The American Society for Testing and Materials (ASTM) is an independent organization devoted to the development of standards.

American Society for Testing and Materials

A new remote sensing index for assessing the spatial heterogeneity in urban ecological quality: A case from Fuzhou City, China

Flash flood susceptibility modeling using an optimized fuzzy rule based feature selection technique and tree based ensemble methods.

Geographic variations of ecosystem service intensity in Fuzhou City, China

This review describes the context of the Canadian Wetland Inventory (CWI) and then defines the specifications, establishes the most appropriate dataset, and explores the options for its implementation. The CWI is based on the assumption that a consistent map for all areas of Canada using satellite imagery can be produced for a minimum mapping unit of 1 ha. The CWI will establish the state of land cover for wetlands around year 2000, and the CWI map will classify wetlands according to the following scheme defined in the Canadian Wetland Classification System: bog, fen, marsh, swamp, and shallow water. It will then be possible to roll the map up into simpler classes (i.e., upland, wetland, open waters, and coastal waters) compatible with other map products. A wide array of datasets is available, and their potential use in the context of the CWI is explored. The best options regarding the mapping method and the evaluation of the map products are both described, keeping in mind that the most suitable method m...

Towards a strategy to implement the Canadian Wetland Inventory using satellite remote sensing

Geostatistically based methods that utilize textural information are frequently used to analyze remote sensing (RS) images. The role of these methods in analyzing forested areas increased rapidly during the last several years following advancements in highresolution RS technology. The results of numerous applications of geostatistical methods for processing RS forest images are encouraging. This paper summarizes such results. Three closely related topics are reviewed: 1) specific properties of geostatistical measures of spatial variability calculated from digital images of forested areas, 2) determination of biophysical forest parameters using semivariograms and characterization of forest ecosystem structure at the stand level, and 3) forest classification methods based on spatial information.

Applying geostatistics for investigations of forest ecosystems using remote sensing imagery

Spatially explicit sustainability analysis of long-term fiber supply in Georgia, USA

We estimated wood quality parameters for a specific tree trunk using samples of this tree’s branch, and auxiliary samples from other similar species, based on analysis of wood density, modulus of elasticity (MOE), and microfibril angle (MFA), measured with the near-infrared spectroscopy (NIRS) and SilviScan processes.  The measured materials included a branch sample from the subject tree, also known as the smolensk birch 1 , and stem analysis disk samples from silver birch ( Betula pendula Roth) trees collected in central Poland.  We analyzed and modeled the pith-to-bark and base-to-tip density changes in the silver birch samples, and using developed models estimated the subject tree trunk air-dry wood quality parameters and compared them with published yellow birch ( Betula alleghaniensis ) and silver birch tabular data.  Then we compared the corresponding surrogated green wood parameters of the subject tree against the standard American utility wood pole parameters, and identified environmental adjustments necessary for a realistic and accurate representation of the final subject tree wood characteristics.  The final conclusions from this study are that the subject tree dry wood parameters are not significantly different (in the statistical sense) from the well-documented yellow birch parameters, which were used as their surrogate, and even without the due reductions in parameters for excessive amount of whorls and branches, and for the height of the tree brake (5 to 7 m above ground), the structural parameters of the subject tree green wood, as applicable to live tree and as surrogated by appropriate yellow birch parameters, are generally weaker than corresponding dry wood parameters for the standard American wood poles and weaker then the southern yellow pine parameters. The adjustments for the whorls and knots and height of the brake may yield some additional 50% reduction in the estimates for the subject tree structural wood parameter values.

https://mcfns.net/index.php/Journal/article/download/155/MCFNS_155

Wood quality assessment of tree trunk from the tree branch sample and auxiliary data based on NIR Spectroscopy and SilviScan

