scholarly journals A Multi Sensor Approach to Forest Type Mapping for Advancing Monitoring of Sustainable Development Goals (SDG) in Myanmar

2020 ◽  
Vol 12 (19) ◽  
pp. 3220
Author(s):  
Sumalika Biswas ◽  
Qiongyu Huang ◽  
Anupam Anand ◽  
Myat Su Mon ◽  
Franz-Eugen Arnold ◽  
...  
Keyword(s):  
Sustainable Development ◽  
Satellite Data ◽  
Classification System ◽  
Sustainable Development Goals ◽  
Near Infrared ◽  
Short Wave ◽  
Landsat 8 ◽  
Forest Types ◽  
Development Goals ◽  
Sentinel 2

Monitoring forests is important for measuring overall success of the 2030 Agenda because forests play an essential role in meeting many Sustainable Development Goals (SDG), especially SDG 15. Our study evaluates the contribution of three satellite data sources (Landsat-8, Sentinel-2 and Sentinel-1) for mapping diverse forest types in Myanmar. This assessment is especially important because Myanmar is currently revising its classification system for forests and it is critical that these new forest types can be accurately mapped and monitored over time using satellite imagery. Our results show that using a combination of Sentinel-1 and Sentinel-2 yields the highest accuracy (89.6% ± 0.16 percentage point(pp)), followed by Sentinel-2 alone (87.97% ± 0.11 pp) and Landsat-8 (82.68% ± 0.13 pp). The higher spatial resolution of Sentinel-2 Blue, Green, Red, Narrow Near Infrared and Short Wave Infrared bands enhances accuracy by 4.83% compared to Landsat-8. The addition of the Sentinel-2 Near Infrared and three Vegetation Red Edge bands further improve accuracy by 0.46% compared to using only Sentinel-2 Blue, Green, Red, Narrow Near Infrared and Short Wave Infrared bands. Adding the radar information from Sentinel-1 further increases the accuracy by 1.63%. We were able to map the two major forest types, Upper Moist and Upper Dry Mixed Deciduous Forest, which comprise 90% of our study area. Accuracies for these forest types ranged from 77 to 96% depending on the sensors used, demonstrating the feasibility of using satellite data to map forest categories from a newly revised classification system. Our results advance the ongoing development of the National Forest Monitoring System (NFMS) by the Myanmar Forest Department and United Nations-Food and Agriculture Organization (UN-FAO) and facilitates future monitoring of progress towards the SDGs.

scholarly journals A workflow for Sustainable Development Goals indicators assessment based on high-resolution satellite data

2019 ◽  
Vol 13 (2) ◽  
pp. 309-321 ◽  
Author(s):  
Nataliia Kussul ◽  
Mykola Lavreniuk ◽  
Andrii Kolotii ◽  
Sergii Skakun ◽  
Olena Rakoid ◽  
...  
Keyword(s):  
Sustainable Development ◽  
High Resolution ◽  
Satellite Data ◽  
Sustainable Development Goals ◽  
Development Goals

Problems of using satellite data to the assessment of sustainable development goals of Ukraine

2019 ◽  
Vol 25 (3) ◽  
pp. 40-56
Author(s):  
O.P. Fedorov ◽  
◽  
L.I. Samoylenko ◽  
L.M. Kolos ◽  
L.V. Pidgorodetska ◽  
...  
Keyword(s):  
Sustainable Development ◽  
Satellite Data ◽  
Sustainable Development Goals ◽  
Development Goals

SDG-Specific Country Groups: Subregional Analysis of the Arab Region

2018 ◽  
Vol 14 (2) ◽  
Author(s):  
Aljaž Kunčič
Keyword(s):  
Cluster Analysis ◽  
Sustainable Development ◽  
Classification System ◽  
Sustainable Development Goals ◽  
Arab Countries ◽  
Policy Recommendation ◽  
Arab Region ◽  
Learning Capacity ◽  
Development Goals ◽  
Specific Country

AbstractThis paper examines a classification system for grouping the Arab countries together based on characteristics most relevant to sustainable development goals (SDGs). It analyzes SDGs in Arab countries with cluster analysis, identifies the most appropriate decomposition of the region for each of the SDGs separately and describes the characteristics of the unique SDG performance groups. The results show that countries move often from a better to a worse group or vice versa, implying that different and SDG-specific subregional groups should be used for work on each individual SDG. Examining the overlap of cluster memberships by countries through a network perspective further identifies the most tightly knit country groups. The implications of findings are relevant for informative monitoring of SDGs on the subregional level, as well as policy recommendation sharing for and between similar countries, and enhancing peer learning capacity.

scholarly journals Introducing WIW for Detecting the Presence of Water in Wetlands with Landsat and Sentinel Satellites

