Predicting membrane protein types using various decision tree classifiers based on various modes of general PseAAC for imbalanced datasets.

PubMed

Sankari, E Siva; Manimegalai, D

2017-12-21

Predicting membrane protein types is an important and challenging research area in bioinformatics and proteomics. Traditional biophysical methods are used to classify membrane protein types. Due to large exploration of uncharacterized protein sequences in databases, traditional methods are very time consuming, expensive and susceptible to errors. Hence, it is highly desirable to develop a robust, reliable, and efficient method to predict membrane protein types. Imbalanced datasets and large datasets are often handled well by decision tree classifiers. Since imbalanced datasets are taken, the performance of various decision tree classifiers such as Decision Tree (DT), Classification And Regression Tree (CART), C4.5, Random tree, REP (Reduced Error Pruning) tree, ensemble methods such as Adaboost, RUS (Random Under Sampling) boost, Rotation forest and Random forest are analysed. Among the various decision tree classifiers Random forest performs well in less time with good accuracy of 96.35%. Another inference is RUS boost decision tree classifier is able to classify one or two samples in the class with very less samples while the other classifiers such as DT, Adaboost, Rotation forest and Random forest are not sensitive for the classes with fewer samples. Also the performance of decision tree classifiers is compared with SVM (Support Vector Machine) and Naive Bayes classifier. Copyright © 2017 Elsevier Ltd. All rights reserved.

Classifying forest inventory data into species-based forest community types at broad extents: exploring tradeoffs among supervised and unsupervised approaches

Treesearch

Jennifer K. Costanza; Don Faber-Langendoen; John W. Coulston; David N. Wear

2018-01-01

Background: Knowledge of the different kinds of tree communities that currently exist can provide a baseline for assessing the ecological attributes of forests and monitoring future changes. Forest inventory data can facilitate the development of this baseline knowledge across broad extents, but they first must be classified into forest...

BitterSweetForest: A random forest based binary classifier to predict bitterness and sweetness of chemical compounds

NASA Astrophysics Data System (ADS)

Banerjee, Priyanka; Preissner, Robert

2018-04-01

Taste of a chemical compounds present in food stimulates us to take in nutrients and avoid poisons. However, the perception of taste greatly depends on the genetic as well as evolutionary perspectives. The aim of this work was the development and validation of a machine learning model based on molecular fingerprints to discriminate between sweet and bitter taste of molecules. BitterSweetForest is the first open access model based on KNIME workflow that provides platform for prediction of bitter and sweet taste of chemical compounds using molecular fingerprints and Random Forest based classifier. The constructed model yielded an accuracy of 95% and an AUC of 0.98 in cross-validation. In independent test set, BitterSweetForest achieved an accuracy of 96 % and an AUC of 0.98 for bitter and sweet taste prediction. The constructed model was further applied to predict the bitter and sweet taste of natural compounds, approved drugs as well as on an acute toxicity compound data set. BitterSweetForest suggests 70% of the natural product space, as bitter and 10 % of the natural product space as sweet with confidence score of 0.60 and above. 77 % of the approved drug set was predicted as bitter and 2% as sweet with a confidence scores of 0.75 and above. Similarly, 75% of the total compounds from acute oral toxicity class were predicted only as bitter with a minimum confidence score of 0.75, revealing toxic compounds are mostly bitter. Furthermore, we applied a Bayesian based feature analysis method to discriminate the most occurring chemical features between sweet and bitter compounds from the feature space of a circular fingerprint.

BitterSweetForest: A Random Forest Based Binary Classifier to Predict Bitterness and Sweetness of Chemical Compounds

PubMed Central

Banerjee, Priyanka; Preissner, Robert

2018-01-01

Taste of a chemical compound present in food stimulates us to take in nutrients and avoid poisons. However, the perception of taste greatly depends on the genetic as well as evolutionary perspectives. The aim of this work was the development and validation of a machine learning model based on molecular fingerprints to discriminate between sweet and bitter taste of molecules. BitterSweetForest is the first open access model based on KNIME workflow that provides platform for prediction of bitter and sweet taste of chemical compounds using molecular fingerprints and Random Forest based classifier. The constructed model yielded an accuracy of 95% and an AUC of 0.98 in cross-validation. In independent test set, BitterSweetForest achieved an accuracy of 96% and an AUC of 0.98 for bitter and sweet taste prediction. The constructed model was further applied to predict the bitter and sweet taste of natural compounds, approved drugs as well as on an acute toxicity compound data set. BitterSweetForest suggests 70% of the natural product space, as bitter and 10% of the natural product space as sweet with confidence score of 0.60 and above. 77% of the approved drug set was predicted as bitter and 2% as sweet with a confidence score of 0.75 and above. Similarly, 75% of the total compounds from acute oral toxicity class were predicted only as bitter with a minimum confidence score of 0.75, revealing toxic compounds are mostly bitter. Furthermore, we applied a Bayesian based feature analysis method to discriminate the most occurring chemical features between sweet and bitter compounds using the feature space of a circular fingerprint. PMID:29696137

Integrating a process-based ecosystem model with Landsat imagery to assess impacts of forest disturbance on terrestrial carbon dynamics: Case studies in Alabama and Mississippi

DOE PAGES

Chen, Guangsheng; Tian, Hanqin; Huang, Chengquan; ...

2013-07-01

Forest ecosystems in the southern United States are dramatically altered by three major disturbances: timber harvesting, hurricane, and permanent land conversion. Understanding and quantifying effects of disturbance on forest carbon, nitrogen, and water cycles is critical for sustainable forest management in this region. In this study, we introduced a process-based ecosystem model for simulating forest disturbance impacts on ecosystem carbon, nitrogen, and water cycles. Based on forest mortality data classified from Landsat TM/ETM + images, this model was then applied to estimate changes in carbon storage using Mississippi and Alabama as a case study. Mean annual forest mortality rate formore » these states was 2.37%. Due to frequent disturbance, over 50% of the forest land in the study region was less than 30 years old. Forest disturbance events caused a large carbon source (138.92 Tg C, 6.04 Tg C yr -1; 1 Tg = 10 12 g) for both states during 1984–2007, accounting for 2.89% (4.81% if disregard carbon storage changes in wood products) of the total forest carbon storage in this region. Large decreases and slow recovery of forest biomass were the main causes for carbon release. Forest disturbance could result in a carbon sink in few areas if wood product carbon was considered as a local carbon pool, indicating the importance of accounting for wood product carbon when assessing forest disturbance effects. The legacy effects of forest disturbance on ecosystem carbon storage could last over 50 years. Lastly, this study implies that understanding forest disturbance impacts on carbon dynamics is of critical importance for assessing regional carbon budgets.« less

Estimates of phytomass and net primary productivity in terrestrial ecosystems of the former Soviet Union identified by classified Global Vegetation Index

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gaston, G.G.; Kolchugina, T.P.

1995-12-01

Forty-two regions with similar vegetation and landcover were identified in the former Soviet Union (FSU) by classifying Global Vegetation Index (GVI) images. Image classes were described in terms of vegetation and landcover. Image classes appear to provide more accurate and precise descriptions for most ecosystems when compared to general thematic maps. The area of forest lands were estimated at 1,330 Mha and the actual area of forest ecosystems at 875 Mha. Arable lands were estimated to be 211 Mha. The area of the tundra biome was estimated at 261 Mha. The areas of the forest-tundra/dwarf forest, taiga, mixed-deciduous forest andmore » forest-steppe biomes were estimated t 153, 882, 196, and 144 Mha, respectively. The areas of desert-semidesert biome and arable land with irrigated land and meadows, were estimated at 126 and 237 Mha, respectively. Vegetation and landcover types were associated with the Bazilevich database of phytomass and NPP for vegetation in the FSU. The phytomass in the FSU was estimated at 97.1 Gt C, with 86.8 in forest vegetation, 9.7 in natural non-forest and 0.6 Gt C in arable lands. The NPP was estimated at 8.6 Gt C/yr, with 3.2, 4.8, and 0.6 Gt C/yr of forest, natural non-forest, and arable ecosystems, respectively. The phytomass estimates for forests were greater than previous assessments which considered the age-class distribution of forest stands in the FSU. The NPP of natural ecosystems estimated in this study was 23% greater than previous estimates which used thematic maps to identify ecosystems. 47 refs., 4 figs., 2 tabs.« less

Machine Learning Methods for Production Cases Analysis

NASA Astrophysics Data System (ADS)

Mokrova, Nataliya V.; Mokrov, Alexander M.; Safonova, Alexandra V.; Vishnyakov, Igor V.

2018-03-01

Approach to analysis of events occurring during the production process were proposed. Described machine learning system is able to solve classification tasks related to production control and hazard identification at an early stage. Descriptors of the internal production network data were used for training and testing of applied models. k-Nearest Neighbors and Random forest methods were used to illustrate and analyze proposed solution. The quality of the developed classifiers was estimated using standard statistical metrics, such as precision, recall and accuracy.

Correcting Classifiers for Sample Selection Bias in Two-Phase Case-Control Studies

PubMed Central

Theis, Fabian J.

2017-01-01

Epidemiological studies often utilize stratified data in which rare outcomes or exposures are artificially enriched. This design can increase precision in association tests but distorts predictions when applying classifiers on nonstratified data. Several methods correct for this so-called sample selection bias, but their performance remains unclear especially for machine learning classifiers. With an emphasis on two-phase case-control studies, we aim to assess which corrections to perform in which setting and to obtain methods suitable for machine learning techniques, especially the random forest. We propose two new resampling-based methods to resemble the original data and covariance structure: stochastic inverse-probability oversampling and parametric inverse-probability bagging. We compare all techniques for the random forest and other classifiers, both theoretically and on simulated and real data. Empirical results show that the random forest profits from only the parametric inverse-probability bagging proposed by us. For other classifiers, correction is mostly advantageous, and methods perform uniformly. We discuss consequences of inappropriate distribution assumptions and reason for different behaviors between the random forest and other classifiers. In conclusion, we provide guidance for choosing correction methods when training classifiers on biased samples. For random forests, our method outperforms state-of-the-art procedures if distribution assumptions are roughly fulfilled. We provide our implementation in the R package sambia. PMID:29312464

Microscale photo interpretation of forest and nonforest land classes

NASA Technical Reports Server (NTRS)

Aldrich, R. C.; Greentree, W. J.

1972-01-01

Remote sensing of forest and nonforest land classes are discussed, using microscale photointerpretation. Results include: (1.) Microscale IR color photography can be interpreted within reasonable limits of error to estimate forest area. (2.) Forest interpretation is best on winter photography with 97 percent or better accuracy. (3.) Broad forest types can be classified on microscale photography. (4.) Active agricultural land is classified most accurately on early summer photography. (5.) Six percent of all nonforest observations were misclassified as forest.

Forest Stewardship Council (FSC) pesticide policy and integrated pest management in certified tropical plantations.

PubMed

Lemes, Pedro Guilherme; Zanuncio, José Cola; Serrão, José Eduardo; Lawson, Simon A

2017-01-01

The Forest Stewardship Council (FSC) was the first non-governmental organization composed of multi-stakeholders to ensure the social, environmental, and economic sustainability of forest resources. FSC prohibits certain chemicals and active ingredients in certified forest plantations. A company seeking certification must discontinue use of products so listed and many face problems to comply with these constraints. The aim of this study was to assess the impacts of certification on pest management from the perspective of Brazilian private forestry sector. Ninety-three percent of Brazilian FSC-certified forest companies rated leaf-cutting ants as "very important" pests. Chemical control was the most important management technique used and considered very important by 82 % of respondents. The main chemical used to control leaf-cutting ants, sulfluramid, is in the derogation process and was classified as very important by 96.5 % of the certified companies. Certified companies were generally satisfied in relation to FSC certification and the integrated management of forest pests, but 27.6 % agreed that the prohibitions of pesticides for leaf-cutting ant and termite control could be considered as a non-tariff barrier on high-productivity Brazilian forest plantations. FSC forest certification has encouraged the implementation of more sustainable techniques and decisions in pest management in forest plantations in Brazil. The prohibition on pesticides like sulfluramid and the use of alternatives without the same efficiency will result in pest mismanagement, production losses, and higher costs. This work has shown that the application of global rules for sustainable forest management needs to adapt to each local reality.

Forest Cover Estimation in Ireland Using Radar Remote Sensing: A Comparative Analysis of Forest Cover Assessment Methodologies.

PubMed

Devaney, John; Barrett, Brian; Barrett, Frank; Redmond, John; O Halloran, John

2015-01-01

Quantification of spatial and temporal changes in forest cover is an essential component of forest monitoring programs. Due to its cloud free capability, Synthetic Aperture Radar (SAR) is an ideal source of information on forest dynamics in countries with near-constant cloud-cover. However, few studies have investigated the use of SAR for forest cover estimation in landscapes with highly sparse and fragmented forest cover. In this study, the potential use of L-band SAR for forest cover estimation in two regions (Longford and Sligo) in Ireland is investigated and compared to forest cover estimates derived from three national (Forestry2010, Prime2, National Forest Inventory), one pan-European (Forest Map 2006) and one global forest cover (Global Forest Change) product. Two machine-learning approaches (Random Forests and Extremely Randomised Trees) are evaluated. Both Random Forests and Extremely Randomised Trees classification accuracies were high (98.1-98.5%), with differences between the two classifiers being minimal (<0.5%). Increasing levels of post classification filtering led to a decrease in estimated forest area and an increase in overall accuracy of SAR-derived forest cover maps. All forest cover products were evaluated using an independent validation dataset. For the Longford region, the highest overall accuracy was recorded with the Forestry2010 dataset (97.42%) whereas in Sligo, highest overall accuracy was obtained for the Prime2 dataset (97.43%), although accuracies of SAR-derived forest maps were comparable. Our findings indicate that spaceborne radar could aid inventories in regions with low levels of forest cover in fragmented landscapes. The reduced accuracies observed for the global and pan-continental forest cover maps in comparison to national and SAR-derived forest maps indicate that caution should be exercised when applying these datasets for national reporting.

Forest Cover Estimation in Ireland Using Radar Remote Sensing: A Comparative Analysis of Forest Cover Assessment Methodologies

PubMed Central

Devaney, John; Barrett, Brian; Barrett, Frank; Redmond, John; O`Halloran, John

2015-01-01

Quantification of spatial and temporal changes in forest cover is an essential component of forest monitoring programs. Due to its cloud free capability, Synthetic Aperture Radar (SAR) is an ideal source of information on forest dynamics in countries with near-constant cloud-cover. However, few studies have investigated the use of SAR for forest cover estimation in landscapes with highly sparse and fragmented forest cover. In this study, the potential use of L-band SAR for forest cover estimation in two regions (Longford and Sligo) in Ireland is investigated and compared to forest cover estimates derived from three national (Forestry2010, Prime2, National Forest Inventory), one pan-European (Forest Map 2006) and one global forest cover (Global Forest Change) product. Two machine-learning approaches (Random Forests and Extremely Randomised Trees) are evaluated. Both Random Forests and Extremely Randomised Trees classification accuracies were high (98.1–98.5%), with differences between the two classifiers being minimal (<0.5%). Increasing levels of post classification filtering led to a decrease in estimated forest area and an increase in overall accuracy of SAR-derived forest cover maps. All forest cover products were evaluated using an independent validation dataset. For the Longford region, the highest overall accuracy was recorded with the Forestry2010 dataset (97.42%) whereas in Sligo, highest overall accuracy was obtained for the Prime2 dataset (97.43%), although accuracies of SAR-derived forest maps were comparable. Our findings indicate that spaceborne radar could aid inventories in regions with low levels of forest cover in fragmented landscapes. The reduced accuracies observed for the global and pan-continental forest cover maps in comparison to national and SAR-derived forest maps indicate that caution should be exercised when applying these datasets for national reporting. PMID:26262681

A technique for extrapolating and validating forest cover across large regions. Calibrating AVHRR data with TM data

Treesearch

L.R. Iverson; E.A. Cook; R.L. Graham

1989-01-01

An approach to extending high-resolution forest cover information across large regions is presented and validated. Landsat Thematic Mapper (TM) data were classified into forest and nonforest for a portion of Jackson County, Illinois. The classified TM image was then used to determine the relationship between forest cover and the spectral signature of Advanced Very High...

[Object-oriented segmentation and classification of forest gap based on QuickBird remote sensing image.

PubMed

Mao, Xue Gang; Du, Zi Han; Liu, Jia Qian; Chen, Shu Xin; Hou, Ji Yu

2018-01-01

Traditional field investigation and artificial interpretation could not satisfy the need of forest gaps extraction at regional scale. High spatial resolution remote sensing image provides the possibility for regional forest gaps extraction. In this study, we used object-oriented classification method to segment and classify forest gaps based on QuickBird high resolution optical remote sensing image in Jiangle National Forestry Farm of Fujian Province. In the process of object-oriented classification, 10 scales (10-100, with a step length of 10) were adopted to segment QuickBird remote sensing image; and the intersection area of reference object (RA or ) and intersection area of segmented object (RA os ) were adopted to evaluate the segmentation result at each scale. For segmentation result at each scale, 16 spectral characteristics and support vector machine classifier (SVM) were further used to classify forest gaps, non-forest gaps and others. The results showed that the optimal segmentation scale was 40 when RA or was equal to RA os . The accuracy difference between the maximum and minimum at different segmentation scales was 22%. At optimal scale, the overall classification accuracy was 88% (Kappa=0.82) based on SVM classifier. Combining high resolution remote sensing image data with object-oriented classification method could replace the traditional field investigation and artificial interpretation method to identify and classify forest gaps at regional scale.

Technical Change in the North American Forestry Sector: A Review

Treesearch

Jeffery C. Stier; David N. Bengston

1992-01-01

Economists have examined the impact of technical change on the forest products sector using the historical, index number, and econometric approaches. This paper reviews econometric analyses of the rate and bias of technical change, examining functional form, factors included, and empirical results. Studies are classified as first- second-, or third-generation...

Multi-Cohort Stand Structural Classification: Ground- and LiDAR-based Approaches for Boreal Mixedwood and Black Spruce Forest Types of Northeastern Ontario

NASA Astrophysics Data System (ADS)

Kuttner, Benjamin George

Natural fire return intervals are relatively long in eastern Canadian boreal forests and often allow for the development of stands with multiple, successive cohorts of trees. Multi-cohort forest management (MCM) provides a strategy to maintain such multi-cohort stands that focuses on three broad phases of increasingly complex, post-fire stand development, termed "cohorts", and recommends different silvicultural approaches be applied to emulate different cohort types. Previous research on structural cohort typing has relied upon primarily subjective classification methods; in this thesis, I develop more comprehensive and objective methods for three common boreal mixedwood and black spruce forest types in northeastern Ontario. Additionally, I examine relationships between cohort types and stand age, productivity, and disturbance history and the utility of airborne LiDAR to retrieve ground-based classifications and to extend structural cohort typing from plot- to stand-levels. In both mixedwood and black spruce forest types, stand age and age-related deadwood features varied systematically with cohort classes in support of an age-based interpretation of increasing cohort complexity. However, correlations of stand age with cohort classes were surprisingly weak. Differences in site productivity had a significant effect on the accrual of increasingly complex multi-cohort stand structure in both forest types, especially in black spruce stands. The effects of past harvesting in predictive models of class membership were only significant when considered in isolation of age. As an age-emulation strategy, the three cohort model appeared to be poorly suited to black spruce forests where the accrual of structural complexity appeared to be more a function of site productivity than age. Airborne LiDAR data appear to be particularly useful in recovering plot-based cohort types and extending them to the stand-level. The main gradients of structural variability detected using LiDAR were similar between boreal mixedwood and black spruce forest types; the best LiDAR-based models of cohort type relied upon combinations of tree size, size heterogeneity, and tree density related variables. The methods described here to measure, classify, and predict cohort-related structural complexity assist in translating the conceptual three cohort model to a more precise, measurement-based management system. In addition, the approaches presented here to measure and classify stand structural complexity promise to significantly enhance the detail of structural information in operational forest inventories in support of a wide array of forest management and conservation applications.

Spatial and Temporal Analysis of Industrial Forest Clearcuts in the Conterminous United States

NASA Astrophysics Data System (ADS)

Huo, L. Z.; Boschetti, L.

2015-12-01

Remote sensing has been widely used for mapping and characterizing changes in forest cover, but the available remote sensing forest change products are not discriminating between deforestation (permanent transition from forest to non forest) and industrial forest management (logging followed by regrowth, with no land cover/ land use class change) (Hansen et al, 2010). Current estimates of carbon-equivalent emissions report the contribution of deforestation as 12% of total anthropogenic carbon emissions (van der Werf et al., 2009), but accurate monitoring of forest carbon balance should discriminate between land use change related to forest natural disturbances, and forest management. The total change in forest cover (Gross Forest Cover Loss, GFLC) needs to be characterized based on the cause (natural/human) and on the outcome of the change (regeneration to forest/transition to non/forest)(Kurtz et al, 2010). This paper presents the methodology used to classify the forest loss detected by the University of Maryland Global Forest Change product (Hansen, 2013) into deforestation, disturbances (fires, insect outbreaks) and industrial forest clearcuts. The industrial forest clearcuts were subsequently analysed by converting the pixel based detections into objects, and applying patch level metrics (e.g. size, compactness, straightness of boundaries) and contextual measures. The analysis is stratified by region and by dominant forest specie, to highlight changes in the rate of forest resource utilization in the 2003-2013 period covered by the Maryland Forest Cover Change Product. References Hansen, M.C., Stehman, S.V., & Potapov, P.V. (2010). Reply to Wernick et al.: Global scale quantification of forest change. Proceedings of the National Academy of Sciences, 107, E148-E148 Hansen, M.C., Potapov, P.V., Moore, R et al., (2013), "High resolution Global Maps for the 21stCentury Forest Cover Change", Science 342: 850-853 Kurz, W.A. (2010). An ecosystem context for global gross forest cover loss estimates. Proceedings of the National Academy of Sciences, 107, 9025-9026 van der Werf, G.R., Morton, D.C., DeFries, R.S., Olivier, J.G., Kasibhatla, P.S., Jackson, R.B., Collatz, G.J., & Randerson, J. (2009). CO2 emissions from forest loss. Nature Geoscience, 2, 737-738

Index for characterizing post-fire soil environments in temperate coniferous forests

Treesearch

Theresa B. Jain; David S. Pilliod; Russell T. Graham; Leigh B. Lentile; Jonathan E. Sandquist

2012-01-01

Many scientists and managers have an interest in describing the environment following a fire to understand the effects on soil productivity, vegetation growth, and wildlife habitat, but little research has focused on the scientific rationale for classifying the post-fire environment. We developed an empirically-grounded soil post-fire index (PFI) based on available...

Polynuclear aromatic hydrocarbons in forest fire smoke

Treesearch

Charles K. McMahon; Skevos N. Tsoukalas

1978-01-01

The occurrence of polynuclear aromatic hydrocarbons (PAH) in the combustion products of carbonaceous fuels is a well known phenomenon. Several PAW are known to be carcinogenic in animals. Benzo[a]pyrene (BaP) is the most well-known and studied compound of those classified by the National Academy of Science (NAS) as strongly carcinogenic. Ambient BaP concentrations...

A Random Forest-based ensemble method for activity recognition.

PubMed

Feng, Zengtao; Mo, Lingfei; Li, Meng

2015-01-01

This paper presents a multi-sensor ensemble approach to human physical activity (PA) recognition, using random forest. We designed an ensemble learning algorithm, which integrates several independent Random Forest classifiers based on different sensor feature sets to build a more stable, more accurate and faster classifier for human activity recognition. To evaluate the algorithm, PA data collected from the PAMAP (Physical Activity Monitoring for Aging People), which is a standard, publicly available database, was utilized to train and test. The experimental results show that the algorithm is able to correctly recognize 19 PA types with an accuracy of 93.44%, while the training is faster than others. The ensemble classifier system based on the RF (Random Forest) algorithm can achieve high recognition accuracy and fast calculation.

Water chemistry in 179 randomly selected Swedish headwater streams related to forest production, clear-felling and climate.

PubMed

Löfgren, Stefan; Fröberg, Mats; Yu, Jun; Nisell, Jakob; Ranneby, Bo

2014-12-01

From a policy perspective, it is important to understand forestry effects on surface waters from a landscape perspective. The EU Water Framework Directive demands remedial actions if not achieving good ecological status. In Sweden, 44 % of the surface water bodies have moderate ecological status or worse. Many of these drain catchments with a mosaic of managed forests. It is important for the forestry sector and water authorities to be able to identify where, in the forested landscape, special precautions are necessary. The aim of this study was to quantify the relations between forestry parameters and headwater stream concentrations of nutrients, organic matter and acid-base chemistry. The results are put into the context of regional climate, sulphur and nitrogen deposition, as well as marine influences. Water chemistry was measured in 179 randomly selected headwater streams from two regions in southwest and central Sweden, corresponding to 10 % of the Swedish land area. Forest status was determined from satellite images and Swedish National Forest Inventory data using the probabilistic classifier method, which was used to model stream water chemistry with Bayesian model averaging. The results indicate that concentrations of e.g. nitrogen, phosphorus and organic matter are related to factors associated with forest production but that it is not forestry per se that causes the excess losses. Instead, factors simultaneously affecting forest production and stream water chemistry, such as climate, extensive soil pools and nitrogen deposition, are the most likely candidates The relationships with clear-felled and wetland areas are likely to be direct effects.

Forest cover loss and urban area expansion in the Conterminous Unites States in the first decade of the third millennium

NASA Astrophysics Data System (ADS)

Huo, L. Z.; Boschetti, L.

2016-12-01

Remote sensing has been successfully used for global mapping of changes in forest cover, but further analysis is needed to characterize those changes - and in particular to classify the total loss of forest loss (Gross Forest Cover Loss, GFCL) based on the cause (natural/human) and on the outcome of the change (regeneration to forest/transition to non-forest) (Kurtz et al., 2010). While natural forest disturbances (fires, insect outbreaks) and timber harvest generally involve a temporary change of land cover (vegetated to non-vegetated), they generally do not involve a change in land use, and it is expected that the forest cover loss is followed by recovery. Change of land use, such as the conversion of forest to agricultural or urban areas, is instead generally irreversible. The proper classification of forest cover loss is therefore necessary to properly model the long term effects of the disturbances on the carbon budget. The present study presents a spatial and temporal analysis of the forest cover loss due to urban expansion in the Conterminous United States. The Landsat-derived University of Maryland Global Forest Change product (Hansen et al, 2013) is used to identify all the areas of gross forest cover loss, which are subsequently classified into disturbance type (deforestation, stand-replacing natural disturbances, industrial forest clearcuts) using an object-oriented time series analysis (Huo and Boschetti, 2015). A further refinement of the classification is conducted to identify the areas of transition from forest land use to urban land use based on ancillary datasets such as the National Land Cover Database (Homer et al., 2015) and contextual image analysis techniques (analysis of object proximity, and detection of shapes). Results showed that over 4000 km2of forest were lost to urban area expansion in CONUS over the 2001 to 2010 period (1.8% of the gross forest cover loss). Most of the urban growth was concentrated in large urban areas: Atlanta, GA ranked first, followed by Houston, TX; Charlotte, NC; Jacksonville, FL; and Raleigh, NC. At the state level, the top 10 states with urban growth due to forest loss were GA, FL, TX, NC, SC, AL, LA, MS, VA and WA, which cumulatively accounted for 76 % of the total forest cover loss due to urban growth.

Forest disturbances, deforestation and timber harvest patterns in the Conterminous United States

NASA Astrophysics Data System (ADS)

Boschetti, L.; Huo, L. Z.

2016-12-01

Current estimates of carbon-equivalent emissions report the contribution of deforestation as 12% of total anthropogenic carbon emissions (van der Werf et al., 2009), but accurate monitoring of forest carbon balance should discriminate between land use change related to forest natural disturbances, forest management and deforestation. The total change in forest cover (Gross Forest Cover Loss, GFCL) needs to be characterized based on the cause (natural/human) and on the outcome of the change (regeneration to forest/transition to non-forest)(Kurtz et al, 2010). We developed a multitemporal, object-oriented methodology to classify GFCL as either (a) deforestation, (b) fire and insect disturbances (c) forest management practices. The Landsat-derived University of Maryland Global Forest Change product (Hansen, 2013) is used to identify all the areas forest cover loss: those areas are subsequently converted to objects, and used to extract temporal profiles of spectral reflectances and spectral indices from the Landsat WELD dataset. Finally, the temporal profiles and descriptive parameters of shapes, textures, and spatial relationships of the objects are used in a rule-based classifier to identify the type of disturbance. To pathfind a global disturbance type classification, the methods are demonstrated by wall-to-wall classification of the forest cover loss in the conterminous United States for the 2002-2011 period. The results show that deforestation accounts for a small percentage (approximately 2%) of the GFCL in the CONUS, and are in agreement with the known patterns of logging activity, fire and insect damage. The time series of timber harvest clearcut is also in agreement with the national timber extraction statistics, showing reduced harvesting following the 2008 economic crisis. The results also highlight the different management practices on private and public lands: 36% of the US forests are publicly owned (federal, state and local institutions) but account only for 12% of the clearcuts, whereas private lands (64% of the total) account for 88% of the clearcut area. Conversely, stand replacing fire and insect disturbances affect primarily public lands (85% versus 15% on private lands).

Removal of introduced inorganic content from chipped forest residues via air classification

DOE PAGES

Lacey, Jeffrey A.; Aston, John E.; Westover, Tyler L.; ...

2015-08-04

Inorganic content in biomass decreases the efficiency of conversion processes, especially thermochemical conversions. The combined concentrations of specific ash forming elements are the primary attributes that cause pine residues to be considered a degraded energy conversion feedstock, as compared to clean pine. Air classification is a potentially effective and economical tool to isolate high inorganic content biomass fractions away from primary feedstock sources to reduce their ash content. In this work, loblolly pine forest residues were air classified into 10 fractions whose ash content and composition were measured. Ash concentrations were highest in the lightest fractions (5.8–8.5 wt%), and inmore » a heavy fraction of the fines (8.9–15.1 wt%). The removal of fractions with high inorganic content resulted in a substantial reduction in the ash content of the remaining biomass in forest thinnings (1.69–1.07 wt%) and logging residues (1.09–0.68 wt%). These high inorganic content fractions from both forest residue types represented less than 7.0 wt% of the total biomass, yet they contained greater than 40% of the ash content by mass. Elemental analysis of the air classified fractions revealed the lightest fractions were comprised of high concentrations of soil elements (silicon, aluminum, iron, sodium, and titanium). However, the elements of biological origin including calcium, potassium, magnesium, sulfur, manganese, and phosphorous were evenly distributed throughout all air classified fractions, making them more difficult to isolate into fractions with high mineral concentrations. Under the conditions reported in this study, an economic analysis revealed air classification could be used for ash removal for as little as $2.23 per ton of product biomass. As a result, this study suggests air classification is a potentially attractive technology for the removal of introduced soil minerals from pine forest residues.« less

Evaluating the Suitability of Management Strategies of Pure Norway Spruce Forests in the Black Forest Area of Southwest Germany for Adaptation to or Mitigation of Climate Change

NASA Astrophysics Data System (ADS)

Yousefpour, Rasoul; Hanewinkel, Marc; Le Moguédec, Gilles

2010-02-01

The study deals with the problem of evaluating management strategies for pure stands of Norway spruce ( Picea abies Karst) to balance adaptation to and mitigation of climate change, taking into account multiple objectives of a forest owner. A simulation and optimization approach was used to evaluate the management of a 1000 ha model Age-Class forest, representing the age-class distribution of an area of 66,000 ha of pure Norway spruce forests in the Black Forest region of Southwest Germany. Eight silvicultural scenarios comprising five forest conversion schemes which were interpreted as “adaptation” strategies which aims at increasing the proportion of Beech, that is expected to better cope with climate change than the existing Norway spruce, and three conventional strategies including a “Do-nothing” alternative classified as “mitigation”, trying to keep rather higher levels of growing stock of spruce, were simulated using the empirical growth simulator BWINPro-S. A linear programming approach was adapted to simultaneously maximize the net present values of carbon sequestration and timber production subject to the two constraints of wood even flow and partial protection of the oldest (nature protection). The optimized plan, with the global utility of 11,687 €/ha in forty years, allocated a combination of silvicultural scenarios to the entire forest area. Overall, strategies classified as “mitigation” were favored, while strategies falling into the “adaptation”-category were limited to the youngest age-classes in the optimal solution. Carbon sequestration of the “Do-nothing” alternative was between 1.72 and 1.85 million tons higher than the other alternatives for the entire forest area while the differences between the adaptation and mitigation approaches were approximately 133,000 tons. Sensitivity analysis showed that a carbon price of 21 €/ t is the threshold at which carbon sequestration is promoted, while an interest rate of above 2% would decrease the amount of carbon.

Optimization of spectral bands for hyperspectral remote sensing of forest vegetation

NASA Astrophysics Data System (ADS)

Dmitriev, Egor V.; Kozoderov, Vladimir V.

2013-10-01

Optimization principles of accounting for the most informative spectral channels in hyperspectral remote sensing data processing serve to enhance the efficiency of the employed high-productive computers. The problem of pattern recognition of the remotely sensed land surface objects with the accent on the forests is outlined from the point of view of the spectral channels optimization on the processed hyperspectral images. The relevant computational procedures are tested using the images obtained by the produced in Russia hyperspectral camera that was installed on a gyro-stabilized platform to conduct the airborne flight campaigns. The Bayesian classifier is used for the pattern recognition of the forests with different tree species and age. The probabilistically optimal algorithm constructed on the basis of the maximum likelihood principle is described to minimize the probability of misclassification given by this classifier. The classification error is the major category to estimate the accuracy of the applied algorithm by the known holdout cross-validation method. Details of the related techniques are presented. Results are shown of selecting the spectral channels of the camera while processing the images having in mind radiometric distortions that diminish the classification accuracy. The spectral channels are selected of the obtained subclasses extracted from the proposed validation techniques and the confusion matrices are constructed that characterize the age composition of the classified pine species as well as the broad age-class recognition for the pine and birch species with the fully illuminated parts of their crowns.

Empirical Evaluation of Hunk Metrics as Bug Predictors

NASA Astrophysics Data System (ADS)

Ferzund, Javed; Ahsan, Syed Nadeem; Wotawa, Franz

Reducing the number of bugs is a crucial issue during software development and maintenance. Software process and product metrics are good indicators of software complexity. These metrics have been used to build bug predictor models to help developers maintain the quality of software. In this paper we empirically evaluate the use of hunk metrics as predictor of bugs. We present a technique for bug prediction that works at smallest units of code change called hunks. We build bug prediction models using random forests, which is an efficient machine learning classifier. Hunk metrics are used to train the classifier and each hunk metric is evaluated for its bug prediction capabilities. Our classifier can classify individual hunks as buggy or bug-free with 86 % accuracy, 83 % buggy hunk precision and 77% buggy hunk recall. We find that history based and change level hunk metrics are better predictors of bugs than code level hunk metrics.

Indigenous systems of forest classification: understanding land use patterns and the role of NTFPs in shifting cultivators' subsistence economies.

