1,720,995 research outputs found
Sharpened mapping of tropical forest biophysical properties from coarse spatial resolution satellite sensor data
No full textForest biophysical properties are typically estimated and mapped from remotely sensed data through the application of a vegetation index. This generally does not make full use of the information content of the remotely sensed data, using only the data acquired in a limited number of spectral channels, and may provide a relatively crude spatial representation of the biophysical variable of interest. Using imagery acquired by the NOAA AVHRR, it is shown that a standard neural network may use all the spectral channels available in a remotely sensed data set to derive more accurate estimates of the biophysical properties of tropical forests in Ghana than a series of vegetation indices. Additionally, the spatial representation derived can be refined by fusion with finer spatial resolution imagery, achieved with the application of a further neural network
Transferring remotely sensed predictions of drought events on Bornean rainforests
No full textIn order to inform the development of a remote sensing drought monitoring system
over Sabah, Borneo, this paper explored how a relationship between remotely
sensed data and rainfall developed at one site in Sabah transferred to four other sites in Sabah. By way of developing relationships between rainfall statistics collected at
five Sabah rainforest sites and remotely sensed data (acquired by NOAA AVHRR)
processed to MIR reflectance, the VI3 index, the Ts/NDVI index and Ts/VI3 index,
two points were concluded. The first was that the relationship between remotely
sensed data and rainfall is non-stationary across Sabah during the 1997/1998 ENSO
and the second was that the index Ts/VI3 is an effective means of using the remotely
sensed data available
Spatio-temporal response of extreme events on Bornean rainforests
No full textThe relationship between middle infrared reflectance and various scenarios of preceeding rainfall for a range of tropical forest types is investigated. Statistically significant correlations for the relationship were generally observed when the rainfall data were acquired over a period of about a month with a short time lag before image acquisition
Evolution of approaches for forest cover estimation in the Pacific Northwest, USA, using remote sensing
No full textThe transformation of land cover, in particular coniferous forest, constitutes one of the most notable agents of regional-to-global-scale environmental change. Remote sensing provides an excellent opportunity for providing forest cover information at appropriate spatial and temporal scales. The optimal exploitation of remote sensing relies on the link between known forest cover and the remotely sensed dataset. This paper explores the accuracy of three methods – vegetation indices, regression analysis and neural networks – for estimating coniferous forest cover across the United States Pacific Northwest. All methods achieved a similar accuracy of forest cover estimation. However, in view of the benefits and limitations of each, the neural network approach is recommended for future consideration
Dynamics of ENSO drought events on Sabah rainforests observed by NOAA AVHRR
No full textDrought, associated with the El Nin˜o Southern Oscillation (ENSO), can have
considerable impact on tropical rainforests. Concern over drought, particularly
given the possibility of an increase in its occurrence and intensity, has fostered a
desire for an increased understanding of drought events and their impact to
inform the development of a drought monitoring system. This paper investigates
the use of National Oceanic and Atmospheric Administration (NOAA)
Advanced Very High Resolution Radiometer (AVHRR) data in a drought
monitoring system for the rainforests of Sabah, Borneo. These rainforests are
dynamic with respect to their coupling with ENSO processes and in their
biophysical properties, and such dynamism may have implications for how
NOAA AVHRR data may be used. In particular, this paper explores the
transferability of relationships between a drought indicator (rainfall) and the
response of the rainforest, as measured by four NOAA AVHRR variables
(middle infrared reflectance; VI3; Ts/VI3 and Ts/NDVI), under particular site
conditions. It was found that both spatial variability in forest biophysical
properties and geographical variability in drought impact had implications for
the transferability of relationships developed under local conditions across Sabah
rainforests within a drought monitoring system. Suggestions are presented for
how NOAA AVHRR data could be used, with a new drought monitoring index –
the Ts/VI3 – recommended
Remote monitoring of impacts of ENSO related drought areas on Sabah rainforests
No full textThe performance of the NOAA AVHRR derived V13 vegetation index, relative to the NDVI, for monitoring impacts of the 1997-1998 ENSO related drought stress on Sabah rainforests was explored. Results demonstrated that the V13 index, which incorporates MIR reflectance, was more strongly correlated to rainfall values of this ENSO event than the more widely used NDVI. Moreover, maximum correlation was achieved at a longer lag period using the V13 than the NDVI, demonstrating its possible utility in a forecasting and monitoring system of ENSO related drought impacts on Sabah rainforest
Early results from the first full waveform LiDAR survey over a lowland ombrotrophic peatland, and synthesis with hyperspectral Eagle-Hawk data
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