2019 ◽  
Vol 11 (19) ◽  
pp. 2210 ◽  
Author(s):  
Lefebvre ◽  
Davranche ◽  
Willm ◽  
Campagna ◽  
Redmond ◽  
...  
Keyword(s):  
Vegetation Cover ◽  
Near Infrared ◽  
Ground Truth ◽  
Short Wave ◽  
Water Dynamics ◽  
High Temporal Resolution ◽  
Landsat 8 ◽  
Model Combining ◽  
Wide Range ◽  
Sentinel 2

Many wetlands are characterized by a vegetation cover of variable height and density over time. Tracking spatio-temporal changes in inundation patterns of these wetlands remains a challenge for remote sensing. Water In Wetlands (WIW) was predicted using a dichotomous partitioning of reflectance values encoded based on ground-truth (n = 4038) and optical-space derived (n = 7016) data covering all land cover types (n = 17) found in the Rhône delta, southern France. The models were developed with spectral data from Sentinel 2, Landsat 7, and Landsat 8 sensors, hence providing a monitoring tool that covers a 35-year period (same sensor for Landsat 5 and 7). A single model combining the near infrared (NIR ≤ 0.1558 to 0.1804, depending on sensors) and short-wave infrared (SWIR2 ≤ 0.0871 to 0.1131) wavelengths was identified by three independent analyses, each one using a different satellite. Overall accuracy of water maps ranged from 89% to 94% for the training samples and from 90% to 94% for the validation samples, encompassing standard water indices that systematically underestimate flooding duration under vegetation cover. Sentinel 2 provided the highest performance with a kappa coefficient of 0.82 for both samples. Such tool will be most useful for monitoring the water dynamics of seasonal wetlands, which are particularly sensitive to climate change while providing multiple services to humankind. Considering the high temporal resolution of Sentinel 2 (every 5 days), cumulative water maps built with the WIW logical rule could further be used for mapping a wide range of wetlands which are either periodically or permanently flooded.

scholarly journals A Multi-Channel Algorithm for Mapping Volcanic Thermal Anomalies by Means of Sentinel-2 MSI and Landsat-8 OLI Data

2019 ◽  
Vol 11 (23) ◽  
pp. 2876 ◽  
Author(s):  
Francesco Marchese ◽  
Nicola Genzano ◽  
Marco Neri ◽  
Alfredo Falconieri ◽  
Giuseppe Mazzeo ◽  
...  
Keyword(s):  
Near Infrared ◽  
Short Wave ◽  
Global Scale ◽  
Landsat 8 ◽  
Thermal Anomalies ◽  
Volcanic Plumes ◽  
Processing Times ◽  
Multi Temporal ◽  
Sentinel 2A ◽  
Sentinel 2

The Multispectral Instrument (MSI) and the Operational Land Imager (OLI), respectively onboard Sentinel-2A/2B and Landsat 8 satellites, thanks to their features especially in terms of spatial/spectral resolution, represents two important instruments for investigating thermal volcanic activity from space. In this study, we used data from those sensors to test an original multichannel algorithm, which aims at mapping volcanic thermal anomalies at a global scale. The algorithm, named Normalized Hotspot Indices (NHI), combines two normalized indices, analyzing near infrared (NIR) and short wave infrared (SWIR) radiances, to identify hotspot pixels in daylight conditions. Results, achieved studying a number of active volcanoes located in different geographic areas and characterized by a different eruptive behavior, demonstrated the NHI capacity in mapping both subtle and more intense volcanic thermal anomalies despite some limitations (e.g., missed detections because of clouds/volcanic plumes). In addition, the study shows that the performance of NHI might be further increased using some additional spectral/spatial tests, in view of a possible usage of this algorithm within a known multi-temporal scheme of satellite data analysis. The low processing times and the straight forth exportability to data from other sensors make NHI, which is sensitive even to other high temperature sources, suited for mapping hot volcanic targets integrating information provided by current and well-established satellite-based volcanoes monitoring systems.

Restructuring Educational Systems and Promoting Social Justice for Young People Involved in Drug Trafficking in Brazil

2019 ◽  
Vol 227 (2) ◽  
pp. 139-143 ◽  
Author(s):  
Alex Sandro Gomes Pessoa ◽  
Linda Liebenberg ◽  
Dorothy Bottrell ◽  
Silvia Helena Koller
Keyword(s):  
Sustainable Development ◽  
Social Justice ◽  
Young People ◽  
Latin American ◽  
Drug Trafficking ◽  
Sustainable Development Goals ◽  
Educational Systems ◽  
Economic Changes ◽  
2030 Agenda ◽  
Development Goals

Abstract. Economic changes in the context of globalization have left adolescents from Latin American contexts with few opportunities to make satisfactory transitions into adulthood. Recent studies indicate that there is a protracted period between the end of schooling and entering into formal working activities. While in this “limbo,” illicit activities, such as drug trafficking may emerge as an alternative for young people to ensure their social participation. This article aims to deepen the understanding of Brazilian youth’s involvement in drug trafficking and its intersection with their schooling, work, and aspirations, connecting with Sustainable Development Goals (SDGs) 4 and 16 as proposed in the 2030 Agenda for Sustainable Development adopted by the United Nations in 2015 .

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