980 research outputs found
Multi-source data fusion of optical satellite imagery to characterize habitat selection from wildlife tracking data
This work was supported by CAPES (Coordination for the Improvement of Higher Education Personnel) [BEX-13438-13-1].Wildlife tracking data allow monitoring of how organisms respond to spatio-temporal changes in resource availability. Remote sensing data can be used to quantify and qualify these variations to understand how movement is related to these changes. The use of remote sensing data with concurrent high levels of spatial and temporal detail may hold potential to improve our understanding of habitat selection. However, no current orbital sensor produces data with simultaneous high temporal and high spatial resolution, therefore alternative methods are required to generate remote sensing data that matches the high spatial-temporal resolution of modern wildlife tracking data. We present an analytical framework, not yet used in movement ecology, for data fusion of optical remote sensing data from multiple satellites and wildlife tracking data to study the impact of seasonal vegetation patterns on the movement of maned wolves (Chrysocyon brachyurus). We use multi-source data fusion to combine MODIS data with higher spatial resolution data (ASTER, Landsat 4-5-7-8, CBERS 2-2B) and create a synthetic NDVI product with a 15 m spatial detail and daily temporal resolution. We also use the higher spatial resolution data to create a multi-source NDVI product with same level of spatial detail but coarser temporal resolution and data from MODIS to create a single-source NDVI product with high temporal resolution but coarse spatial resolution. We combine the three different spatial-temporal resolution NDVI products with GPS tracking data of maned wolves to create step-selection functions (SSF), which are models used in ecology to investigate and predict habitat selection by animals. The SSF model based on multi-source NDVI had the best performance predicting the probability of use of visited locations given its NDVI value. The SSF based on the raw MODIS NDVI product, one which is commonly employed by ecologists, had the poorest performance for our study species. These findings indicate that, in contrast with current practice in movement ecology, a detailed spatial resolution of contextual environmental variable may be more important than a detailed temporal resolution, when investigating wildlife habitat selection regarding vegetation, although this result will be highly dependent on species. The choice of data set should therefore take into account not only the scale of movement but also the spatial and temporal scales at which dynamic environmental variables are changing.PostprintPostprintPeer reviewe
The recent developments in cloud removal approaches of MODIS snow cover product
The snow cover products of optical remote sensing systems
play an important role in research into global climate change, the
hydrological cycle, and the energy balance. Moderate Resolution Imaging
Spectroradiometer (MODIS) snow cover products are the most popular datasets
used in the community. However, for MODIS, cloud cover results in spatial
and temporal discontinuity for long-term snow monitoring. In the last few
decades, a large number of cloud removal methods for MODIS snow cover
products have been proposed. In this paper, our goal is to make a
comprehensive summarization of the existing algorithms for generating
cloud-free MODIS snow cover products and to expose the development trends.
The methods of generating cloud-free MODIS snow cover products are
classified into spatial methods, temporal methods, spatio-temporal methods,
and multi-source fusion methods. The spatial methods and temporal methods
remove the cloud cover of the snow product based on the spatial patterns and
temporal changing correlation of the snowpack, respectively. The
spatio-temporal methods utilize the spatial and temporal features of snow
jointly. The multi-source fusion methods utilize the complementary
information among different sources among optical observations, microwave
observations, and station observations.</p
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