Contemporary forest management requires highly-detailed, spatially-contiguous, multi-temporal, and scenarios-comparable forest conditions.
The multi-temporal and multi-stage satellite data sets were firstly produced by using high spatial resolution satellite data and wide observation satellite data.
A numerical optimization method was employed for the inversion of a canopy reflectance model using Terra and Aqua MODIS multi-spectral, multi-temporal, and multi-angle reflectance observations to aid the determination of vegetation-specific physiological and structural canopy parameters.
Using multi-temporal and multi-source satellite data we classify urban footprints of a mega-region – the Hong Kong Shenzhen Guangzhou mega-region in Southern China – for the years 1975, 1990, 2000 and 2011.
We employed segmentation and landscape metrics (edge density and image texture respectively), derived from a pan-European multi-temporal and multi-spectral remote sensing dataset, to generate a consistent European indicator of farmland heterogeneity, the Farmland Heterogeneity Indicator (FHI).
By incorporating and utilizing multi-spectral, multi-temporal and spatial information in remote sensing images and considering the inherit spatial and sequential interdependence among neighboring pixels, we propose a new patch-based recurrent neural network (PB-RNN) systailoredored for classifying multi-temporal remote sensing data.
For example, [31] demonstrate a multi-temporal and multi-sensor land-use change detection that involves a hybrid classification scheme.
For the effective utilization of the available multi-sensor, multi-temporal, multi-spectral and multi-radiometric data from these sensors, fusion of digital image data has become a valuable technique.
This method minimizes measurement and environmental atmospheric effects in the multi-temporal images and provides a complete matrix of the change information.
In any case, the positive results from this study should encourage the use of a multi-temporal SAR and optical/SAR fusion for monitoring semiarid range conditions, and improving management of scarce resources.
Multi-temporal Landsat and RapidEye satellite data formed the basis for creating annual and multi-annual crop maps for 2010 2012 while using supervised classifications.
