Proporsi Indeks Sosial-Ekonomi Dalam Penentuan Indeks Kepekaan Lingkungan (Socio-Economics Index Proportion for Measuring Environmental Sensitivity Index)

2013 ◽  
Author(s):  
Yudi Wahyudin
Keyword(s):  
Sensitivity Index ◽  
Environmental Sensitivity

Hawaii Environmental Sensitivity Index (ESI) Maps and the Spatial Accuracy of ESI Mapping Methodology

2003 ◽  
Vol 2003 (1) ◽  
pp. 679-684
Author(s):  
Colin Plank ◽  
Zach Nixon ◽  
Christine Lord
Keyword(s):  
Human Error ◽  
Error Reporting ◽  
Sensitivity Index ◽  
Spatial Accuracy ◽  
Environmental Sensitivity ◽  
Data Collection And Analysis ◽  
Human Use ◽  
Low Altitude ◽  
First Time ◽  
Circular Error Probable

ABSTRACT Environmental Sensitivity Index (ESI) maps and digital databases have been generated for the Hawaiian Islands. ESI atlases integrate the most recent data available in three general categories: shoreline habitats, biological resources, and human-use resources, to provide spill responders with a synopsis of critical information. The new Hawaii atlas was prepared using standardized ESI data collection and analysis methods. For the first time, the spatial accuracy of the ESI shoreline classification method has been quantitatively measured using GPS. Shoreline classification methodology consisted of low-altitude overflights during the 2.5 hours preceding and following low-tide. Aerial classifications were then checked on the ground throughout the study area. USGS 7.5 minute basemaps were used in the field and shoreline segmentation annotation was transcribed from scanned field maps to a digital shoreline provided by the State. ESI surveys in August of 2000 resulted in nineteen shoreline classes for the Hawaiian chain. The GPS field study was conducted simultaneously with shoreline classification. During GPS surveys boundaries between ESI shoreline segments were located in the field and their position recorded with a hand held GPS. The coordinates of the segment boundary as located on the final ESI map were compared to those recorded with the GPS. Data for over 60 locations were analyzed statistically using three commonly accepted error-reporting methodologies. A Root Mean Squared (RMS) error of 33.5 meters, a Circular Error Probable (CEP) of 28 meters, and a 95% error bound of 58.2 meters were found. Error occurred unsystematically and was attributed to various factors, including inaccuracies in basemap shorelines, human error in the field and cartography, and error expected when mapping natural gradations as discreet points.

Incorporating dynamics factor to the Environmental Sensitivity Index (ESI) shoreline classification – Estonian and Spanish example

2014 ◽  
Vol 70 ◽  
pp. 372-377 ◽  
Author(s):  
Robert Aps ◽  
Hannes Tõnisson ◽  
Giorgio Anfuso ◽  
José A. Perales ◽  
Kaarel Orviku ◽  
...  
Keyword(s):  
Sensitivity Index ◽  
Environmental Sensitivity

scholarly journals A study on the use of Environmental Sensitivity Index map for oil spill considering social information

SOCIOTECHNICA ◽  
2004 ◽  
Vol 2 ◽  
pp. 209-217 ◽  
Author(s):  
Masumi YAZAKI ◽  
Shintaro GOTO ◽  
Seiichi HAMADA ◽  
Nobuhiro SAWANO ◽  
Kunihisa SAO ◽  
...  
Keyword(s):  
Oil Spill ◽  
Social Information ◽  
Sensitivity Index ◽  
Environmental Sensitivity

scholarly journals KenSea – development of an environmental sensitivity atlas for coastal areas of Kenya

2006 ◽  
Vol 10 ◽  
pp. 65-70
Author(s):  
John Tychsen ◽  
Ole Geertz-Hansen ◽  
Jesper Kofoed
Keyword(s):  
United Nations ◽  
Oil Spill ◽  
Oil Pollution ◽  
Development Program ◽  
Maritime Transportation ◽  
Sensitivity Index ◽  
Environmental Sensitivity ◽  
Effective Response ◽  
Kenyan Coast ◽  
The Government

