2018) proposed an alternative methodology to control CAP applications based on Earth Observation data. Accordingly, this work was aimed at designing and implementing a prototype of service based on Copernicus Sentinel-2 (S2) data for the classification of soybean, corn, wheat, rice, and meadow crops. The approach relies on the classification of S2 NDVI time-series (TS) by “user-friendly” supervised classification algorithms: Minimum Distance (MD) and Random Forest (RF). The study area was located in the Vercelli province (NW Italy), which represents a strategic agricultural area in the Piemonte region. Crop classes separability proved to be a key factor during the classification process. Confusion matrices were generated with respect to ground checks (GCs); they showed a high Overall Accuracy (>80%) for both MD and RF approaches. With respect to MD and RF, a new raster layer was generated (hereinafter called Controls Map layer), mapping four levels of classification occurrences, useful for administrative procedures required by PA. The Control Map layer highlighted that only the eight percent of CAP 2019 applications appeared to be critical in terms of consistency between farmers’ declarations and classification results. Only for these ones, a GC was warmly suggested, while the 12% must be desirable and the 80% was not required. This information alone suggested that the proposed methodology is able to optimize GCs, making possible to focus ground checks on a limited number of fields, thus determining an economic saving for PA and/or a more effective strategy of controls.

scholarly journals A New Combined Adjustment Model for Geolocation Accuracy Improvement of Multiple Sources Optical and SAR Imagery

2021 ◽  
Vol 13 (3) ◽  
pp. 491
Author(s):  
Niangang Jiao ◽  
Feng Wang ◽  
Hongjian You
Keyword(s):  
Remote Sensing Data ◽  
Observation Data ◽  
Multiple Sources ◽  
Accuracy Improvement ◽  
Geometric Calibration ◽  
Proposed Model ◽  
Calibration Methods ◽  
Earth Observation Data ◽  
Sar Imagery ◽  
Geolocation Accuracy

Numerous earth observation data obtained from different platforms have been widely used in various fields, and geometric calibration is a fundamental step for these applications. Traditional calibration methods are developed based on the rational function model (RFM), which is produced by image vendors as a substitution of the rigorous sensor model (RSM). Generally, the fitting accuracy of the RFM is much higher than 1 pixel, whereas the result decreases to several pixels in mountainous areas, especially for Synthetic Aperture Radar (SAR) imagery. Therefore, this paper proposes a new combined adjustment for geolocation accuracy improvement of multiple sources satellite SAR and optical imagery. Tie points are extracted based on a robust image matching algorithm, and relationships between the parameters of the range-doppler (RD) model and the RFM are developed by transformed into the same Geodetic Coordinate systems. At the same time, a heterogeneous weight strategy is designed for better convergence. Experimental results indicate that our proposed model can achieve much higher geolocation accuracy with approximately 2.60 pixels in the X direction and 3.50 pixels in the Y direction. Compared with traditional methods developed based on RFM, our proposed model provides a new way for synergistic use of multiple sources remote sensing data.

Sign in / Sign up

Export Citation Format

Share Document