PubMed

Delang, Claudio O

2006-04-01

This article discusses the system of classification of forest types used by the Pwo Karen in Thung Yai Naresuan Wildlife Sanctuary in western Thailand and the role of nontimber forest products (NTFPs), focusing on wild food plants, in Karen livelihoods. The article argues that the Pwo Karen have two methods of forest classification, closely related to their swidden farming practices. The first is used for forest land that has been, or can be, swiddened, and classifies forest types according to growth conditions. The second system is used for land that is not suitable for cultivation and looks at soil properties and slope. The article estimates the relative importance of each forest type in what concerns the collection of wild food plants. A total of 134 wild food plant species were recorded in December 2004. They account for some 80-90% of the amount of edible plants consumed by the Pwo Karen, and have a base value of Baht 11,505 per year, comparable to the cash incomes of many households. The article argues that the Pwo Karen reliance on NTFPs has influenced their land-use and forest management practices. However, by restricting the length of the fallow period, the Thai government has caused ecological changes that are challenging the ability of the Karen to remain subsistence oriented. By ignoring shifting cultivators' dependence on such products, the involvement of governments in forest management, especially through restrictions imposed on swidden farming practices, is likely to have a considerable impact on the livelihood strategies of these communities.

Object-based forest classification to facilitate landscape-scale conservation in the Mississippi Alluvial Valley

USGS Publications Warehouse

Mitchell, Michael; Wilson, R. Randy; Twedt, Daniel J.; Mini, Anne E.; James, J. Dale

2016-01-01

The Mississippi Alluvial Valley is a floodplain along the southern extent of the Mississippi River extending from southern Missouri to the Gulf of Mexico. This area once encompassed nearly 10 million ha of floodplain forests, most of which has been converted to agriculture over the past two centuries. Conservation programs in this region revolve around protection of existing forest and reforestation of converted lands. Therefore, an accurate and up to date classification of forest cover is essential for conservation planning, including efforts that prioritize areas for conservation activities. We used object-based image analysis with Random Forest classification to quickly and accurately classify forest cover. We used Landsat band, band ratio, and band index statistics to identify and define similar objects as our training sets instead of selecting individual training points. This provided a single rule-set that was used to classify each of the 11 Landsat 5 Thematic Mapper scenes that encompassed the Mississippi Alluvial Valley. We classified 3,307,910±85,344 ha (32% of this region) as forest. Our overall classification accuracy was 96.9% with Kappa statistic of 0.96. Because this method of forest classification is rapid and accurate, assessment of forest cover can be regularly updated and progress toward forest habitat goals identified in conservation plans can be periodically evaluated.

Amazon Rain Forest Classification Using J-ERS-1 SAR Data

NASA Technical Reports Server (NTRS)

Freeman, A.; Kramer, C.; Alves, M.; Chapman, B.

1994-01-01

The Amazon rain forest is a region of the earth that is undergoing rapid change. Man-made disturbance, such as clear cutting for agriculture or mining, is altering the rain forest ecosystem. For many parts of the rain forest, seasonal changes from the wet to the dry season are also significant. Changes in the seasonal cycle of flooding and draining can cause significant alterations in the forest ecosystem.Because much of the Amazon basin is regularly covered by thick clouds, optical and infrared coverage from the LANDSAT and SPOT satellites is sporadic. Imaging radar offers a much better potential for regular monitoring of changes in this region. In particular, the J-ERS-1 satellite carries an L-band HH SAR system, which via an on-board tape recorder, can collect data from almost anywhere on the globe at any time of year.In this paper, we show how J-ERS-1 radar images can be used to accurately classify different forest types (i.e., forest, hill forest, flooded forest), disturbed areas such as clear cuts and urban areas, and river courses in the Amazon basin. J-ERS-1 data has also shown significant differences between the dry and wet season, indicating a strong potential for monitoring seasonal change. The algorithm used to classify J-ERS-1 data is a standard maximum-likelihood classifier, using the radar image local mean and standard deviation of texture as input. Rivers and clear cuts are detected using edge detection and region-growing algorithms. Since this classifier is intended to operate successfully on data taken over the entire Amazon, several options are available to enable the user to modify the algorithm to suit a particular image.

Preliminary guides for the management of lodgepole pine in Oregon and Washington.

Treesearch

Edwin L. Mowat

1949-01-01

There are some 2 million acres of lodgepole pine in eastern Oregon and Washington. Much of it was formerly classified as noncommercial; the trees were small, many were limby, and there was very little market for the low-value products. But the type has lately gained in commercial importance, and results of such little forest research as has been devoted to lodgepole...

Can single classifiers be as useful as model ensembles to produce benthic seabed substratum maps?

NASA Astrophysics Data System (ADS)

Turner, Joseph A.; Babcock, Russell C.; Hovey, Renae; Kendrick, Gary A.

2018-05-01

Numerous machine-learning classifiers are available for benthic habitat map production, which can lead to different results. This study highlights the performance of the Random Forest (RF) classifier, which was significantly better than Classification Trees (CT), Naïve Bayes (NB), and a multi-model ensemble in terms of overall accuracy, Balanced Error Rate (BER), Kappa, and area under the curve (AUC) values. RF accuracy was often higher than 90% for each substratum class, even at the most detailed level of the substratum classification and AUC values also indicated excellent performance (0.8-1). Total agreement between classifiers was high at the broadest level of classification (75-80%) when differentiating between hard and soft substratum. However, this sharply declined as the number of substratum categories increased (19-45%) including a mix of rock, gravel, pebbles, and sand. The model ensemble, produced from the results of all three classifiers by majority voting, did not show any increase in predictive performance when compared to the single RF classifier. This study shows how a single classifier may be sufficient to produce benthic seabed maps and model ensembles of multiple classifiers.

Impact of professional foresters on timber harvests on West Virginia nonindustrial private forests

Treesearch

Stuart A. Moss; Eric Heitzman

2013-01-01

Timber harvests conducted on 90 nonindustrial private forest properties in West Virginia were investigated to determine the effects that professional foresters have on harvest and residual stand attributes. Harvests were classified based on the type of forester involved: (1) consulting/state service foresters representing landowners, (2) industry foresters representing...

Forest Cover Mapping in Iskandar Malaysia Using Satellite Data

NASA Astrophysics Data System (ADS)

Kanniah, K. D.; Mohd Najib, N. E.; Vu, T. T.

2016-09-01

Malaysia is the third largest country in the world that had lost forest cover. Therefore, timely information on forest cover is required to help the government to ensure that the remaining forest resources are managed in a sustainable manner. This study aims to map and detect changes of forest cover (deforestation and disturbance) in Iskandar Malaysia region in the south of Peninsular Malaysia between years 1990 and 2010 using Landsat satellite images. The Carnegie Landsat Analysis System-Lite (CLASlite) programme was used to classify forest cover using Landsat images. This software is able to mask out clouds, cloud shadows, terrain shadows, and water bodies and atmospherically correct the images using 6S radiative transfer model. An Automated Monte Carlo Unmixing technique embedded in CLASlite was used to unmix each Landsat pixel into fractions of photosynthetic vegetation (PV), non photosynthetic vegetation (NPV) and soil surface (S). Forest and non-forest areas were produced from the fractional cover images using appropriate threshold values of PV, NPV and S. CLASlite software was found to be able to classify forest cover in Iskandar Malaysia with only a difference between 14% (1990) and 5% (2010) compared to the forest land use map produced by the Department of Agriculture, Malaysia. Nevertheless, the CLASlite automated software used in this study was found not to exclude other vegetation types especially rubber and oil palm that has similar reflectance to forest. Currently rubber and oil palm were discriminated from forest manually using land use maps. Therefore, CLASlite algorithm needs further adjustment to exclude these vegetation and classify only forest cover.

Land Cover Change in Colombia: Surprising Forest Recovery Trends between 2001 and 2010

PubMed Central

Sánchez-Cuervo, Ana María; Aide, T. Mitchell; Clark, Matthew L.; Etter, Andrés

2012-01-01

Background Monitoring land change at multiple spatial scales is essential for identifying hotspots of change, and for developing and implementing policies for conserving biodiversity and habitats. In the high diversity country of Colombia, these types of analyses are difficult because there is no consistent wall-to-wall, multi-temporal dataset for land-use and land-cover change. Methodology/Principal Findings To address this problem, we mapped annual land-use and land-cover from 2001 to 2010 in Colombia using MODIS (250 m) products coupled with reference data from high spatial resolution imagery (QuickBird) in Google Earth. We used QuickBird imagery to visually interpret percent cover of eight land cover classes used for classifier training and accuracy assessment. Based on these maps we evaluated land cover change at four spatial scales country, biome, ecoregion, and municipality. Of the 1,117 municipalities, 820 had a net gain in woody vegetation (28,092 km2) while 264 had a net loss (11,129 km2), which resulted in a net gain of 16,963 km2 in woody vegetation at the national scale. Woody regrowth mainly occurred in areas previously classified as mixed woody/plantation rather than agriculture/herbaceous. The majority of this gain occurred in the Moist Forest biome, within the montane forest ecoregions, while the greatest loss of woody vegetation occurred in the Llanos and Apure-Villavicencio ecoregions. Conclusions The unexpected forest recovery trend, particularly in the Andes, provides an opportunity to expand current protected areas and to promote habitat connectivity. Furthermore, ecoregions with intense land conversion (e.g. Northern Andean Páramo) and ecoregions under-represented in the protected area network (e.g. Llanos, Apure-Villavicencio Dry forest, and Magdalena-Urabá Moist forest ecoregions) should be considered for new protected areas. PMID:22952816

Advanced Subspace Techniques for Modeling Channel and Session Variability in a Speaker Recognition System

DTIC Science & Technology

2012-03-01

with each SVM discriminating between a pair of the N total speakers in the data set. The (( + 1))/2 classifiers then vote on the final...classification of a test sample. The Random Forest classifier is an ensemble classifier that votes amongst decision trees generated with each node using...Forest vote , and the effects of overtraining will be mitigated by the fact that each decision tree is overtrained differently (due to the random

Security authentication with a three-dimensional optical phase code using random forest classifier: an overview

NASA Astrophysics Data System (ADS)

Markman, Adam; Carnicer, Artur; Javidi, Bahram

2017-05-01

We overview our recent work [1] on utilizing three-dimensional (3D) optical phase codes for object authentication using the random forest classifier. A simple 3D optical phase code (OPC) is generated by combining multiple diffusers and glass slides. This tag is then placed on a quick-response (QR) code, which is a barcode capable of storing information and can be scanned under non-uniform illumination conditions, rotation, and slight degradation. A coherent light source illuminates the OPC and the transmitted light is captured by a CCD to record the unique signature. Feature extraction on the signature is performed and inputted into a pre-trained random-forest classifier for authentication.

Contribution of Remote Sensing and GIS for Sustainable Forest Management in Côte d'Ivoire; Case of the classified Forest of TENE in the department of OUME (Côte d'Ivoire).

NASA Astrophysics Data System (ADS)

Yao, N. A.

2015-12-01

The classified forest of TENE located in the department of OUME has a role of timber production characterized by a high logging. This operation requires a measure of preservation of sensitive sites to exploitation in order to maintain ecological functions, ecosystem and biodiversity living there. The parameters such as streams, slopes, wetlands and rivers are indicators of the existence of sensitive sites to preserve. However, no knowledge of the location, boundaries and the surface of these natural habitats makes its protection difficult. Thus, knowledge of the natural and conceptual environment at the forest of TENE is necessary for the preservation of the ecosystem and biodiversity, prerequisite for its sustainability. Furthermore, Remote Sensing and GIS are less expensive techniques for synthetic and fast analysis of these parameters at different scales as well as spatially and temporally. It should be noted that this study is focused on wetlands mapping in the forest of TENE for a sustainable management. The satellite image of December 2014 from Landsat 8 carried on the Operational Land Imager (OLI) sensor was used for analysis. The methodological approach is based primarily on prior knowledge of the spectral signatures of different elements on the image in different wavelengths. Then the thematic layers extraction of hydromorphic soil without and with vegetation are made by thresholding associated luminance values. The combination of the obtained layers allowed to map all wetlands in the forest of TENE. Finally, the superimposition of this layer with the water system was used to assess the conformity of the result with the reality on the ground. The result showed that the wetlands subject of sensitive sites are mainly found in the western part of the forest of TENE. They are also encountered along the rivers. These wetlands extend over a total area of 12,915 ha against 16,898.22 ha for the non wetlands with a coverage rate of 43.32 %. These areas should be protected against logging in order to do not disturb them because they are refuge environments for fauna as well as flora conservation. This study will enable the manager of the classified forest of TENE to target those sites considered sensitive to preserve for a sustainable management. Keywords: Spectral signature, Forest, Wetland, Remote Sensing, GIS, Côte d'Ivoire.

Producer-level benefits of sustainability certification.

PubMed

Blackman, Allen; Rivera, Jorge

2011-12-01

Initiatives certifying that producers of goods and services adhere to defined environmental and social-welfare production standards are increasingly popular. According to proponents, these initiatives create financial incentives for producers to improve their environmental, social, and economic performance. We reviewed the evidence on whether these initiatives have such benefits. We identified peer-reviewed, ex post, producer-level studies in economic sectors in which certification is particularly prevalent (bananas, coffee, fish products, forest products, and tourism operations), classified these studies on the basis of whether their design and methods likely generated credible results, summarized findings from the studies with credible results, and considered how these findings might guide future research. We found 46 relevant studies, most of which focused on coffee and forest products and examined fair-trade and Forest Stewardship Council certification. The methods used in 11 studies likely generated credible results. Of these 11 studies, nine examined the economic effects and two the environmental effects of certification. The results of four of the 11 studies, all of which examined economic effects, showed that certification has producer-level benefits. Hence, the evidence to support the hypothesis that certification benefits the environment or producers is limited. More evidence could be generated by incorporating rigorous, independent evaluation into the design and implementation of projects promoting certification. ©2011 Society for Conservation Biology.

New York Forests, 2012

Treesearch

Richard H. Widmann; Sloane Crawford; Cassandra M. Kurtz; Mark D. Nelson; Patrick D. Miles; Randall S. Morin; Rachel. Riemann

2015-01-01

This report summarizes the second annual inventory of New York's forests, conducted in 2008-2012. New York's forests cover 19.0 million acres; 15.9 million acres are classified as timberland and 3.1 million acres as reserved and other forest land. Forest land is dominated by the maple/beech/birch forest-type group that occupies more than half of the forest...

Distribution and Characterization of Forested Wetlands in the Carolinas and Virginia

Treesearch

Mark J. Brown

1995-01-01

Recent forest inventories of North Carolina, South Carolina, and Virginia, included sampled for hydric soils, and wetland hydrology. Forest samples that met all 3 of these criteria were classified as forested wetland.This study characterizes wetland forests by extent, owner, age, forest type, physiography, volume, growth, and removals, and evaluates its contribution...

A Machine Learning and Cross-Validation Approach for the Discrimination of Vegetation Physiognomic Types Using Satellite Based Multispectral and Multitemporal Data.

PubMed

Sharma, Ram C; Hara, Keitarou; Hirayama, Hidetake

2017-01-01

This paper presents the performance and evaluation of a number of machine learning classifiers for the discrimination between the vegetation physiognomic classes using the satellite based time-series of the surface reflectance data. Discrimination of six vegetation physiognomic classes, Evergreen Coniferous Forest, Evergreen Broadleaf Forest, Deciduous Coniferous Forest, Deciduous Broadleaf Forest, Shrubs, and Herbs, was dealt with in the research. Rich-feature data were prepared from time-series of the satellite data for the discrimination and cross-validation of the vegetation physiognomic types using machine learning approach. A set of machine learning experiments comprised of a number of supervised classifiers with different model parameters was conducted to assess how the discrimination of vegetation physiognomic classes varies with classifiers, input features, and ground truth data size. The performance of each experiment was evaluated by using the 10-fold cross-validation method. Experiment using the Random Forests classifier provided highest overall accuracy (0.81) and kappa coefficient (0.78). However, accuracy metrics did not vary much with experiments. Accuracy metrics were found to be very sensitive to input features and size of ground truth data. The results obtained in the research are expected to be useful for improving the vegetation physiognomic mapping in Japan.

An unsupervised two-stage clustering approach for forest structure classification based on X-band InSAR data - A case study in complex temperate forest stands

NASA Astrophysics Data System (ADS)

Abdullahi, Sahra; Schardt, Mathias; Pretzsch, Hans

2017-05-01

Forest structure at stand level plays a key role for sustainable forest management, since the biodiversity, productivity, growth and stability of the forest can be positively influenced by managing its structural diversity. In contrast to field-based measurements, remote sensing techniques offer a cost-efficient opportunity to collect area-wide information about forest stand structure with high spatial and temporal resolution. Especially Interferometric Synthetic Aperture Radar (InSAR), which facilitates worldwide acquisition of 3d information independent from weather conditions and illumination, is convenient to capture forest stand structure. This study purposes an unsupervised two-stage clustering approach for forest structure classification based on height information derived from interferometric X-band SAR data which was performed in complex temperate forest stands of Traunstein forest (South Germany). In particular, a four dimensional input data set composed of first-order height statistics was non-linearly projected on a two-dimensional Self-Organizing Map, spatially ordered according to similarity (based on the Euclidean distance) in the first stage and classified using the k-means algorithm in the second stage. The study demonstrated that X-band InSAR data exhibits considerable capabilities for forest structure classification. Moreover, the unsupervised classification approach achieved meaningful and reasonable results by means of comparison to aerial imagery and LiDAR data.

Vegetation classification, mapping, and monitoring at Voyageurs National Park, Minnesota: An application of the U.S. National Vegetation Classification

USGS Publications Warehouse

Faber-Langendoen, D.; Aaseng, N.; Hop, K.; Lew-Smith, M.; Drake, J.

2007-01-01

Question: How can the U.S. National Vegetation Classification (USNVC) serve as an effective tool for classifying and mapping vegetation, and inform assessments and monitoring? Location: Voyageurs National Park, northern Minnesota, U.S.A and environs. The park contains 54 243 ha of terrestrial habitat in the sub-boreal region of North America. Methods: We classified and mapped the natural vegetation using the USNVC, with 'alliance' and 'association' as base units. We compiled 259 classification plots and 1251 accuracy assessment test plots. Both plot and type ordinations were used to analyse vegetation and environmental patterns. Color infrared aerial photography (1:15840 scale) was used for mapping. Polygons were manually drawn, then transferred into digital form. Classification and mapping products are stored in publicly available databases. Past fire and logging events were used to assess distribution of forest types. Results and Discussion: Ordination and cluster analyses confirmed 49 associations and 42 alliances, with three associations ranked as globally vulnerable to extirpation. Ordination provided a useful summary of vegetation and ecological gradients. Overall map accuracy was 82.4%. Pinus banksiana - Picea mariana forests were less frequent in areas unburned since the 1930s. Conclusion: The USNVC provides a consistent ecological tool for summarizing and mapping vegetation. The products provide a baseline for assessing forests and wetlands, including fire management. The standardized classification and map units provide local to continental perspectives on park resources through linkages to state, provincial, and national classifications in the U.S. and Canada, and to NatureServe's Ecological Systems classification. ?? IAVS; Opulus Press.

North Carolina's forests, 2002

Treesearch

Mark J. Brown; Barry D. New; Sonja N. Oswalt; Tony G. Johnson; Victor A. Rudis

2006-01-01

In 2002, forests covered 18.3 million acres in North Carolina, of which 17.7 million were classified as timberland. Hardwood forest types prevailed on 72 percent of timberland and planted pine stands occupied 15 percent. Nonindustrial private forest landowners controlled 78 percent of timberland, forest industry holdings declined to 8 percent, and publicly owned...

Wisconsin Forests 2014

Treesearch

Cassandra M. Kurtz; Sally E. Dahir; Andrew M. Stoltman; William H. McWilliams; Brett J. Butler; Mark D. Nelson; Randall S. Morin; Ronald J. Piva; Sarah K. Herrick; Laura J. Lorentz; Mark Guthmiller; Charles H. Perry

2017-01-01

This report summarizes the third annual inventory of Wisconsin’s forests, conducted 2009–2014. Wisconsin’s forests cover 17.1 million acres with 16.6 million acres classified as timberland. Forests are bountiful in the north with Florence, Forest, Menominee, and Vilas Counties having over 90 percent forest cover. In the southeastern part of the State, forest cover is...

Ecological species group—Environmental factors relationships in unharvested beech forests in the north of Iran

Treesearch

Mohammad Naghi Adel; Hassan Pourbabaei; Daniel C. Dey

2014-01-01

Beech forests are the richest forest community in Iran because they are both economically and environmentally valuable. The greatest forest volume occurs in Iran's beech forests. Forests dominated by oriental beech (Fagus orientalis Lipskey) cover about 565,000 ha and represent the total area of indigenous forests in Guilan Province. A system for classifying beech...

36 CFR 1256.46 - National security-classified information.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 36 Parks, Forests, and Public Property 3 2010-07-01 2010-07-01 false National security-classified... Restrictions § 1256.46 National security-classified information. In accordance with 5 U.S.C. 552(b)(1), NARA... properly classified under the provisions of the pertinent Executive Order on Classified National Security...

New York's Forests 2007

Treesearch

Richard H. Widmann; Sloane Crawford; Charles Barnett; Brett J. Butler; Grant M. Domke; Douglas M. Griffith; Mark A. Hatfield; Cassandra M. Kurtz; Tonya W. Lister; Randall S. Morin; W. Keith Moser; Charles H. Perry; Rachel Riemann; Christopher W. Woodall

2012-01-01

This report summarizes the first full annual inventory of New York's forests, conducted in 2002-2007 by the U.S. Forest Service, Northern Research Station. New York's forests cover 19.0 million acres; 15.9 million acres are classified as timberland and 3.1 million acres as reserved and other forest land. Forest land is dominated by the maple/beech/birch...

Satellite inventory of Minnesota forest resources

NASA Technical Reports Server (NTRS)

Bauer, Marvin E.; Burk, Thomas E.; Ek, Alan R.; Coppin, Pol R.; Lime, Stephen D.; Walsh, Terese A.; Walters, David K.; Befort, William; Heinzen, David F.

1993-01-01

The methods and results of using Landsat Thematic Mapper (TM) data to classify and estimate the acreage of forest covertypes in northeastern Minnesota are described. Portions of six TM scenes covering five counties with a total area of 14,679 square miles were classified into six forest and five nonforest classes. The approach involved the integration of cluster sampling, image processing, and estimation. Using cluster sampling, 343 plots, each 88 acres in size, were photo interpreted and field mapped as a source of reference data for classifier training and calibration of the TM data classifications. Classification accuracies of up to 75 percent were achieved; most misclassification was between similar or related classes. An inverse method of calibration, based on the error rates obtained from the classifications of the cluster plots, was used to adjust the classification class proportions for classification errors. The resulting area estimates for total forest land in the five-county area were within 3 percent of the estimate made independently by the USDA Forest Service. Area estimates for conifer and hardwood forest types were within 0.8 and 6.0 percent respectively, of the Forest Service estimates. A trial of a second method of estimating the same classes as the Forest Service resulted in standard errors of 0.002 to 0.015. A study of the use of multidate TM data for change detection showed that forest canopy depletion, canopy increment, and no change could be identified with greater than 90 percent accuracy. The project results have been the basis for the Minnesota Department of Natural Resources and the Forest Service to define and begin to implement an annual system of forest inventory which utilizes Landsat TM data to detect changes in forest cover.

Using Landsat and a Bayesian hard classifier to study forest change in the Salmon Creek Watershed area from 1972-2013

NASA Astrophysics Data System (ADS)

Mullis, David Stone

The Salmon Creek Watershed in Sonoma County, California, USA, is home to a variety of wildlife, and many of its residents are mindful of their place in its ecology. In the past half century, several of its native and rare species have become threatened, endangered, or extinct, most notably the once common Coho salmon and Chinook salmon. The cause of this decline is believed to be a combination of global climate change, local land use, and land cover change. More specifically, the clearing of forested land to create vineyards, as well as other agricultural and residential uses, has led to a decline in biodiversity and habitat structure. I studied sub-scenes of Landsat data from 1972 to 2013 for the Salmon Creek Watershed area to estimate forest cover over this period. I used a maximum likelihood hard classifier to determine forest area, a Mahalanobis distance soft classifier to show the software's uncertainty in classification, and manually digitized forest cover to test and compare results for the 2013 30 m image. Because the earliest images were lower spatial resolution, I also tested the effects of resolution on these statistics. The images before 1985 are at 60 m spatial resolution while the later images are at 30 m resolution. Each image was processed individually and the training data were based on knowledge of the area and a mosaic of aerial photography. Each sub-scene was classified into five categories: water, forest, pasture, vineyard/orchard, and developed/barren. The research shows a decline in forest area from 1972 to around the mid-1990s, then an increase in forest area from the mid-1990s to present. The forest statistics can be helpful for conservation and restoration purposes, while the study on resolution can be helpful for landscape analysis on many levels.

Ecological modeling for forest management in the Shawnee National Forest

Treesearch

Richard G. Thurau; J.F. Fralish; S. Hupe; B. Fitch; A.D. Carver

2008-01-01

Land managers of the Shawnee National Forest in southern Illinois are challenged to meet the needs of a diverse populace of stakeholders. By classifying National Forest holdings into management units, U.S. Forest Service personnel can spatially allocate resources and services to meet local management objectives. Ecological Classification Systems predict ecological site...

The effect of using complete and partial forested FIA plot data on biomass and forested area classifications from MODIS satellite data

Treesearch

Dumitru Salajanu; Dennis M. Jacobs

2006-01-01

Authors’ objective was to determine at what level biomass and forest area obtained from partial and complete forested plot inventory data compares with forested area and biomass estimates from the national inventory data. A subset of 3819 inventory plots (100% forested, 100% non-forested, mixed-forest/non-forest) was used to classify the land cover and model the...

Forest resources of the Umatilla National Forest.

Treesearch

Glenn A. Christensen; Paul Dunham; David C. Powell; Bruce. Hiserote

2007-01-01

Current resource statistics for the Umatilla National Forest, based on two separate inventories conducted in 1993–96 and in 1997–2002, are presented in this report. Currently on the Umatilla National Forest, 89 percent of the land area is classified as forest land. The predominant forest type is grand fir (26 percent of forested acres) followed by the interior Douglas-...

EFO-LCI: A New Life Cycle Inventory Database of Forestry Operations in Europe

NASA Astrophysics Data System (ADS)

Cardellini, Giuseppe; Valada, Tatiana; Cornillier, Claire; Vial, Estelle; Dragoi, Marian; Goudiaby, Venceslas; Mues, Volker; Lasserre, Bruno; Gruchala, Arkadiusz; Rørstad, Per Kristian; Neumann, Mathias; Svoboda, Miroslav; Sirgmets, Risto; Näsärö, Olli-Pekka; Mohren, Frits; Achten, Wouter M. J.; Vranken, Liesbet; Muys, Bart

2018-06-01

Life cycle assessment (LCA) has become a common methodology to analyze environmental impacts of forestry systems. Although LCA has been widely applied to forestry since the 90s, the LCAs are still often based on generic Life Cycle Inventory (LCI). With the purpose of improving LCA practices in the forestry sector, we developed a European Life Cycle Inventory of Forestry Operations (EFO-LCI) and analyzed the available information to check if within the European forestry sector national differences really exist. We classified the European forests on the basis of "Forest Units" (combinations of tree species and silvicultural practices). For each Forest Unit, we constructed the LCI of their forest management practices on the basis of a questionnaire filled out by national silvicultural experts. We analyzed the data reported to evaluate how they vary over Europe and how they affect LCA results and made freely available the inventory data collected for future use. The study shows important variability in rotation length, type of regeneration, amount and assortments of wood products harvested, and machinery used due to the differences in management practices. The existing variability on these activities sensibly affect LCA results of forestry practices and raw wood production. Although it is practically unfeasible to collect site-specific data for all the LCAs involving forest-based products, the use of less generic LCI data of forestry practice is desirable to improve the reliability of the studies. With the release of EFO-LCI we made a step toward the construction of regionalized LCI for the European forestry sector.

EFO-LCI: A New Life Cycle Inventory Database of Forestry Operations in Europe.

PubMed

Cardellini, Giuseppe; Valada, Tatiana; Cornillier, Claire; Vial, Estelle; Dragoi, Marian; Goudiaby, Venceslas; Mues, Volker; Lasserre, Bruno; Gruchala, Arkadiusz; Rørstad, Per Kristian; Neumann, Mathias; Svoboda, Miroslav; Sirgmets, Risto; Näsärö, Olli-Pekka; Mohren, Frits; Achten, Wouter M J; Vranken, Liesbet; Muys, Bart

2018-06-01

Life cycle assessment (LCA) has become a common methodology to analyze environmental impacts of forestry systems. Although LCA has been widely applied to forestry since the 90s, the LCAs are still often based on generic Life Cycle Inventory (LCI). With the purpose of improving LCA practices in the forestry sector, we developed a European Life Cycle Inventory of Forestry Operations (EFO-LCI) and analyzed the available information to check if within the European forestry sector national differences really exist. We classified the European forests on the basis of "Forest Units" (combinations of tree species and silvicultural practices). For each Forest Unit, we constructed the LCI of their forest management practices on the basis of a questionnaire filled out by national silvicultural experts. We analyzed the data reported to evaluate how they vary over Europe and how they affect LCA results and made freely available the inventory data collected for future use. The study shows important variability in rotation length, type of regeneration, amount and assortments of wood products harvested, and machinery used due to the differences in management practices. The existing variability on these activities sensibly affect LCA results of forestry practices and raw wood production. Although it is practically unfeasible to collect site-specific data for all the LCAs involving forest-based products, the use of less generic LCI data of forestry practice is desirable to improve the reliability of the studies. With the release of EFO-LCI we made a step toward the construction of regionalized LCI for the European forestry sector.

36 CFR 1256.74 - How does NARA process Freedom of Information Act (FOIA) requests for classified information?

Code of Federal Regulations, 2010 CFR

2010-07-01

... 36 Parks, Forests, and Public Property 3 2010-07-01 2010-07-01 false How does NARA process Freedom of Information Act (FOIA) requests for classified information? 1256.74 Section 1256.74 Parks, Forests, and Public Property NATIONAL ARCHIVES AND RECORDS ADMINISTRATION PUBLIC AVAILABILITY AND USE ACCESS TO RECORDS AND DONATED HISTORICAL MATERIALS...

Analysis of the 1996 Wisconsin forest statistics by habitat type.

Treesearch

John Kotar; Joseph A. Kovach; Gary Brand

1999-01-01

The fifth inventory of Wisconsin's forests is presented from the perspective of habitat type as a classification tool. Habitat type classifies forests based on the species composition of the understory plant community. Various forest attributes are summarized by habitat type and management implications are discussed.

Multiple baseline radar interferometry applied to coastal land cover classification and change analyses

USGS Publications Warehouse

Ramsey, Elijah W.; Lu, Z.; Rangoonwala, A.; Rykhus, Russ

2006-01-01

ERS-1 and ERS-2 SAR data were collected in tandem over a four-month period and used to generate interferometric coherence, phase, and intensity products that we compared to a classified land cover coastal map of Big Bend, Florida. Forests displayed the highest intensity, and marshes the lowest. The intensity for fresh marsh and forests progressively shifted while saline marsh intensity variance distribution changed with the season. Intensity variability suggested instability between temporal comparisons. Forests, especially hardwoods, displayed lower coherences and marshes higher. Only marshes retained coherence after 70 days. Coherence was more responsive to land cover class than intensity and provided discrimination in winter. Phase distributions helped reveal variation in vegetation structure, identify broad land cover classes and unique within-class variations, and estimate water-level changes. Copyright ?? 2006 by V. H. Winston & Son, Inc. All rights reserved.

36 CFR 1256.70 - What controls access to national security-classified information?

Code of Federal Regulations, 2010 CFR

2010-07-01

... national security-classified information? 1256.70 Section 1256.70 Parks, Forests, and Public Property... HISTORICAL MATERIALS Access to Materials Containing National Security-Classified Information § 1256.70 What controls access to national security-classified information? (a) The declassification of and public access...

Dynamics and pattern of a managed coniferous forest landscape in Oregon.

Treesearch

T.A. Spies; W.J. Ripple; G.A. Bradshaw

1994-01-01

We examined the process of fragmentation in a managed forest landscape by comparing rates and patterns of disturbance (primarily clear-cutting) and regrowth between 1972 and 1988 using Landsat imagery. A 2589-km2 managed forest landscape in western Oregon was classified into two forest types, closed-canopy conifer forest (CF) (typically, > 60% conifer cover) and...

Iowa's forest resources, 2005

Treesearch

Susan J. Crocker; Gary J. Brand; Aron Flickinger

2007-01-01

Report presents Iowa's annual inventory results for 2005. Estimates show that Iowa has more than 2.8 million acres of forest land. Total live-tree volume on forest land is 4.0 billion cubic feet. Ninety-eight percent of forest land is classified as timberland. Oak/hickory is the predominant forest-type group, representing 54 percent of timberland area. Growing-...

North Carolina’s forests, 2013

Treesearch

Mark J. Brown; James T. Vogt

2015-01-01

The principal findings from five panels of the ninth forest survey of North Carolina are presented. In 2013, forests covered 18.6 million acres of the State, of which 17.9 million were classified as timberland. Oak-hickory was the most common forest-type group and covered 7.0 million acres of the timberland. The second most common forest-type group was...

D Semantic Labeling of ALS Data Based on Domain Adaption by Transferring and Fusing Random Forest Models

NASA Astrophysics Data System (ADS)

Wu, J.; Yao, W.; Zhang, J.; Li, Y.

2018-04-01

Labeling 3D point cloud data with traditional supervised learning methods requires considerable labelled samples, the collection of which is cost and time expensive. This work focuses on adopting domain adaption concept to transfer existing trained random forest classifiers (based on source domain) to new data scenes (target domain), which aims at reducing the dependence of accurate 3D semantic labeling in point clouds on training samples from the new data scene. Firstly, two random forest classifiers were firstly trained with existing samples previously collected for other data. They were different from each other by using two different decision tree construction algorithms: C4.5 with information gain ratio and CART with Gini index. Secondly, four random forest classifiers adapted to the target domain are derived through transferring each tree in the source random forest models with two types of operations: structure expansion and reduction-SER and structure transfer-STRUT. Finally, points in target domain are labelled by fusing the four newly derived random forest classifiers using weights of evidence based fusion model. To validate our method, experimental analysis was conducted using 3 datasets: one is used as the source domain data (Vaihingen data for 3D Semantic Labelling); another two are used as the target domain data from two cities in China (Jinmen city and Dunhuang city). Overall accuracies of 85.5 % and 83.3 % for 3D labelling were achieved for Jinmen city and Dunhuang city data respectively, with only 1/3 newly labelled samples compared to the cases without domain adaption.

Wetland forest statistics for the South Atlantic States

Treesearch

Mark J. Brown; Greg M. Smith; Joseph McCollum

2001-01-01

Twenty-one percent, or 17.6 million acres, of the timberland in the South Atlantic States was classified as wetland timberland. Sixty percent of the region’s wetland timberland was under nonindustrial private forest ownership. Forty-eight percent of the region’s wetland timberland was classified as sawtimber-sized stands. Lowland hardwood types made up 62 percent of...

New Remote Sensing Methods for Labeling Disturbance Agents in Appalachian Forests

NASA Astrophysics Data System (ADS)

Hughes, M. J.; Hayes, D. J.