The forest volumes are essential as they are directly related to the economic and environmental values of the forests. Satellite-based forest volume estimation was first developed in the 1990s, and the accuracy of the estimation has been improved over time. One of the satellite-based forest volume estimation issues is that it tends to overestimate the large volume class and underestimate the small volume class. Free availability of the major satellite imagery and the development of cloud-based computational platforms facilitate an immense amount of satellite imagery in the estimation. In this paper, we set three objectives: (1) to examine whether the long Landsat time series contributes to the improvement of the estimation accuracy, (2) to explore the effectiveness of forest disturbance record and land cover data as ancillary spatial data on the accuracy of the estimation, and (3) to apply the bias correction method to reduce the bias of the estimation. We computed three Tasseled-cap components from the Landsat data for preparation of short (2014–2016) and long (1984–2016) time series. Each data entity was analyzed with harmonic regressions resulting in the coefficients and the fitted values recorded as pixel values in a multilayer raster database. Data included Forest Inventory and Analysis (FIA) unit field inventory measurements provided by the United States Department of Agriculture Forest Service and the National Land Cover Database and disturbance history data added as ancillary information. The totality of the available data was organized into seven distinct Random Forest (RF) models with different variables compared against each other to identify the ones with the most satisfactory performance. A bias correction method was then applied to all the RF models to examine the effectiveness of the method. Among the seven models, the worst one used the coefficients and fitted values of the short Landsat time series only, and the best one used coefficients and fitted values of both short and long Landsat time series. Using the Out-of-bag (OOB) score, the best model was found to be 34.4% better than the worst one. The model that used only the long time series data had almost the same OOB score as the best model. The results indicate that the use of the long Landsat time series improves model performance. Contrary to the previous research employing forest disturbance data as a feature variable had almost no effect on OOB. The bias correction method reduced the relative size of the bias in the estimates of the best model from 3.79% to −1.47%, the bottom 10% bias by 12.5 points, and the top 10% bias by 9.9 points. Depending on the types of forest, important feature variables were differed, reflecting the relationship between the time series remote sensing data we computed for this research and the forests’ phenological characteristics. The availability of Light Detection And Ranging (LiDAR) data and accessibility of the precise locations of the FIA data are likely to improve the model estimates further.

Random Forest Regression Model for Estimation of the Growing Stock Volumes in Georgia, USA, Using Dense Landsat Time Series and FIA Dataset

Forest resources have a high economic value in the State of Georgia (USA) and the landscape is frequently disturbed as a part of forest management activities, such as plantation forest management activities. Thus, tracking the stand-clearing disturbance history in a spatially referenced manner might be pivotal in discussions of forest resource sustainability within the State. The two major objectives of this research are (i) to develop and test a reliable methodology for statewide tracking of forest disturbances in Georgia, (ii) to consider and discuss the use and implications of the information derived from the forest disturbance map. Two primary disturbance detection methods, a threshold algorithm and a statistical boundary method, were combined to develop a robust estimation of recent forest disturbance history. The developed model was used to create a forest disturbance record for the years 1987–2016, through the use of all available Landsat Thematic Mapper (TM)/Enhanced Thematic Mapper (ETM+) data. The final product was a raster database, where each pixel was assigned a value corresponding to the last disturbance year. The overall accuracy of the forest disturbance map was 87%, and it indicated that 4,503,253 ha, equivalent to 29.2% of the total land area in Georgia, experienced disturbances between 1987 and 2016. The estimated disturbed area in each year was highly variable and ranged between 84,651 ha (±36,354 ha) to 211,780 ha (±49,504 ha). By combining the use of the disturbance map along with the 2016 database from the National Land Cover Database (NLCD), we also analyzed the regional variation in the disturbance history. This analysis indicated that disturbed forests in urban areas were more likely to be converted to other land-uses. The forest disturbance record created in this research provides the necessary spatial data and address forest resource sustainability in Georgia. Additionally, the methodology used has application in the analysis of other resources, such as the estimation of the aboveground forest biomass.

Mapping Forest Disturbances between 1987–2016 Using All Available Time Series Landsat TM/ETM+ Imagery: Developing a Reliable Methodology for Georgia, United States

Roger C. Lowe

Papers

Applying geostatistics for investigations of forest ecosystems using remote sensing imagery

Spatially explicit sustainability analysis of long-term fiber supply in Georgia, USA

Wood quality assessment of tree trunk from the tree branch sample and auxiliary data based on NIR Spectroscopy and SilviScan

Random Forest Regression Model for Estimation of the Growing Stock Volumes in Georgia, USA, Using Dense Landsat Time Series and FIA Dataset

Mapping Forest Disturbances between 1987–2016 Using All Available Time Series Landsat TM/ETM+ Imagery: Developing a Reliable Methodology for Georgia, United States