A land cover map of Africa at 100 meters resolution using a mosaic of alos PALSAR dual-polarization data: Preliminary developments
24 Jul 2011-pp 3526-3529
TL;DR: A visual assessment indicates that the classified maps are very satisfying, at least for the tree cover class, with a significantly improved spatial resolution compared to existing large-scale land cover products.
Abstract: In this paper, we investigate the potential of large-scale mosaics of Synthetic Aperture Radar (SAR) data for land cover mapping. The study is based on a wall-to-wall mosaic of double-polarization data (HH and HV) from the L-band sensor PALSAR, covering the whole African continent at a spatial resolution of about 100m. The GlobCover 2009 global land cover map is taken as a reference for the training of the classification algorithm. The joint use of the PALSAR mosaic, the GlobCover 2009 map and the Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM) allows retrieving backscatter signatures that take into account local topography (slope angle and slope orientation) for each of the 22 GlobCover classes. These signatures are then used to assign each pixel of the mosaic to one class. The 22 classes are finally combined to coarser classes: tree cover, shrub cover, herbaceous cover/bare soil, and water. Because of ecological and phenological variations over the continent, this land cover mapping scheme is actually applied to 5° by 5° tiles. The methodology has been developed so far on two tiles which illustrate different ecoregions. A visual assessment indicates that the classified maps are very satisfying, at least for the tree cover class, with a significantly improved spatial resolution compared to existing large-scale land cover products. Validation is ongoing before applying the method to the whole Africa in the future.
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TL;DR: This paper demonstrates that the use of the standard EO-based proxy for ANPP, summed normalized difference vegetation index (NDVI) over the year, and the blended EO/rain gauge based data-set for annual precipitation (Climate Prediction Center Merged Analysis of) is feasible.
Abstract: The 'rain use efficiency' (RUE) may be defined as the ratio of above-ground net primary productivity (ANPP) to annual precipitation, and it is claimed to be a conservative property of the vegetation cover in drylands, if the vegetation cover is not subject to non-precipitation related land degradation. Consequently, RUE may be regarded as means of normalizing ANPP for the impact of annual precipitation, and as an indicator of non-precipitation related land degradation. Large scale and long term identification and monitoring of land degradation in drylands, such as the Sahel, can only be achieved by use of Earth Observation (EO) data. This paper demonstrates that the use of the standard EO-based proxy for ANPP, summed normalized difference vegetation index (NDVI) (National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) Global Inventory Modeling and Mapping Studies 3rd generation (GIMMS3g)) over the year (ΣNDVI), and the blended EO/rain gauge based data-set for annual precipitation (Climate Prediction Center Merged Analysis of
186 citations
Cites background from "A land cover map of Africa at 100 m..."
...The northern parts of the Sahel are primarily dominated by open and sparse grasslands and shrublands, while cropland, open woody vegetation and deciduous shrubland characterize the southern parts [39,40]....
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16 Dec 2012
TL;DR: The 2010 State of the Forest report does not differ fundamentally from that of the 2008 report and relies on indicators decided on collectively by about sixty contributors Data collection was organized from 2009 to 2010 using national groups consisting of four to ten members depending on the countries, all of whom worked for public administrations dealing with forest issues as discussed by the authors.
Abstract: The design of the 2010 State of the Forest report does not differ fundamentally from that of the 2008 report and relies on indicators decided on collectively by about sixty contributors Data collection was organized from 2009 to 2010 using national groups consisting of four to ten members, depending on the countries, all of whom worked for public administrations dealing with forest issues Data collected, covering for the most part 2008 and 2009, was validated during national workshops for public administration officials, environmental NGO representatives, the private sector and development projects The data served to support the inputs provided by contributors of chapters for the current report, under the supervision of internationally recognized scientific committees Furthermore, a State of the Forest 2010 sub-regional validation workshop was organized from 29 to 30 March 2011 for about 100 participants working in forest management, comprising representatives from the ten COMIFAC member countries, and several of its partners
179 citations
TL;DR: In this paper, Taylor et al. used the SPOT (Satellite Pour l'Observation de la Terre)-VEGETATION sensor and its 13-year time series of reflectance values to produce a reference data set describing the seasonal and inter-annual variability of the land surface phenology on a global scale.