2014-12-01

Forests in the eastern United States are species rich and affected by a variety of disturbance agents such as fire, invasive insects, diseases, and storm events. Millions of hectares of forest are disturbed each year, altering the forest carbon sink and changing forest nutrient cycles. The magnitude and direction of these changes, though, can be different for different disturbance agents. For example, trees that burn in severe fire rapidly release stored carbon into the atmosphere whereas standing deadwood from insect attacks decompose slowly while atmospheric carbon is fixed in regenerating vegetation. The diagnosis and attribution of these processes require accurate and reliable estimates of the extent and frequency of different disturbance agents. Here, a new method is presented that classifies disturbance events identified using time-series analysis of Landsat TM imagery. The method exploits information about changes in the canopy heterogeneity as measured by several texture indices within forest patches. Classifiers were trained using data from the US Forest Service Aerial Detection Surveys and currently differentiate between fires, southern pine beetle, gypsy moth, hemlock woolly adelgid, beech bark disease, anthracnose, and storm events. In addition, the classifier returns a value of 'uncertain' when it is unable to make a clear determination, which is currently approximately 10% of identified disturbances. Classification accuracy for the remainder is 81%, though is variable between agents. For example, the classifier performs well in identifying southern pine beetle and gypsy moth affected areas, but poorly in identifying storms. Reliabilities are similar to accuracies for each agent. The results presented are the first yearly, regional-scale estimates of forest disturbance partitioned by disturbance agent. We find good correspondence with previously described patterns of disturbance and distribution, including direct observational evidence of their predicted periodicities over entire ecoregions. Such estimates are vital for forest monitoring and to better understand the role of the dynamic forest carbon sink in order to reduce uncertainty in atmospheric carbon models. Future work must focus on the inclusion of direct anthropogenic changes such as harvest and urbanization.

Nationwide classification of forest types of India using remote sensing and GIS.

PubMed

Reddy, C Sudhakar; Jha, C S; Diwakar, P G; Dadhwal, V K

2015-12-01

India, a mega-diverse country, possesses a wide range of climate and vegetation types along with a varied topography. The present study has classified forest types of India based on multi-season IRS Resourcesat-2 Advanced Wide Field Sensor (AWiFS) data. The study has characterized 29 land use/land cover classes including 14 forest types and seven scrub types. Hybrid classification approach has been used for the classification of forest types. The classification of vegetation has been carried out based on the ecological rule bases followed by Champion and Seth's (1968) scheme of forest types in India. The present classification scheme has been compared with the available global and national level land cover products. The natural vegetation cover was estimated to be 29.36% of total geographical area of India. The predominant forest types of India are tropical dry deciduous and tropical moist deciduous. Of the total forest cover, tropical dry deciduous forests occupy an area of 2,17,713 km(2) (34.80%) followed by 2,07,649 km(2) (33.19%) under tropical moist deciduous forests, 48,295 km(2) (7.72%) under tropical semi-evergreen forests and 47,192 km(2) (7.54%) under tropical wet evergreen forests. The study has brought out a comprehensive vegetation cover and forest type maps based on inputs critical in defining the various categories of vegetation and forest types. This spatially explicit database will be highly useful for the studies related to changes in various forest types, carbon stocks, climate-vegetation modeling and biogeochemical cycles.

Analysis of Radarsat-2 Full Polarimetric Data for Forest Mapping

NASA Astrophysics Data System (ADS)

Maghsoudi, Yasser

Forests are a major natural resource of the Earth and control a wide range of environmental processes. Forests comprise a major part of the planet's plant biodiversity and have an important role in the global hydrological and biochemical cycles. Among the numerous potential applications of remote sensing in forestry, forest mapping plays a vital role for characterization of the forest in terms of species. Particularly, in Canada where forests occupy 45% of the territory, representing more than 400 million hectares of the total Canadian continental area. In this thesis, the potential of polarimetric SAR (PolSAR) Radarsat-2 data for forest mapping is investigated. This thesis has two principle objectives. First is to propose algorithms for analyzing the PolSAR image data for forest mapping. There are a wide range of SAR parameters that can be derived from PolSAR data. In order to make full use of the discriminative power offered by all these parameters, two categories of methods are proposed. The methods are based on the concept of feature selection and classifier ensemble. First, a nonparametric definition of the evaluation function is proposed and hence the methods NFS and CBFS. Second, a fast wrapper algorithm is proposed for the evaluation function in feature selection and hence the methods FWFS and FWCBFS. Finally, to incorporate the neighboring pixels information in classification an extension of the FWCBFS method i.e. CCBFS is proposed. The second objective of this thesis is to provide a comparison between leaf-on (summer) and leaf-off (fall) season images for forest mapping. Two Radarsat-2 images acquired in fine quad-polarized mode were chosen for this study. The images were collected in leaf-on and leaf-off seasons. We also test the hypothesis whether combining the SAR parameters obtained from both images can provide better results than either individual datasets. The rationale for this combination is that every dataset has some parameters which may be useful for forest mapping. To assess the potential of the proposed methods their performance have been compared with each other and with the baseline classifiers. The baseline methods include the Wishart classifier, which is a commonly used classification method in PolSAR community, as well as an SVM classifier with the full set of parameters. Experimental results showed a better performance of the leaf-off image compared to that of leaf-on image for forest mapping. It is also shown that combining leaf-off parameters with leaf-on parameters can significantly improve the classification accuracy. Also, the classification results (in terms of the overall accuracy) compared to the baseline classifiers demonstrate the effectiveness of the proposed nonparametric scheme for forest mapping.

Combination of support vector machine, artificial neural network and random forest for improving the classification of convective and stratiform rain using spectral features of SEVIRI data

NASA Astrophysics Data System (ADS)

Lazri, Mourad; Ameur, Soltane

2018-05-01

A model combining three classifiers, namely Support vector machine, Artificial neural network and Random forest (SAR) is designed for improving the classification of convective and stratiform rain. This model (SAR model) has been trained and then tested on a datasets derived from MSG-SEVIRI (Meteosat Second Generation-Spinning Enhanced Visible and Infrared Imager). Well-classified, mid-classified and misclassified pixels are determined from the combination of three classifiers. Mid-classified and misclassified pixels that are considered unreliable pixels are reclassified by using a novel training of the developed scheme. In this novel training, only the input data corresponding to the pixels in question to are used. This whole process is repeated a second time and applied to mid-classified and misclassified pixels separately. Learning and validation of the developed scheme are realized against co-located data observed by ground radar. The developed scheme outperformed different classifiers used separately and reached 97.40% of overall accuracy of classification.

Comparison of Hybrid Classifiers for Crop Classification Using Normalized Difference Vegetation Index Time Series: A Case Study for Major Crops in North Xinjiang, China

PubMed Central

Hao, Pengyu; Wang, Li; Niu, Zheng

2015-01-01

A range of single classifiers have been proposed to classify crop types using time series vegetation indices, and hybrid classifiers are used to improve discriminatory power. Traditional fusion rules use the product of multi-single classifiers, but that strategy cannot integrate the classification output of machine learning classifiers. In this research, the performance of two hybrid strategies, multiple voting (M-voting) and probabilistic fusion (P-fusion), for crop classification using NDVI time series were tested with different training sample sizes at both pixel and object levels, and two representative counties in north Xinjiang were selected as study area. The single classifiers employed in this research included Random Forest (RF), Support Vector Machine (SVM), and See 5 (C 5.0). The results indicated that classification performance improved (increased the mean overall accuracy by 5%~10%, and reduced standard deviation of overall accuracy by around 1%) substantially with the training sample number, and when the training sample size was small (50 or 100 training samples), hybrid classifiers substantially outperformed single classifiers with higher mean overall accuracy (1%~2%). However, when abundant training samples (4,000) were employed, single classifiers could achieve good classification accuracy, and all classifiers obtained similar performances. Additionally, although object-based classification did not improve accuracy, it resulted in greater visual appeal, especially in study areas with a heterogeneous cropping pattern. PMID:26360597

AUTOCLASSIFICATION OF THE VARIABLE 3XMM SOURCES USING THE RANDOM FOREST MACHINE LEARNING ALGORITHM

DOE Office of Scientific and Technical Information (OSTI.GOV)

Farrell, Sean A.; Murphy, Tara; Lo, Kitty K., E-mail: s.farrell@physics.usyd.edu.au

In the current era of large surveys and massive data sets, autoclassification of astrophysical sources using intelligent algorithms is becoming increasingly important. In this paper we present the catalog of variable sources in the Third XMM-Newton Serendipitous Source catalog (3XMM) autoclassified using the Random Forest machine learning algorithm. We used a sample of manually classified variable sources from the second data release of the XMM-Newton catalogs (2XMMi-DR2) to train the classifier, obtaining an accuracy of ∼92%. We also evaluated the effectiveness of identifying spurious detections using a sample of spurious sources, achieving an accuracy of ∼95%. Manual investigation of amore » random sample of classified sources confirmed these accuracy levels and showed that the Random Forest machine learning algorithm is highly effective at automatically classifying 3XMM sources. Here we present the catalog of classified 3XMM variable sources. We also present three previously unidentified unusual sources that were flagged as outlier sources by the algorithm: a new candidate supergiant fast X-ray transient, a 400 s X-ray pulsar, and an eclipsing 5 hr binary system coincident with a known Cepheid.« less

Michigan's forest resources in 2001

Treesearch

Earl C. Leatherberry; Gary J. Brand

2003-01-01

Results of the annual forest inventory of Michigan show an estimated 19.4 million acres of forest land in the State. The estimate of total all live tree volume on forest land is 29.8 billion cubic feet. Nearly 19 million acres of forest land in Michigan is classified as timberland. The estimagte of growing-stock volume on timberland is 27.2 billion cubic feet. All...

A preview of West Virginia's forest resource

Treesearch

Joseph E. Barnard; Teresa M. Bowers

1977-01-01

Forest land occupies 75 percent of the total land area of West Virginia. Sixty percent of the forest land is classified in the oak-hickory forest type and only 6 percent in all the softwood forest types. Since 1961, growing-stock volume increased 24 percent. Yellow-poplar increased 39 percent in volume and is now the prevalent species in the State.

North Carolina’s forests, 2007

Treesearch

Mark J. Brown; Barry D. New; Tony G. Johnson; James L. Chamberlain

2014-01-01

The principal findings of the eighth forest survey of North Carolina are presented. In 2007, forests covered 18.6 million acres of the State, of which 18.1 million were classified as timberland. Oak-hickory was the most common forest-type group and covered 7.3 million acres of the timberland. The second most common forest-type group was loblolly-shortleaf pine, which...

Forested land cover classification on the Cumberland Plateau, Jackson County, Alabama: a comparison of Landsat ETM+ and SPOT5 images

Treesearch

Yong Wang; Shanta Parajuli; Callie Schweitzer; Glendon Smalley; Dawn Lemke; Wubishet Tadesse; Xiongwen Chen

2010-01-01

Forest cover classifications focus on the overall growth form (physiognomy) of the community, dominant vegetation, and species composition of the existing forest. Accurately classifying the forest cover type is important for forest inventory and silviculture. We compared classification accuracy based on Landsat Enhanced Thematic Mapper Plus (Landsat ETM+) and Satellite...

Florida's forests, 2013

Treesearch

Mark J. Brown; Jarek Nowak; James T. Vogt

2017-01-01

The principal findings from the five panels of the ninth forest survey of Florida are presented. In 2013, forests covered 17.3 million acres of the State, of which 15.4 million were classified as timberland. Longleaf-slash pine was the most common forest-type group and covered 5.3 million acres of the timberland. The second most common...

Forest fragmentation in Massachusetts, USA: a town-level assessment using Morphological Spatial Pattern Analysis and affinity propagation

Treesearch

J. Rogan; T.M. Wright; J. Cardille; H. Pearsall; Y. Ogneva-Himmelberger; Rachel Riemann; Kurt Riitters; K. Partington

2016-01-01

Forest fragmentation has been studied extensively with respect to biodiversity loss, disruption of ecosystem services, and edge effects although the relationship between forest fragmentation and human activities is still not well understood. We classified the pattern of forests in Massachusetts using fragmentation indicators to address...

The stage-classified matrix models project a significant increase in biomass carbon stocks in China’s forests between 2005 and 2050

PubMed Central

Hu, Huifeng; Wang, Shaopeng; Guo, Zhaodi; Xu, Bing; Fang, Jingyun

2015-01-01

China’s forests are characterized by young age, low carbon (C) density and a large plantation area, implying a high potential for increasing C sinks in the future. Using data of provincial forest area and biomass C density from China’s forest inventories between 1994 and 2008 and the planned forest coverage of the country by 2050, we developed a stage-classified matrix model to predict biomass C stocks of China’s forests from 2005 to 2050. The results showed that total forest biomass C stock would increase from 6.43 Pg C (1 Pg = 1015 g) in 2005 to 9.97 Pg C (95% confidence interval: 8.98 ~ 11.07 Pg C) in 2050, with an overall net C gain of 78.8 Tg C yr−1 (56.7 ~ 103.3 Tg C yr−1; 1 Tg = 1012 g). Our findings suggest that China’s forests will be a large and persistent biomass C sink through 2050. PMID:26110831

The stage-classified matrix models project a significant increase in biomass carbon stocks in China's forests between 2005 and 2050.

PubMed

Hu, Huifeng; Wang, Shaopeng; Guo, Zhaodi; Xu, Bing; Fang, Jingyun

2015-06-25

China's forests are characterized by young age, low carbon (C) density and a large plantation area, implying a high potential for increasing C sinks in the future. Using data of provincial forest area and biomass C density from China's forest inventories between 1994 and 2008 and the planned forest coverage of the country by 2050, we developed a stage-classified matrix model to predict biomass C stocks of China's forests from 2005 to 2050. The results showed that total forest biomass C stock would increase from 6.43 Pg C (1 Pg = 10(15) g) in 2005 to 9.97 Pg C (95% confidence interval: 8.98 ~ 11.07 Pg C) in 2050, with an overall net C gain of 78.8 Tg C yr(-1) (56.7 ~ 103.3 Tg C yr(-1); 1 Tg = 10(12) g). Our findings suggest that China's forests will be a large and persistent biomass C sink through 2050.

[Ecological regulation services of Hainan Island ecosystem and their valuation].

PubMed

Ouyang, Zhiyun; Zhao, Tongqian; Zhao, Jingzhu; Xiao, Han; Wang, Xiaoke

2004-08-01

Ecosystem services imply the natural environmental conditions on which human life relies for existence, and their effectiveness formed and sustained by ecosystem and its ecological processes. In newly research reports, they were divided into four groups, i. e., provisioning services, regulation services, cultural services, and supporting services. To assess and valuate ecosystem services is the foundation of regional environmental reserve and development. Taking Hainan Island as an example and based on the structure and processes of natural ecosystem, this paper discussed the proper methods for regulation services assessment. The ecosystems were classified into 13 types including valley rain forest, mountainous rain forest, tropical monsoon forest, mountainous coppice forest, mountainous evergreen forest, tropical coniferous forest, shrubs, plantation, timber forest, windbreak forest, mangrove, savanna, and cropland, and then, the regulation services and their economic values of Hainan Island ecosystem were assessed and evaluated by terms of water-holding, soil conservancy, nutrient cycle, C fixation, and windbreak function. The economic value of the regulation services of Hainan Island ecosystem was estimated as 2035.88 x 10(8)-2153.39 x 10(8) RMB yuan, 8 times higher to its provisioning services (wood and agricultural products) which were estimated as only 254.06 x 10(8) RMB yuan. The result implied that ecosystem regulation services played an even more important role in the sustainable development of society and economy in Hainan Island.

Effect of Extreme Drought on Tropical Dry Forests

NASA Astrophysics Data System (ADS)

Castro, Saulo; Sanchez-Azofeifa, Arturo; Sato, Hiromitsu; Cowling, Sharon; Vega-Araya, Mauricio

2017-04-01

Tropical dry forests (TDFs) hold a strong economic and cultural connection to human development in the Neotropics. Historically, TDFs not only represent a source of agricultural and urban land but also an important source of goods and ecosystem services for the communities that live around them. Such is the close connection of TDFs to human activity that they are considered the most heavily utilized and disturbed ecosystem in the world. However, TDF have been largely understudied and represent only a fraction of research devoted to globally tropical ecosystems. Thus we lack the framework to properly project how predicted increases in drought events due to climate change will impact TDFs and human society which depend on its services. Our study aims to show the effect of extreme drought on water, food security, and tropical dry forest productivity in the Guanacaste province of Costa Rica. Two pre-ENSO years (2013-2014) and an ENSO year (2015) were compared. The 2013 and 2014 pre-ENSO years were classified as a normal precipitation (1470 mm) and drought year (1027mm), respectively. The 2015 ENSO year was classified as a severe drought (654mm), with amplified effects resulting by the drought experienced during the previous (2014) growing cycle. Effects of the ENSO drought on agriculture and livestock sectors in the province included losses of US13million and US6.5million, respectively. Crop land losses equaled 2,118 hectares and 11,718 hectares were affected. Hydroelectricity generation decreased by 10% and potable water shortages were observed. The Agriculture and Livestock Ministry (MAG) and the National Emergency Commission (CNE) distributed animal feed and supplies to 4,000 farmers affected by the extreme droughts. Eddy covariance flux measurements were used to identify productivity changes during the extreme drought. Changes in phenologic stages and the transitions between CO2 sink to source during mid-growing cycle were observed. Drought significantly delayed the onset of green-up, as well as prolonged the growth season by extending senescence by approximately 30 days beyond the normal season. Comparison of total accumulated forest productivity for each growth cycle indicated significantly lower carbon sequestration during drought years, with decreasing total accumulation as drought severity increased. TDF appeared to compensate for the decreases in productivity rates during drought by lengthening the growth cycle, potentially to allow a minimum productivity threshold to survive the yearly dry season. The dynamic changes occurring in TDF carbon cycling emphasizes the importance of further studies of this ecosystem as it has a direct impact on biodiversity, ecosystem services, and water and food security.

FRAGMENTATION OF CONTINENTAL UNITES STATES FORESTS

EPA Science Inventory

We report a multiple-scale analysis of forest fragmentation based on 30-m land-cover maps for the conterminous United States. Each 0.09-ha unit of forest was classified according to fragmentation indices measured within the surrounding landscape, for five landscape sizes from 2....

Land-use change in Missouri, 1959-1972.

Treesearch

Pamela J. Jakes; John S. Jr. Spencer; Burton L. Essex

1978-01-01

Missouri's third Forest Survey showed an 11% decline in commercial forest area between 1959 and 1972. Most of this land was converted to nonforest uses, primarily pasture. Of the land that remained classified as commercial forest, 75% underwent little or no treatment between surveys.

Exploring diversity in ensemble classification: Applications in large area land cover mapping

NASA Astrophysics Data System (ADS)

Mellor, Andrew; Boukir, Samia

2017-07-01

Ensemble classifiers, such as random forests, are now commonly applied in the field of remote sensing, and have been shown to perform better than single classifier systems, resulting in reduced generalisation error. Diversity across the members of ensemble classifiers is known to have a strong influence on classification performance - whereby classifier errors are uncorrelated and more uniformly distributed across ensemble members. The relationship between ensemble diversity and classification performance has not yet been fully explored in the fields of information science and machine learning and has never been examined in the field of remote sensing. This study is a novel exploration of ensemble diversity and its link to classification performance, applied to a multi-class canopy cover classification problem using random forests and multisource remote sensing and ancillary GIS data, across seven million hectares of diverse dry-sclerophyll dominated public forests in Victoria Australia. A particular emphasis is placed on analysing the relationship between ensemble diversity and ensemble margin - two key concepts in ensemble learning. The main novelty of our work is on boosting diversity by emphasizing the contribution of lower margin instances used in the learning process. Exploring the influence of tree pruning on diversity is also a new empirical analysis that contributes to a better understanding of ensemble performance. Results reveal insights into the trade-off between ensemble classification accuracy and diversity, and through the ensemble margin, demonstrate how inducing diversity by targeting lower margin training samples is a means of achieving better classifier performance for more difficult or rarer classes and reducing information redundancy in classification problems. Our findings inform strategies for collecting training data and designing and parameterising ensemble classifiers, such as random forests. This is particularly important in large area remote sensing applications, for which training data is costly and resource intensive to collect.

Applying a weighted random forests method to extract karst sinkholes from LiDAR data

NASA Astrophysics Data System (ADS)

Zhu, Junfeng; Pierskalla, William P.

2016-02-01

Detailed mapping of sinkholes provides critical information for mitigating sinkhole hazards and understanding groundwater and surface water interactions in karst terrains. LiDAR (Light Detection and Ranging) measures the earth's surface in high-resolution and high-density and has shown great potentials to drastically improve locating and delineating sinkholes. However, processing LiDAR data to extract sinkholes requires separating sinkholes from other depressions, which can be laborious because of the sheer number of the depressions commonly generated from LiDAR data. In this study, we applied the random forests, a machine learning method, to automatically separate sinkholes from other depressions in a karst region in central Kentucky. The sinkhole-extraction random forest was grown on a training dataset built from an area where LiDAR-derived depressions were manually classified through a visual inspection and field verification process. Based on the geometry of depressions, as well as natural and human factors related to sinkholes, 11 parameters were selected as predictive variables to form the dataset. Because the training dataset was imbalanced with the majority of depressions being non-sinkholes, a weighted random forests method was used to improve the accuracy of predicting sinkholes. The weighted random forest achieved an average accuracy of 89.95% for the training dataset, demonstrating that the random forest can be an effective sinkhole classifier. Testing of the random forest in another area, however, resulted in moderate success with an average accuracy rate of 73.96%. This study suggests that an automatic sinkhole extraction procedure like the random forest classifier can significantly reduce time and labor costs and makes its more tractable to map sinkholes using LiDAR data for large areas. However, the random forests method cannot totally replace manual procedures, such as visual inspection and field verification.

Minnesota's forest resources in 2002

Treesearch

Patrick D. Miles; Gary J. Brand; Manfred E. Mielke

2003-01-01

Results of the combined 1999, 2000, 2001, and 2002 annual forest inventories of Minesota show that 16.3 million acres or 32 percent of the total land area is forested. The estimate of total all live tree volume on forest land is 17.6 billion cubic feet or approximately 1,080 cubic feet per acre. Just over 15.0 million acres of forest land in Minnesota is classified...

Minnesota's forest resources in 2001

Treesearch

Patrick D. Miles; Manfred E. Mielke; Gary J. Brand

2003-01-01

Results of the combined 1999, 2000, and 2001 annual forest inventories of Minnesota show that 16.3 million acres or 32 percent of the total land area is forested. The estimate of total all live tree volume on forest land is 17.4 billion cubic feet or approximately 1,068 cubic feet per acre. Nearly 15.0 million acres of forest land in Minnesota are classified as...

Assessing biomass and forest area classifications from modis satellite data while incrementing the number of FIA data panels

Treesearch

Dumitru Salajanu; Dennis M. Jacobs

2005-01-01

Our objective was to determine at what level biomass and forest area obtained from 2, 3, 4, or 5 panels of forest inventory data compares well with forested area and biomass estimates from the national inventory data. A subset of 2605 inventory plots (100% forested, 100% non-forested) was used to classify the land cover and model the biomass in South Carolina. Mixed...

Nonmarket Economic Impacts of Forest Insect Pests: A Literature Review

Treesearch

Randall S. Rosenberger; Eric L. Smith

1997-01-01

This report summarizes the results of research on the nonmarket economic impacts of forest insect pests. The majority of the research reports are journal articles or fulfillment of three USDA Forest Service research contracts. This report also reviews the foundations for methodologies used and classifies the forest insect pests studied, the regions in which research...

Placing man in regional landscape classification: Use of Forest Survey data to assess human influences for southern U.S. forest ecosystems

Treesearch

Victor A. Rudis; John B. Tansey

1991-01-01

Information from plots surveyed by U.S.D.A., Forest Service, Forest Inventory and Analysis (FIA) units provides a basis for classifying human-dominated ecosystems at the regional scale of resolution.Attributes include forest stand measures, evidence of human influence, and other disturbances.Data from recent FIA surveys suggest that human influences are common to...

Illinois's Forest Resources in 2002.

Treesearch

Earl C. Leatherberry; Gary J Brand; Dick C Little

2004-01-01

Results of the 2002 annual inventory of Illinois shows an estimated 4.3 million acres of forest land. The estimate of total volume of all live trees on forest land is 7.5 billion cubic feet. Nearly 4.1 million acres of forest land are classified as timberland. The estimate of growing-stock volume on timberland is 6.3 billion cubic feet. All live aboveground tree...

Forest Area in Michigan Counties, 1966

Treesearch

Arnold J. Ostrom

1967-01-01

In 1966, Michigan had 19.4 million acres of forest land. Almost half of the 18.9 million acres classified as commercial forest land was in Upper Michigan. Since 1955 commercial forest land has increased from 2.4 million to 2.8 million acres in Southern Lower Michigan, and decreased from 7.7 million to 7.0 million acres in Northern Lower Michigan.

Northeastern Regional Forest Fragmentation Assessment: Rationale, Methods, and Comparisons With Other Studies

Treesearch

Andrew Lister; Rachel Riemann; Tonya Lister; Will McWilliams

2005-01-01

Forest fragmentation is thought to impact many biotic and abiotic processes important to ecosystem function. We assessed forest fragmentation in 13 Northeastern States to gain a greater understanding of the trends in and status of this region?s forests. We reclassified and then statistically filtered and updated classified Landsat imagery from the early 1990s, and...

Regional forest cover estimation via remote sensing: the calibration center concept

Treesearch

Louis R. Iverson; Elizabeth A. Cook; Robin L. Graham; Robin L. Graham

1994-01-01

A method for combining Landsat Thematic Mapper (TM), Advanced Very High Resolution Radiometer (AVHRR) imagery, and other biogeographic data to estimate forest cover over large regions is applied and evaluated at two locations. In this method, TM data are used to classify a small area (calibration center) into forest/nonforest; the resulting forest cover map is then...

Patterns of exotic plant invasions in Pennsylvania's Allegheny National Forest using intensive Forest Inventory and Analysis plots

Treesearch

Cynthia D. Huebner; Randall S. Morin; Ann Zurbriggen; Robert L. White

2009-01-01

Intensive Forest Inventory and Analysis (FIA) plot data collected in the Allegheny National Forest (ANF), Pennsylvania, between 1999 and 2006 were evaluated for their ability to predict ANF's vulnerability to invasion by exotic plants. A total of 26 variables classified by biotic, abiotic, or disturbance characteristics were examined. Likelihood of colonization by...

Delineation of landform and lithologic units for Ecological Landtype-Association analysis in Glacier Bay National Park, Southeast Alaska

USGS Publications Warehouse

Brew, David A.

2008-01-01

In this study, landforms were classified-by using topographic maps and personal experience-into eight categories similar to those used by the U.S. Forest Service. The 90 bedrock-lithologic units on the current Glacier Bay geologic map were classified into 13 generalized lithologic units corresponding exactly to those used by the U.S. Forest Service. Incomplete storm-track, storm-intensity, and limited climatic information have also been compiled.

Coast redwood ecological types of southern Monterey County, California

Treesearch

Mark Borchert; Daniel Segotta; Michael D. Purser

1988-01-01

An ecological classification system has been developed for the Pacific Southwest Region of the Forest Service. As part of this classification effort, coast redwood (Sequoia sempervirens) forests of southern Monterey County in the Los Padres National Forest were classified into six ecological types using vegetation, soils and geomorphology taken from...

PPCM: Combing multiple classifiers to improve protein-protein interaction prediction

DOE PAGES

Yao, Jianzhuang; Guo, Hong; Yang, Xiaohan

2015-08-01

Determining protein-protein interaction (PPI) in biological systems is of considerable importance, and prediction of PPI has become a popular research area. Although different classifiers have been developed for PPI prediction, no single classifier seems to be able to predict PPI with high confidence. We postulated that by combining individual classifiers the accuracy of PPI prediction could be improved. We developed a method called protein-protein interaction prediction classifiers merger (PPCM), and this method combines output from two PPI prediction tools, GO2PPI and Phyloprof, using Random Forests algorithm. The performance of PPCM was tested by area under the curve (AUC) using anmore » assembled Gold Standard database that contains both positive and negative PPI pairs. Our AUC test showed that PPCM significantly improved the PPI prediction accuracy over the corresponding individual classifiers. We found that additional classifiers incorporated into PPCM could lead to further improvement in the PPI prediction accuracy. Furthermore, cross species PPCM could achieve competitive and even better prediction accuracy compared to the single species PPCM. This study established a robust pipeline for PPI prediction by integrating multiple classifiers using Random Forests algorithm. Ultimately, this pipeline will be useful for predicting PPI in nonmodel species.« less

Forest cover dynamics of shifting cultivation in the Democratic Republic of Congo: a remote sensing-based assessment for 2000-2010

NASA Astrophysics Data System (ADS)

Molinario, G.; Hansen, M. C.; Potapov, P. V.

2015-09-01

Shifting cultivation has traditionally been practiced in the Democratic Republic of Congo by carving agricultural fields out of primary and secondary forest, resulting in the rural complex: a characteristic land cover mosaic of roads, villages, active and fallow fields and secondary forest. Forest clearing has varying impacts depending on where it occurs relative to this area: whether inside it, along its primary forest interface, or in more isolated primary forest areas. The spatial contextualization of forest cover loss is therefore necessary to understand its impacts and plan its management. We characterized forest clearing using spatial models in a Geographical Information System, applying morphological image processing to the Forets d’Afrique Central Evaluee par Teledetection product. This process allowed us to create forest fragmentation maps for 2000, 2005 and 2010, classifying previously homogenous primary forest into separate patch, edge, perforated, fragmented and core forest subtypes. Subsequently we used spatial rules to map the established rural complex separately from isolated forest perforations, tracking the growth of these areas in time. Results confirm that the expansion of the rural complex and forest perforations has high variance throughout the country, with consequent differences in local impacts on forest ecology and habitat fragmentation. Between 2000 and 2010 the rural complex grew by 10.2% (46 182 ha), increasing from 11.9% to 13.1% of the total land area (1.2% change) while perforated forest grew by 74.4% (23 856 ha), from 0.8% to 1.5%. Core forest decreased by 3.8% (54 852 ha), from 38% to 36.6% of the 2010 land area. Of particular concern is the nearly doubling of perforated forest, a land dynamic that represents greater spatial intrusion of forest clearing within core forest areas and a move away from the established rural complex.

Amount of Future Forest Edge at a 2 Hectare Scale

EPA Pesticide Factsheets

Forests provide economic and ecological value. High amounts of forest edge indicates a highly fragmented forest, which generally diminishes those economic and ecological values. EDGE2 is the percent of forest that is classified as edge using a 2 ha scale. More information about these resources, including the variables used in this study, may be found here: https://edg.epa.gov/data/Public/ORD/NERL/ReVA/ReVA_Data.zip.

Amount of Future Forest Edge at a 65 Hectare scale

EPA Pesticide Factsheets

Forests provide economic and ecological value. High amounts of forest edge indicates a highly fragmented forest, which generally diminishes those economic and ecological values. EDGE65 is the percent of forest that is classified as edge using a 65 ha scale. More information about these resources, including the variables used in this study, may be found here: https://edg.epa.gov/data/Public/ORD/NERL/ReVA/ReVA_Data.zip.

Amount of Forest Edge at a 2 Hectare Scale

EPA Pesticide Factsheets

Forests provide economic and ecological value. High amounts of forest edge indicates a highly fragmented forest, which generally diminishes those economic and ecological values. EDGE2 is the percent of forest that is classified as edge using a 2 ha scale. More information about these resources, including the variables used in this study, may be found here: https://edg.epa.gov/data/Public/ORD/NERL/ReVA/ReVA_Data.zip.

Amount of Forest Edge at a 65 Hectare Scale

EPA Pesticide Factsheets

Forests provide economic and ecological value. High amounts of forest edge indicates a highly fragmented forest, which generally diminishes those economic and ecological values. EDGE65 is the percent of forest that is classified as edge using a 65 ha scale. More information about these resources, including the variables used in this study, may be found here: https://edg.epa.gov/data/Public/ORD/NERL/ReVA/ReVA_Data.zip.

The Zoning of Forest Fire Potential of Gulestan Province Forests Using Granular Computing and MODIS Images

NASA Astrophysics Data System (ADS)

Jalilzadeh Shadlouei, A.; Delavar, M. R.

2013-09-01

There are many vegetation in Iran. This is because of extent of Iran and its width. One of these vegetation is forest vegetation most prevalent in Northern provinces named Guilan, Mazandaran, Gulestan, Ardebil as well as East Azerbaijan. These forests are always threatened by natural forest fires so much so that there have been reports of tens of fires in recent years. Forest fires are one of the major environmental as well as economic, social and security concerns in the world causing much damages. According to climatology, forest fires are one of the important factors in the formation and dispersion of vegetation. Also, regarding the environment, forest fires cause the emission of considerable amounts of greenhouse gases, smoke and dust into the atmosphere which in turn causes the earth temperature to rise up and are unhealthy to humans, animals and vegetation. In agriculture droughts are the usual side effects of these fires. The causes of forest fires could be categorized as either Human or Natural Causes. Naturally, it is impossible to completely contain forest fires; however, areas with high potentials of fire could be designated and analysed to decrease the risk of fires. The zoning of forest fire potential is a multi-criteria problem always accompanied by inherent uncertainty like other multi-criteria problems. So far, various methods and algorithm for zoning hazardous areas via Remote Sensing (RS) and Geospatial Information System (GIS) have been offered. This paper aims at zoning forest fire potential of Gulestan Province of Iran forests utilizing Remote Sensing, Geospatial Information System, meteorological data, MODIS images and granular computing method. Granular computing is part of granular mathematical and one way of solving multi-criteria problems such forest fire potential zoning supervised by one expert or some experts , and it offers rules for classification with the least inconsistencies. On the basis of the experts' opinion, 6 determinative criterias contributing to forest fires have been designated as follows: vegetation (NDVI), slope, aspect, temperature, humidity and proximity to roadways. By applying these variables on several tentatively selected areas and formation information tables and producing granular decision tree and extraction of rules, the zoning rules (for the areas in question) were extracted. According to them the zoning of the entire area has been conducted. The zoned areas have been classified into 5 categories: high hazard, medium hazard (high), medium hazard (low), low hazard (high), low hazard (low). According to the map, the zoning of most of the areas fall into the low hazard (high) class while the least number of areas have been classified as low hazard (low). Comparing the forest fires in these regions in 2010 with the MODIS data base for forest fires, it is concluded that areas with high hazards of forest fire have been classified with a 64 percent precision. In other word 64 percent of pixels that are in high hazard classification are classified according to MODIS data base. Using this method we obtain a good range of Perception. Manager will reduce forest fire concern using precautionary proceeding on hazardous area.

Detailed forest formation mapping in the land cover map series for the Caribbean islands

NASA Astrophysics Data System (ADS)

Helmer, E. H.; Schill, S.; Pedreros, D. H.; Tieszen, L. L.; Kennaway, T.; Cushing, M.; Ruzycki, T.

