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Flores, Africa

Publications and source records attributed to Flores, Africa.

At least 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2019

A Spatial Pattern Analysis of Forest Loss in the Madre de Dios Region, Peru

Previous studies have quantified the expansion of gold mining-related forest loss (Espejo et al., 2018; Asner et al., 2017; Swenson et al., 2011) in the Madre de Dios region of Peru. This study uses Spectral Mixture Analysis (SMA) in a cloud-computing platform to map general forest loss within and outside key land tenure areas in this region. Landsat 7 Enhanced Thematic Mapper plus (ETM+) and Landsat 8 Operational Land Imager (OLI) Surface Reflectance data were utilized spanning 2013 and 2018 and spectral unmixing was performed to identify patterns of forest loss for each year. Planet Scope and RapidEye imagery were used to conduct an accuracy assessment and to identify potential drivers.

Puzzi Nicolau, Andrea↗
NASA NTRS2018

GC11H _ 0998: A spatial pattern analysis of forest loss in the Madre de Dios region, Peru

Previous studies have quantified the extension of gold mining activities and associated it with forest loss in the Madre de Dios region of Peru. This study uses Spectral Mixture Analysis (SMA) in a cloud-computing platform to map forest loss within and outside key land tenure areas in this region, Landsat 7 Enhanced Thematic Mapper plus (ETM+) and Landsat 8 Operational land Imager (OLI) Surface Reflectance data were utilized spanning 2013 and 2017 and spectral unmixing was performed to identify patterns of forest loss for each year. Planet Scope and RapidEye imagery were used to conduct an accuracy assessment and to identify potential drivers.

Puzzi Nicolau, Andrea↗
NASA NTRS2017

GeoDash: Assisting Visual Image Interpretation in Collect Earth Online by Leveraging Big Data on Google Earth Engine

Collect Earth Online (CEO) is a free and open online implementation of the FAO Collect Earth system for collaboratively collecting environmental data through the visual interpretation of Earth observation imagery. The primary collection mechanism in CEO is human interpretation of land surface characteristics in imagery served via Web Map Services (WMS). However, interpreters may not have enough contextual information to classify samples by only viewing the imagery served via WMS, be they high resolution or otherwise. To assist in the interpretation and collection processes in CEO, SERVIR, a joint NASA-USAID initiative that brings Earth observations to improve environmental decision making in developing countries, developed the GeoDash system, an embedded and critical component of CEO. GeoDash leverages Google Earth Engine (GEE) by allowing users to set up custom browser-based widgets that pull from GEE's massive public data catalog. These widgets can be quick looks of other satellite imagery, time series graphs of environmental variables, and statistics panels of the same. Users can customize widgets with any of GEE's image collections, such as the historical Landsat collection with data available since the 1970s, select date ranges, image stretch parameters, graph characteristics, and create custom layouts, all on-the-fly to support plot interpretation in CEO. This presentation focuses on the implementation and potential applications, including the back-end links to GEE and the user interface with custom widget building. GeoDash takes large data volumes and condenses them into meaningful, relevant information for interpreters. While designed initially with national and global forest resource assessments in mind, the system will complement disaster assessments, agriculture management, project monitoring and evaluation, and more.

SERVI↗
NASA NTRS2017

Collaborative, Rapid Mapping of Water Extents During Hurricane Harvey Using Optical and Radar Satellite Sensors

On August 25, 2017, Hurricane Harvey made landfall between Port Aransas and Port O'Connor, Texas, bringing with it unprecedented amounts of rainfall and record flooding. In times of natural disasters of this nature, emergency responders require timely and accurate information about the hazard in order to assess and plan for disaster response. Due to the extreme flooding impacts associated with Hurricane Harvey, delineations of water extent were crucial to inform resource deployment. Through the USGS's Hazards Data Distribution System, government and commercial vendors were able to acquire and distribute various satellite imagery to analysts to create value-added products that can be used by these emergency responders. Rapid-response water extent maps were created through a collaborative multi-organization and multi-sensor approach. One team of researchers created Synthetic Aperture Radar (SAR) water extent maps using modified Copernicus Sentinel data (2017), processed by ESA. This group used backscatter images, pre-processed by the Alaska Satellite Facility's Hybrid Pluggable Processing Pipeline (HyP3), to identify and apply a threshold to identify water in the image. Quality control was conducted by manually examining the image and correcting for potential errors. Another group of researchers and graduate student volunteers derived water masks from high resolution DigitalGlobe and SPOT images. Through a system of standardized image processing, quality control measures, and communication channels the team provided timely and fairly accurate water extent maps to support a larger NASA Disasters Program response. The optical imagery was processed through a combination of various band thresholds and by using Normalized Difference Water Index (NDWI), Modified Normalized Water Index (MNDWI), Normalized Difference Vegetation Index (NDVI), and cloud masking. Several aspects of the pre-processing and image access were run on internal servers to expedite the provision of images to analysts who could focus on manipulating thresholds and quality control checks for maximum accuracy within the time constraints. The combined results of the radar- and optical-derived value-added products through the coordination of multiple organizations provided timely information for emergency response and recovery efforts.

SERVIR↗
NASA NTRS2017

Mapping Threats to Agriculture in East Africa: Performance of MODIS Derived LST for Frost Identification in Kenya's Tea Plantations

Increased prevalence of weather related hazards in eastern Africa including drought, floods, hail and frost threatening agricultural productivity. Kenya is heavily dependent on agriculture for economic growth (FAO 2013); (1) Agriculture contributed 23.5% and 21.5% of GDP in 2009 and 2010 respectively, (2) Employment to half a million households of smallholders and 150,000 on large tea estates. Tea growing in Kenya depends on stability of the weather; (1) Weather is unpredictable, (2) Frost has contributed 30% of tea leaf losses, (3) Drought has contributed 14-30%, (4) The losses are experienced between January and march - frost and dry season.

remote sensing↗
NASA NTRS2016

Linking Land Use Changes to Surface Water Quality Variability in Lake Victoria: Some Insights From Remote Sensing (GC41B-1101)

Various land use changes driven by urbanization, conversion of grasslands and woodlands into farmlands, intensification of agricultural practices, deforestation, land fragmentation and degradation are taking place in Africa. In Kenya, agriculture is the main driver of land use conversions. The impacts of these land use changes are observable in land cover maps, and eventually in the hydrological systems. Reduction or change of natural vegetation cover types increases the speed of surface runoff and reduces water and nutrient retention capacities. This can lead to high nutrient inputs into lakes, resulting in eutrophication, siltation and infestation of floating aquatic vegetation. To assess if changes in land use could be contributing to increased phytoplankton blooms and sediment loads into Lake Victoria, we analyzed land use land cover data from Landsat, as well as surface chlorophyll-a and total suspended matter from MODIS-Aqua sensor.

water quality↗
NASA NTRS2016

Leveraging Technology in Invasive Species Mapping

Kenya has had several invasions of alien species that have had negative impacts on biodiversity, agriculture and human development. For instance, prickly pear out-competes native plants, precludes grazing and browsing near it, and inhibits the proliferation of indigenous species. The Northern Kenya Rangelands in the recent decades has experienced increased infestation by invasive plant species shrinking forage space available for both livestock and wildlife. An Invasive Species App has been co-developed and is currently being used in tracking locations and sightings of invasive species of trees or shrubs and the extensiveness of their effects. The App is customized to include any list of local invasive plants present in an area, take photos of the invasive species and is also able to work offline in cases of no internet connection in the remote areas.

remote sensing↗