Abstract: Time series of vegetation indices (VIs) obtained by remote sensing are widely used to study phenology on regional and global scales. The aim of the study is to design a method and to produce a reference data set describing the seasonal and inter-annual variability of the land-surface phenology on a global scale. Specific constraints are inherent in the design of such a global reference data set: (1) the high diversity of vegetation types and the heterogeneous conditions of observation, (2) a near-daily resolution is needed to follow the rapid changes in phenology, (3) the time series used to depict the baseline vegetation cycle must be long enough to be representative of the current vegetation dynamic and encompass anomalies, and (4) a spatial resolution consistent with a land-cover-specific analysis should be privileged. This study focuses on the SPOT (Satellite Pour l'Observation de la Terre)-VEGETATION sensor and its 13-year time series of reflectance values. Five steps addressing the noise and the missing data in the reflectance time series were selected to process the daily multispectral reflectance observations. The final product provides, for every pixel, three profiles for 52 × 7-day periods: a mean, a median, and a standard deviation profile. The mean and median profiles represent the reference seasonal pattern for variation of the vegetation at a specific location whereas the standard deviation profile expresses the inter-annual variability of VIs. A quality flag at the pixel level demonstrated that the reference data set can be considered as a reliable representation of the vegetation phenology in most parts of the Earth. © 2014 Taylor & Francis.
49 citations
Cites background from "A land cover map of Africa at 100 m..."
...(a) Global land-cover map for Africa for the year 2000 (Mayaux et al. 2003)....
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TL;DR: In this paper, a new water balance model for Lake Victoria was presented with climate simulations available through the Coordinated Regional Climate Downscaling Experiment (CORDEX) Africa framework.
Abstract: . Lake Victoria, the second largest freshwater lake in the world, is one of the
major sources of the Nile river. The outlet to the Nile is controlled by two
hydropower dams of which the allowed discharge is dictated by the Agreed
Curve, an equation relating outflow to lake level. Some regional climate
models project a decrease in precipitation and an increase in evaporation
over Lake Victoria, with potential important implications for its water
balance and resulting level. Yet, little is known about the potential
consequences of climate change for the water balance of Lake Victoria. In
this second part of a two-paper series, we feed a new water balance model for
Lake Victoria presented in the first part with climate simulations available
through the COordinated Regional Climate Downscaling
Experiment (CORDEX) Africa
framework. Our results reveal that most regional climate models are not
capable of giving a realistic representation of the water balance of Lake
Victoria and therefore require bias correction. For two emission scenarios
(RCPs 4.5 and 8.5), the decrease in precipitation over the lake and an
increase in evaporation are compensated by an increase in basin precipitation
leading to more inflow. The future lake level projections show that the dam
management scenario and not the emission scenario is the main controlling
factor of the future water level evolution. Moreover, inter-model
uncertainties are larger than emission scenario uncertainties. The comparison
of four idealized future management scenarios pursuing certain policy
objectives (electricity generation, navigation reliability and environmental
conservation) uncovers that the only sustainable management scenario is
mimicking natural lake level fluctuations by regulating outflow according to
the Agreed Curve. The associated outflow encompasses, however, ranges from
14 m 3 day −1 ( −85 %) to 200 m 3 day −1 ( +100 %)
within this ensemble, highlighting that future hydropower generation and
downstream water availability may strongly change in the next decades even if
dam management adheres to he Agreed Curve. Our results overall underline that
managing the dam according to the Agreed Curve is a key prerequisite for
sustainable future lake levels, but that under this management scenario,
climate change might potentially induce profound changes in lake level and
outflow volume.
35 citations
Cites background from "A land cover map of Africa at 100 m..."
...The same land cover classes based on the Global Land Cover 2000 dataset (GLC 2000; Mayaux et al., 2003) and the hydrological soil groups are applied to all CORDEX simulations....
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References
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01 Jan 2008
553 citations
"A land cover map of Africa at 100 m..." refers background in this paper
...A description of this product can be found in [2]....
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TL;DR: The generation of a regional dual-polarization (HH and HV) mosaic for the entire African continent at spatial resolution on the order of 100 m.
Abstract: The Japan Space Exploration Agency Kyoto and Carbon (K&C) Initiative seeks to demonstrate the potential of the Advanced Land Observing Satellite (ALOS) Phased Arrayed L-band Synthetic Aperture Radar (PALSAR) data for addressing regional applications relating to climate change, carbon cycle science, and environmental conservation. This paper outlines the generation of a regional dual-polarization (HH and HV) mosaic for the entire African continent at spatial resolution on the order of 100 m. The main computational and radar science issues undertaken to generate a seamless mosaic with good radiometric and geometric accuracy are summarized. Preliminary investigations into the thematic information provided by the K&C Africa mosaic and comparisons with the JERS-1 SAR mosaic generated as part of the Global Rain Forest Mapping Project are reported, with emphasis placed on characterizing and detecting change in forests and savannas.
