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Journal ArticleDOI

Unmixing multiple land-cover type reflectances from coarse spatial resolution satellite data

TLDR
In this paper, a Gaussian filter is used to spatially degrade a set of fine spatial resolution images [based on, e.g., Landsat Thematic Mapper (TM)], each of which represents the spatial structure and extent of a land cover type.
About
This article is published in Remote Sensing of Environment.The article was published on 1995-11-01. It has received 81 citations till now. The article focuses on the topics: Temporal resolution & Image resolution.

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Citations
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Journal ArticleDOI

Integration of lidar and Landsat ETM+ data for estimating and mapping forest canopy height

TL;DR: In this paper, the authors compared five aspatial and spatial methods for predicting canopy height, using an airborne lidar system (Aeroscan) and Landsat Enhanced Thematic Mapper (ETM+) data: regression, kriging and cokriging.
Journal ArticleDOI

Downscaling land surface temperatures at regional scales with random forest regression

TL;DR: In this paper, a random forest (RF) regression approach was used to increase the spatial resolution of LST maps from 1.1 km to 250 m. The approach was tested for a complex landscape in the Eastern Mediterranean, the Jordan River Region, with LST fields from aggregated Landsat-7 ETM+ and MODIS (MODIS/Terra LST product MOD11A1) data; as reference at the finer scale, they used Landsat 7 derived LST data.
Journal ArticleDOI

Global continuous fields of vegetation characteristics : a linear mixture model applied to multi-year 8 km AVHRR data

TL;DR: This article applied a linear mixture model to derive global continuous fields of percentage woody vegetation, herbaceous vegetation, and bare ground from 8 km Advanced Very High Resolution Radiometer (AVHRR) Pathfinder Land data.
Journal ArticleDOI

Land cover mapping at sub-pixel scales using linear optimization techniques

TL;DR: A newly proposed sub-pixel mapping algorithm was first applied to a synthetic data set with a 1-km resolution, derived from a 20-m resolution image and yielded land cover maps at 500, 200, and 100 m resolution with accuracies close to 89%.
Journal ArticleDOI

Continuous fields of vegetation characteristics at the global scale at 1‐km resolution

TL;DR: In this paper, a linear mixture model is applied to 1-km advanced very high resolution radiometer data to estimate proportional cover for three important vegetation characteristics: life form (percent woody vegetation, percent herbaceous vegetation, and percent bare ground), leaf type (percent needleleaf and percent broadleaf), and leaf duration (percent evergreen and percent deciduous).
References
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Book

Data Reduction and Error Analysis for the Physical Sciences

TL;DR: In this paper, Monte Carlo techniques are used to fit dependent and independent variables least squares fit to a polynomial least-squares fit to an arbitrary function fitting composite peaks direct application of the maximum likelihood.
Journal ArticleDOI

Data Reduction and Error Analysis for the Physical Sciences.

TL;DR: Numerical methods matrices graphs and tables histograms and graphs computer routines in Pascal and Monte Carlo techniques dependent and independent variables least-squares fit to a polynomial least-square fit to an arbitrary function fitting composite peaks direct application of the maximum likelihood.
Book

Numerical Recipes in FORTRAN

TL;DR: The Diskette v 2.04, 3.5'' (720k) for IBM PC, PS/2 and compatibles [DOS] Reference Record created on 2004-09-07, modified on 2016-08-08.
Book

Probability and Statistics for Engineering and the Sciences

Jay L. Devore
TL;DR: In this article, the authors present a comprehensive overview of the statistical properties of point estimates and their relationship with the probability of a given point in a single-sample set of data.
Journal ArticleDOI

Probability and Statistics for Engineering and the Sciences.

G. M. Clarke, +1 more
- 01 Mar 1983 - 
TL;DR: This paper presents a meta-modelling framework for estimating the probabilities of different types of population sizes using a simple linear Regression model, and some examples show how this model can be modified to accommodate diverse population sizes.
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