2006-12-01

Forest formation and land cover maps for several Caribbean islands were developed from Landsat ETM+ imagery as part of a multi-organizational project. The spatially explicit data on forest formation types will permit more refined estimates of some forest attributes. The woody vegetation classification scheme relates closely to that of Areces-Malea et al. (1), who classify Caribbean vegetation according to standards of the US Federal Geographic Data Committee (FGDC, 1997), with modifications similar to those in Helmer et al. (2). For several of the islands, we developed image mosaics that filled cloudy parts of scenes with data from other scene dates after using regression tree normalization (3). The regression tree procedure permitted us to develop mosaics for wet and drought seasons for a few of the islands. The resulting multiseason imagery facilitated separation between classes such as seasonal evergreen forest, semi-deciduous forest (including semi-evergreen forest), and drought deciduous forest or woodland formations. We used decision tree classification methods to classify the Landsat image mosaics to detailed forest formations and land cover for Puerto Rico (4), St. Kitts and Nevis, St. Lucia, St. Vincent and the Grenadines and Grenada. The decision trees classified a stack of raster layers for each mapping area that included the Landsat image bands and various ancillary raster data layers. For Puerto Rico, for example, the ancillary data included climate parameters (5). For some islands, the ancillary data included topographic derivatives such as aspect, slope and slope position, SRTM (6) or other topographic data. Mapping forest formations with decision tree classifiers, ancillary geospatial data, and cloud-free image mosaics, accurately distinguished spectrally similar forest formations, without the aid of ecological zone maps, on the islands where the approach was used. The approach resulted in maps of forest formations with comparable or better detail than when IKONOS or Landsat imagery was hand-digitized, as it was for the Dominican Republic (7) and Barbados. 1. T. Kennaway, E. H. Helmer. (Intl Inst of Tropical Forestry, USDA Forest Service, Río Piedras, Puerto Rico, 2006). 2. A. Areces-Mallea et al. (The Nature Conservancy, 1999). 3. E. H. Helmer, O. Ramos, T. Lopez, M. Quiñones, W. Diaz, Carib J Sci 38, 165-183 (2002). 4. C. Daly, E. H. Helmer, M. Quiñones, Int J Climatology 23, 1359-1381 (2003). 5. T. G. Farr, M. Kobrick, Eos Transactions 81, 583-585 (2000). 6. E. H. Helmer, B. Ruefenacht, Photogrammetric Eng Rem Sens 71, 1079-1089 (2005). 7. S. Hernández, M. Pérez. (Secretaría de Estado de Medio Ambiente y Recursos Naturales de la República Dominicana, Santo Domingo, Dominican Republic, 2005).

Forest community classification of the Porcupine River drainage, interior Alaska, and its application to forest management.

Treesearch

John Yarie

1983-01-01

The forest vegetation of 3,600,000 hectares in northeast interior Alaska was classified. A total of 365 plots located in a stratified random design were run through the ordination programs SIMORD and TWINSPAN. A total of 40 forest communities were described vegetatively and, to a limited extent, environmentally. The area covered by each community was similar, ranging...

Forest growth of Mississippi's north unit - A case study of the Southern Forest surveys growth estimation procedures

Treesearch

Dennis M. May

1988-01-01

This report presents the procedures by which the Southern Forest Inventory and Analysis unit estimates forest growth from permanent horizontal point samples. Inventory data from the 1977-87 survey of Mississippi's north unit were used to demonstrate how trees on the horizontal point samples are classified into one of eight components of growth and, in turn, how...

Iowa's forest resources in 2002.

Treesearch

Earl C Leatherberry; Gary J. Brand

2004-01-01

Results of the 2002 annual inventory of Iowa show an estimated 2.7 million acres of forest land. The estimate of total all live tree volume on forest land is 3.9 billion cubic feet. Nearly 2.6 million acres of forest land in Iowa are classified as timberland. The estimate of growing-stock volume on timberland is 3.0 billion cubic feet. All live aboveground tree biomass...

Iowa's forest resources in 2001

Treesearch

Earl C. Leatherberry; Steve Pennington; Gary J. Brand

2003-01-01

Results of the 2001 annual inventory of Iowa show an estimated 2.6 million acres of forest land in the State. The estimate of total all live tree volume on forest land is 3.6 billion cubic feet. Nearly 2.5 million acres of forest land in Iowa are classified as timberland. The estimate of growing-stock volume on timberland is 2.7 billion cubic feet. All live aboveground...

Nebraska's forest resources in 2002.

Treesearch

Katherine P. O' Neill; Earl C. Leatherberry; William R. Lovett

2004-01-01

Results of the 2002 annual inventory of Nebraska show an estimated 1,346.5 thousand acres of forest land in the State. The estimated total volume of all live trees on forest land is 1.9 billion cubic feet. An estimated 1,297.4 thousand acres of forest land are classified as timberland. The estimate of growing-stock volume on timberland is 1.6 billion cubic feet. All...

Appendix 1: Regional summaries - Alaska

Treesearch

Jane M. Wolken; Teresa N. Hollingsworth

2012-01-01

Alaskan forests cover one-third of the state’s 52 million ha of land (Parson et al. 2001), and are regionally and globally significant. Ninety percent of Alaskan forests are classified as boreal, representing 4 percent of the world’s boreal forests, and are located throughout interior and south-central Alaska (fig. A1-1). The remaining 10 percent of Alaskan forests are...

State and regional water-quality characteristics and trophic conditions of Michigan's inland lakes, 2001-2005

USGS Publications Warehouse

Fuller, L.M.; Minnerick, R.J.

2008-01-01

The U.S. Geological Survey and the Michigan Department of Environmental Quality are jointly monitoring selected water-quality constituents of inland lakes through 2015 as part of Michigan’s Lake Water Quality Assessment program. During 2001–2005, 433 lake basins from 364 inland lakes were monitored for baseline water-quality conditions and trophic status. This report summarizes the water-quality characteristics and trophic conditions of those monitored lake basins throughout the State. Regional variation of water quality in lake basins was examined by grouping on the basis of the five Omernik level III ecoregions within Michigan. Concentrations of most constituents measured were significantly different between ecoregions. Less regional variation of phosphorus concentrations was noted between Northern Lakes and Forests (50) and North Central Hardwoods (51) ecoregions during summer possibly because water samples were collected when lake productivity was high; hence the utilization of the limited amount of phosphorus by algae and macrophytes may have resulted in the more uniform concentrations between these two ecoregions. Concentrations of common ions (calcium, magnesium, potassium, sodium, chloride, and sulfate) measured in the spring typically were higher in the Michigan southern Lower Peninsula in the Eastern Corn Belt Plains (55), Southern Michigan/Northern Indiana Drift Plains (56), and Huron/Erie Lake Plains (57) ecoregions. Most ions whose concentrations were less than the minimum reporting levels or were nondetectable were from lakes in the Michigan northern Lower Peninsula and the Upper Peninsula in the Northern Lakes and Forests (50) and North Central Hardwoods (51) ecoregions. Chlorophyll a concentrations followed a similar distribution pattern. Measured properties such as pH and specific conductance (indicative of dissolved solids) also showed a regional relation. The lakes with the lowest pH and specific conductance were generally in the western Upper Peninsula (Northern Lakes and Forests (50) ecoregion). The Michigan Department of Environmental Quality classifies Michigan lakes on the basis of their primary biological productivity or trophic characteristics using the Carlson Trophic State Index. Trophic evaluations based on data collected from 2001 through 2005 indicate 17 percent of the lakes are oligotrophic, 53 percent are mesotrophic, 22 percent are eutrophic, 4 percent are hypereutrophic, and less than 5 percent are classified into transition classes between each major class. Although the distribution of lakes throughout Michigan or between Omernik level III ecoregions is not uniform, about 85 percent of the lakes classified as oligotrophic are in the Northern Lakes and Forests (50) or North Central Hardwoods (51) ecoregions. Nearly 28 percent of all the lakes in each of these two ecoregions were classified as oligotrophic. Historical trophic-state classes were compared to the current (2001 through 2005) trophic-state classes. Approximately 72 percent of lakes remained in the same trophic-state class, 11 percent moved up a partial or full class (indicating a decrease in water clarity) and 18 percent moved down a partial or full class (indicating an increase in water clarity).

Forest cover type analysis of New England forests using innovative WorldView-2 imagery

NASA Astrophysics Data System (ADS)

Kovacs, Jenna M.

For many years, remote sensing has been used to generate land cover type maps to create a visual representation of what is occurring on the ground. One significant use of remote sensing is the identification of forest cover types. New England forests are notorious for their especially complex forest structure and as a result have been, and continue to be, a challenge when classifying forest cover types. To most accurately depict forest cover types occurring on the ground, it is essential to utilize image data that have a suitable combination of both spectral and spatial resolution. The WorldView-2 (WV2) commercial satellite, launched in 2009, is the first of its kind, having both high spectral and spatial resolutions. WV2 records eight bands of multispectral imagery, four more than the usual high spatial resolution sensors, and has a pixel size of 1.85 meters at the nadir. These additional bands have the potential to improve classification detail and classification accuracy of forest cover type maps. For this reason, WV2 imagery was utilized on its own, and in combination with Landsat 5 TM (LS5) multispectral imagery, to evaluate whether these image data could more accurately classify forest cover types. In keeping with recent developments in image analysis, an Object-Based Image Analysis (OBIA) approach was used to segment images of Pawtuckaway State Park and nearby private lands, an area representative of the typical complex forest structure found in the New England region. A Classification and Regression Tree (CART) analysis was then used to classify image segments at two levels of classification detail. Accuracies for each forest cover type map produced were generated using traditional and area-based error matrices, and additional standard accuracy measures (i.e., KAPPA) were generated. The results from this study show that there is value in analyzing imagery with both high spectral and spatial resolutions, and that WV2's new and innovative bands can be useful for the classification of complex forest structures.

76 FR 24511 - Cabo Rojo National Wildlife Refuge, Cabo Rojo, Puerto Rico; Draft Comprehensive Conservation Plan...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-05-02

... the Gu[aacute]nica Commonwealth Forest remaining in public ownership. The native vegetation is classified as subtropical dry forest under the Holdridge classification of world life zones. At least 245... restore and maintain existing sub-tropical dryland forests, salt lagoons, and grassland habitats. Active...

The population dynamics of goldenseal by habitat type on the Hoosier National Forest

Treesearch

S. P. Meyer; G. R. Parker

2003-01-01

Goldenseal (Hydrastis canadensis L.) is an herbaceous species found throughout the central hardwood forest ecosystem that is harvested from the wild for the medicinal herb trade. A total of 147 goldenseal populations were classified according to the Ecological Classification Guide developed for the Hoosier National Forest, and change in population...

Remote sensing change detection methods to track deforestation and growth in threatened rainforests in Madre de Dios, Peru

USGS Publications Warehouse

Shermeyer, Jacob S.; Haack, Barry N.

2015-01-01

Two forestry-change detection methods are described, compared, and contrasted for estimating deforestation and growth in threatened forests in southern Peru from 2000 to 2010. The methods used in this study rely on freely available data, including atmospherically corrected Landsat 5 Thematic Mapper and Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation continuous fields (VCF). The two methods include a conventional supervised signature extraction method and a unique self-calibrating method called MODIS VCF guided forest/nonforest (FNF) masking. The process chain for each of these methods includes a threshold classification of MODIS VCF, training data or signature extraction, signature evaluation, k-nearest neighbor classification, analyst-guided reclassification, and postclassification image differencing to generate forest change maps. Comparisons of all methods were based on an accuracy assessment using 500 validation pixels. Results of this accuracy assessment indicate that FNF masking had a 5% higher overall accuracy and was superior to conventional supervised classification when estimating forest change. Both methods succeeded in classifying persistently forested and nonforested areas, and both had limitations when classifying forest change.

Assessment of vegetation change in a fire-altered forest landscape

NASA Technical Reports Server (NTRS)

Jakubauskas, Mark E.; Lulla, Kamlesh P.; Mausel, Paul W.

1990-01-01

This research focused on determining the degree to which differences in burn severity relate to postfire vegetative cover within a Michigan pine forest. Landsat MSS data from June 1973 and TM data from October 1982 were classified using an unsupervised approach to create prefire and postfire cover maps of the study area. Using a raster-based geographic information system (GIS), the maps were compared, and a map of vegetation change was created. An IR/red band ratio from a June 1980 Landsat scene was classified to create a map of three degres of burn severity, which was then compared with the vegetation change map using a GIS. Classification comparisons of pine and deciduous forest classes (1973 to 1982) revealed that the most change in vegetation occurred in areas subjected to the most intense burn. Two classes of regenerating forest comprised the majority of the change, while the remaining change was associated with shrub vegetation or another forest class.

Soy moratorium impacts on soybean and deforestation dynamics in Mato Grosso, Brazil.

PubMed

Kastens, Jude H; Brown, J Christopher; Coutinho, Alexandre Camargo; Bishop, Christopher R; Esquerdo, Júlio César D M

2017-01-01

Previous research has established the usefulness of remotely sensed vegetation index (VI) data from the Moderate Resolution Imaging Spectroradiometer (MODIS) to characterize the spatial dynamics of agriculture in the state of Mato Grosso (MT), Brazil. With these data it has become possible to track MT agriculture, which accounts for ~85% of Brazilian Amazon soy production, across periods of several years. Annual land cover (LC) maps support investigation of the spatiotemporal dynamics of agriculture as they relate to forest cover and governance and policy efforts to lower deforestation rates. We use a unique, spatially extensive 9-year (2005-2013) ground reference dataset to classify, with approximately 80% accuracy, MODIS VI data, merging the results with carefully processed annual forest and sugarcane coverages developed by Brazil's National Institute for Space Research to produce LC maps for MT for the 2001-2014 crop years. We apply the maps to an evaluation of forest and agricultural intensification dynamics before and after the Soy Moratorium (SoyM), a governance effort enacted in July 2006 to halt deforestation for the purpose of soy production in the Brazilian Amazon. We find the pre-SoyM deforestation rate to be more than five times the post-SoyM rate, while simultaneously observing the pre-SoyM forest-to-soy conversion rate to be more than twice the post-SoyM rate. These observations support the hypothesis that SoyM has played a role in reducing both deforestation and subsequent use for soy production. Additional analyses explore the land use tendencies of deforested areas and the conceptual framework of horizontal and vertical agricultural intensification, which distinguishes production increases attributable to cropland expansion into newly deforested areas as opposed to implementation of multi-cropping systems on existing cropland. During the 14-year study period, soy production was found to shift from predominantly single-crop systems to majority double-crop systems.

CRF: detection of CRISPR arrays using random forest.

PubMed

Wang, Kai; Liang, Chun

2017-01-01

CRISPRs (clustered regularly interspaced short palindromic repeats) are particular repeat sequences found in wide range of bacteria and archaea genomes. Several tools are available for detecting CRISPR arrays in the genomes of both domains. Here we developed a new web-based CRISPR detection tool named CRF (CRISPR Finder by Random Forest). Different from other CRISPR detection tools, a random forest classifier was used in CRF to filter out invalid CRISPR arrays from all putative candidates and accordingly enhanced detection accuracy. In CRF, particularly, triplet elements that combine both sequence content and structure information were extracted from CRISPR repeats for classifier training. The classifier achieved high accuracy and sensitivity. Moreover, CRF offers a highly interactive web interface for robust data visualization that is not available among other CRISPR detection tools. After detection, the query sequence, CRISPR array architecture, and the sequences and secondary structures of CRISPR repeats and spacers can be visualized for visual examination and validation. CRF is freely available at http://bioinfolab.miamioh.edu/crf/home.php.

Linear Subpixel Learning Algorithm for Land Cover Classification from WELD using High Performance Computing

NASA Technical Reports Server (NTRS)

Kumar, Uttam; Nemani, Ramakrishna R.; Ganguly, Sangram; Kalia, Subodh; Michaelis, Andrew

2017-01-01

In this work, we use a Fully Constrained Least Squares Subpixel Learning Algorithm to unmix global WELD (Web Enabled Landsat Data) to obtain fractions or abundances of substrate (S), vegetation (V) and dark objects (D) classes. Because of the sheer nature of data and compute needs, we leveraged the NASA Earth Exchange (NEX) high performance computing architecture to optimize and scale our algorithm for large-scale processing. Subsequently, the S-V-D abundance maps were characterized into 4 classes namely, forest, farmland, water and urban areas (with NPP-VIIRS-national polar orbiting partnership visible infrared imaging radiometer suite nighttime lights data) over California, USA using Random Forest classifier. Validation of these land cover maps with NLCD (National Land Cover Database) 2011 products and NAFD (North American Forest Dynamics) static forest cover maps showed that an overall classification accuracy of over 91 percent was achieved, which is a 6 percent improvement in unmixing based classification relative to per-pixel-based classification. As such, abundance maps continue to offer an useful alternative to high-spatial resolution data derived classification maps for forest inventory analysis, multi-class mapping for eco-climatic models and applications, fast multi-temporal trend analysis and for societal and policy-relevant applications needed at the watershed scale.

Linear Subpixel Learning Algorithm for Land Cover Classification from WELD using High Performance Computing

NASA Astrophysics Data System (ADS)

Ganguly, S.; Kumar, U.; Nemani, R. R.; Kalia, S.; Michaelis, A.

2017-12-01

In this work, we use a Fully Constrained Least Squares Subpixel Learning Algorithm to unmix global WELD (Web Enabled Landsat Data) to obtain fractions or abundances of substrate (S), vegetation (V) and dark objects (D) classes. Because of the sheer nature of data and compute needs, we leveraged the NASA Earth Exchange (NEX) high performance computing architecture to optimize and scale our algorithm for large-scale processing. Subsequently, the S-V-D abundance maps were characterized into 4 classes namely, forest, farmland, water and urban areas (with NPP-VIIRS - national polar orbiting partnership visible infrared imaging radiometer suite nighttime lights data) over California, USA using Random Forest classifier. Validation of these land cover maps with NLCD (National Land Cover Database) 2011 products and NAFD (North American Forest Dynamics) static forest cover maps showed that an overall classification accuracy of over 91% was achieved, which is a 6% improvement in unmixing based classification relative to per-pixel based classification. As such, abundance maps continue to offer an useful alternative to high-spatial resolution data derived classification maps for forest inventory analysis, multi-class mapping for eco-climatic models and applications, fast multi-temporal trend analysis and for societal and policy-relevant applications needed at the watershed scale.

Evaluating the Effectiveness of Flood Control Strategies in Contrasting Urban Watersheds and Implications for Houston's Future Flood Vulnerability

NASA Astrophysics Data System (ADS)

Ganguly, S.; Kumar, U.; Nemani, R. R.; Kalia, S.; Michaelis, A.

2016-12-01

In this work, we use a Fully Constrained Least Squares Subpixel Learning Algorithm to unmix global WELD (Web Enabled Landsat Data) to obtain fractions or abundances of substrate (S), vegetation (V) and dark objects (D) classes. Because of the sheer nature of data and compute needs, we leveraged the NASA Earth Exchange (NEX) high performance computing architecture to optimize and scale our algorithm for large-scale processing. Subsequently, the S-V-D abundance maps were characterized into 4 classes namely, forest, farmland, water and urban areas (with NPP-VIIRS - national polar orbiting partnership visible infrared imaging radiometer suite nighttime lights data) over California, USA using Random Forest classifier. Validation of these land cover maps with NLCD (National Land Cover Database) 2011 products and NAFD (North American Forest Dynamics) static forest cover maps showed that an overall classification accuracy of over 91% was achieved, which is a 6% improvement in unmixing based classification relative to per-pixel based classification. As such, abundance maps continue to offer an useful alternative to high-spatial resolution data derived classification maps for forest inventory analysis, multi-class mapping for eco-climatic models and applications, fast multi-temporal trend analysis and for societal and policy-relevant applications needed at the watershed 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

Treesearch

Ronald E. McRoberts; Geoffrey R. Holden; Mark D. Nelson; Greg C. Liknes; Dale D. Gormanson

2006-01-01

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...

Gis-Based Multi-Criteria Decision Analysis for Forest Fire Risk Mapping

NASA Astrophysics Data System (ADS)

Akay, A. E.; Erdoğan, A.

2017-11-01

The forested areas along the coastal zone of the Mediterranean region in Turkey are classified as first-degree fire sensitive areas. Forest fires are major environmental disaster that affects the sustainability of forest ecosystems. Besides, forest fires result in important economic losses and even threaten human lives. Thus, it is critical to determine the forested areas with fire risks and thereby minimize the damages on forest resources by taking necessary precaution measures in these areas. The risk of forest fire can be assessed based on various factors such as forest vegetation structures (tree species, crown closure, tree stage), topographic features (slope and aspect), and climatic parameters (temperature, wind). In this study, GIS-based Multi-Criteria Decision Analysis (MCDA) method was used to generate forest fire risk map. The study was implemented in the forested areas within Yayla Forest Enterprise Chiefs at Dursunbey Forest Enterprise Directorate which is classified as first degree fire sensitive area. In the solution process, "extAhp 2.0" plug-in running Analytic Hierarchy Process (AHP) method in ArcGIS 10.4.1 was used to categorize study area under five fire risk classes: extreme risk, high risk, moderate risk, and low risk. The results indicated that 23.81 % of the area was of extreme risk, while 25.81 % was of high risk. The result indicated that the most effective criterion was tree species, followed by tree stages. The aspect had the least effective criterion on forest fire risk. It was revealed that GIS techniques integrated with MCDA methods are effective tools to quickly estimate forest fire risk at low cost. The integration of these factors into GIS can be very useful to determine forested areas with high fire risk and also to plan forestry management after fire.

Classification of Strawberry Fruit Shape by Machine Learning

NASA Astrophysics Data System (ADS)

Ishikawa, T.; Hayashi, A.; Nagamatsu, S.; Kyutoku, Y.; Dan, I.; Wada, T.; Oku, K.; Saeki, Y.; Uto, T.; Tanabata, T.; Isobe, S.; Kochi, N.

2018-05-01

Shape is one of the most important traits of agricultural products due to its relationships with the quality, quantity, and value of the products. For strawberries, the nine types of fruit shape were defined and classified by humans based on the sampler patterns of the nine types. In this study, we tested the classification of strawberry shapes by machine learning in order to increase the accuracy of the classification, and we introduce the concept of computerization into this field. Four types of descriptors were extracted from the digital images of strawberries: (1) the Measured Values (MVs) including the length of the contour line, the area, the fruit length and width, and the fruit width/length ratio; (2) the Ellipse Similarity Index (ESI); (3) Elliptic Fourier Descriptors (EFDs), and (4) Chain Code Subtraction (CCS). We used these descriptors for the classification test along with the random forest approach, and eight of the nine shape types were classified with combinations of MVs + CCS + EFDs. CCS is a descriptor that adds human knowledge to the chain codes, and it showed higher robustness in classification than the other descriptors. Our results suggest machine learning's high ability to classify fruit shapes accurately. We will attempt to increase the classification accuracy and apply the machine learning methods to other plant species.

Assessment and monitoring of deforestation and forest fragmentation in South Asia since the 1930s

NASA Astrophysics Data System (ADS)

Sudhakar Reddy, C.; Saranya, K. R. L.; Vazeed Pasha, S.; Satish, K. V.; Jha, C. S.; Diwakar, P. G.; Dadhwal, V. K.; Rao, P. V. N.; Krishna Murthy, Y. V. N.

2018-02-01

The present study, first of its kind, has analyzed the land cover and investigated the spatial patterns of deforestation and forest fragmentation in South Asian region since the 1930's. This region comprises of eight countries: India, Bangladesh, Bhutan, Nepal, Pakistan, Afghanistan, Sri Lanka and Maldives. In South Asia, agricultural land is predominant constituting 43% of the total geographical area followed by barren land (19.99%) and forests (14.72%). The long-term change analysis using the classified maps of 1930 and 2014 indicated a loss of 29.62% of the forest cover. Higher annual net deforestation rates were observed in the period from 1930-1975 (0.68%) followed by 1975-1985 (0.23%), 1985-1995 (0.12%), 1995-2005 (0.06%) and 2005-2014 (0.04%) for the region. Forest fragmentation had significant spatio-temporal variation across the South Asian countries. In 1930, 88.91% of the South Asian forest was classified as large core forest, 8.18% as edge forest and 1.18% as perforated forest. The large core forest category has decreased significantly in area over last eight decades. The results of the present study are expected to serve as a reference for the evaluation of globally agreed Aichi biodiversity target 5 for South Asian countries. This study will be a valuable basis for developing management strategies and restoration programs as it tracks the spatial changes in deforestation and forest fragmentation.

Analysis of zone of vulnurability and impact of forest fires in forest ecosystems in north algeria by susing remote sensing

NASA Astrophysics Data System (ADS)

Zegrar, Ahmed

2010-05-01

The Forest in steppe present ecological diversity, and seen climatic unfavourable conditions in zone and impact of forest fires; we notes deterioration of physical environment particularly, deterioration of natural forest. This deterioration of forests provokes an unbalance of environment witch provokes a process of deterioration advanced in the ultimate stadium is desertification. By elsewhere, where climatic conditions are favourable, the fire is an ecological and acted agent like integral part of evolution of the ecosystems, the specific regeneration of plants are influenced greatly by the regime of fire (season of fire, intensity, interval), witch leads to the recuperation of the vegetation of meadow- fire. In this survey we used the pictures ALSAT-1 for detection of zones with risk of forest fire and their impact on the naturals forests in region named TLEMCEN in the north west of Algeria. A thematic detailed analysis of forests well attended ecosystems some processing on the picture ALSAT-1, we allowed to identify and classifying the forests in there opinion components flowers. We identified ampleness of fire on this zone also. Some parameters as the slope, the proximity to the road and the forests formations were studied in the goal of determining the zones to risk of forest fire. A crossing of diaper of information in a GIS according to a very determined logic allowed classifying the zones in degree of risk of fire in semi arid zone witch forest zone not encouraging the regeneration but permitting the installation of cash of steppe which encourages the desertification.

Integration of carbon conservation into sustainable forest management using high resolution satellite imagery: A case study in Sabah, Malaysian Borneo

NASA Astrophysics Data System (ADS)

Langner, Andreas; Samejima, Hiromitsu; Ong, Robert C.; Titin, Jupiri; Kitayama, Kanehiro

2012-08-01

Conservation of tropical forests is of outstanding importance for mitigation of climate change effects and preserving biodiversity. In Borneo most of the forests are classified as permanent forest estates and are selectively logged using conventional logging techniques causing high damage to the forest ecosystems. Incorporation of sustainable forest management into climate change mitigation measures such as Reducing Emissions from Deforestation and Forest Degradation (REDD+) can help to avert further forest degradation by synergizing sustainable timber production with the conservation of biodiversity. In order to evaluate the efficiency of such initiatives, monitoring methods for forest degradation and above-ground biomass in tropical forests are urgently needed. In this study we developed an index using Landsat satellite data to describe the crown cover condition of lowland mixed dipterocarp forests. We showed that this index combined with field data can be used to estimate above-ground biomass using a regression model in two permanent forest estates in Sabah, Malaysian Borneo. Tangkulap represented a conventionally logged forest estate while Deramakot has been managed in accordance with sustainable forestry principles. The results revealed that conventional logging techniques used in Tangkulap during 1991 and 2000 decreased the above-ground biomass by an annual amount of average -6.0 t C/ha (-5.2 to -7.0 t C/ha, 95% confidential interval) whereas the biomass in Deramakot increased by 6.1 t C/ha per year (5.3-7.2 t C/ha, 95% confidential interval) between 2000 and 2007 while under sustainable forest management. This indicates that sustainable forest management with reduced-impact logging helps to protect above-ground biomass. In absolute terms, a conservative amount of 10.5 t C/ha per year, as documented using the methodology developed in this study, can be attributed to the different management systems, which will be of interest when implementing REDD+ that rewards the enhancement of carbon stocks.

Insect Pollinators of Three Rare Plants in a Florida Longleaf Pine Forest

Treesearch

Theresa Pitts-Singer; James L. Hanula; Joan L. Walker

2002-01-01

As a result of human activity, longleaf pine (Pinus palustris Miller) forests in the southern United States have been lost or drastically altered. Many of the plant species that historically occupied those forests now persist only as remnants and are classified as threatened or endangered. In order to safeguard such species, a better understanding of...

Blue oak plant communities of southern San Luis Obispo and northern Santa Barbara Counties, California

Treesearch

Mark I. Borchert; Nancy D. Cunha; Patricia C. Krosse; Marcee L. Lawrence

1993-01-01

An ecological classification system has been developed for the Pacific Southwest Region of the Forest Service. As part of that classification effort, blue oak (Quercus douglasii) woodlands and forests of southern San Luis Obispo and northern Santa Barbara Counties in Los Padres National Forest were classified into I3 plant communities using...

Vegetation and environmental features of forest and range ecosystems

Treesearch

George A. Garrison; Ardell J. Bjugstad; Don A. Duncan; Mont E. Lewis; Dixie R. Smith

1977-01-01

This publication describes the 34 ecosystems into which all the land of the 48 contiguous states has been classified in the Forest-Range Environmental Study (FRES) of the Forest Service, U.S. Department of Agriculture. The description of each ecosystem discusses physiography, climate, vegetation, fauna, soils, and land use. For a number of the ecosystems, the...

Fragmentation of Continental United States Forests

Treesearch

Kurt H. Riitters; James D. Wickham; Robert V. O' Neill; K. Bruce Jones; Elizabeth R. Smith; John W. Coulston; Timothy G. Wade; Jonathan H. Smith

2002-01-01

We report a multiple-scale analysis of forest fragmentation based on 30-m (0.09 ha pixel-1) land- cover maps for the conterminous United States. Each 0.09-ha unit of forest was classified according to fragmentation indexes measured within the surrounding landscape, for five landscape sizes including 2.25, 7.29, 65.61, 590.49, and 5314.41 ha....

Evaluating Classified MODIS Satellite Imagery as a Stratification Tool

Treesearch

Greg C. Liknes; Mark D. Nelson; Ronald E. McRoberts

2004-01-01

The Forest Inventory and Analysis (FIA) program of the USDA Forest Service collects forest attribute data on permanent plots arranged on a hexagonal network across all 50 states and Puerto Rico. Due to budget constraints, sample sizes sufficient to satisfy national FIA precision standards are seldom achieved for most inventory variables unless the estimation process is...

Forest Species Diversity in Upper Elevation Hardwood Forests in the Southern Appalachian Mountains

Treesearch

Katherine J. Elliott; Deidre Hewitt

1997-01-01

Overstory, shrub-layer, and herb-layer flora composition and abundance patterns in eleven forest sites were studied to evaluate species diversity and richness before implementing three types of harvest treat- ments. The sites were within the Wine Spring Creek Watershed and were classified as high elevation, dry, Quercus rubra-Rhododendron calendulaceum based on...

Characterizing the Status (Disturbed, Hybrid or Novel) of Swamp Forest Fragments in a Caribbean Ramsar Wetland: The Impact of Anthropogenic Degradation and Invasive Plant Species.

PubMed

Prospere, Kurt; McLaren, Kurt P; Wilson, Byron

2016-10-01

The last remaining Amazonian-type swamp forest fragments in Black River Lower Morass, Jamaica, have been subjected to a myriad of anthropogenic disturbances, compounded by the establishment and spread of several invasive plant species. We established 44 permanent sample plots (covering 3.92 ha) across 10 of these swamp forest fragments and sampled all non-woody plants and all trees ≥2 cm DBH found in the plots. These data were used to (1) identify thresholds of hybridity and novelty, (2) derive several diversity and structural descriptors used to characterize the swamp forest fragments and (3) identify possible indicators of anthropogenic degradation. These were incorporated into a framework and used to determine the status of the swamp forest fragments so that appropriate management and conservation measures can be implemented. We recorded 43 woody plant species (9 endemic, 28 native and 4 non-native) and 21 non-tree species. The composition and structure of all the patches differed significantly due to the impact of the herbaceous invasive plant Alpinia allughas, the presence and diversity of other non-native plants, and differing intensities of anthropogenic disturbance (e.g., burning, cutting and harvesting of non-timber forest products). We ranked forest patches along a continuum representing deviations from a historical proxy (least disturbed) swamp forest to those with dramatically altered structural and floristic attributes (=novel swamp forests). Only one fragment overrun with A. allughas was classified as novel. If effective conservation and management does not come to the BRLM, the remaining swamp forest fragments appear doomed to further degradation and will soon disappear altogether.

Characterizing the Status (Disturbed, Hybrid or Novel) of Swamp Forest Fragments in a Caribbean Ramsar Wetland: The Impact of Anthropogenic Degradation and Invasive Plant Species

NASA Astrophysics Data System (ADS)

Prospere, Kurt; McLaren, Kurt P.; Wilson, Byron

2016-10-01

The last remaining Amazonian-type swamp forest fragments in Black River Lower Morass, Jamaica, have been subjected to a myriad of anthropogenic disturbances, compounded by the establishment and spread of several invasive plant species. We established 44 permanent sample plots (covering 3.92 ha) across 10 of these swamp forest fragments and sampled all non-woody plants and all trees ≥2 cm DBH found in the plots. These data were used to (1) identify thresholds of hybridity and novelty, (2) derive several diversity and structural descriptors used to characterize the swamp forest fragments and (3) identify possible indicators of anthropogenic degradation. These were incorporated into a framework and used to determine the status of the swamp forest fragments so that appropriate management and conservation measures can be implemented. We recorded 43 woody plant species (9 endemic, 28 native and 4 non-native) and 21 non-tree species. The composition and structure of all the patches differed significantly due to the impact of the herbaceous invasive plant Alpinia allughas, the presence and diversity of other non-native plants, and differing intensities of anthropogenic disturbance (e.g., burning, cutting and harvesting of non-timber forest products). We ranked forest patches along a continuum representing deviations from a historical proxy (least disturbed) swamp forest to those with dramatically altered structural and floristic attributes (=novel swamp forests). Only one fragment overrun with A. allughas was classified as novel. If effective conservation and management does not come to the BRLM, the remaining swamp forest fragments appear doomed to further degradation and will soon disappear altogether.

Operational use of Landsat data for timber inventory

NASA Technical Reports Server (NTRS)

Price, Curtis V.; Bowlin, Harry L.

1987-01-01

Landsat TM data, digital elevation model (DEM) data, and field observations were used to generate a timber type/structure and land-cover strata map of the Sequoia National Forest in California, U.S. and to create a classification data set. The spectral classes were identified as 32 information classes of land cover or timber type and structure. DEM data were used for the determination of major timber specie types by topographic regions of natural occurrence. The results suggest that, for inventories over large areas, traditional per-pixel classifiers are not appropriate for TM-resolution data sets over spatially complex regions such as forest lands; either resolution must be degraded, or more context-dependent classifiers, such as the ECHO classifier described by Landgrebe (1979), must be used.

Tree species classification in subtropical forests using small-footprint full-waveform LiDAR data

NASA Astrophysics Data System (ADS)

Cao, Lin; Coops, Nicholas C.; Innes, John L.; Dai, Jinsong; Ruan, Honghua; She, Guanghui

2016-07-01

The accurate classification of tree species is critical for the management of forest ecosystems, particularly subtropical forests, which are highly diverse and complex ecosystems. While airborne Light Detection and Ranging (LiDAR) technology offers significant potential to estimate forest structural attributes, the capacity of this new tool to classify species is less well known. In this research, full-waveform metrics were extracted by a voxel-based composite waveform approach and examined with a Random Forests classifier to discriminate six subtropical tree species (i.e., Masson pine (Pinus massoniana Lamb.)), Chinese fir (Cunninghamia lanceolata (Lamb.) Hook.), Slash pines (Pinus elliottii Engelm.), Sawtooth oak (Quercus acutissima Carruth.) and Chinese holly (Ilex chinensis Sims.) at three levels of discrimination. As part of the analysis, the optimal voxel size for modelling the composite waveforms was investigated, the most important predictor metrics for species classification assessed and the effect of scan angle on species discrimination examined. Results demonstrate that all tree species were classified with relatively high accuracy (68.6% for six classes, 75.8% for four main species and 86.2% for conifers and broadleaved trees). Full-waveform metrics (based on height of median energy, waveform distance and number of waveform peaks) demonstrated high classification importance and were stable among various voxel sizes. The results also suggest that the voxel based approach can alleviate some of the issues associated with large scan angles. In summary, the results indicate that full-waveform LIDAR data have significant potential for tree species classification in the subtropical forests.