The Kenya coastline extends 600 km from the border of Tanzania in the south to the border of Somalia in the north (Fig. 1). The Kenyan coast features a diverse marine environment, including estuaries, mangroves, sea grass beds and intertidal reef platforms and coral reefs, which are vital for the reproduction of marine organisms. These coastal ecosystems are regarded as some of the most valuable in Kenya but face serious threats from the ever increasing human pressure of tourism, industrial pollution, destructive fishing, mangrove logging and other unsustainable uses of marine resources. Another serious threat is the maritime transportation activities along the coast and at the ports. It is estimated that at any given time more than 50 ships operate in the major shipping lanes off the Kenyan coast, of which about nine are oil tankers with capacities ranging from 50 000 to 250 000 tonnes. Furthermore, the harbour of Mombasa serves as the major port for countries in East Africa. In recognition of the risks posed by oil pollution the government of Kenya and the commercial petroleum industry agreed to develop a National Oil Spill Response Contingency Plan (NOSRCP) with the purpose of enabling a speedy and effective response to any oil spill within the territorial waters of Kenya. An important element of this plan was the mapping of the coastal resources and the development of an environmental sensitivity atlas showing the vulnerability of the coast to marine oil spills. In 2004, the Government of Kenya approached the United Nations Development Program (UNDP) in Kenya for financial support to develop an environmental sensitivity atlas. The project was approved and forwarded for funding by the Danish Consultancy Trust Fund administrated by United Nations Operational Program (UNOPS) in Copenhagen. The project was announced in Denmark, and the KenSea group headed by the Geological Survey of Denmark and Greenland (GEUS) was awarded the contract. The project comprises four phases: (1) data compilation and development of the KenSea database, (2) development of a coastal classification for Kenya, (3) development of the sensitivity index jointly with a group of stakeholders, and (4) compilation of the KenSea environmental sensitivity atlas (Tychsen 2006).

scholarly journals ANALISYS AND MAPPING OF ENVIRONMENTAL SENSITIVITY INDEX IN BANGGAI REGENCY AND BANGGAI ISLANDS REGENCY, CENTRAL SULAWESI

2017 ◽  
Vol 9 (1) ◽  
pp. 357-374
Author(s):  
Sugeng Putranto ◽  
Neviaty P. Zamani ◽  
Harpasis S. Sanusi ◽  
Etty Riani ◽  
Achmad Fahrudin
Keyword(s):  
Spatial Analysis ◽  
Cultural Values ◽  
Local Community ◽  
Oil Spills ◽  
Mangrove Ecosystem ◽  
Sensitivity Index ◽  
Environmental Sensitivity ◽  
Moderate Sensitivity ◽  
Information System Technology ◽  
Central Sulawesi

The Environmental Sensitivity Index (ESI) is a description of the biological, socio-economic and socio-cultural values of a particular coastal and marine area used as a priority response to oil spills, shown on a map by applying spatial analysis using geographic information system technology. The purpose of this research was to analyse mangrove ecosystem area in Banggai and Banggai Islands Regency based on its vulnerability value by analyzing mangrove community structure and socio-economic services for local community. The research was conducted in nine sub-districts, among others: South Batui, Batui, East Luwuk, Lamala, Masama, Balantak, Bualemo (Banggai) and Bulagi and Buko (Banggai Islands). The study was conducted from August to November 2016, with field observations, direct interviews with communities and local government and literature review. The results of spatial analysis of ESI in the coastal areas of Banggai and Banggai Islands are obtained from sensitivity values of medium and sensitive. Areas with moderate sensitivity are Batui, East Luwuk, Masama, Lamala, Balantak and Bualemo sub-districts with grades of 16,78 – 24,35. The value of ESI with sensitive category ranges from 38,24 – 57,54 in Bulagi, Buko and South Batui sub-districts. Keywords: mangrove ecosystem, Environmental Sensitivity Index (ESI)

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