Mapping, Remote Sensing, and GIS

Mangrove Ecosystem Recovery and Restoration from Oil Spill in the Niger Delta: The GIS Perspective

Background

In Nigeria, Mangrove's provide critical environmental and economic services, including maintaining water quality, serving as breeding grounds for important fish and crustacean, and as a source of food and materials.  Yet, the country also has a history of oil spills that threaten these important ecosystems. In order to understand the effects of oil spills on mangroves, this study examines the land cover change of mangrove ecosystems in the Niger Delta between 1986 and 2008.

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Modelos espaciales aplicados al manejo de los recursos naturales: una propuesta en la sub-cuenca del Río Pilón, Nuevo León, México (Spacial modeling applied to natural resources management)

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Monitoring of Black Mangrove Restoration with Nursery-Reared Seedlings on an Arid Coastal Lagoon

background

This paper describes a reforestation experiment with black mangrove (Avicennia germinans) in an arid mangrove forest of Baja California Sur, Mexico. In arid mangrove systems, natural regeneration and small-scale reforestation are not adequate to restore mangrove forests, as they may be in the humid tropics. Thus, alternative nursery techniques for arid mangroves must be developed.

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Integration of Hyperion Satellite Data and A Household Social Survey to Characterize the Causes and Consequences of Reforestation Patterns in the Northern Ecuadorian Amazon

background

This paper describes reforestation in the Northern Ecuadorian Amazon (NEA) using 2002 remotely sensed Hyperion images and 2001 Ikonos images.

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The Causes of the Reforestation in Vietnam

background

Wood exploitation and agricultural expansion led to large-scale deforestation in Vietnam.  Since the mid-1990s, forest cover in many areas has increased both in the form of natural regeneration and tree plantations. Policies such as the 1993 Land Law offered households rights to forestland and tree planting campaigns such as the Five Million Hectare Reforestation Programme made people responsible for owning and protecting forest land.

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Importance of Input Classification to Graph Automata Simulations of Forest Cover Change in the Peruvian Amazon

Background

In an area of Peru difficult for remote sensing imaging of deforestation and regeneration, the authors evaluate landcover and detect changes in landuse using novel data simulation techniques.

Research goals & Methods

The authors aim to compensate for remote assessments of deforestation or reforestation that may be strongly dependent on the seasonality of input images. To do this, they ran graph automata simulations while varying forest cover inputs to model land cover change. 

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