36 CFR 1260.28 - Who is responsible for declassifying records that contain information classified under the Atomic...

Code of Federal Regulations, 2011 CFR

2011-07-01

... 36 Parks, Forests, and Public Property 3 2011-07-01 2011-07-01 false Who is responsible for declassifying records that contain information classified under the Atomic Energy Act of 1954, as amended... records that contain information classified under the Atomic Energy Act of 1954, as amended, commonly...

36 CFR 1260.28 - Who is responsible for declassifying records that contain information classified under the Atomic...

Code of Federal Regulations, 2010 CFR

2010-07-01

... 36 Parks, Forests, and Public Property 3 2010-07-01 2010-07-01 false Who is responsible for declassifying records that contain information classified under the Atomic Energy Act of 1954, as amended... records that contain information classified under the Atomic Energy Act of 1954, as amended, commonly...

A multiscale curvature algorithm for classifying discrete return LiDAR in forested environments

Treesearch

Jeffrey S. Evans; Andrew T. Hudak

2007-01-01

One prerequisite to the use of light detection and ranging (LiDAR) across disciplines is differentiating ground from nonground returns. The objective was to automatically and objectively classify points within unclassified LiDAR point clouds, with few model parameters and minimal postprocessing. Presented is an automated method for classifying LiDAR returns as ground...

Spatial and temporal dimensions of landscape fragmentation across the Brazilian Amazon.

PubMed

Rosa, Isabel M D; Gabriel, Cristina; Carreiras, Joāo M B

2017-01-01

The Brazilian Amazon in the past decades has been suffering severe landscape alteration, mainly due to anthropogenic activities, such as road building and land clearing for agriculture. Using a high-resolution time series of land cover maps (classified as mature forest, non-forest, secondary forest) spanning from 1984 through 2011, and four uncorrelated fragmentation metrics (edge density, clumpiness index, area-weighted mean patch size and shape index), we examined the temporal and spatial dynamics of forest fragmentation in three study areas across the Brazilian Amazon (Manaus, Santarém and Machadinho d'Oeste), inside and outside conservation units. Moreover, we compared the impacts on the landscape of: (1) different land uses (e.g. cattle ranching, crop production), (2) occupation processes (spontaneous vs. planned settlements) and (3) implementation of conservation units. By 2010/2011, municipalities located along the Arc of Deforestation had more than 55% of the remaining mature forest strictly confined to conservation units. Further, the planned settlement showed a higher rate of forest loss, a more persistent increase in deforested areas and a higher relative incidence of deforestation inside conservation units. Distinct agricultural activities did not lead to significantly different landscape structures; the accessibility of the municipality showed greater influence in the degree of degradation of the landscapes. Even with a high proportion of the landscapes covered by conservation units, which showed a strong inhibitory effect on forest fragmentation, we show that dynamic agriculturally driven economic activities, in municipalities with extensive road development, led to more regularly shaped, heavily fragmented landscapes, with higher densities of forest edge.

Forest fires impact in semi arid lands in Algeria, analysis and followed of desertification by using remote sensing and GIS

NASA Astrophysics Data System (ADS)

Zegrar, Ahmed

The Forest in steppe present ecological diversity, and seen climatic unfavourable conditions in zone and impact of forest fires; we notes deterioration of physical environment particularly, deterioration of natural forest. This deterioration of forests provokes an unbalance of environment witch provokes a process of deterioration advanced in the ultimate stadium is desertification. By elsewhere, where climatic conditions are favourable, the fire is an ecological and acted agent like integral part of evolution of the ecosystems, the specific regeneration of plants are influenced greatly by the regime of fire (season of fire, intensity, interval), who leads to the recuperation of the vegetation of meadow- fire. In this survey we used the pictures ALSAT-1 for detection of zones with risk of forest fire and their impact on the naturals forests in region of Tlemcen. A thematic detailed analysis of forests well attended ecosystems some processing on the picture ALSAT-1, we allowed to identify and classifying the forests in there opinion components flowers. we identified ampleness of fire on this zone also. Some parameters as the slope, the proximity to the road and the forests formations were studied in the goal of determining the zones to risk of forest fire. A crossing of diaper of information in a SIG according to a very determined logic allowed to classify the zones in degree of risk of fire in a middle arid in a forest zone not encouraging the regeneration on the other hand permitting the installation of cash of steppe which encourages the desertification.

An evaluation of the impact of forest biomass harvest for biofuels on carbon storage in the US west coast states under different management scenarios

NASA Astrophysics Data System (ADS)

Hudiburg, T. M.; Law, B. E.

2009-12-01

Mitigation strategies to reduce fossil fuel emissions of carbon dioxide have lead to investigation of alternative sources of fuels. National and state forest policies have been implemented to both reduce risk of wildfire and promote use of forest biomass as a secondary biofuels energy source. However, the cost and biomass availability have been estimated without quantifying the impact on current and future terrestrial carbon balances. This study uses a combination of Federal Inventory Analysis data (FIA) and supplementary plot data for Washington, Oregon and California to quantify the current forest carbon stocks, net ecosystem production (NEP), and net biome production (NBP = NEP - removals) for the period from 2001-2006. Varying management treatments were applied to determine the net cost, carbon debt, and biofuels energy potential. The treatments were designed to meet multiple objectives emphasizing carbon storage, economic gain, or energy production. The hazardous fuels reduction treatment minimizes carbon loss by only harvesting biomass in forested areas classified by moderate to high risk fire condition classes (FRCC class). This scenario assumes no additional harvest in ecoregions characterized by long fire return intervals (>100 years) such as the Coast Range and the West Cascades and limits removals to an 18 in diameter at breast height (DBH). The energy production treatment maximizes biomass removal by harvesting areas regardless of FRCC class and allows removals up to a 24 inch DBH. Statewide estimates of carbon for 2001-2006 prior to harvest scenarios for California, Oregon, and Washington respectively are as follows: 1) Total land-based carbon stocks (excluding soils) averages 1680, 1663, and 1278 Tg C; 2) NEP is positive in most ecoregions averaging 213, 180, and 191 g C m-2 yr-1; 3) Actual harvest removals averaged 2.7, 6.5, and 5.1 Tg C yr-1 for the same period. In Oregon, the amount of biomass available for biofuels varies from 128 g C m-2 in the hazardous fuels reduction treatment versus 185 g C m-2 in the energy production treatment. Removal of this biomass over the next 20 years is estimated to result in an additional 4 Tg C yr-1 (a 60% increase) in harvest removals for the hazardous fuels reduction treatment and an additional 14 Tg C yr-1 (216% increase) for the energy production treatment. Even in a minimal removals scenario and assuming no other disturbance losses (i.e. insects and fire), Oregon forest NBP will be significantly reduced with the potential to become a carbon source.

Classifying Wildfire Causes in the USDA Forest Service: Problems and Alternatives

Treesearch

Linda R. Donoghue

1982-01-01

Discusses problems associated with fire-cause data on USDA Forest Service wildfire reports, traces the historical development of wildfire-cause categories, and presents the pros and cons of retaining current wildfire-cause reporting systems or adopting new systems.

Examining conifer canopy structural complexity across forest ages and elevations with LiDAR data

Treesearch

Van R. Kane; Jonathan D. Bakker; Robert J. McGaughey; James A. Lutz; Rolf F. Gersonde; Jerry F. Franklin

2010-01-01

LiDAR measurements of canopy structure can be used to classify forest stands into structural stages to study spatial patterns of canopy structure, identify habitat, or plan management actions. A key assumption in this process is that differences in canopy structure based on forest age and elevation are consistent with predictions from models of stand development. Three...

Classification of the forest vegetation on the National Forests of Arizona and New Mexico

Treesearch

Robert R. Alexander; Frank Ronco

1987-01-01

Forest vegetation classified by habitat types and community types in Arizona and New Mexico are tabulated. Eleven series and 123 habitat and community types are identified; however, these habitat types and community types have been grouped into a lesser number of categories having similar characteristics or synonymous names. The table includes the name, location,...

New Tree-Classification System Used by the Southern Forest Inventory and Analysis Unit

Treesearch

Dennis M. May; John S. Vissage; D. Vince Few

1990-01-01

Trees at USDA Forest Service, Southern Forest Inventory and Analysis, sample locations are classified as growing stock or cull based on their ability to produce sawlogs. The old and new classification systems are compared, and the impacts of the new system on the reporting of tree volumes are illustrated with inventory data from north Alabama.

Forested Communities of the Upper Montane in the Central and Southern Sierra Nevada

Treesearch

Donald A. Potter

1998-01-01

Upper montane forests in the central and southern Sierra Nevada of California were classified into 26 plant associations by using information collected from 0.1-acre circular plots. Within this region, the forested environment including the physiographic setting, geology, soils, and vegetation is described in detail. A simulation model is presented for this portion of...

A preview of New Jersey's forest resource

Treesearch

Joseph E. Barnard; Teresa M. Bowers

1973-01-01

The recently completed forest survey of New Jersey indicates that 54 percent of the land area has tree cover on it. Thirty-eight percent of the state is classified as commercial forest land. Total growing-stock volume has increased, although the softwood component of the resource has decreased in both cubic-foot volume and area occupied by the softwood types. Average...

Integrating support vector machines and random forests to classify crops in time series of Worldview-2 images

NASA Astrophysics Data System (ADS)

Zafari, A.; Zurita-Milla, R.; Izquierdo-Verdiguier, E.

2017-10-01

Crop maps are essential inputs for the agricultural planning done at various governmental and agribusinesses agencies. Remote sensing offers timely and costs efficient technologies to identify and map crop types over large areas. Among the plethora of classification methods, Support Vector Machine (SVM) and Random Forest (RF) are widely used because of their proven performance. In this work, we study the synergic use of both methods by introducing a random forest kernel (RFK) in an SVM classifier. A time series of multispectral WorldView-2 images acquired over Mali (West Africa) in 2014 was used to develop our case study. Ground truth containing five common crop classes (cotton, maize, millet, peanut, and sorghum) were collected at 45 farms and used to train and test the classifiers. An SVM with the standard Radial Basis Function (RBF) kernel, a RF, and an SVM-RFK were trained and tested over 10 random training and test subsets generated from the ground data. Results show that the newly proposed SVM-RFK classifier can compete with both RF and SVM-RBF. The overall accuracies based on the spectral bands only are of 83, 82 and 83% respectively. Adding vegetation indices to the analysis result in the classification accuracy of 82, 81 and 84% for SVM-RFK, RF, and SVM-RBF respectively. Overall, it can be observed that the newly tested RFK can compete with SVM-RBF and RF classifiers in terms of classification accuracy.

Random forests in non-invasive sensorimotor rhythm brain-computer interfaces: a practical and convenient non-linear classifier.

PubMed

Steyrl, David; Scherer, Reinhold; Faller, Josef; Müller-Putz, Gernot R

2016-02-01

There is general agreement in the brain-computer interface (BCI) community that although non-linear classifiers can provide better results in some cases, linear classifiers are preferable. Particularly, as non-linear classifiers often involve a number of parameters that must be carefully chosen. However, new non-linear classifiers were developed over the last decade. One of them is the random forest (RF) classifier. Although popular in other fields of science, RFs are not common in BCI research. In this work, we address three open questions regarding RFs in sensorimotor rhythm (SMR) BCIs: parametrization, online applicability, and performance compared to regularized linear discriminant analysis (LDA). We found that the performance of RF is constant over a large range of parameter values. We demonstrate - for the first time - that RFs are applicable online in SMR-BCIs. Further, we show in an offline BCI simulation that RFs statistically significantly outperform regularized LDA by about 3%. These results confirm that RFs are practical and convenient non-linear classifiers for SMR-BCIs. Taking into account further properties of RFs, such as independence from feature distributions, maximum margin behavior, multiclass and advanced data mining capabilities, we argue that RFs should be taken into consideration for future BCIs.

Renewal of Collaborative Research: Economically Viable Forest Harvesting Practices That Increase Carbon Sequestration

DOE Office of Scientific and Technical Information (OSTI.GOV)

Davidson, E.A.; Dail, D.B., Hollinger, D.; Scott, N.

Forests provide wildlife habitat, water and air purification, climate moderation, and timber and nontimber products. Concern about climate change has put forests in the limelight as sinks of atmospheric carbon. The C stored in the global vegetation, mostly in forests, is nearly equivalent to the amount present in atmospheric CO{sub 2}. Both voluntary and government-mandated carbon trading markets are being developed and debated, some of which include C sequestration resulting from forest management as a possible tradeable commodity. However, uncertainties regarding sources of variation in sequestration rates, validation, and leakage remain significant challenges for devising strategies to include forest managementmore » in C markets. Hence, the need for scientifically-based information on C sequestration by forest management has never been greater. The consequences of forest management on the US carbon budget are large, because about two-thirds of the {approx}300 million hectare US forest resource is classified as 'commercial forest.' In most C accounting budgets, forest harvesting is usually considered to cause a net release of C from the terrestrial biosphere to the atmosphere. However, forest management practices could be designed to meet the multiple goals of providing wood and paper products, creating economic returns from natural resources, while sequestering C from the atmosphere. The shelterwood harvest strategy, which removes about 30% of the basal area of the overstory trees in each of three successive harvests spread out over thirty years as part of a stand rotation of 60-100 years, may improve net C sequestration compared to clear-cutting because: (1) the average C stored on the land surface over a rotation increases, (2) harvesting only overstory trees means that a larger fraction of the harvested logs can be used for long-lived sawtimber products, compared to more pulp resulting from clearcutting, (3) the shelterwood cut encourages growth of subcanopy trees by opening up the forest canopy to increasing light penetration. Decomposition of onsite harvest slash and of wastes created during timber processing releases CO{sub 2} to the atmosphere, thus offsetting some of the C sequestered in vegetation. Decomposition of soil C and dead roots may also be temporarily stimulated by increased light penetration and warming of the forest floor. Quantification of these processes and their net effect is needed. We began studying C sequestration in a planned shelterwood harvest at the Howland Forest in central Maine in 2000. The harvest took place in 2002 by the International Paper Corporation, who assisted us to track the fates of harvest products (Scott et al., 2004, Environmental Management 33: S9-S22). Here we present the results of intensive on-site studies of the decay of harvest slash, soil respiration, growth of the remaining trees, and net ecosystem exchange (NEE) of CO{sub 2} during the first six years following the harvest. These results are combined with calculations of C in persisting off-site harvest products to estimate the net C consequences to date of this commercial shelterwood harvest operation. Tower-based eddy covariance is an ideal method for this study, as it integrates all C fluxes in and out of the forest over a large 'footprint' area and can reveal how the net C flux, as well as gross primary productivity and respiration, change following harvest. Because the size of this experiment precludes large-scale replication, we are use a paired-airshed approach, similar to classic large-scale paired watershed experiments. Measurements of biomass and C fluxes in control and treatment stands were compared during a pre-treatment calibration period, and then divergence from pre-treatment relationships between the two sites measured after the harvest treatment. Forests store carbon (C) as they accumulate biomass. Many forests are also commercial sources of timber and wood fiber. In most C accounting budgets, forest harvesting is usually considered to cause a net release of C from the terrestrial biosphere to the atmosphere. However, it might also be possible for commercial use of forests to contribute to terrestrial sequestration of C. The objective of the our research project is to determine whether shelterwood cutting regimes now being adopted in the commercial forests of Maine and other areas of the country can achieve these multiple goals.« less

Neonatal Seizure Detection Using Deep Convolutional Neural Networks.

PubMed

Ansari, Amir H; Cherian, Perumpillichira J; Caicedo, Alexander; Naulaers, Gunnar; De Vos, Maarten; Van Huffel, Sabine

2018-04-02

Identifying a core set of features is one of the most important steps in the development of an automated seizure detector. In most of the published studies describing features and seizure classifiers, the features were hand-engineered, which may not be optimal. The main goal of the present paper is using deep convolutional neural networks (CNNs) and random forest to automatically optimize feature selection and classification. The input of the proposed classifier is raw multi-channel EEG and the output is the class label: seizure/nonseizure. By training this network, the required features are optimized, while fitting a nonlinear classifier on the features. After training the network with EEG recordings of 26 neonates, five end layers performing the classification were replaced with a random forest classifier in order to improve the performance. This resulted in a false alarm rate of 0.9 per hour and seizure detection rate of 77% using a test set of EEG recordings of 22 neonates that also included dubious seizures. The newly proposed CNN classifier outperformed three data-driven feature-based approaches and performed similar to a previously developed heuristic method.

Drought-induced changes in Amazon forest structure from repeat airborne lidar

NASA Astrophysics Data System (ADS)

Morton, D. C.; Leitold, V.; Longo, M.; Keller, M.; dos-Santos, M. N.; Scaranello, M. A., Sr.

2017-12-01

Drought events in tropical forests, including the 2015-2016 El Niño, may reduce net primary productivity and increase canopy tree mortality, thereby altering the short and long-term net carbon balance of tropical forests. Given the broad extent of drought impacts, forest inventory plots or eddy flux towers may not capture regional variability in forest response to drought. Here, we analyzed repeat airborne lidar data to evaluate canopy turnover from branch and tree fall before (2013-2014) and during (2014-2016) the recent El Niño drought in the eastern and central Brazilian Amazon. Coincident field surveys for a 16-ha subset of the lidar coverage provided complementary information to classify turnover areas by mechanism (branch, multiple branch, tree fall, multiple tree fall) and estimate the total coarse woody debris volume from canopy and understory tree mortality. Annualized rates of canopy turnover increased by 50%, on average, during the drought period in both intact and fragmented forests near Santarém, Pará. Turnover increased uniformly across all size classes, and there was limited evidence that taller trees contributed a greater proportion of turnover events in any size class in 2014-2016 compared to 2013-2014. This short-term increase in canopy turnover differs from findings in multi-year rainfall exclusion experiments that large trees were more sensitive to drought impacts. Field measurements confirmed the separability of the smallest (single branch) and largest damage classes (multiple tree falls), but single tree and multiple branch fall events generated similar coarse woody debris production and lidar-derived changes in canopy volume. Large-scale sampling possible with repeat airborne lidar data also captured strong local and regional gradients in canopy turnover. Differences in slope partially explained the north-south gradient in canopy turnover dynamics near Santarém, with larger increases in turnover on flatter terrain. Regional variability in canopy turnover in response to drought conditions highlights the need for a mechanistic representation of branch and tree fall dynamics in ecosystem models to resolve changes in net carbon balance from the increase in coarse woody debris production and reorganization of canopy light environments during drought years.

Intelligent Fault Diagnosis of HVCB with Feature Space Optimization-Based Random Forest

PubMed Central

Ma, Suliang; Wu, Jianwen; Wang, Yuhao; Jia, Bowen; Jiang, Yuan

2018-01-01

Mechanical faults of high-voltage circuit breakers (HVCBs) always happen over long-term operation, so extracting the fault features and identifying the fault type have become a key issue for ensuring the security and reliability of power supply. Based on wavelet packet decomposition technology and random forest algorithm, an effective identification system was developed in this paper. First, compared with the incomplete description of Shannon entropy, the wavelet packet time-frequency energy rate (WTFER) was adopted as the input vector for the classifier model in the feature selection procedure. Then, a random forest classifier was used to diagnose the HVCB fault, assess the importance of the feature variable and optimize the feature space. Finally, the approach was verified based on actual HVCB vibration signals by considering six typical fault classes. The comparative experiment results show that the classification accuracy of the proposed method with the origin feature space reached 93.33% and reached up to 95.56% with optimized input feature vector of classifier. This indicates that feature optimization procedure is successful, and the proposed diagnosis algorithm has higher efficiency and robustness than traditional methods. PMID:29659548

Classifier ensemble construction with rotation forest to improve medical diagnosis performance of machine learning algorithms.

PubMed

Ozcift, Akin; Gulten, Arif

2011-12-01

Improving accuracies of machine learning algorithms is vital in designing high performance computer-aided diagnosis (CADx) systems. Researches have shown that a base classifier performance might be enhanced by ensemble classification strategies. In this study, we construct rotation forest (RF) ensemble classifiers of 30 machine learning algorithms to evaluate their classification performances using Parkinson's, diabetes and heart diseases from literature. While making experiments, first the feature dimension of three datasets is reduced using correlation based feature selection (CFS) algorithm. Second, classification performances of 30 machine learning algorithms are calculated for three datasets. Third, 30 classifier ensembles are constructed based on RF algorithm to assess performances of respective classifiers with the same disease data. All the experiments are carried out with leave-one-out validation strategy and the performances of the 60 algorithms are evaluated using three metrics; classification accuracy (ACC), kappa error (KE) and area under the receiver operating characteristic (ROC) curve (AUC). Base classifiers succeeded 72.15%, 77.52% and 84.43% average accuracies for diabetes, heart and Parkinson's datasets, respectively. As for RF classifier ensembles, they produced average accuracies of 74.47%, 80.49% and 87.13% for respective diseases. RF, a newly proposed classifier ensemble algorithm, might be used to improve accuracy of miscellaneous machine learning algorithms to design advanced CADx systems. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

Discriminating Canopy Structural Types from Optical Properties using AVIRIS Data in the Sierra National Forest in Central California

NASA Astrophysics Data System (ADS)

Huesca Martinez, M.; Garcia, M.; Roth, K. L.; Casas, A.; Ustin, S.

2015-12-01

There is a well-established need within the remote sensing community for improved estimation of canopy structure and understanding of its influence on the retrieval of leaf biochemical properties. The aim of this project was to evaluate the estimation of structural properties directly from hyperspectral data, with the broader goal that these might be used to constrain retrievals of canopy chemistry. We used NASA's Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) to discriminate different canopy structural types, defined in terms of biomass, canopy height and vegetation complexity, and compared them to estimates of these properties measured by LiDAR data. We tested a large number of optical metrics, including single narrow band reflectance and 1st derivative, sub-pixel cover fractions, narrow-band indices, spectral absorption features, and Principal Component Analysis components. Canopy structural types were identified and classified from different forest types by integrating structural traits measured by optical metrics using the Random Forest (RF) classifier. The classification accuracy was above 70% in most of the vegetation scenarios. The best overall accuracy was achieved for hardwood forest (>80% accuracy) and the lowest accuracy was found in mixed forest (~70% accuracy). Furthermore, similarly high accuracy was found when the RF classifier was applied to a spatially independent dataset, showing significant portability for the method used. Results show that all spectral regions played a role in canopy structure assessment, thus the whole spectrum is required. Furthermore, optical metrics derived from AVIRIS proved to be a powerful technique for structural attribute mapping. This research illustrates the potential for using optical properties to distinguish several canopy structural types in different forest types, and these may be used to constrain quantitative measurements of absorbing properties in future research.

Supervised fully polarimetric classification of the Black Forest test site: From MAESTROI to MAC Europe

NASA Technical Reports Server (NTRS)

Degrandi, G.; Lavalle, C.; Degroof, H.; Sieber, A.

1992-01-01

A study on the performance of a supervised fully polarimetric maximum likelihood classifier for synthetic aperture radar (SAR) data when applied to a specific classification context: forest classification based on age classes and in the presence of a sloping terrain is presented. For the experimental part, the polarimetric AIRSAR data at P, L, and C-band, acquired over the German Black Forest near Freiburg in the frame of the 1989 MAESTRO-1 campaign and the 1991 MAC Europe campaign was used, MAESTRO-1 with an ESA/JRC sponsored campaign, and MAC Europe (Multi-sensor Aircraft Campaign); in both cases the multi-frequency polarimetric JPL Airborne Synthetic Aperture Radar (AIRSAR) radar was flown over a number of European test sites. The study is structured as follows. At first, the general characteristics of the classifier and the dependencies from some parameters, like frequency bands, feature vector, calibration, using test areas lying on a flat terrain are investigated. Once it is determined the optimal conditions for the classifier performance, we then move on to the study of the slope effect. The bulk of this work is performed using the Maestrol data set. Next the classifier performance with the MAC Europe data is considered. The study is divided into two stages: first some of the tests done on the Maestro data are repeated, to highlight the improvements due to the new processing scheme that delivers 16 look data. Second we experiment with multi images classification with two goals: to assess the possibility of using a training set measured from one image to classify areas in different images; and to classify areas on critical slopes using different viewing angles. The main points of the study are listed and some of the results obtained so far are highlighted.

An assessment of the effectiveness of a random forest classifier for land-cover classification

NASA Astrophysics Data System (ADS)

Rodriguez-Galiano, V. F.; Ghimire, B.; Rogan, J.; Chica-Olmo, M.; Rigol-Sanchez, J. P.

2012-01-01

Land cover monitoring using remotely sensed data requires robust classification methods which allow for the accurate mapping of complex land cover and land use categories. Random forest (RF) is a powerful machine learning classifier that is relatively unknown in land remote sensing and has not been evaluated thoroughly by the remote sensing community compared to more conventional pattern recognition techniques. Key advantages of RF include: their non-parametric nature; high classification accuracy; and capability to determine variable importance. However, the split rules for classification are unknown, therefore RF can be considered to be black box type classifier. RF provides an algorithm for estimating missing values; and flexibility to perform several types of data analysis, including regression, classification, survival analysis, and unsupervised learning. In this paper, the performance of the RF classifier for land cover classification of a complex area is explored. Evaluation was based on several criteria: mapping accuracy, sensitivity to data set size and noise. Landsat-5 Thematic Mapper data captured in European spring and summer were used with auxiliary variables derived from a digital terrain model to classify 14 different land categories in the south of Spain. Results show that the RF algorithm yields accurate land cover classifications, with 92% overall accuracy and a Kappa index of 0.92. RF is robust to training data reduction and noise because significant differences in kappa values were only observed for data reduction and noise addition values greater than 50 and 20%, respectively. Additionally, variables that RF identified as most important for classifying land cover coincided with expectations. A McNemar test indicates an overall better performance of the random forest model over a single decision tree at the 0.00001 significance level.

Acute and long-term effects of irradiation on pine (Pinus silvestris) strands post-Chernobyl.

PubMed

Arkhipov, N P; Kuchma, N D; Askbrant, S; Pasternak, P S; Musica, V V

1994-12-11

The effect of ionizing irradiation on the viability of pine stands after the fallout from the damaged nuclear energy plant at Chernobyl (ChNPP) was shown within the territory of the 10-km zone. During the period 1986-1991, irradiated and damaged forest stands, so-called 'red forest', located in this area were systematically classified by observation. Mortality rate, re-establishment, development of tree canopies, reproduction anomalies and stand viability were shown to be dependent on absorbed irradiation dose, on the age of the stand and on forest composition. For pine stands in the acutely affected zone, doses of more than 60 Gy resulted in a massive mortality and no regeneration of pine trees since 1987. The injured trees had burned or had dried-up. The drying process was accelerated by a massive production of pathogenic insects invading the dying trees. Specifically, irradiation doses of 10-60 Gy, 1-10 Gy and 0.1-1 Gy caused high, medium and low injury to the forest stands, respectively. Doses of less than 0.1 Gy did not cause any visible damage to the trees. In 1987, repair processes were displayed by the tree canopies and practically the entire viability of the forest stands had recovered except for trees in the acute and highly affected zones. The young forest was reestablished in the same place as the perished trees and new pine saplings were planted on the reclaimed areas.

Next-Generation Terrestrial Laser Scanning to Measure Forest Canopy Structure

NASA Astrophysics Data System (ADS)

Danson, M.

2015-12-01

Terrestrial laser scanners (TLS) are now capable of semi-automatic reconstruction of the structure of complete trees or forest stands and have the potential to provide detailed information on tree architecture and foliage biophysical properties. The trends for the next generation of TLS are towards higher resolution, faster scanning and full-waveform data recording, with mobile, multispectral laser devices. The convergence of these technological advances in the next generation of TLS will allow the production of information for forest and woodland mapping and monitoring that is far more detailed, more accurate, and more comprehensive than any available today. This paper describes recent scientific advances in the application of TLS for characterising forest and woodland areas, drawing on the authors' development of the Salford Advanced Laser Canopy Analyser (SALCA), the activities of the Terrestrial Laser Scanner International Interest Group (TLSIIG), and recent advances in laser scanner technology around the world. The key findings illustrated in the paper are that (i) a complete understanding of system measurement characteristics is required for quantitative analysis of TLS data, (ii) full-waveform data recording is required for extraction of forest biophysical variables and, (iii) multi-wavelength systems provide additional spectral information that is essential for classifying different vegetation components. The paper uses a range of recent experimental TLS measurements to support these findings, and sets out a vision for new research to develop an information-rich future-forest information system, populated by mobile autonomous multispectral TLS devices.

Stratified estimation of forest area using satellite imagery, inventory data, and the k-nearest neighbors technique

Treesearch

Ronald E. McRoberts; Mark D. Nelson; Daniel G. Wendt

2002-01-01

For two large study areas in Minnesota, USA, stratified estimation using classified Landsat Thematic Mapper satellite imagery as the basis for stratification was used to estimate forest area. Measurements of forest inventory plots obtained for a 12-month period in 1998 and 1999 were used as the source of data for within-stratum estimates. These measurements further...

Urban forest cover of the Chicago region and its relation to household density and income

Treesearch

Louis R. Iverson; Elizabeth A. Cook; Elizabeth A. Cook

2000-01-01

Urban forests and herbaceous open space play a vital role in the environmental and aesthetic ?health? of cities, yet they are rarely identified in land-use inventories of urban areas. To provide information on urban forests and other vegetative land cover in Illinois cities, Landsat Thematic Mapper (TM) data from June 27, 1988, were classified for the Chicago...

Filling of Cloud-Induced Gaps for Land Use and Land Cover Classifications Around Refugee Camps

NASA Astrophysics Data System (ADS)

Braun, Andreas; Hagensieker, Ron; Hochschild, Volker

2016-08-01

Clouds cover is one of the main constraints in the field of optical remote sensing. Especially the use of multispectral imagery is affected by either fully obscured data or parts of the image which remain unusable. This study compares four algorithms for the filling of cloud induced gaps in classified land cover products based on Markov Random Fields (MRF), Random Forest (RF), Closest Spectral Fit (CSF) operators. They are tested on a classified image of Sentinel-2 where artificial clouds are filled by information derived from a scene of Sentinel-1. The approaches rely on different mathematical principles and therefore produced results varying in both pattern and quality. Overall accuracies for the filled areas range from 57 to 64 %. Best results are achieved by CSF, however some classes (e.g. sands and grassland) remain critical through all approaches.

Integrating reconstructed scatterometer and advanced very high resolution radiometer data for tropical forest inventory

NASA Astrophysics Data System (ADS)

Hardin, Perry J.; Long, David G.

1995-11-01

A scientific effort is currently underway to assess tropical forest degradation and its potential impact on Earth's climate. Because of the large continental regions involved, Advanced Very High Resolution Radiometer (AVHRR) imagery and its derivative vegetation index products with resolutions between 1 and 12 km are typically used to inventory the Earth's equatorial vegetation. Archival AVHRR imagery is also used to obtain a temporal baseline of historical forest extent. Recently however, 50-km Seasat-A Scatterometer (SASS) Ku-band imagery (acquired in 1978) has been reconstructed to approximately equals 4-km resolution, making it a supplement to AVHRR imagery for historical vegetation assessment. In order to test the utility of reconstructed Ku-band scatterometer imagery for this purpose, seasonal AVHRR vegetation index and SASS images of identical resolutions were constructed. Using the imagery, discrimination experiments involving 18 vegetation categories were conducted for a central South America study area. The results of these experiments indicate that AVHRR vegetation- index images are slightly superior to reconstructed SASS images for differentiating between equatorial vegetation classes when used alone. However, combining the scatterometer imagery with the vegetation-index images provides discrimination superior to any other combination of the data sets. Using the two data sets together, 90.3% of the test data could be correctly classified into broad classes of equatorial forest, degraded woodland/forest, woodland/savanna, and caatinga.

Vegetation types, dominant compositions, woody plant diversity and stand structure in Trishna Wildlife Sanctuary of Northeast India.

PubMed

Majumdar, Koushik; Datta, B K

2015-03-01

Present study was carried out to assess the vegetation types, diversity and phytosociological status of woody plants in Trishna Wildlife Sanctuary of Tripura, Northeast India. Vegetation data was derived by 25 line transects (10 m wide and 500 m length, each 0.5 ha size). All woody species at >10 cm gbh (Girth at Breast Height) within each plots were measured and counted. A total of six forest types were classified by cluster analysis using Importance Value Index (IVI) of 289 woody species. Species diversity, forest structure and woody community associations were evaluated and discussed. One way ANOVA revealed significant differences in all species diversity measures and stand structure along the forest types. Distribution of stem density at ten different gbh classes showed reverse J-shaped curves. Population status of woody plants was also examined through grouping of all individuals into four population age stages viz. sapling (<30 cm gbh), adult (> or = 30 - <120 cm gbh), mature (>120 - 210 cm gbh) and old (> or =210 cm). To observe dominant composition and species population trend, IVI of top ten dominant species from all forest types were tabulated. The present study suggested that Trishna Wildlife Sanctuary is an important habitat in Tripura from floristic point of view and it should be conserved on priority basis for remaining wildlife endurances and monitor for forest livelihoods products for sustainable biodiversity conservation in this region.

BLM forest lands report -- 2006 status and condition

Treesearch

Tim Bottomley; Jim Menlove

2006-01-01

The Bureau of Land Management (BLM), an agency within the U.S. Department of the Interior (DOI), administers over 261 million surface acres of public land in the western United States, including Alaska. Approximately 69 million acres, or 26 percent, are classified as forested.

Evaluation of Classifier Performance for Multiclass Phenotype Discrimination in Untargeted Metabolomics.

PubMed

Trainor, Patrick J; DeFilippis, Andrew P; Rai, Shesh N

2017-06-21

Statistical classification is a critical component of utilizing metabolomics data for examining the molecular determinants of phenotypes. Despite this, a comprehensive and rigorous evaluation of the accuracy of classification techniques for phenotype discrimination given metabolomics data has not been conducted. We conducted such an evaluation using both simulated and real metabolomics datasets, comparing Partial Least Squares-Discriminant Analysis (PLS-DA), Sparse PLS-DA, Random Forests, Support Vector Machines (SVM), Artificial Neural Network, k -Nearest Neighbors ( k -NN), and Naïve Bayes classification techniques for discrimination. We evaluated the techniques on simulated data generated to mimic global untargeted metabolomics data by incorporating realistic block-wise correlation and partial correlation structures for mimicking the correlations and metabolite clustering generated by biological processes. Over the simulation studies, covariance structures, means, and effect sizes were stochastically varied to provide consistent estimates of classifier performance over a wide range of possible scenarios. The effects of the presence of non-normal error distributions, the introduction of biological and technical outliers, unbalanced phenotype allocation, missing values due to abundances below a limit of detection, and the effect of prior-significance filtering (dimension reduction) were evaluated via simulation. In each simulation, classifier parameters, such as the number of hidden nodes in a Neural Network, were optimized by cross-validation to minimize the probability of detecting spurious results due to poorly tuned classifiers. Classifier performance was then evaluated using real metabolomics datasets of varying sample medium, sample size, and experimental design. We report that in the most realistic simulation studies that incorporated non-normal error distributions, unbalanced phenotype allocation, outliers, missing values, and dimension reduction, classifier performance (least to greatest error) was ranked as follows: SVM, Random Forest, Naïve Bayes, sPLS-DA, Neural Networks, PLS-DA and k -NN classifiers. When non-normal error distributions were introduced, the performance of PLS-DA and k -NN classifiers deteriorated further relative to the remaining techniques. Over the real datasets, a trend of better performance of SVM and Random Forest classifier performance was observed.