30 citations
01 Jan 2009
11 citations
"A land cover map of Africa at 100 m..." refers methods in this paper
...This data consists of 20 km x 20km boxes of Landsat data at 30 m resolution, located every square degree, that have been classified into the following classes: tree cover, shrub cover, herbaceous, bare/nonvegetated, wetlands, and water [3] [4] [5]....
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01 Jan 2009
TL;DR: In this paper, the authors developed a methodology to monitor the tropical forest cover in Latin America, Southeast Asia and Africa, based on the FAO global Forest Resources Assessment 2010 (FRA2010) systematic sample, in support to FRA2010 remote sensing survey.
Abstract: Reggiani SpA, via Tonale 133, 21100 Varese, Italy; - (dario.simonetti, silvia.carboni)@ext.jrc.it Abstract – At the JRC, a methodology is developed to monitor the tropical forest cover in Latin America, Southeast Asia and Africa. The results will provide quantitative measurements of changes for the year 1990 and 2000 and will be a major input for the FAO FRA 2010 remote sensing survey (Global Forest Resources Assessment). The project is based on object-based classification of a systematic sampling of Landsat imagery at each longitude and latitude intersect. The area covered at each sample site is a box of 20km x 20km for which Landsat data are used for both dates. Prior to the classification and the change detection, a robust approach applicable to a very large amount of data had to be developed to put the multi-temporal and multi-scene data on the same radiometric scale. The paper presents all the pre-processing steps applied to a total of circa 4,000 image pairs. Starting with the conversion to TOA (Top of Atmosphere) reflectance, the image normalization is described as well as the haze correction process. A two-level segmentation is applied on the multi-date imagery. The results of these processing steps prior to the supervised classification are presented. Keywords: Land cover change, pre-processing, Landsat, tropical forest 1. INTRODUCTION Tropical regions are currently undergoing rapid changes in land cover (Achard et al ., 2002). These changes, in particular forest clearing can have different impacts such as greenhouse gas contribution and biodiversity loss. A global and systematic estimation of the status and monitoring of forest cover changes is important to accurately access these potential impacts and to inform better decision-makers at national and international scale (Mayaux et al. , 2005; Lepers et al. 2005). Remote sensing imagery offers repetitive data acquisition, a synoptic view of inaccessible areas and consistent image quality. Deforestation and vegetation changes have been thus widely studied with remotely sensing technologies. The JRC is developing a methodology to monitor the tropical forest cover in Latin America, Southeast Asia and Africa, based on the FAO global Forest Resources Assessment 2010 (FRA2010) systematic sample, in support to FRA2010 remote sensing survey. The project is based on an automatic object-based classification of Landsat imagery validated by visual interpretation of regional experts (Achard et al. , 2009). To produce accurate change detection results, radiometric and atmospheric correction of multi-date images is of crucial importance. This paper presents all the pre-processing steps applied to a total of 4,000 image pairs covering Latin America, Southeast Asia and Africa. Starting with the conversion to Top of Atmosphere (TOA) reflectance, the image normalization is described in details as well as the haze correction process. To facilitate the interpretation and improve the classification, a two-level segmentation is applied on the multi-date imagery providing objects of similar pixels both spectrally and temporally. 2. SAMPLING STRATEGY Figure 1 depicts the study areas covered by the JRC. The grid system selected for the global systematic sampling is a rectilinear grid based on degrees of geographical latitude and longitude enabling a straightforward implementation, easy location and understanding (FAO, 2007). To monitor changes at a scale relevant to land management, boxes of 10 km by 10 km plus 5 km buffer to facilitate interpretations have been extracted at each intersection point. On our study areas, the one by one degree sampling frame results in 2055 intersection points in Sub-Saharan Africa, 1230 in Central and South America and the Caribbean and 741 in South and Southeast Asia. This sampling scheme results in a circa 3 % sampling rate (Achard et al. , 2009).
9 citations
"A land cover map of Africa at 100 m..." refers methods in this paper
...This data consists of 20 km x 20km boxes of Landsat data at 30 m resolution, located every square degree, that have been classified into the following classes: tree cover, shrub cover, herbaceous, bare/nonvegetated, wetlands, and water [3] [4] [5]....
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