Integration of Satellite Imagery and Forest Inventory in Mapping Dominant and Associated Species at a Regional Scale

2009 ◽  
Vol 44 (2) ◽  
pp. 312-323 ◽  
Author(s):  
Yangjian Zhang ◽  
Hong S. He ◽  
William D. Dijak ◽  
Jian Yang ◽  
Stephen R. Shifley ◽  
...  
Keyword(s):  
Satellite Imagery ◽  
Forest Inventory ◽  
Regional Scale ◽  
Associated Species

Forest Inventory

2019 ◽  
pp. 174-193
Author(s):  
Said Lahssini ◽  
Loubna El Mansouri ◽  
Hicham Mharzi Alaoui ◽  
Said Moukrim
Keyword(s):  
Satellite Imagery ◽  
Forest Inventory ◽  
Social Systems ◽  
Forest Resources ◽  
Lidar Data ◽  
Aerial Photogrammetry ◽  
Spatio Temporal ◽  
Land Inventory ◽  
Forest Resources Management

Forest resources management requires a variety of information related to social systems and to land and its supported resources and their dynamics (land cover, forest stocking, and growth). Such information is, by nature, spatio-temporal and scale dependent and its quality relay on costs for obtaining it. Geosciences and forest geomatics offer valuable methods for ensuring a good compromise between the quality of required information and its costs. This chapter will review and discuss the contribution of geoscience to forest and land inventory. After presentation of information needed and their acquisition methods, through traditional forest inventory, the chapter will focus on technologies aiming at forest resources characterization and assessment such as aerial photogrammetry, satellite imagery, LiDAR data.

scholarly journals An estimate of ice wedge volume for a High Arctic polar desert environment, Fosheim Peninsula, Ellesmere Island

2018 ◽  
Vol 12 (11) ◽  
pp. 3589-3604 ◽  
Author(s):  
Claire Bernard-Grand'Maison ◽  
Wayne Pollard
Keyword(s):  
Satellite Imagery ◽  
Regional Scale ◽  
High Arctic ◽  
Ellesmere Island ◽  
The Arctic ◽  
Ground Ice ◽  
Polar Desert ◽  
Ice Volume ◽  
Carbon Release ◽  
Wedge Volume

Abstract. Quantifying ground-ice volume on a regional scale is necessary to assess the vulnerability of permafrost landscapes to thaw-induced disturbance like terrain subsidence and to quantify potential carbon release. Ice wedges (IWs) are a ubiquitous ground-ice landform in the Arctic. Their high spatial variability makes generalizing their potential role in landscape change problematic. IWs form polygonal networks that are visible on satellite imagery from surface troughs. This study provides a first approximation of IW ice volume for the Fosheim Peninsula, Ellesmere Island, a continuous permafrost area characterized by polar desert conditions and extensive ground ice. We perform basic GIS analyses on high-resolution satellite imagery to delineate IW troughs and estimate the associated IW ice volume using a 3-D subsurface model. We demonstrate the potential of two semi-automated IW trough delineation methods, one newly developed and one marginally used in previous studies, to increase the time efficiency of this process compared to manual delineation. Our methods yield acceptable IW ice volume estimates, validating the value of GIS to estimate IW volume on much larger scales. We estimate that IWs are potentially present on 50 % of the Fosheim Peninsula (∼3000 km2), where 3.81 % of the top 5.9 m of permafrost could be IW ice.

scholarly journals Use of Satellite Imagery to Predict Corn Phenology at a Regional Scale

2019 ◽  
Vol 5 (6) ◽  
Author(s):  
L. Nieto ◽  
R. Schwalbert ◽  
I. A. Ciampitti
Keyword(s):  
Satellite Imagery ◽  
Regional Scale

Using satellite imagery as ancillary data for increasing the precision of estimates for the Forest Inventory and Analysis program of the USDA Forest Service

2005 ◽  
Vol 35 (12) ◽  
pp. 2968-2980 ◽  
Author(s):  
Ronald E McRoberts ◽  
Geoffrey R Holden ◽  
Mark D Nelson ◽  
Greg C Liknes ◽  
Dale D Gormanson
Keyword(s):  
Satellite Imagery ◽  
Forest Inventory ◽  
Forest Service ◽  
Analysis Program ◽  
Ancillary Data ◽  
Usda Forest Service ◽  
Forest Inventory And Analysis ◽  
New Approach ◽  
Areas Of Interest ◽  
Effective Source

Forest inventory programs report estimates of forest variables for areas of interest ranging in size from municipalities, to counties, to states or provinces. Because of numerous factors, sample sizes are often insufficient to estimate attributes as precisely as is desired, unless the estimation process is enhanced using ancillary data. Classified satellite imagery has been shown to be an effective source of ancillary data that, when used with stratified estimation techniques, contributes to increased precision with little corresponding increase in cost. Stratification investigations conducted by the Forest Inventory and Analysis program of the USDA Forest Service are reviewed, and a new approach to stratification using satellite imagery is proposed. The results indicate that precision may be substantially increased for estimates of both forest area and volume per unit area.

Forest inventory stand height estimates from very high spatial resolution satellite imagery calibrated with lidar plots

2013 ◽  
Vol 34 (12) ◽  
pp. 4406-4424 ◽  
Author(s):  
Brice Mora ◽  
Michael A. Wulder ◽  
Geordie W. Hobart ◽  
Joanne C. White ◽  
Christopher W. Bater ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Satellite Imagery ◽  
Forest Inventory ◽  
High Spatial Resolution ◽  
Height Estimates ◽  
Very High Spatial Resolution ◽  
Stand Height ◽  
Very High

scholarly journals Regional scale crop mapping using multi-temporal satellite imagery

2015 ◽  
Vol XL-7/W3 ◽  
pp. 45-52 ◽  
Author(s):  
N. Kussul ◽  
S. Skakun ◽  
A. Shelestov ◽  
M. Lavreniuk ◽  
B. Yailymov ◽  
...  
Keyword(s):  
Neural Networks ◽  
Missing Data ◽  
Satellite Imagery ◽  
Missing Values ◽  
Spectral Band ◽  
Regional Scale ◽  
Test Site ◽  
Multilayer Perceptrons ◽  
Landsat 8 ◽  
Multi Temporal

One of the problems in dealing with optical images for large territories (more than 10,000 sq. km) is the presence of clouds and shadows that result in having missing values in data sets. In this paper, a new approach to classification of multi-temporal optical satellite imagery with missing data due to clouds and shadows is proposed. First, self-organizing Kohonen maps (SOMs) are used to restore missing pixel values in a time series of satellite imagery. SOMs are trained for each spectral band separately using nonmissing values. Missing values are restored through a special procedure that substitutes input sample's missing components with neuron's weight coefficients. After missing data restoration, a supervised classification is performed for multi-temporal satellite images. An ensemble of neural networks, in particular multilayer perceptrons (MLPs), is proposed. Ensembling of neural networks is done by the technique of average committee, i.e. to calculate the average class probability over classifiers and select the class with the highest average posterior probability for the given input sample. The proposed approach is applied for regional scale crop classification using multi temporal Landsat-8 images for the JECAM test site in Ukraine in 2013. It is shown that ensemble of MLPs provides better performance than a single neural network in terms of overall classification accuracy, kappa coefficient, and producer's and user's accuracies for separate classes. The overall accuracy more than 85% is achieved. The obtained classification map is also validated through estimated crop areas and comparison to official statistics.

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