Fangorn Forest (F2): a machine learning approach to classify genes and genera in the family Geminiviridae.

PubMed

Silva, José Cleydson F; Carvalho, Thales F M; Fontes, Elizabeth P B; Cerqueira, Fabio R

2017-09-30

Geminiviruses infect a broad range of cultivated and non-cultivated plants, causing significant economic losses worldwide. The studies of the diversity of species, taxonomy, mechanisms of evolution, geographic distribution, and mechanisms of interaction of these pathogens with the host have greatly increased in recent years. Furthermore, the use of rolling circle amplification (RCA) and advanced metagenomics approaches have enabled the elucidation of viromes and the identification of many viral agents in a large number of plant species. As a result, determining the nomenclature and taxonomically classifying geminiviruses turned into complex tasks. In addition, the gene responsible for viral replication (particularly, the viruses belonging to the genus Mastrevirus) may be spliced due to the use of the transcriptional/splicing machinery in the host cells. However, the current tools have limitations concerning the identification of introns. This study proposes a new method, designated Fangorn Forest (F2), based on machine learning approaches to classify genera using an ab initio approach, i.e., using only the genomic sequence, as well as to predict and classify genes in the family Geminiviridae. In this investigation, nine genera of the family Geminiviridae and their related satellite DNAs were selected. We obtained two training sets, one for genus classification, containing attributes extracted from the complete genome of geminiviruses, while the other was made up to classify geminivirus genes, containing attributes extracted from ORFs taken from the complete genomes cited above. Three ML algorithms were applied on those datasets to build the predictive models: support vector machines, using the sequential minimal optimization training approach, random forest (RF), and multilayer perceptron. RF demonstrated a very high predictive power, achieving 0.966, 0.964, and 0.995 of precision, recall, and area under the curve (AUC), respectively, for genus classification. For gene classification, RF could reach 0.983, 0.983, and 0.998 of precision, recall, and AUC, respectively. Therefore, Fangorn Forest is proven to be an efficient method for classifying genera of the family Geminiviridae with high precision and effective gene prediction and classification. The method is freely accessible at www.geminivirus.org:8080/geminivirusdw/discoveryGeminivirus.jsp .

36 CFR 223.215 - Applicability.

Code of Federal Regulations, 2012 CFR

2012-07-01

... DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS Special... Public Law 108-108, special forest products that are also forest botanical products shall be sold, or... forest botanical pilot program. A commercial sale of special forest products shall be governed by a...

36 CFR 223.215 - Applicability.

Code of Federal Regulations, 2013 CFR

2013-07-01

... DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS Special... Public Law 108-108, special forest products that are also forest botanical products shall be sold, or... forest botanical pilot program. A commercial sale of special forest products shall be governed by a...

Appendix 2: Risk-based framework and risk case studies. Risk-based framework for evaluating changes in response thresholds and vulnerabilities.

Treesearch

Dennis S. Ojima; Louis R. Iverson; Brent L. Sohngen

2012-01-01

Alaskan forests cover one-third of the state’s 52 million ha of land (Parson et al. 2001), and are regionally and globally significant. Ninety percent of Alaskan forests are classified as boreal, representing 4 percent of the world’s boreal forests, and are located throughout interior and south-central Alaska (fig. A1-1). The remaining 10 percent of Alaskan forests are...

Appendix 3: Western mountain initiative synthesis. Response of western mountain ecosystems to climatic variability and change: a synthesis from the western mountain initiative

Treesearch

Crystal L. Raymond

2012-01-01

Alaskan forests cover one-third of the state’s 52 million ha of land (Parson et al. 2001), and are regionally and globally significant. Ninety percent of Alaskan forests are classified as boreal, representing 4 percent of the world’s boreal forests, and are located throughout interior and south-central Alaska (fig. A1-1). The remaining 10 percent of Alaskan forests are...

Field evaluation of a random forest activity classifier for wrist-worn accelerometer data.

PubMed

Pavey, Toby G; Gilson, Nicholas D; Gomersall, Sjaan R; Clark, Bronwyn; Trost, Stewart G

2017-01-01

Wrist-worn accelerometers are convenient to wear and associated with greater wear-time compliance. Previous work has generally relied on choreographed activity trials to train and test classification models. However, validity in free-living contexts is starting to emerge. Study aims were: (1) train and test a random forest activity classifier for wrist accelerometer data; and (2) determine if models trained on laboratory data perform well under free-living conditions. Twenty-one participants (mean age=27.6±6.2) completed seven lab-based activity trials and a 24h free-living trial (N=16). Participants wore a GENEActiv monitor on the non-dominant wrist. Classification models recognising four activity classes (sedentary, stationary+, walking, and running) were trained using time and frequency domain features extracted from 10-s non-overlapping windows. Model performance was evaluated using leave-one-out-cross-validation. Models were implemented using the randomForest package within R. Classifier accuracy during the 24h free living trial was evaluated by calculating agreement with concurrently worn activPAL monitors. Overall classification accuracy for the random forest algorithm was 92.7%. Recognition accuracy for sedentary, stationary+, walking, and running was 80.1%, 95.7%, 91.7%, and 93.7%, respectively for the laboratory protocol. Agreement with the activPAL data (stepping vs. non-stepping) during the 24h free-living trial was excellent and, on average, exceeded 90%. The ICC for stepping time was 0.92 (95% CI=0.75-0.97). However, sensitivity and positive predictive values were modest. Mean bias was 10.3min/d (95% LOA=-46.0 to 25.4min/d). The random forest classifier for wrist accelerometer data yielded accurate group-level predictions under controlled conditions, but was less accurate at identifying stepping verse non-stepping behaviour in free living conditions Future studies should conduct more rigorous field-based evaluations using observation as a criterion measure. Copyright © 2016 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved.

An integrated classifier for computer-aided diagnosis of colorectal polyps based on random forest and location index strategies

NASA Astrophysics Data System (ADS)

Hu, Yifan; Han, Hao; Zhu, Wei; Li, Lihong; Pickhardt, Perry J.; Liang, Zhengrong

2016-03-01

Feature classification plays an important role in differentiation or computer-aided diagnosis (CADx) of suspicious lesions. As a widely used ensemble learning algorithm for classification, random forest (RF) has a distinguished performance for CADx. Our recent study has shown that the location index (LI), which is derived from the well-known kNN (k nearest neighbor) and wkNN (weighted k nearest neighbor) classifier [1], has also a distinguished role in the classification for CADx. Therefore, in this paper, based on the property that the LI will achieve a very high accuracy, we design an algorithm to integrate the LI into RF for improved or higher value of AUC (area under the curve of receiver operating characteristics -- ROC). Experiments were performed by the use of a database of 153 lesions (polyps), including 116 neoplastic lesions and 37 hyperplastic lesions, with comparison to the existing classifiers of RF and wkNN, respectively. A noticeable gain by the proposed integrated classifier was quantified by the AUC measure.

Examining change detection approaches for tropical mangrove monitoring

USGS Publications Warehouse

Myint, Soe W.; Franklin, Janet; Buenemann, Michaela; Kim, Won; Giri, Chandra

2014-01-01

This study evaluated the effectiveness of different band combinations and classifiers (unsupervised, supervised, object-oriented nearest neighbor, and object-oriented decision rule) for quantifying mangrove forest change using multitemporal Landsat data. A discriminant analysis using spectra of different vegetation types determined that bands 2 (0.52 to 0.6 μm), 5 (1.55 to 1.75 μm), and 7 (2.08 to 2.35 μm) were the most effective bands for differentiating mangrove forests from surrounding land cover types. A ranking of thirty-six change maps, produced by comparing the classification accuracy of twelve change detection approaches, was used. The object-based Nearest Neighbor classifier produced the highest mean overall accuracy (84 percent) regardless of band combinations. The automated decision rule-based approach (mean overall accuracy of 88 percent) as well as a composite of bands 2, 5, and 7 used with the unsupervised classifier and the same composite or all band difference with the object-oriented Nearest Neighbor classifier were the most effective approaches.

Automated segmentation of dental CBCT image with prior-guided sequential random forests

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, Li; Gao, Yaozong; Shi, Feng

Purpose: Cone-beam computed tomography (CBCT) is an increasingly utilized imaging modality for the diagnosis and treatment planning of the patients with craniomaxillofacial (CMF) deformities. Accurate segmentation of CBCT image is an essential step to generate 3D models for the diagnosis and treatment planning of the patients with CMF deformities. However, due to the image artifacts caused by beam hardening, imaging noise, inhomogeneity, truncation, and maximal intercuspation, it is difficult to segment the CBCT. Methods: In this paper, the authors present a new automatic segmentation method to address these problems. Specifically, the authors first employ a majority voting method to estimatemore » the initial segmentation probability maps of both mandible and maxilla based on multiple aligned expert-segmented CBCT images. These probability maps provide an important prior guidance for CBCT segmentation. The authors then extract both the appearance features from CBCTs and the context features from the initial probability maps to train the first-layer of random forest classifier that can select discriminative features for segmentation. Based on the first-layer of trained classifier, the probability maps are updated, which will be employed to further train the next layer of random forest classifier. By iteratively training the subsequent random forest classifier using both the original CBCT features and the updated segmentation probability maps, a sequence of classifiers can be derived for accurate segmentation of CBCT images. Results: Segmentation results on CBCTs of 30 subjects were both quantitatively and qualitatively validated based on manually labeled ground truth. The average Dice ratios of mandible and maxilla by the authors’ method were 0.94 and 0.91, respectively, which are significantly better than the state-of-the-art method based on sparse representation (p-value < 0.001). Conclusions: The authors have developed and validated a novel fully automated method for CBCT segmentation.« less

Decision support framework for evaluating the operational environment of forest bioenergy production and use: Case of four European countries.

PubMed

Pezdevšek Malovrh, Špela; Kurttila, Mikko; Hujala, Teppo; Kärkkäinen, Leena; Leban, Vasja; Lindstad, Berit H; Peters, Dörte Marie; Rhodius, Regina; Solberg, Birger; Wirth, Kristina; Zadnik Stirn, Lidija; Krč, Janez

2016-09-15

Complex policy-making situations around bioenergy production and use require examination of the operational environment of the society and a participatory approach. This paper presents and demonstrates a three-phase decision-making framework for analysing the operational environment of strategies related to increased forest bioenergy targets. The framework is based on SWOT (strengths, weaknesses, opportunities and threats) analysis and the Simple Multi-Attribute Rating Technique (SMART). Stakeholders of four case countries (Finland, Germany, Norway and Slovenia) defined the factors that affect the operational environments, classified in four pre-set categories (Forest Characteristics and Management, Policy Framework, Technology and Science, and Consumers and Society). The stakeholders participated in weighting of SWOT items for two future scenarios with SMART technique. The first scenario reflected the current 2020 targets (the Business-as-Usual scenario), and the second scenario contained a further increase in the targets (the Increase scenario). This framework can be applied to various problems of environmental management and also to other fields where public decision-making is combined with stakeholders' engagement. The case results show that the greatest differences between the scenarios appear in Germany, indicating a notably negative outlook for the Increase scenario, while the smallest differences were found in Finland. Policy Framework was a highly rated category across the countries, mainly with respect to weaknesses and threats. Intensified forest bioenergy harvesting and utilization has potentially wide country-specific impacts which need to be anticipated and considered in national policies and public dialogue. Copyright © 2016 Elsevier Ltd. All rights reserved.

Pheromone Production by an Invasive Bark Beetle Varies with Monoterpene Composition of its Naïve Host.

PubMed

Taft, Spencer; Najar, Ahmed; Erbilgin, Nadir

2015-06-01

The secondary chemistry of host plants can have cascading impacts on the establishment of new insect herbivore populations, their long-term population dynamics, and their invasion potential in novel habitats. Mountain pine beetle, Dendroctonus ponderosae Hopkins (Coleoptera: Curculionidae) has recently expanded its range into forests of jack pine, Pinus banksiana Lamb., in western Canada. We investigated whether variations in jack pine monoterpenes affect beetle pheromone production, as the primary components of the beetle's aggregation pheromone, (-)-trans-verbenol and anti-aggregation pheromone (-)-verbenone, are biosynthesized from the host monoterpene α-pinene. Jack pine bolts were collected from five Canadian provinces east of the beetle's current range, live D. ponderosae were introduced into them, and their monoterpene compositions were characterized. Production of (-)-trans-verbenol and (-)-verbenone emitted by beetles was measured to determine whether pheromone production varies with monoterpene composition of jack pines. Depending on particular ratios of major monoterpenes in host phloem, jack pine could be classified into three monoterpenoid groups characterized by high amounts of (+)-α-pinene, 3-carene, or a more moderate blend of monoterpenes, and beetle pheromone production varied among these groups. Specifically, beetles reared in trees characterized by high (+)-α-pinene produced the most (-)-trans-verbenol and (-)-verbenone, while beetles in trees characterized by high 3-carene produced the least. Our results indicate that pheromone production by D. ponderosae will remain a significant aspect and important predictor of its survival and persistence in the boreal forest.

Neighbourhood-Scale Urban Forest Ecosystem Classification

Treesearch

James W.N. Steenberg; Andrew A. Millward; Peter N. Duinker; David J. Nowak; Pamela J. Robinson

2015-01-01

Urban forests are now recognized as essential components of sustainable cities, but there remains uncertainty concerning how to stratify and classify urban landscapes into units of ecological significance at spatial scales appropriate for management. Ecosystem classification is an approach that entails quantifying the social and ecological processes that shape...

Dynamics and pattern of a managed coniferous forest landscape in Oregon

NASA Technical Reports Server (NTRS)

Spies, Thomas A.; Ripple, William J.; Bradshaw, G. A.

1995-01-01

We examined the process of fragmentation in a managed forest landscape by comparing rates and patterns of disturbance (primarily clear-cutting) and regrowth between 1972 and 1988 using Landsat imagery. A 2589-km(exp 2) managed forest landscape in western Oregon was classified into two forest types, closed-canopy conifer forest (CF) (typically, greater than 60% conifer cover) and other forest and nonforest types (OT) (typically, less than 40 yr old or deciduous forest). The percentage of CF declined from 71 to 58% between 1972 and 1988. Declines were greatest on private land, least in wilderness, and intermediate in public nonwilderness. High elevations (greater than 914 m) maintained a greater percentage of CF than lower elevations (less than 914 m). The percentage of the area at the edge of the two cover types increased on all ownerships and in both elevational zones, whereas the amount of interior habitat (defined as CF at least 100 m from OT) decreased on all ownerships and elevational zones. By 1988 public lands contained approximately 45% interior habitat while private lands had 12% interior habitat. Mean interior patch area declined from 160 to 62 ha. The annual rate of disturbance (primarily clear-cutting) for the entire area including the wilderness was 1.19%, which corresponds to a cutting rotation of 84 yr. The forest landscape was not in a steady state or regulated condition which is not projected to occur for at least 40 yr under current forest plans. Variability in cutting rates within ownerships was higher on private land than on nonreserve public land. However, despite the use of dispersed cutting patterns on public land, spatial patterns of cutting and remnant forest patches were nonuniform across the entire public ownership. Large remaining patches (less than 5000 ha) of contiguous interior forest were restricted to public lands designated for uses other than timber production such as wilderness areas and research natural areas.

Soils characterisation along ecological forest zones in the Eastern Himalayas

NASA Astrophysics Data System (ADS)

Simon, Alois; Dhendup, Kuenzang; Bahadur Rai, Prem; Gratzer, Georg

2017-04-01

Elevational gradients are commonly used to characterise vegetation patterns and, to a lesser extent, also to describe soil development. Furthermore, interactions between vegetation cover and soil characteristics are repeatedly observed. Combining information on soil development and easily to distinguish forest zones along elevational gradients, creates an added value for forest management decisions especially in less studied mountain regions. For this purpose, soil profiles along elevational gradients in the temperate conifer forests of Western and Central Bhutan, ranging from 2600-4000m asl were investigated. Thereby, 82 soil profiles were recorded and classified according to the World Reference Base for Soil Resources. Based on 19 representative profiles, genetic horizons were sampled and analysed. We aim to provide fundamental information on forest soil characteristics along these elevational transects. The results are presented with regard to ecological forest zones. The elevational distribution of the reference soil groups showed distinct distribution ranges for most of the soils. Cambisols were the most frequently recorded reference soil group with 58% of the sampled profiles, followed by Podzols in higher elevations, and Stagnosols, at intermediate elevations. Fluvisols occurred only at the lower end of the elevational transects and Phaeozems only at drier site conditions in the cool conifer dry forest zone. The humus layer thickness differs between forest zones and show a shift towards increased organic layer (O-layer) with increasing elevation. The reduced biomass productivity with increasing elevation and subsequently lower litter input compensates for the slow decomposition rates. The increasing O-layer thickness is an indicator of restrained intermixing of organic and mineral components by soil organisms at higher elevation. Overall, the soil types and soil characteristics along the elevational gradient showed a continuous and consistent change, instead of abrupt changes. We interpret these as manifestations of changes of temperature and precipitation with elevation which also drives forest zonation in these least anthropogenically influenced forest ecosystems. The elevational distribution of forest zones is correlated with the distribution of soil types and thus also reflects soil characteristics.

Evaluating data mining algorithms using molecular dynamics trajectories.

PubMed

Tatsis, Vasileios A; Tjortjis, Christos; Tzirakis, Panagiotis

2013-01-01

Molecular dynamics simulations provide a sample of a molecule's conformational space. Experiments on the mus time scale, resulting in large amounts of data, are nowadays routine. Data mining techniques such as classification provide a way to analyse such data. In this work, we evaluate and compare several classification algorithms using three data sets which resulted from computer simulations, of a potential enzyme mimetic biomolecule. We evaluated 65 classifiers available in the well-known data mining toolkit Weka, using 'classification' errors to assess algorithmic performance. Results suggest that: (i) 'meta' classifiers perform better than the other groups, when applied to molecular dynamics data sets; (ii) Random Forest and Rotation Forest are the best classifiers for all three data sets; and (iii) classification via clustering yields the highest classification error. Our findings are consistent with bibliographic evidence, suggesting a 'roadmap' for dealing with such data.

A comparison of rule-based and machine learning approaches for classifying patient portal messages.

PubMed

Cronin, Robert M; Fabbri, Daniel; Denny, Joshua C; Rosenbloom, S Trent; Jackson, Gretchen Purcell

2017-09-01

Secure messaging through patient portals is an increasingly popular way that consumers interact with healthcare providers. The increasing burden of secure messaging can affect clinic staffing and workflows. Manual management of portal messages is costly and time consuming. Automated classification of portal messages could potentially expedite message triage and delivery of care. We developed automated patient portal message classifiers with rule-based and machine learning techniques using bag of words and natural language processing (NLP) approaches. To evaluate classifier performance, we used a gold standard of 3253 portal messages manually categorized using a taxonomy of communication types (i.e., main categories of informational, medical, logistical, social, and other communications, and subcategories including prescriptions, appointments, problems, tests, follow-up, contact information, and acknowledgement). We evaluated our classifiers' accuracies in identifying individual communication types within portal messages with area under the receiver-operator curve (AUC). Portal messages often contain more than one type of communication. To predict all communication types within single messages, we used the Jaccard Index. We extracted the variables of importance for the random forest classifiers. The best performing approaches to classification for the major communication types were: logistic regression for medical communications (AUC: 0.899); basic (rule-based) for informational communications (AUC: 0.842); and random forests for social communications and logistical communications (AUCs: 0.875 and 0.925, respectively). The best performing classification approach of classifiers for individual communication subtypes was random forests for Logistical-Contact Information (AUC: 0.963). The Jaccard Indices by approach were: basic classifier, Jaccard Index: 0.674; Naïve Bayes, Jaccard Index: 0.799; random forests, Jaccard Index: 0.859; and logistic regression, Jaccard Index: 0.861. For medical communications, the most predictive variables were NLP concepts (e.g., Temporal_Concept, which maps to 'morning', 'evening' and Idea_or_Concept which maps to 'appointment' and 'refill'). For logistical communications, the most predictive variables contained similar numbers of NLP variables and words (e.g., Telephone mapping to 'phone', 'insurance'). For social and informational communications, the most predictive variables were words (e.g., social: 'thanks', 'much', informational: 'question', 'mean'). This study applies automated classification methods to the content of patient portal messages and evaluates the application of NLP techniques on consumer communications in patient portal messages. We demonstrated that random forest and logistic regression approaches accurately classified the content of portal messages, although the best approach to classification varied by communication type. Words were the most predictive variables for classification of most communication types, although NLP variables were most predictive for medical communication types. As adoption of patient portals increases, automated techniques could assist in understanding and managing growing volumes of messages. Further work is needed to improve classification performance to potentially support message triage and answering. Copyright © 2017 Elsevier B.V. All rights reserved.

Peculiarities of use of ECOC and AdaBoost based classifiers for thematic processing of hyperspectral data

NASA Astrophysics Data System (ADS)

Dementev, A. O.; Dmitriev, E. V.; Kozoderov, V. V.; Egorov, V. D.

2017-10-01

Hyperspectral imaging is up-to-date promising technology widely applied for the accurate thematic mapping. The presence of a large number of narrow survey channels allows us to use subtle differences in spectral characteristics of objects and to make a more detailed classification than in the case of using standard multispectral data. The difficulties encountered in the processing of hyperspectral images are usually associated with the redundancy of spectral information which leads to the problem of the curse of dimensionality. Methods currently used for recognizing objects on multispectral and hyperspectral images are usually based on standard base supervised classification algorithms of various complexity. Accuracy of these algorithms can be significantly different depending on considered classification tasks. In this paper we study the performance of ensemble classification methods for the problem of classification of the forest vegetation. Error correcting output codes and boosting are tested on artificial data and real hyperspectral images. It is demonstrates, that boosting gives more significant improvement when used with simple base classifiers. The accuracy in this case in comparable the error correcting output code (ECOC) classifier with Gaussian kernel SVM base algorithm. However the necessity of boosting ECOC with Gaussian kernel SVM is questionable. It is demonstrated, that selected ensemble classifiers allow us to recognize forest species with high enough accuracy which can be compared with ground-based forest inventory data.

Stratifying FIA Ground Plots Using A 3-Year Old MRLC Forest Cover Map and Current TM Derived Variables Selected By "Decision Tree" Classification

Treesearch

Michael Hoppus; Stan Arner; Andrew Lister

2001-01-01

A reduction in variance for estimates of forest area and volume in the state of Connecticut was accomplished by stratifying FIA ground plots using raw, transformed and classified Landsat Thematic Mapper (TM) imagery. A US Geological Survey (USGS) Multi-Resolution Landscape Characterization (MRLC) vegetation cover map for Connecticut was used to produce a forest/non-...

Synergistic use of FIA plot data and Landsat 7 ETM+ images for large area forest mapping

Treesearch

Chengquan Huang; Limin Yang; Collin Homer; Michael Coan; Russell Rykhus; Zheng Zhang; Bruce Wylie; Kent Hegge; Andrew Lister; Michael Hoppus; Ronald Tymcio; Larry DeBlander; William Cooke; Ronald McRoberts; Daniel Wendt; Dale Weyermann

2002-01-01

FIA plot data were used to assist in classifying forest land cover from Landsat imagery and relevant ancillary data in two regions of the U.S.: one around the Chesapeake Bay area and the other around Utah. The overall accuracies for the forest/nonforest classification were over 90 percent and about 80 percent, respectively, in the two regions. The accuracies for...

A minimum spanning forest based classification method for dedicated breast CT images

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pike, Robert; Sechopoulos, Ioannis; Fei, Baowei, E-mail: bfei@emory.edu

Purpose: To develop and test an automated algorithm to classify different types of tissue in dedicated breast CT images. Methods: Images of a single breast of five different patients were acquired with a dedicated breast CT clinical prototype. The breast CT images were processed by a multiscale bilateral filter to reduce noise while keeping edge information and were corrected to overcome cupping artifacts. As skin and glandular tissue have similar CT values on breast CT images, morphologic processing is used to identify the skin based on its position information. A support vector machine (SVM) is trained and the resulting modelmore » used to create a pixelwise classification map of fat and glandular tissue. By combining the results of the skin mask with the SVM results, the breast tissue is classified as skin, fat, and glandular tissue. This map is then used to identify markers for a minimum spanning forest that is grown to segment the image using spatial and intensity information. To evaluate the authors’ classification method, they use DICE overlap ratios to compare the results of the automated classification to those obtained by manual segmentation on five patient images. Results: Comparison between the automatic and the manual segmentation shows that the minimum spanning forest based classification method was able to successfully classify dedicated breast CT image with average DICE ratios of 96.9%, 89.8%, and 89.5% for fat, glandular, and skin tissue, respectively. Conclusions: A 2D minimum spanning forest based classification method was proposed and evaluated for classifying the fat, skin, and glandular tissue in dedicated breast CT images. The classification method can be used for dense breast tissue quantification, radiation dose assessment, and other applications in breast imaging.« less

Ensemble Methods for Classification of Physical Activities from Wrist Accelerometry.

PubMed

Chowdhury, Alok Kumar; Tjondronegoro, Dian; Chandran, Vinod; Trost, Stewart G

2017-09-01

To investigate whether the use of ensemble learning algorithms improve physical activity recognition accuracy compared to the single classifier algorithms, and to compare the classification accuracy achieved by three conventional ensemble machine learning methods (bagging, boosting, random forest) and a custom ensemble model comprising four algorithms commonly used for activity recognition (binary decision tree, k nearest neighbor, support vector machine, and neural network). The study used three independent data sets that included wrist-worn accelerometer data. For each data set, a four-step classification framework consisting of data preprocessing, feature extraction, normalization and feature selection, and classifier training and testing was implemented. For the custom ensemble, decisions from the single classifiers were aggregated using three decision fusion methods: weighted majority vote, naïve Bayes combination, and behavior knowledge space combination. Classifiers were cross-validated using leave-one subject out cross-validation and compared on the basis of average F1 scores. In all three data sets, ensemble learning methods consistently outperformed the individual classifiers. Among the conventional ensemble methods, random forest models provided consistently high activity recognition; however, the custom ensemble model using weighted majority voting demonstrated the highest classification accuracy in two of the three data sets. Combining multiple individual classifiers using conventional or custom ensemble learning methods can improve activity recognition accuracy from wrist-worn accelerometer data.

Classifier for gravitational-wave inspiral signals in nonideal single-detector data

NASA Astrophysics Data System (ADS)

Kapadia, S. J.; Dent, T.; Dal Canton, T.

2017-11-01

We describe a multivariate classifier for candidate events in a templated search for gravitational-wave (GW) inspiral signals from neutron-star-black-hole (NS-BH) binaries, in data from ground-based detectors where sensitivity is limited by non-Gaussian noise transients. The standard signal-to-noise ratio (SNR) and chi-squared test for inspiral searches use only properties of a single matched filter at the time of an event; instead, we propose a classifier using features derived from a bank of inspiral templates around the time of each event, and also from a search using approximate sine-Gaussian templates. The classifier thus extracts additional information from strain data to discriminate inspiral signals from noise transients. We evaluate a random forest classifier on a set of single-detector events obtained from realistic simulated advanced LIGO data, using simulated NS-BH signals added to the data. The new classifier detects a factor of 1.5-2 more signals at low false positive rates as compared to the standard "reweighted SNR" statistic, and does not require the chi-squared test to be computed. Conversely, if only the SNR and chi-squared values of single-detector events are available, random forest classification performs nearly identically to the reweighted SNR.

36 CFR 223.215 - Applicability.

Code of Federal Regulations, 2011 CFR

2011-07-01

... DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS Special Forest Products § 223.215 Applicability. The regulations contained in this subpart govern the disposal of... Public Law 108-108, special forest products that are also forest botanical products shall be sold, or...

Prediction of Chemical Function: Model Development and ...

EPA Pesticide Factsheets

The United States Environmental Protection Agency’s Exposure Forecaster (ExpoCast) project is developing both statistical and mechanism-based computational models for predicting exposures to thousands of chemicals, including those in consumer products. The high-throughput (HT) screening-level exposures developed under ExpoCast can be combined with HT screening (HTS) bioactivity data for the risk-based prioritization of chemicals for further evaluation. The functional role (e.g. solvent, plasticizer, fragrance) that a chemical performs can drive both the types of products in which it is found and the concentration in which it is present and therefore impacting exposure potential. However, critical chemical use information (including functional role) is lacking for the majority of commercial chemicals for which exposure estimates are needed. A suite of machine-learning based models for classifying chemicals in terms of their likely functional roles in products based on structure were developed. This effort required collection, curation, and harmonization of publically-available data sources of chemical functional use information from government and industry bodies. Physicochemical and structure descriptor data were generated for chemicals with function data. Machine-learning classifier models for function were then built in a cross-validated manner from the descriptor/function data using the method of random forests. The models were applied to: 1) predict chemi

Comparison of Random Forest, k-Nearest Neighbor, and Support Vector Machine Classifiers for Land Cover Classification Using Sentinel-2 Imagery

PubMed Central

Thanh Noi, Phan; Kappas, Martin

2017-01-01

In previous classification studies, three non-parametric classifiers, Random Forest (RF), k-Nearest Neighbor (kNN), and Support Vector Machine (SVM), were reported as the foremost classifiers at producing high accuracies. However, only a few studies have compared the performances of these classifiers with different training sample sizes for the same remote sensing images, particularly the Sentinel-2 Multispectral Imager (MSI). In this study, we examined and compared the performances of the RF, kNN, and SVM classifiers for land use/cover classification using Sentinel-2 image data. An area of 30 × 30 km2 within the Red River Delta of Vietnam with six land use/cover types was classified using 14 different training sample sizes, including balanced and imbalanced, from 50 to over 1250 pixels/class. All classification results showed a high overall accuracy (OA) ranging from 90% to 95%. Among the three classifiers and 14 sub-datasets, SVM produced the highest OA with the least sensitivity to the training sample sizes, followed consecutively by RF and kNN. In relation to the sample size, all three classifiers showed a similar and high OA (over 93.85%) when the training sample size was large enough, i.e., greater than 750 pixels/class or representing an area of approximately 0.25% of the total study area. The high accuracy was achieved with both imbalanced and balanced datasets. PMID:29271909

Comparison of Random Forest, k-Nearest Neighbor, and Support Vector Machine Classifiers for Land Cover Classification Using Sentinel-2 Imagery.

PubMed

Thanh Noi, Phan; Kappas, Martin

2017-12-22

In previous classification studies, three non-parametric classifiers, Random Forest (RF), k-Nearest Neighbor (kNN), and Support Vector Machine (SVM), were reported as the foremost classifiers at producing high accuracies. However, only a few studies have compared the performances of these classifiers with different training sample sizes for the same remote sensing images, particularly the Sentinel-2 Multispectral Imager (MSI). In this study, we examined and compared the performances of the RF, kNN, and SVM classifiers for land use/cover classification using Sentinel-2 image data. An area of 30 × 30 km² within the Red River Delta of Vietnam with six land use/cover types was classified using 14 different training sample sizes, including balanced and imbalanced, from 50 to over 1250 pixels/class. All classification results showed a high overall accuracy (OA) ranging from 90% to 95%. Among the three classifiers and 14 sub-datasets, SVM produced the highest OA with the least sensitivity to the training sample sizes, followed consecutively by RF and kNN. In relation to the sample size, all three classifiers showed a similar and high OA (over 93.85%) when the training sample size was large enough, i.e., greater than 750 pixels/class or representing an area of approximately 0.25% of the total study area. The high accuracy was achieved with both imbalanced and balanced datasets.

A classification of forest environments in the south Umpqua Basin.

Treesearch

Don Minore

1972-01-01

Forest environments are classified by elevation, temperature, moisture, potential solar radiation, and soil type. Broad elevation classes are derived from topographic maps or altimeter measurements, measured temperature and moisture conditions are related to vegetation by using plant indicator species (illustrated), and tabular values are employed in estimating...

A Model-Based Approach to Inventory Stratification

Treesearch

Ronald E. McRoberts

2006-01-01

Forest inventory programs report estimates of forest variables for areas of interest ranging in size from municipalities to counties to States and Provinces. 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...

National forest trail users: planning for recreation opportunities

Treesearch

John J. Daigle; Alan E. Watson; Glenn E. Haas

1994-01-01

National forest trail users in four geographical regions of the United States are described based on participation in clusters of recreation activities. Visitors are classified into day hiking, undeveloped recreation, and two developed camping and hiking activity clusters for the Appalachian, Pacific, Rocky Mountain, and Southwestern regions. Distance and time traveled...

36 CFR 223.279 - Personal use.

Code of Federal Regulations, 2013 CFR

2013-07-01

... DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS Forest Botanical Products § 223.279 Personal use. (a) Personal use. A person may harvest forest botanical products... specific forest botanical products, which shall be equal to the amount or quantity authorized for free use...

36 CFR 223.279 - Personal use.

Code of Federal Regulations, 2011 CFR

2011-07-01

... DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS Forest Botanical Products § 223.279 Personal use. (a) Personal use. A person may harvest forest botanical products... specific forest botanical products, which shall be equal to the amount or quantity authorized for free use...

36 CFR 223.279 - Personal use.

Code of Federal Regulations, 2012 CFR

2012-07-01

... DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS Forest Botanical Products § 223.279 Personal use. (a) Personal use. A person may harvest forest botanical products... specific forest botanical products, which shall be equal to the amount or quantity authorized for free use...

36 CFR 223.279 - Personal use.

Code of Federal Regulations, 2014 CFR

2014-07-01

... DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS Forest Botanical Products § 223.279 Personal use. (a) Personal use. A person may harvest forest botanical products... specific forest botanical products, which shall be equal to the amount or quantity authorized for free use...

Decision-Tree, Rule-Based, and Random Forest Classification of High-Resolution Multispectral Imagery for Wetland Mapping and Inventory

EPA Science Inventory

Efforts are increasingly being made to classify the world’s wetland resources, an important ecosystem and habitat that is diminishing in abundance. There are multiple remote sensing classification methods, including a suite of nonparametric classifiers such as decision-tree...

Structure Measurements of Leaf and Woody Components of Forests with Dual-Wavelength Lidar Scanning Data

NASA Astrophysics Data System (ADS)

Strahler, A. H.; Li, Z.; Schaaf, C.; Howe, G.; Martel, J.; Hewawasam, K.; Douglas, E. S.; Chakrabarti, S.; Cook, T.; Paynter, I.; Saenz, E. J.; Wang, Z.; Woodcock, C. E.; Jupp, D. L. B.; Schaefer, M.; Newnham, G.

2014-12-01

Forest structure plays a critical role in the exchange of energy, carbon and water between land and atmosphere and nutrient cycle. We can provide detailed forest structure measurements of leaf and woody components with the Dual Wavelength Echidna® Lidar (DWEL), which acquires full-waveform scans at both near-infrared (NIR, 1064 nm) and shortwave infrared (SWIR, 1548 nm) wavelengths from simultaneous laser pulses. We collected DWEL scans at a broadleaf forest stand and a conifer forest stand at Harvard Forest in June 2014. Power returned from leaves is much lower than from woody materials such as trunks and branches at the SWIR wavelength due to the liquid water absorption by leaves, whereas returned power at the NIR wavelength is similar from both leaves and woody materials. We threshold a normalized difference index (NDI), defined as the difference between returned power at the two wavelengths divided by their sum, to classify each return pulse as a leaf or trunk/branch hit. We obtain leaf area index (LAI), woody area index (WAI) and vertical profiles of leaf and woody components directly from classified lidar hits without empirical wood-to-total ratios as are commonly used in optical methods of LAI estimation. Tree heights, diameter at breast height (DBH), and stem count density are the other forest structure parameters estimated from our DWEL scans. The separation of leaf and woody components in tandem with fine-scale forest structure measurements will benefit studies on carbon allocation of forest ecosystems and improve our understanding of the effects of forest structure on ecosystem functions. This research is supported by NSF grant, MRI-0923389

Deforestation change detection in North Korea between 1999 and 2008 using multi temporal satellite image

NASA Astrophysics Data System (ADS)

KIM, K. M.

2017-12-01

After the mid-1990s, North Korea has gone through a hard time of shortage of food and fuel due to the large scale flood and landslide. This became a vicious circle, which has kept accelerating the deforestation in North Korea. This study aims to analyze the change of deforestation in North Korea using two different seasonal satellite images of Landsat 5-TM and SPOT-5 between 1999 and 2008. The Land cover was classified into 6 categories: forest, cropland, grassland, bare land, built area and water body. And the deforested and degraded forest area was extracted considering forest land boundary and classified into 3 categories: the cultivated, the unstocked forest land and the bare mountain. For the all classification process, unsupervised classification method was used since North Korea is inaccessible area. The results of the study showed that the stocked forest area has decreased 1,379,000 ha compared with those in 1999, whereas the deforested and degraded forest area has increased 1,207,000 ha in 2008. The increase of 880,000 ha in the unstocked forest land was the biggest expansion among 3 categories of the deforested and degraded forest area during 9 yrs. It is resulted from an increase of firewood usage, which is presumably owing to the severe shortage of fuel and food. I look forward for the outcome of this study to being used as baseline data for inter-Korean forest cooperation. Especially, it is expected to serve as important input data for the potential REDD project site selection with results of the 3rd forest monitoring(2018) of North Korea.

Data mining methods in the prediction of Dementia: A real-data comparison of the accuracy, sensitivity and specificity of linear discriminant analysis, logistic regression, neural networks, support vector machines, classification trees and random forests

PubMed Central

2011-01-01

Background Dementia and cognitive impairment associated with aging are a major medical and social concern. Neuropsychological testing is a key element in the diagnostic procedures of Mild Cognitive Impairment (MCI), but has presently a limited value in the prediction of progression to dementia. We advance the hypothesis that newer statistical classification methods derived from data mining and machine learning methods like Neural Networks, Support Vector Machines and Random Forests can improve accuracy, sensitivity and specificity of predictions obtained from neuropsychological testing. Seven non parametric classifiers derived from data mining methods (Multilayer Perceptrons Neural Networks, Radial Basis Function Neural Networks, Support Vector Machines, CART, CHAID and QUEST Classification Trees and Random Forests) were compared to three traditional classifiers (Linear Discriminant Analysis, Quadratic Discriminant Analysis and Logistic Regression) in terms of overall classification accuracy, specificity, sensitivity, Area under the ROC curve and Press'Q. Model predictors were 10 neuropsychological tests currently used in the diagnosis of dementia. Statistical distributions of classification parameters obtained from a 5-fold cross-validation were compared using the Friedman's nonparametric test. Results Press' Q test showed that all classifiers performed better than chance alone (p < 0.05). Support Vector Machines showed the larger overall classification accuracy (Median (Me) = 0.76) an area under the ROC (Me = 0.90). However this method showed high specificity (Me = 1.0) but low sensitivity (Me = 0.3). Random Forest ranked second in overall accuracy (Me = 0.73) with high area under the ROC (Me = 0.73) specificity (Me = 0.73) and sensitivity (Me = 0.64). Linear Discriminant Analysis also showed acceptable overall accuracy (Me = 0.66), with acceptable area under the ROC (Me = 0.72) specificity (Me = 0.66) and sensitivity (Me = 0.64). The remaining classifiers showed overall classification accuracy above a median value of 0.63, but for most sensitivity was around or even lower than a median value of 0.5. Conclusions When taking into account sensitivity, specificity and overall classification accuracy Random Forests and Linear Discriminant analysis rank first among all the classifiers tested in prediction of dementia using several neuropsychological tests. These methods may be used to improve accuracy, sensitivity and specificity of Dementia predictions from neuropsychological testing. PMID:21849043

Assessment of water availability and its relationship with vegetation distribution over a tropical montane system

NASA Astrophysics Data System (ADS)

Streher, A. S.; Sobreiro, J. F. F.; Silva, T. S. F.

2017-12-01

Water availability is one of the main drivers of vegetation distribution, but assessing it over mountainous regions is difficult given the effects of rugged topography on hydroclimatic dynamics (orographic rainfall, soil water, and runoff). We assessed how water availability may influence the distribution of vegetation types in the Espinhaço Range, a South American tropical mountain landscape comprised of savannas, grasslands, rock outcrops, cloud forests, and semi-deciduous/deciduous forests. For precipitation, we used CHIRPS monthly and daily products (1981- 2016) and 112 rain gauge ground stations, and assessed potential evapotranspiration (PET) using the MODIS MOD16A3 (2000-2013) product. Vegetation types were classified according to the Global Ecoregions by WWF. We show that rainfall has well-defined rainy and dry seasons with a strong latitudinal pattern, there is evidence for local orographic effects. Dry forests (907 mm/yr; 8% cv) and caatinga vegetation (795 mm/yr; 7% cv) had the lowest average annual precipitation and low variance, whilst Atlantic tropical forest in the southeast (1267 mm/yr; 15% cv), cerrado savanna vegetation in the west (1086 mm/yr; 15% cv) and rupestrian grasslands above 800m (1261 mm/yr; 20% cv) received the highest annual precipitation, with the largest observed variance due to their wide latitudinal distribution. Forests and rupestrian grasslands in the windward side of the mountain had a higher frequency of intense rainfall events (> 20mm), accounting for 6% of the CHIRPS daily time series, suggesting orographic effects on precipitation. Annual average PET was highest for dry forests (2437 mm/yr) and caatinga (2461 mm/yr), intermediate for cerrado (2264 mm/yr) and lowest for Atlantic tropical forest (2083 mm/yr) and rupestrian grasslands (2136 mm/yr). All vegetation types received less rainfall than its PET capacity based on yearly data, emphasizing the need for ecophysiological adaptations to water use. Climate change threatens these ecosystems by possible alterations on the hydrological cycle and, consequently, capacity for adaptations on water use. These could lead to shifts in vegetation composition and distribution within the studied region. Further investigation of seasonal trends on water availability and edaphic factors would improve these analyses.

Population and harvest trends of big game and small game species: a technical document supporting the USDA Forest Service Interim Update of the 2000 RPA Assessment

Treesearch

Curtis H. Flather; Michael S. Knowles; Stephen J. Brady

2009-01-01

This technical document supports the Forest Service's requirement to assess the status of renewable natural resources as mandated by the Forest and Rangeland Renewable Resources Planning Act of 1974 (RPA). It updates past reports on national and regional trends in population and harvest estimates for species classified as big game and small game. The trends...

Developing Land Use Land Cover Maps for the Lower Mekong Basin to Aid SWAT Hydrologic Modeling

NASA Astrophysics Data System (ADS)

Spruce, J.; Bolten, J. D.; Srinivasan, R.

2017-12-01

This presentation discusses research to develop Land Use Land Cover (LULC) maps for the Lower Mekong Basin (LMB). Funded by a NASA ROSES Disasters grant, the main objective was to produce updated LULC maps to aid the Mekong River Commission's (MRC's) Soil and Water Assessment Tool (SWAT) hydrologic model. In producing needed LULC maps, temporally processed MODIS monthly NDVI data for 2010 were used as the primary data source for classifying regionally prominent forest and agricultural types. The MODIS NDVI data was derived from processing MOD09 and MYD09 8-day reflectance data with the Time Series Product Tool, a custom software package. Circa 2010 Landsat multispectral data from the dry season were processed into top of atmosphere reflectance mosaics and then classified to derive certain locally common LULC types, such as urban areas and industrial forest plantations. Unsupervised ISODATA clustering was used to derive most LULC classifications. GIS techniques were used to merge MODIS and Landsat classifications into final LULC maps for Sub-Basins (SBs) 1-8 of the LMB. The final LULC maps were produced at 250-meter resolution and delivered to the MRC for use in SWAT modeling for the LMB. A map accuracy assessment was performed for the SB 7 LULC map with 14 classes. This assessment was performed by comparing random locations for sampled LULC types to geospatial reference data such as Landsat RGBs, MODIS NDVI phenologic profiles, high resolution satellite data from Google Map/Earth, and other reference data from the MRC (e.g., crop calendars). LULC accuracy assessment results for SB 7 indicated an overall agreement to reference data of 81% at full scheme specificity. However, by grouping 3 deciduous forest classes into 1 class, the overall agreement improved to 87%. The project enabled updated LULC maps, plus more specific rice types were classified compared to the previous LULC maps. The LULC maps from this project should improve the use of SWAT for modeling hydrology in the LMB, plus improve water and disaster management in a region vulnerable to flooding, droughts, and anthropogenic change (e.g., from dam building and other LULC change).

36 CFR 223.276 - Applicability.

Code of Federal Regulations, 2014 CFR

2014-07-01

... DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS Forest Botanical Products § 223.276 Applicability. This subpart applies to the sale and free use of forest botanical products, as defined in § 223.277, from National Forest System lands, until September 30, 2009...

36 CFR 223.276 - Applicability.

Code of Federal Regulations, 2011 CFR

2011-07-01

... DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS Forest Botanical Products § 223.276 Applicability. This subpart applies to the sale and free use of forest botanical products, as defined in § 223.277, from National Forest System lands, until September 30, 2009...

36 CFR 223.276 - Applicability.

Code of Federal Regulations, 2013 CFR

2013-07-01

... DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS Forest Botanical Products § 223.276 Applicability. This subpart applies to the sale and free use of forest botanical products, as defined in § 223.277, from National Forest System lands, until September 30, 2009...

36 CFR 223.276 - Applicability.

Code of Federal Regulations, 2012 CFR

2012-07-01

... DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS Forest Botanical Products § 223.276 Applicability. This subpart applies to the sale and free use of forest botanical products, as defined in § 223.277, from National Forest System lands, until September 30, 2009...

Evaluating differences in forest fragmentation and restoration between western natural forests and southeastern plantation forests in the United States.

PubMed

Ren, Xinyu; Lv, Yingying; Li, Mingshi

2017-03-01

Changes in forest ecosystem structure and functions are considered some of the research issues in landscape ecology. In this study, advancing Forman's theory, we considered five spatially explicit processes associated with fragmentation, including perforation, dissection, subdivision, shrinkage, and attrition, and two processes associated with restoration, i.e., increment and expansion processes. Following this theory, a forest fragmentation and restoration process model that can detect the spatially explicit processes and ecological consequences of forest landscape change was developed and tested in the current analysis. Using the National Land Cover Databases (2001, 2006 and 2011), the forest fragmentation and restoration process model was applied to US western natural forests and southeastern plantation forests to quantify and classify forest patch losses into one of the four fragmentation processes (the dissection process was merged into the subdivision process) and to classify the newly gained forest patches based on the two restoration processes. At the same time, the spatio-temporal differences in fragmentation and restoration patterns and trends between natural forests and plantations were further compared. Then, through overlaying the forest fragmentation/restoration processes maps with targeting year land cover data and land ownership vectors, the results from forest fragmentation and the contributors to forest restoration in federal and nonfederal lands were identified. Results showed that, in natural forests, the forest change patches concentrated around the urban/forest, cultivated/forest, and shrubland/forest interfaces, while the patterns of plantation change patches were scattered sparsely and irregularly. The shrinkage process was the most common type in forest fragmentation, and the average size was the smallest. Expansion, the most common restoration process, was observed in both natural forests and plantations and often occurred around the previous expansion or covered the previous subdivision or shrinkage processes. The overall temporal fragmentation pattern of natural forests had a "perforation-subdivision/shrinkage-attrition" pathway, which corresponded to Forman's landscape fragmentation rule, while the plantation forests did not follow the rule strictly. The main land cover types resulted from forest fragmentation in natural forests and plantation forests were shrubland and herbaceous, mainly through subdivision and shrinkages process. The processes and effects of restoration of plantation forests were more diverse and efficient, compared to the natural forest, which were simpler with a lower regrowth rate. The fragmentation mostly occurred in nonfederal lands. In natural forests, forest fragmentation pattern differed in different land tenures, yet plantations remained the same in federal and nonfederal lands. Copyright © 2016 Elsevier Ltd. All rights reserved.

Forest products research in IUFRO history and potential

Treesearch

Robert L. Youngs; John A. Youngquist

1999-01-01

When silviculture researchers in central Europe were gathering together to form IUFRO in 1892, forest products researchers were occupied with making useful forest products and conserving the forest resource through wise use. Forest products researchers did not become an active part of IUFRO until 50 years later. Research in forest products was stimulated by World War I...

36 CFR 223.216 - Special Forest Products definitions.

Code of Federal Regulations, 2013 CFR

2013-07-01

... 36 Parks, Forests, and Public Property 2 2013-07-01 2013-07-01 false Special Forest Products definitions. 223.216 Section 223.216 Parks, Forests, and Public Property FOREST SERVICE, DEPARTMENT OF AGRICULTURE SALE AND DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL...

36 CFR 223.216 - Special Forest Products definitions.

Code of Federal Regulations, 2012 CFR

2012-07-01

... 36 Parks, Forests, and Public Property 2 2012-07-01 2012-07-01 false Special Forest Products definitions. 223.216 Section 223.216 Parks, Forests, and Public Property FOREST SERVICE, DEPARTMENT OF AGRICULTURE SALE AND DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL...

36 CFR 223.216 - Special Forest Products definitions.

Code of Federal Regulations, 2014 CFR

2014-07-01

... 36 Parks, Forests, and Public Property 2 2014-07-01 2014-07-01 false Special Forest Products definitions. 223.216 Section 223.216 Parks, Forests, and Public Property FOREST SERVICE, DEPARTMENT OF AGRICULTURE SALE AND DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL...

36 CFR 223.216 - Special Forest Products definitions.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 36 Parks, Forests, and Public Property 2 2011-07-01 2011-07-01 false Special Forest Products definitions. 223.216 Section 223.216 Parks, Forests, and Public Property FOREST SERVICE, DEPARTMENT OF AGRICULTURE SALE AND DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL...

36 CFR 223.275 - Establishment of a pilot program.

Code of Federal Regulations, 2012 CFR

2012-07-01

... AGRICULTURE SALE AND DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS Forest Botanical Products § 223.275 Establishment of a pilot program. This subpart governs the Forest Service's pilot program for the disposal of forest botanical products, as authorized by the...

36 CFR 223.275 - Establishment of a pilot program.

Code of Federal Regulations, 2013 CFR

2013-07-01

... AGRICULTURE SALE AND DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS Forest Botanical Products § 223.275 Establishment of a pilot program. This subpart governs the Forest Service's pilot program for the disposal of forest botanical products, as authorized by the...

36 CFR 223.275 - Establishment of a pilot program.

Code of Federal Regulations, 2014 CFR

2014-07-01

... AGRICULTURE SALE AND DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS Forest Botanical Products § 223.275 Establishment of a pilot program. This subpart governs the Forest Service's pilot program for the disposal of forest botanical products, as authorized by the...

Small-scale, private lands forestry in the Lake States

Treesearch

Kathryn Fernholz

2004-01-01

Of the approximately 750 million acres of forests in the United States, approximately 46 percent is classified as "nonindustrial private forest" (NIPF). These are lands owned by private individuals, jointly or through family partnerships. According to the most recent USDA survey, there are 10,565,000 private woodland owners in the United States, collectively...

Triple nitrate isotopes indicate differing nitrate source contributions to streams across a nitrogen saturation gradient

Treesearch

Lucy A. Rose; Emily M. Elliott; Mary Beth. Adams

2015-01-01

Nitrogen (N) deposition affects forest biogeochemical cycles worldwide, often contributing to N saturation. Using long-term (>30-year) records of stream nitrate (NO3-) concentrations at Fernow Experimental Forest (West Virginia, USA), we classified four watersheds into N saturation stages ranging from Stage 0 (N-...

Plant community classification for alpine vegetation on the Beaverhead National Forest, Montana

Treesearch

Stephen V. Cooper; Peter Lesica; Deborah Page-Dumroese

1997-01-01

Vegetation of the alpine zone of eight mountain ranges in southwestern Montana was classified using IWINSPAN, DECORAN, and STRATA-algorithms embedded within the U.S. Forest Service Northern Region's ECADS (ecological classification and description system) program. Quantitative estimates of vegetation and soil attributes were sampled from 138 plots. Vegetation...

Sensitivity of FIA Volume Estimates to Changes in Stratum Weights and Number of Strata

Treesearch

James A. Westfall; Michael Hoppus

2005-01-01

In the Northeast region, the USDA Forest Service Forest Inventory and Analysis (FIA) program utilizes stratified sampling techniques to improve the precision of population estimates. Recently, interpretation of aerial photographs was replaced with classified remotely sensed imagery to determine stratum weights and plot stratum assignments. However, stratum weights...

Coho salmon and steelhead trout of JDSF

Treesearch

Peter Cafferata; Karen Walton; Weldon Jones

1989-01-01

Spawning and rearing habitat for anadromous fish is the dominant use of Jackson Demonstration State Forest's (JDSF) many miles of streams. Both coho (silver) salmon and steelhead migrate from the ocean up our rivers in the fall and winter to spawn. About 90 miles of the Forest's streams have been classified as habitat for these fish.

Landsat for practical forest type mapping - A test case

NASA Technical Reports Server (NTRS)

Bryant, E.; Dodge, A. G., Jr.; Warren, S. D.

1980-01-01

Computer classified Landsat maps are compared with a recent conventional inventory of forest lands in northern Maine. Over the 196,000 hectare area mapped, estimates of the areas of softwood, mixed wood and hardwood forest obtained by a supervised classification of the Landsat data and a standard inventory based on aerial photointerpretation, probability proportional to prediction, field sampling and a standard forest measurement program are found to agree to within 5%. The cost of the Landsat maps is estimated to be $0.065/hectare. It is concluded that satellite techniques are worth developing for forest inventories, although they are not yet refined enough to be incorporated into current practical inventories.

Mapping of taiga forest units using AIRSAR data and/or optical data, and retrieval of forest parameters

NASA Technical Reports Server (NTRS)

Rignot, Eric; Williams, Cynthia; Way, Jobea; Viereck, Leslie

1993-01-01

A maximum a posteriori Bayesian classifier for multifrequency polarimetric SAR data is used to perform a supervised classification of forest types in the floodplains of Alaska. The image classes include white spruce, balsam poplar, black spruce, alder, non-forests, and open water. The authors investigate the effect on classification accuracy of changing environmental conditions, and of frequency and polarization of the signal. The highest classification accuracy (86 percent correctly classified forest pixels, and 91 percent overall) is obtained combining L- and C-band frequencies fully polarimetric on a date where the forest is just recovering from flooding. The forest map compares favorably with a vegetation map assembled from digitized aerial photos which took five years for completion, and address the state of the forest in 1978, ignoring subsequent fires, changes in the course of the river, clear-cutting of trees, and tree growth. HV-polarization is the most useful polarization at L- and C-band for classification. C-band VV (ERS-1 mode) and L-band HH (J-ERS-1 mode) alone or combined yield unsatisfactory classification accuracies. Additional data acquired in the winter season during thawed and frozen days yield classification accuracies respectively 20 percent and 30 percent lower due to a greater confusion between conifers and deciduous trees. Data acquired at the peak of flooding in May 1991 also yield classification accuracies 10 percent lower because of dominant trunk-ground interactions which mask out finer differences in radar backscatter between tree species. Combination of several of these dates does not improve classification accuracy. For comparison, panchromatic optical data acquired by SPOT in the summer season of 1991 are used to classify the same area. The classification accuracy (78 percent for the forest types and 90 percent if open water is included) is lower than that obtained with AIRSAR although conifers and deciduous trees are better separated due to the presence of leaves on the deciduous trees. Optical data do not separate black spruce and white spruce as well as SAR data, cannot separate alder from balsam poplar, and are of course limited by the frequent cloud cover in the polar regions. Yet, combining SPOT and AIRSAR offers better chances to identify vegetation types independent of ground truth information using a combination of NDVI indexes from SPOT, biomass numbers from AIRSAR, and a segmentation map from either one.

Statewide land cover derived from multiseasonal Landsat TM data: A retrospective of the WISCLAND project

USGS Publications Warehouse

Reese, H.M.; Lillesand, T.M.; Nagel, D.E.; Stewart, J.S.; Goldmann, R.A.; Simmons, T.E.; Chipman, J.W.; Tessar, P.A.

2002-01-01

Landsat Thematic Mapper (TM) data were the basis in production of a statewide land cover data set for Wisconsin, undertaken in partnership with U.S. Geological Survey's (USGS) Gap Analysis Program (GAP). The data set contained seven classes comparable to Anderson Level I and 24 classes comparable to Anderson Level II/III. Twelve scenes of dual-date TM data were processed with methods that included principal components analysis, stratification into spectrally consistent units, separate classification of upland, wetland, and urban areas, and a hybrid supervised/unsupervised classification called "guided clustering." The final data had overall accuracies of 94% for Anderson Level I upland classes, 77% for Level II/III upland classes, and 84% for Level II/III wetland classes. Classification accuracies for deciduous and coniferous forest were 95% and 93%, respectively, and forest species' overall accuracies ranged from 70% to 84%. Limited availability of acceptable imagery necessitated use of an early May date in a majority of scene pairs, perhaps contributing to lower accuracy for upland deciduous forest species. The mixed deciduous/coniferous forest class had the lowest accuracy, most likely due to distinctly classifying a purely mixed class. Mixed forest signatures containing oak were often confused with pure oak. Guided clustering was seen as an efficient classification method, especially at the tree species level, although its success relied in part on image dates, accurate ground troth, and some analyst intervention. ?? 2002 Elsevier Science Inc. All rights reserved.

Detecting understory plant invasion in urban forests using LiDAR

NASA Astrophysics Data System (ADS)

Singh, Kunwar K.; Davis, Amy J.; Meentemeyer, Ross K.

2015-06-01

Light detection and ranging (LiDAR) data are increasingly used to measure structural characteristics of urban forests but are rarely used to detect the growing problem of exotic understory plant invaders. We explored the merits of using LiDAR-derived metrics alone and through integration with spectral data to detect the spatial distribution of the exotic understory plant Ligustrum sinense, a rapidly spreading invader in the urbanizing region of Charlotte, North Carolina, USA. We analyzed regional-scale L. sinense occurrence data collected over the course of three years with LiDAR-derived metrics of forest structure that were categorized into the following groups: overstory, understory, topography, and overall vegetation characteristics, and IKONOS spectral features - optical. Using random forest (RF) and logistic regression (LR) classifiers, we assessed the relative contributions of LiDAR and IKONOS derived variables to the detection of L. sinense. We compared the top performing models developed for a smaller, nested experimental extent using RF and LR classifiers, and used the best overall model to produce a predictive map of the spatial distribution of L. sinense across our country-wide study extent. RF classification of LiDAR-derived topography metrics produced the highest mapping accuracy estimates, outperforming IKONOS data by 17.5% and the integration of LiDAR and IKONOS data by 5.3%. The top performing model from the RF classifier produced the highest kappa of 64.8%, improving on the parsimonious LR model kappa by 31.1% with a moderate gain of 6.2% over the county extent model. Our results demonstrate the superiority of LiDAR-derived metrics over spectral data and fusion of LiDAR and spectral data for accurately mapping the spatial distribution of the forest understory invader L. sinense.

Management of tropical forests for products and energy

Treesearch

John I. Zerbe

1992-01-01

Tropical forests have always been sources for prized timbers, rubber, tannin, and other forest products for use worldwide. However, with the recent concern regarding global change, the importance of effective forest products management and utilization has increased significantly. The USDA Forest Service's Forest Products Laboratory at Madison, Wisconsin, has...

36 CFR 223.280 - Waiver of fees and/or fair market value.

Code of Federal Regulations, 2012 CFR

2012-07-01

... AGRICULTURE SALE AND DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS Forest Botanical Products § 223.280 Waiver of fees and/or fair market value. The Forest Service...) For all federally-recognized Tribes seeking to harvest forest botanical products for cultural...

36 CFR 223.280 - Waiver of fees and/or fair market value.

Code of Federal Regulations, 2013 CFR

2013-07-01

... AGRICULTURE SALE AND DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS Forest Botanical Products § 223.280 Waiver of fees and/or fair market value. The Forest Service...) For all federally-recognized Tribes seeking to harvest forest botanical products for cultural...

36 CFR 223.280 - Waiver of fees and/or fair market value.

Code of Federal Regulations, 2011 CFR

2011-07-01

... AGRICULTURE SALE AND DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS Forest Botanical Products § 223.280 Waiver of fees and/or fair market value. The Forest Service...) For all federally-recognized Tribes seeking to harvest forest botanical products for cultural...

36 CFR 223.280 - Waiver of fees and/or fair market value.

Code of Federal Regulations, 2014 CFR

2014-07-01

... AGRICULTURE SALE AND DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS Forest Botanical Products § 223.280 Waiver of fees and/or fair market value. The Forest Service...) For all federally-recognized Tribes seeking to harvest forest botanical products for cultural...

Ultrasonic Sensor Signals and Optimum Path Forest Classifier for the Microstructural Characterization of Thermally-Aged Inconel 625 Alloy

PubMed Central

de Albuquerque, Victor Hugo C.; Barbosa, Cleisson V.; Silva, Cleiton C.; Moura, Elineudo P.; Rebouças Filho, Pedro P.; Papa, João P.; Tavares, João Manuel R. S.

2015-01-01

Secondary phases, such as laves and carbides, are formed during the final solidification stages of nickel-based superalloy coatings deposited during the gas tungsten arc welding cold wire process. However, when aged at high temperatures, other phases can precipitate in the microstructure, like the γ” and δ phases. This work presents an evaluation of the powerful optimum path forest (OPF) classifier configured with six distance functions to classify background echo and backscattered ultrasonic signals from samples of the inconel 625 superalloy thermally aged at 650 and 950 °C for 10, 100 and 200 h. The background echo and backscattered ultrasonic signals were acquired using transducers with frequencies of 4 and 5 MHz. The potentiality of ultrasonic sensor signals combined with the OPF to characterize the microstructures of an inconel 625 thermally aged and in the as-welded condition were confirmed by the results. The experimental results revealed that the OPF classifier is sufficiently fast (classification total time of 0.316 ms) and accurate (accuracy of 88.75% and harmonic mean of 89.52) for the application proposed. PMID:26024416

Evaluation of Semi-supervised Learning for Classification of Protein Crystallization Imagery.

PubMed

Sigdel, Madhav; Dinç, İmren; Dinç, Semih; Sigdel, Madhu S; Pusey, Marc L; Aygün, Ramazan S

2014-03-01

In this paper, we investigate the performance of two wrapper methods for semi-supervised learning algorithms for classification of protein crystallization images with limited labeled images. Firstly, we evaluate the performance of semi-supervised approach using self-training with naïve Bayesian (NB) and sequential minimum optimization (SMO) as the base classifiers. The confidence values returned by these classifiers are used to select high confident predictions to be used for self-training. Secondly, we analyze the performance of Yet Another Two Stage Idea (YATSI) semi-supervised learning using NB, SMO, multilayer perceptron (MLP), J48 and random forest (RF) classifiers. These results are compared with the basic supervised learning using the same training sets. We perform our experiments on a dataset consisting of 2250 protein crystallization images for different proportions of training and test data. Our results indicate that NB and SMO using both self-training and YATSI semi-supervised approaches improve accuracies with respect to supervised learning. On the other hand, MLP, J48 and RF perform better using basic supervised learning. Overall, random forest classifier yields the best accuracy with supervised learning for our dataset.

Ultrasonic sensor signals and optimum path forest classifier for the microstructural characterization of thermally-aged inconel 625 alloy.

PubMed

de Albuquerque, Victor Hugo C; Barbosa, Cleisson V; Silva, Cleiton C; Moura, Elineudo P; Filho, Pedro P Rebouças; Papa, João P; Tavares, João Manuel R S

2015-05-27

Secondary phases, such as laves and carbides, are formed during the final solidification stages of nickel-based superalloy coatings deposited during the gas tungsten arc welding cold wire process. However, when aged at high temperatures, other phases can precipitate in the microstructure, like the γ'' and δ phases. This work presents an evaluation of the powerful optimum path forest (OPF) classifier configured with six distance functions to classify background echo and backscattered ultrasonic signals from samples of the inconel 625 superalloy thermally aged at 650 and 950 °C for 10, 100 and 200 h. The background echo and backscattered ultrasonic signals were acquired using transducers with frequencies of 4 and 5 MHz. The potentiality of ultrasonic sensor signals combined with the OPF to characterize the microstructures of an inconel 625 thermally aged and in the as-welded condition were confirmed by the results. The experimental results revealed that the OPF classifier is sufficiently fast (classification total time of 0.316 ms) and accurate (accuracy of 88.75%" and harmonic mean of 89.52) for the application proposed.

Random forests ensemble classifier trained with data resampling strategy to improve cardiac arrhythmia diagnosis.

PubMed

Ozçift, Akin

2011-05-01

Supervised classification algorithms are commonly used in the designing of computer-aided diagnosis systems. In this study, we present a resampling strategy based Random Forests (RF) ensemble classifier to improve diagnosis of cardiac arrhythmia. Random forests is an ensemble classifier that consists of many decision trees and outputs the class that is the mode of the class's output by individual trees. In this way, an RF ensemble classifier performs better than a single tree from classification performance point of view. In general, multiclass datasets having unbalanced distribution of sample sizes are difficult to analyze in terms of class discrimination. Cardiac arrhythmia is such a dataset that has multiple classes with small sample sizes and it is therefore adequate to test our resampling based training strategy. The dataset contains 452 samples in fourteen types of arrhythmias and eleven of these classes have sample sizes less than 15. Our diagnosis strategy consists of two parts: (i) a correlation based feature selection algorithm is used to select relevant features from cardiac arrhythmia dataset. (ii) RF machine learning algorithm is used to evaluate the performance of selected features with and without simple random sampling to evaluate the efficiency of proposed training strategy. The resultant accuracy of the classifier is found to be 90.0% and this is a quite high diagnosis performance for cardiac arrhythmia. Furthermore, three case studies, i.e., thyroid, cardiotocography and audiology, are used to benchmark the effectiveness of the proposed method. The results of experiments demonstrated the efficiency of random sampling strategy in training RF ensemble classification algorithm. Copyright © 2011 Elsevier Ltd. All rights reserved.

Application of remote sensing and GIS techniques for forest cover monitoring in the southern part of Laos

NASA Astrophysics Data System (ADS)

Keonuchan, Ammala; Liu, Yaolin

2008-12-01

Forest resource is the important material foundation of national sustainable development. And it need to master the status and change of forest resource timely for reasonable exploitation of forest and its renewal. Laos is located in the heart of the Indochinese peninsular, in southeast Asia, latitude 14° to 23 °north and longitude 100°to 108°east, covered a total 236, 800 square kilometers, and country of nearly 6 million people. The forest of Laos dropped from close to two-third in the 1970's to less than half by the 1990's. This deforestation has been attributed to two human activities : a traditional of shifting cultivation or slash and burn farming, and logging without reforestation. Remote sensing and GIS are the most modern technologies which have been widely used in the field of natural resource management and monitoring. These technologies provide very powerful tools to observe and collect information on natural resources and dynamic phenomenon on the earth surface, and ability to integrate different data and present data in different formats. In this study, using forest cover map and Landsat 7 ETM data, we analyze and compare forest cover change from 1997 to 2002. And the maximum likelihood method of supervised classification was used to classify the remote sensing data, we processed Spectral Enhancement, including Normalized Difference Vegetation Index (NDVI) ,and re-classify data again base on Principle Components Analysis (PCA) and NDVI.

Forest Productivity, Leaf Area, and Terrain in Southern Appalachian Deciduous Forests

Treesearch

Paul V. Bolstad; James M. Vose; Steven G. McNulty

2000-01-01

Leaf area index (LAI) is an important structural characteristic of forest ecosystems which has been shown to be strongly related to forest mass and energy cycles and forest productivity. LAI is more easily measured than forest productivity, and so a strong relationship between LAI and productivity would be a valuable tool in forest management. While a linear...

The importance of forest structure to biodiversity–productivity relationships

PubMed Central

Huth, Andreas

2017-01-01

While various relationships between productivity and biodiversity are found in forests, the processes underlying these relationships remain unclear and theory struggles to coherently explain them. In this work, we analyse diversity–productivity relationships through an examination of forest structure (described by basal area and tree height heterogeneity). We use a new modelling approach, called ‘forest factory’, which generates various forest stands and calculates their annual productivity (above-ground wood increment). Analysing approximately 300 000 forest stands, we find that mean forest productivity does not increase with species diversity. Instead forest structure emerges as the key variable. Similar patterns can be observed by analysing 5054 forest plots of the German National Forest Inventory. Furthermore, we group the forest stands into nine forest structure classes, in which we find increasing, decreasing, invariant and even bell-shaped relationships between productivity and diversity. In addition, we introduce a new index, called optimal species distribution, which describes the ratio of realized to the maximal possible productivity (by shuffling species identities). The optimal species distribution and forest structure indices explain the obtained productivity values quite well (R2 between 0.7 and 0.95), whereby the influence of these attributes varies within the nine forest structure classes. PMID:28280550

Sensitivity of snow process simulations to precipitation-phase transition method in forested and open areas

NASA Astrophysics Data System (ADS)

Lundberg, A.; Gustafsson, D.

2009-04-01

Modeling of forest snow processes is complicated and especially problematic seems to be the separation of precipitation phase in climates where a large part of the precipitation falls at temperatures near zero degrees Celsius. When the precipitation is classified as snow, the tree crowns can carry an order of magnitude more canopy storage as compared to when the precipitation is classified as rain, and snow in the trees also alters the albedo of the forest while rain does not. Many different schemes for the precipitation phase separation are used by various snow models. Some models use just one air temperature threshold (TR/S) below which all precipitation is assumed to be snow and above which all precipitation is classified as rain. A more common approach for forest snow models is to use two temperature thresholds. The snow fraction (SF) is then set to one below the snow threshold (TS) and to zero above the rain threshold (TR) and SF is assumed to decrease linearly between these two thresholds. Also more sophisticated schemes exist, but three seems to be a lack of agreement on how the precipitation phase separations should be performed. The aim with this study is to use a hydrological model including canopy snow processes to illustrate the sensitivity for different formulations of the precipitation phase separation on a) the simulated maximum snow pack storage b) the interception evaporation loss and c) snow melt runoff. In other words, to investigate of the choice of precipitation phase separation has an impact on the simulated wintertime water balance. Simulations are made for sites in different climates and for both open fields and forest sites in different regions of Sweden from north to south. In general, precipitation phase separation methods that classified snowfall at higher temperatures resulted in a larger proportion of the precipitation lost by interception evaporation as a result of the increased interception capacity. However, the maximum snow accumulation was also increased in some cases due to the overall increased snowfall, depending on canopy density and precipitation and temperature regimes. Results show that the choice of precipitation phase separation method can have an significant impact on the simulated wintertime water balance, especially in forested regions.

A multivariate study of mangrove morphology (Rhizophora mangle) using both above and below-water plant architecture

USGS Publications Warehouse

Brooks, R.A.; Bell, S.S.

2005-01-01

A descriptive study of the architecture of the red mangrove, Rhizophora mangle L., habitat of Tampa Bay, FL, was conducted to assess if plant architecture could be used to discriminate overwash from fringing forest type. Seven above-water (e.g., tree height, diameter at breast height, and leaf area) and 10 below-water (e.g., root density, root complexity, and maximum root order) architectural features were measured in eight mangrove stands. A multivariate technique (discriminant analysis) was used to test the ability of different models comprising above-water, below-water, or whole tree architecture to classify forest type. Root architectural features appear to be better than classical forestry measurements at discriminating between fringing and overwash forests but, regardless of the features loaded into the model, misclassification rates were high as forest type was only correctly classified in 66% of the cases. Based upon habitat architecture, the results of this study do not support a sharp distinction between overwash and fringing red mangrove forests in Tampa Bay but rather indicate that the two are architecturally undistinguishable. Therefore, within this northern portion of the geographic range of red mangroves, a more appropriate classification system based upon architecture may be one in which overwash and fringing forest types are combined into a single, "tide dominated" category. ?? 2005 Elsevier Ltd. All rights reserved.

Burnscar analysis using normalized burning ratio (NBR) index during 2015 forest fire at Merang-Kepahyang peat forest, South Sumatra, Indonesia

NASA Astrophysics Data System (ADS)

Saputra, Agus Dwi; Setiabudidaya, Dedi; Setyawan, Dwi; Khakim, M. Yusup Nur; Iskandar, Iskhaq

2017-07-01

Forest fire, classified as a natural hazard or human-induced hazard, has negative impacts on humans. These negative impacts are including economic loss, health problems, transportation disruption and land degradation or even biodiversity loss. During 2015, forest fire had occurred at the Merang-Kepahyang peat forest that has a total area of about 69.837,00 ha. In order to set a rehabilitation plan for recovering the impact of forest fire, information on the total burnscar area and severity level is required. In this study, the total burnscar area and severity level is evaluated using a calculation on the Normalized Burning Ratio (NBR) Index. The calculation is based on the Near Infra Red (NIR) and Short Wave Infra Red (SWIR) of the satellite imageries from LANDSAT. The images of pre-and post-fire are used to evaluate the severity level, which is defined as a difference in NBR Index of pre- and post-fire. It is found that about 42.906,00 ha of the total area of Merang-Kepahyang peat area have been fired in 2015. These burned area are classified into four categories, i.e., unburned, low, extreme and moderate extreme. By overlying the spatial map of burning level with other thematic maps, it is expected that strategy for rehabilitation plan can be well developed.

An Effective Antifreeze Protein Predictor with Ensemble Classifiers and Comprehensive Sequence Descriptors.

PubMed

Yang, Runtao; Zhang, Chengjin; Gao, Rui; Zhang, Lina

2015-09-07

Antifreeze proteins (AFPs) play a pivotal role in the antifreeze effect of overwintering organisms. They have a wide range of applications in numerous fields, such as improving the production of crops and the quality of frozen foods. Accurate identification of AFPs may provide important clues to decipher the underlying mechanisms of AFPs in ice-binding and to facilitate the selection of the most appropriate AFPs for several applications. Based on an ensemble learning technique, this study proposes an AFP identification system called AFP-Ensemble. In this system, random forest classifiers are trained by different training subsets and then aggregated into a consensus classifier by majority voting. The resulting predictor yields a sensitivity of 0.892, a specificity of 0.940, an accuracy of 0.938 and a balanced accuracy of 0.916 on an independent dataset, which are far better than the results obtained by previous methods. These results reveal that AFP-Ensemble is an effective and promising predictor for large-scale determination of AFPs. The detailed feature analysis in this study may give useful insights into the molecular mechanisms of AFP-ice interactions and provide guidance for the related experimental validation. A web server has been designed to implement the proposed method.

Combination of mass spectrometry-based targeted lipidomics and supervised machine learning algorithms in detecting adulterated admixtures of white rice.

PubMed

Lim, Dong Kyu; Long, Nguyen Phuoc; Mo, Changyeun; Dong, Ziyuan; Cui, Lingmei; Kim, Giyoung; Kwon, Sung Won

2017-10-01

The mixing of extraneous ingredients with original products is a common adulteration practice in food and herbal medicines. In particular, authenticity of white rice and its corresponding blended products has become a key issue in food industry. Accordingly, our current study aimed to develop and evaluate a novel discrimination method by combining targeted lipidomics with powerful supervised learning methods, and eventually introduce a platform to verify the authenticity of white rice. A total of 30 cultivars were collected, and 330 representative samples of white rice from Korea and China as well as seven mixing ratios were examined. Random forests (RF), support vector machines (SVM) with a radial basis function kernel, C5.0, model averaged neural network, and k-nearest neighbor classifiers were used for the classification. We achieved desired results, and the classifiers effectively differentiated white rice from Korea to blended samples with high prediction accuracy for the contamination ratio as low as five percent. In addition, RF and SVM classifiers were generally superior to and more robust than the other techniques. Our approach demonstrated that the relative differences in lysoGPLs can be successfully utilized to detect the adulterated mixing of white rice originating from different countries. In conclusion, the present study introduces a novel and high-throughput platform that can be applied to authenticate adulterated admixtures from original white rice samples. Copyright © 2017 Elsevier Ltd. All rights reserved.

Forest Resource Information System (FRIS)

NASA Technical Reports Server (NTRS)

1983-01-01

The technological and economical feasibility of using multispectral digital image data as acquired from the LANDSAT satellites in an ongoing operational forest information system was evaluated. Computer compatible multispectral scanner data secured from the LANDSAT satellites were demonstrated to be a significant contributor to ongoing information systems by providing the added dimensions of synoptic and repeat coverage of the Earth's surface. Major forest cover types of conifer, deciduous, mixed conifer-deciduous and non-forest, were classified well within the bounds of the statistical accuracy of the ground sample. Further, when overlayed with existing maps, the acreage of cover type retains a high level of positional integrity. Maps were digitized by a graphics design system, overlayed and registered onto LANDSAT imagery such that the map data with associated attributes were displayed on the image. Once classified, the analysis results were converted back to map form as a cover type of information. Existing tabular information as represented by inventory is registered geographically to the map base through a vendor provided data management system. The notion of a geographical reference base (map) providing the framework to which imagery and tabular data bases are registered and where each of the three functions of imagery, maps and inventory can be accessed singly or in combination is the very essence of the forest resource information system design.

Insects colonising carcasses in open and forest habitats of Central Europe: search for indicators of corpse relocation.

PubMed

Matuszewski, Szymon; Szafałowicz, Michał; Jarmusz, Mateusz

2013-09-10

Several traces may reveal the post-mortem relocation of a corpse. Insects are particularly useful for that purpose. The use of insects for inferring the transfer of a corpse rests on a premise that particular species colonise corpses in different habitats. However, only some insects reveal a strong preference for a given type of habitat. In order to find those insects which colonise corpses exclusively in open habitats, as opposed to forest habitats, a pig carrion study was made in rural open and rural forest habitats of Central Europe. Lucilia sericata (Diptera: Calliphoridae), Dermestes frischi, Dermestes laniarius (Coleoptera: Dermestidae), Omosita colon, some species of Nitidula (Coleoptera: Nitidulidae) and Necrobia rufipes (Coleoptera: Cleridae) were found to breed exclusively in open habitats. Only Oiceoptoma thoracicum (Coleoptera: Silphidae) avoided definitely breeding in open habitats. Sarcophaga caerulescens (Diptera: Sarcophagidae) regularly bred in open habitats but rarely bred in forests. Accordingly, L. sericata, D. frischi, O. colon, species of Nitidula and supposedly N. rufipes may be classified as indicators of corpse relocation from rural open to rural forest habitats of Central Europe. Only O. thoracicum may be classified as an indicator of the relocation in an opposite direction. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Floating Forests: Validation of a Citizen Science Effort to Answer Global Ecological Questions

NASA Astrophysics Data System (ADS)

Rosenthal, I.; Byrnes, J.; Cavanaugh, K. C.; Haupt, A. J.; Trouille, L.; Bell, T. W.; Rassweiler, A.; Pérez-Matus, A.; Assis, J.

2017-12-01

Researchers undertaking long term, large-scale ecological analyses face significant challenges for data collection and processing. Crowdsourcing via citizen science can provide an efficient method for analyzing large data sets. However, many scientists have raised questions about the quality of data collected by citizen scientists. Here we use Floating-Forests (http://floatingforests.org), a citizen science platform for creating a global time series of giant kelp abundance, to show that ensemble classifications of satellite data can ensure data quality. Citizen scientists view satellite images of coastlines and classify kelp forests by tracing all visible patches of kelp. Each image is classified by fifteen citizen scientists before being retired. To validate citizen science results, all fifteen classifications are converted to a raster and overlaid on a calibration dataset generated from previous studies. Results show that ensemble classifications from citizen scientists are consistently accurate when compared to calibration data. Given that all source images were acquired by Landsat satellites, we expect this consistency to hold across all regions. At present, we have over 6000 web-based citizen scientists' classifications of almost 2.5 million images of kelp forests in California and Tasmania. These results are not only useful for remote sensing of kelp forests, but also for a wide array of applications that combine citizen science with remote sensing.

Mapping Species Composition of Forests and Tree Plantations in Northeastern Costa Rica with an Integration of Hyperspectral and Multitemporal Landsat Imagery

NASA Technical Reports Server (NTRS)

Fagan, Matthew E.; Defries, Ruth S.; Sesnie, Steven E.; Arroyo-Mora, J. Pablo; Soto, Carlomagno; Singh, Aditya; Townsend, Philip A.; Chazdon, Robin L.

2015-01-01

An efficient means to map tree plantations is needed to detect tropical land use change and evaluate reforestation projects. To analyze recent tree plantation expansion in northeastern Costa Rica, we examined the potential of combining moderate-resolution hyperspectral imagery (2005 HyMap mosaic) with multitemporal, multispectral data (Landsat) to accurately classify (1) general forest types and (2) tree plantations by species composition. Following a linear discriminant analysis to reduce data dimensionality, we compared four Random Forest classification models: hyperspectral data (HD) alone; HD plus interannual spectral metrics; HD plus a multitemporal forest regrowth classification; and all three models combined. The fourth, combined model achieved overall accuracy of 88.5%. Adding multitemporal data significantly improved classification accuracy (p less than 0.0001) of all forest types, although the effect on tree plantation accuracy was modest. The hyperspectral data alone classified six species of tree plantations with 75% to 93% producer's accuracy; adding multitemporal spectral data increased accuracy only for two species with dense canopies. Non-native tree species had higher classification accuracy overall and made up the majority of tree plantations in this landscape. Our results indicate that combining occasionally acquired hyperspectral data with widely available multitemporal satellite imagery enhances mapping and monitoring of reforestation in tropical landscapes.

Evaluation of ERTS-1 data for inventory of forest and rangeland and detection of forest stress. [Atlanta, Georgia, Manitou, Colorado, and Black Hills

NASA Technical Reports Server (NTRS)

Heller, R. C. (Principal Investigator); Aldrich, R. C.; Driscoll, R. S.; Francis, R. E.; Weber, F. P.

1974-01-01

The author has identified the following significant results. Results of photointerpretation indicated that ERTS is a good classifier of forest and nonforest lands (90 to 95 percent accurate). Photointerpreters could make this separation as accurately as signature analysis of the computer compatible tapes. Further breakdowns of cover types at each site could not be accurately classified by interpreters (60 percent) or computer analysts (74 percent). Exceptions were water, wet meadow, and coniferous stands. At no time could the large bark beetle infestations (many over 300 meters in size) be detected on ERTS images. The ERTS wavebands are too broad to distinguish the yellow, yellow-red, and red colors of the dying pine foliage from healthy green-yellow foliage. Forest disturbances could be detected on ERTS color composites about 90 percent of the time when compared with six-year-old photo index mosaics. ERTS enlargements (1:125,000 scale, preferably color prints) would be useful to forest managers of large ownerships over 5,000 hectares (12,500 acres) for broad area planning. Black-and-white enlargements can be used effectively as aerial navigation aids for precision aerial photography where maps are old or not available.

Are forest disturbances amplifying or canceling out climate change-induced productivity changes in European forests?

NASA Astrophysics Data System (ADS)

Reyer, Christopher P. O.; Bathgate, Stephen; Blennow, Kristina; Borges, Jose G.; Bugmann, Harald; Delzon, Sylvain; Faias, Sonia P.; Garcia-Gonzalo, Jordi; Gardiner, Barry; Gonzalez-Olabarria, Jose Ramon; Gracia, Carlos; Guerra Hernández, Juan; Kellomäki, Seppo; Kramer, Koen; Lexer, Manfred J.; Lindner, Marcus; van der Maaten, Ernst; Maroschek, Michael; Muys, Bart; Nicoll, Bruce; Palahi, Marc; Palma, João HN; Paulo, Joana A.; Peltola, Heli; Pukkala, Timo; Rammer, Werner; Ray, Duncan; Sabaté, Santiago; Schelhaas, Mart-Jan; Seidl, Rupert; Temperli, Christian; Tomé, Margarida; Yousefpour, Rasoul; Zimmermann, Niklaus E.; Hanewinkel, Marc

2017-03-01

Recent studies projecting future climate change impacts on forests mainly consider either the effects of climate change on productivity or on disturbances. However, productivity and disturbances are intrinsically linked because 1) disturbances directly affect forest productivity (e.g. via a reduction in leaf area, growing stock or resource-use efficiency), and 2) disturbance susceptibility is often coupled to a certain development phase of the forest with productivity determining the time a forest is in this specific phase of susceptibility. The objective of this paper is to provide an overview of forest productivity changes in different forest regions in Europe under climate change, and partition these changes into effects induced by climate change alone and by climate change and disturbances. We present projections of climate change impacts on forest productivity from state-of-the-art forest models that dynamically simulate forest productivity and the effects of the main European disturbance agents (fire, storm, insects), driven by the same climate scenario in seven forest case studies along a large climatic gradient throughout Europe. Our study shows that, in most cases, including disturbances in the simulations exaggerate ongoing productivity declines or cancel out productivity gains in response to climate change. In fewer cases, disturbances also increase productivity or buffer climate-change induced productivity losses, e.g. because low severity fires can alleviate resource competition and increase fertilization. Even though our results cannot simply be extrapolated to other types of forests and disturbances, we argue that it is necessary to interpret climate change-induced productivity and disturbance changes jointly to capture the full range of climate change impacts on forests and to plan adaptation measures.

Are forest disturbances amplifying or canceling out climate change-induced productivity changes in European forests?

PubMed Central

Reyer, Christopher P O; Bathgate, Stephen; Blennow, Kristina; Borges, Jose G; Bugmann, Harald; Delzon, Sylvain; Faias, Sonia P; Garcia-Gonzalo, Jordi; Gardiner, Barry; Gonzalez-Olabarria, Jose Ramon; Gracia, Carlos; Hernández, Juan Guerra; Kellomäki, Seppo; Kramer, Koen; Lexer, Manfred J; Lindner, Marcus; van der Maaten, Ernst; Maroschek, Michael; Muys, Bart; Nicoll, Bruce; Palahi, Marc; Palma, João HN; Paulo, Joana A; Peltola, Heli; Pukkala, Timo; Rammer, Werner; Ray, Duncan; Sabaté, Santiago; Schelhaas, Mart-Jan; Seidl, Rupert; Temperli, Christian; Tomé, Margarida; Yousefpour, Rasoul; Zimmermann, Niklaus E; Hanewinkel, Marc

2017-01-01

Recent studies projecting future climate change impacts on forests mainly consider either the effects of climate change on productivity or on disturbances. However, productivity and disturbances are intrinsically linked because 1) disturbances directly affect forest productivity (e.g. via a reduction in leaf area, growing stock or resource-use efficiency), and 2) disturbance susceptibility is often coupled to a certain development phase of the forest with productivity determining the time a forest is in this specific phase of susceptibility. The objective of this paper is to provide an overview of forest productivity changes in different forest regions in Europe under climate change, and partition these changes into effects induced by climate change alone and by climate change and disturbances. We present projections of climate change impacts on forest productivity from state-of-the-art forest models that dynamically simulate forest productivity and the effects of the main European disturbance agents (fire, storm, insects), driven by the same climate scenario in seven forest case studies along a large climatic gradient throughout Europe. Our study shows that, in most cases, including disturbances in the simulations exaggerate ongoing productivity declines or cancel out productivity gains in response to climate change. In fewer cases, disturbances also increase productivity or buffer climate-change induced productivity losses, e.g. because low severity fires can alleviate resource competition and increase fertilization. Even though our results cannot simply be extrapolated to other types of forests and disturbances, we argue that it is necessary to interpret climate change-induced productivity and disturbance changes jointly to capture the full range of climate change impacts on forests and to plan adaptation measures. PMID:28855959

Are forest disturbances amplifying or canceling out climate change-induced productivity changes in European forests?

PubMed

Reyer, Christopher P O; Bathgate, Stephen; Blennow, Kristina; Borges, Jose G; Bugmann, Harald; Delzon, Sylvain; Faias, Sonia P; Garcia-Gonzalo, Jordi; Gardiner, Barry; Gonzalez-Olabarria, Jose Ramon; Gracia, Carlos; Hernández, Juan Guerra; Kellomäki, Seppo; Kramer, Koen; Lexer, Manfred J; Lindner, Marcus; van der Maaten, Ernst; Maroschek, Michael; Muys, Bart; Nicoll, Bruce; Palahi, Marc; Palma, João Hn; Paulo, Joana A; Peltola, Heli; Pukkala, Timo; Rammer, Werner; Ray, Duncan; Sabaté, Santiago; Schelhaas, Mart-Jan; Seidl, Rupert; Temperli, Christian; Tomé, Margarida; Yousefpour, Rasoul; Zimmermann, Niklaus E; Hanewinkel, Marc

2017-03-16

Recent studies projecting future climate change impacts on forests mainly consider either the effects of climate change on productivity or on disturbances. However, productivity and disturbances are intrinsically linked because 1) disturbances directly affect forest productivity (e.g. via a reduction in leaf area, growing stock or resource-use efficiency), and 2) disturbance susceptibility is often coupled to a certain development phase of the forest with productivity determining the time a forest is in this specific phase of susceptibility. The objective of this paper is to provide an overview of forest productivity changes in different forest regions in Europe under climate change, and partition these changes into effects induced by climate change alone and by climate change and disturbances. We present projections of climate change impacts on forest productivity from state-of-the-art forest models that dynamically simulate forest productivity and the effects of the main European disturbance agents (fire, storm, insects), driven by the same climate scenario in seven forest case studies along a large climatic gradient throughout Europe. Our study shows that, in most cases, including disturbances in the simulations exaggerate ongoing productivity declines or cancel out productivity gains in response to climate change. In fewer cases, disturbances also increase productivity or buffer climate-change induced productivity losses, e.g. because low severity fires can alleviate resource competition and increase fertilization. Even though our results cannot simply be extrapolated to other types of forests and disturbances, we argue that it is necessary to interpret climate change-induced productivity and disturbance changes jointly to capture the full range of climate change impacts on forests and to plan adaptation measures.

Automated time activity classification based on global positioning system (GPS) tracking data

PubMed Central

2011-01-01

Background Air pollution epidemiological studies are increasingly using global positioning system (GPS) to collect time-location data because they offer continuous tracking, high temporal resolution, and minimum reporting burden for participants. However, substantial uncertainties in the processing and classifying of raw GPS data create challenges for reliably characterizing time activity patterns. We developed and evaluated models to classify people's major time activity patterns from continuous GPS tracking data. Methods We developed and evaluated two automated models to classify major time activity patterns (i.e., indoor, outdoor static, outdoor walking, and in-vehicle travel) based on GPS time activity data collected under free living conditions for 47 participants (N = 131 person-days) from the Harbor Communities Time Location Study (HCTLS) in 2008 and supplemental GPS data collected from three UC-Irvine research staff (N = 21 person-days) in 2010. Time activity patterns used for model development were manually classified by research staff using information from participant GPS recordings, activity logs, and follow-up interviews. We evaluated two models: (a) a rule-based model that developed user-defined rules based on time, speed, and spatial location, and (b) a random forest decision tree model. Results Indoor, outdoor static, outdoor walking and in-vehicle travel activities accounted for 82.7%, 6.1%, 3.2% and 7.2% of manually-classified time activities in the HCTLS dataset, respectively. The rule-based model classified indoor and in-vehicle travel periods reasonably well (Indoor: sensitivity > 91%, specificity > 80%, and precision > 96%; in-vehicle travel: sensitivity > 71%, specificity > 99%, and precision > 88%), but the performance was moderate for outdoor static and outdoor walking predictions. No striking differences in performance were observed between the rule-based and the random forest models. The random forest model was fast and easy to execute, but was likely less robust than the rule-based model under the condition of biased or poor quality training data. Conclusions Our models can successfully identify indoor and in-vehicle travel points from the raw GPS data, but challenges remain in developing models to distinguish outdoor static points and walking. Accurate training data are essential in developing reliable models in classifying time-activity patterns. PMID:22082316

Automated time activity classification based on global positioning system (GPS) tracking data.

PubMed

Wu, Jun; Jiang, Chengsheng; Houston, Douglas; Baker, Dean; Delfino, Ralph

2011-11-14

Air pollution epidemiological studies are increasingly using global positioning system (GPS) to collect time-location data because they offer continuous tracking, high temporal resolution, and minimum reporting burden for participants. However, substantial uncertainties in the processing and classifying of raw GPS data create challenges for reliably characterizing time activity patterns. We developed and evaluated models to classify people's major time activity patterns from continuous GPS tracking data. We developed and evaluated two automated models to classify major time activity patterns (i.e., indoor, outdoor static, outdoor walking, and in-vehicle travel) based on GPS time activity data collected under free living conditions for 47 participants (N = 131 person-days) from the Harbor Communities Time Location Study (HCTLS) in 2008 and supplemental GPS data collected from three UC-Irvine research staff (N = 21 person-days) in 2010. Time activity patterns used for model development were manually classified by research staff using information from participant GPS recordings, activity logs, and follow-up interviews. We evaluated two models: (a) a rule-based model that developed user-defined rules based on time, speed, and spatial location, and (b) a random forest decision tree model. Indoor, outdoor static, outdoor walking and in-vehicle travel activities accounted for 82.7%, 6.1%, 3.2% and 7.2% of manually-classified time activities in the HCTLS dataset, respectively. The rule-based model classified indoor and in-vehicle travel periods reasonably well (Indoor: sensitivity > 91%, specificity > 80%, and precision > 96%; in-vehicle travel: sensitivity > 71%, specificity > 99%, and precision > 88%), but the performance was moderate for outdoor static and outdoor walking predictions. No striking differences in performance were observed between the rule-based and the random forest models. The random forest model was fast and easy to execute, but was likely less robust than the rule-based model under the condition of biased or poor quality training data. Our models can successfully identify indoor and in-vehicle travel points from the raw GPS data, but challenges remain in developing models to distinguish outdoor static points and walking. Accurate training data are essential in developing reliable models in classifying time-activity patterns.

Mapping Fuels on the Okanogan and Wenatchee National Forests

Treesearch

Crystal L. Raymond; Lara-Karena B. Kellogg; Donald McKenzie

2006-01-01

Resource managers need spatially explicit fuels data to manage fire hazard and evaluate the ecological effects of wildland fires and fuel treatments. For this study, fuels were mapped on the Okanogan and Wenatchee National Forests (OWNF) using a rule-based method and the Fuels Characteristic Classification System (FCCS). The FCCS classifies fuels based on their...

Forest statistics for the Piedmont of North Carolina, 1990

Treesearch

Mark J. Brown

1990-01-01

Since 1984, are of timberland in the Piedmont of North Carolina decreased less than 1 percent and now totals under 5.8 million acres. Nonindustrial private owners control 93 percent of the region's timberland, the highest percentage in the Southeast. About 32 percent of the timberland in the unit is classified as a pine forest type. Artificial regeneration...

Drought impacts on tree growth and mortality of southern Appalachian forests

Treesearch

Brian D. Kloeppel; Barton D. Clinton; James M. Vose; Aaron R. Cooper

2003-01-01

The Coweeta LTER Program represents the eastern deciduous forests of the southem Appalachian Mountains in the United States. Coweeta Hydrologic Laboratory was established in 1934 and hence has a long record of climate measurement and vegetation response to both natural and human disturbance (Swank and Crossley 1988). The general climate of the area is classified as...

Implementing a land cover stratification on-the-fly

Treesearch

Ronald E. McRoberts; Daniel G. Wendt

2002-01-01

Stratified estimation is used by the Forest Inventory and Analysis program of the USDA Forest Service to increase the precision of county-level inventory estimates. Stratified estimation requires that plots be assigned to strata and that proportions of land area in each strata be determined. Classified satellite imagery has been found to be an efficient and effective...

Forest statistics for North Carolina, 1990

Treesearch

Tony G. Johnson

1991-01-01

Since 1984, area of timberland in North Carolina declined almost 78,000 acres to 18.7 million acres. Nonindustrial private forest landowners control 76 percent of the State's timberland. Area classified as a pine type declined 3 percent to 6.3 million acres. Nearly 295,000 acres were harvested annually, while 357,000 per year were regenerated both by artificial...

Analyzing Body Movements within the Laban Effort Framework Using a Single Accelerometer

PubMed Central

Kikhia, Basel; Gomez, Miguel; Jiménez, Lara Lorna; Hallberg, Josef; Karvonen, Niklas; Synnes, Kåre

2014-01-01

This article presents a study on analyzing body movements by using a single accelerometer sensor. The investigated categories of body movements belong to the Laban Effort Framework: Strong—Light, Free—Bound and Sudden—Sustained. All body movements were represented by a set of activities used for data collection. The calculated accuracy of detecting the body movements was based on collecting data from a single wireless tri-axial accelerometer sensor. Ten healthy subjects collected data from three body locations (chest, wrist and thigh) simultaneously in order to analyze the locations comparatively. The data was then processed and analyzed using Machine Learning techniques. The wrist placement was found to be the best single location to record data for detecting Strong—Light body movements using the Random Forest classifier. The wrist placement was also the best location for classifying Bound—Free body movements using the SVM classifier. However, the data collected from the chest placement yielded the best results for detecting Sudden—Sustained body movements using the Random Forest classifier. The study shows that the choice of the accelerometer placement should depend on the targeted type of movement. In addition, the choice of the classifier when processing data should also depend on the chosen location and the target movement. PMID:24662408

36 CFR 223.217 - Authority to dispose of special forest products.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 36 Parks, Forests, and Public Property 2 2010-07-01 2010-07-01 false Authority to dispose of special forest products. 223.217 Section 223.217 Parks, Forests, and Public Property FOREST SERVICE, DEPARTMENT OF AGRICULTURE SALE AND DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER Special Forest Products § 223...

Accuracy and efficiency of area classifications based on tree tally

Treesearch

Michael S. Williams; Hans T. Schreuder; Raymond L. Czaplewski

2001-01-01

Inventory data are often used to estimate the area of the land base that is classified as a specific condition class. Examples include areas classified as old-growth forest, private ownership, or suitable habitat for a given species. Many inventory programs rely on classification algorithms of varying complexity to determine condition class. These algorithms can be...

Adapting a tourism crime typology: classifying outdoor recreation crime

Treesearch

Joanne F. Tynon; Deborah J. Chavez

2006-01-01

Using a qualitative aproach, the authors tested a crime typology developed for tourism destinations in a U.S. National Forest recreation setting. Specific objectives were to classify the attributes of crime and violence, examine the effects of crime and violence on visitor demand, and suggest methods of prevention and recovery. A key modification to the crime typology...

Classifying forest and nonforest land on space photographs

NASA Technical Reports Server (NTRS)

Aldrich, R. C.

1970-01-01

Although the research reported is in its preliminary stages, results show that: (1) infrared color film is the best single multiband sensor available; (2) there is a good possibility that forest can be separated from all nonforest land uses by microimage evaluation techniques on IR color film coupled with B/W infrared and panchromatic films; and (3) discrimination of forest and nonforest classes is possible by either of two methods: interpreters with appropriate viewing and mapping instruments, or programmable automatic scanning microdensitometers and automatic data processing.

Nonlinguistic vocalizations from online amateur videos for emotion research: A validated corpus.

PubMed

Anikin, Andrey; Persson, Tomas

2017-04-01

This study introduces a corpus of 260 naturalistic human nonlinguistic vocalizations representing nine emotions: amusement, anger, disgust, effort, fear, joy, pain, pleasure, and sadness. The recognition accuracy in a rating task varied greatly per emotion, from <40% for joy and pain, to >70% for amusement, pleasure, fear, and sadness. In contrast, the raters' linguistic-cultural group had no effect on recognition accuracy: The predominantly English-language corpus was classified with similar accuracies by participants from Brazil, Russia, Sweden, and the UK/USA. Supervised random forest models classified the sounds as accurately as the human raters. The best acoustic predictors of emotion were pitch, harmonicity, and the spacing and regularity of syllables. This corpus of ecologically valid emotional vocalizations can be filtered to include only sounds with high recognition rates, in order to study reactions to emotional stimuli of known perceptual types (reception side), or can be used in its entirety to study the association between affective states and vocal expressions (production side).

Classification of the Gabon SAR Mosaic Using a Wavelet Based Rule Classifier

NASA Technical Reports Server (NTRS)

Simard, Marc; Saatchi, Sasan; DeGrandi, Gianfranco

2000-01-01

A method is developed for semi-automated classification of SAR images of the tropical forest. Information is extracted using the wavelet transform (WT). The transform allows for extraction of structural information in the image as a function of scale. In order to classify the SAR image, a Desicion Tree Classifier is used. The method of pruning is used to optimize classification rate versus tree size. The results give explicit insight on the type of information useful for a given class.

25 CFR 163.22 - Payment for forest products.

Code of Federal Regulations, 2010 CFR

2010-04-01

...) Terms and conditions for payment of forest products under lump sum (predetermined volume) sales shall be... Forest Management and Operations § 163.22 Payment for forest products. (a) The basis of volume determination for forest products sold shall be the Scribner Decimal C log rules, cubic volume, lineal...

36 CFR 223.282 - Deposit and expenditure of collected fees.

Code of Federal Regulations, 2013 CFR

2013-07-01

... AGRICULTURE SALE AND DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS Forest Botanical Products § 223.282 Deposit and expenditure of collected fees. (a) Funds collected under the pilot program for the harvest and sale of forest botanical products shall be deposited into a...

36 CFR 223.282 - Deposit and expenditure of collected fees.

Code of Federal Regulations, 2014 CFR

2014-07-01

... AGRICULTURE SALE AND DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS Forest Botanical Products § 223.282 Deposit and expenditure of collected fees. (a) Funds collected under the pilot program for the harvest and sale of forest botanical products shall be deposited into a...

36 CFR 223.282 - Deposit and expenditure of collected fees.

Code of Federal Regulations, 2011 CFR

2011-07-01

... AGRICULTURE SALE AND DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS Forest Botanical Products § 223.282 Deposit and expenditure of collected fees. (a) Funds collected under the pilot program for the harvest and sale of forest botanical products shall be deposited into a...

36 CFR 223.282 - Deposit and expenditure of collected fees.

Code of Federal Regulations, 2012 CFR

2012-07-01

... AGRICULTURE SALE AND DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS Forest Botanical Products § 223.282 Deposit and expenditure of collected fees. (a) Funds collected under the pilot program for the harvest and sale of forest botanical products shall be deposited into a...

29 CFR 780.1015 - Other forest products.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 29 Labor 3 2010-07-01 2010-07-01 false Other forest products. 780.1015 Section 780.1015 Labor... Provisions Under Section 13(d) Requirements for Exemption § 780.1015 Other forest products. The homeworker may also harvest “other forest products” for use in making wreaths. The term other forest products...

Species composition and forest structure explain the temperature sensitivity patterns of productivity in temperate forests

NASA Astrophysics Data System (ADS)

Bohn, Friedrich J.; May, Felix; Huth, Andreas

2018-03-01

Rising temperatures due to climate change influence the wood production of forests. Observations show that some temperate forests increase their productivity, whereas others reduce their productivity. This study focuses on how species composition and forest structure properties influence the temperature sensitivity of aboveground wood production (AWP). It further investigates which forests will increase their productivity the most with rising temperatures. We described forest structure by leaf area index, forest height and tree height heterogeneity. Species composition was described by a functional diversity index (Rao's Q) and a species distribution index (ΩAWP). ΩAWP quantified how well species are distributed over the different forest layers with regard to AWP. We analysed 370 170 forest stands generated with a forest gap model. These forest stands covered a wide range of possible forest types. For each stand, we estimated annual aboveground wood production and performed a climate sensitivity analysis based on 320 different climate time series (of 1-year length). The scenarios differed in mean annual temperature and annual temperature amplitude. Temperature sensitivity of wood production was quantified as the relative change in productivity resulting from a 1 °C rise in mean annual temperature or annual temperature amplitude. Increasing ΩAWP positively influenced both temperature sensitivity indices of forest, whereas forest height showed a bell-shaped relationship with both indices. Further, we found forests in each successional stage that are positively affected by temperature rise. For such forests, large ΩAWP values were important. In the case of young forests, low functional diversity and small tree height heterogeneity were associated with a positive effect of temperature on wood production. During later successional stages, higher species diversity and larger tree height heterogeneity were an advantage. To achieve such a development, one could plant below the closed canopy of even-aged, pioneer trees a climax-species-rich understorey that will build the canopy of the mature forest. This study highlights that forest structure and species composition are both relevant for understanding the temperature sensitivity of wood production.

Environmentally friendly use of non-coal ashes in Sweden.

PubMed

Ribbing, C

2007-01-01

The Swedish Thermal Engineering Research Institute (Värmeforsk) initiated an applied research program "Environmentally friendly use of non-coal ashes", in 2002. The program aims at increasing knowledge on the by-products of energy production and their application. The goal of formulating technical and environmental guidelines and assessments is a major point of the program, which is supported by about forty authorities and private organisations. The programme has been divided into four areas: recycling of ashes to forests, geotechnical applications, use in landfilling, and environmental aspects and chemistry. Among all results obtained, the following progress is shown: *Evidence for the positive effects of spreading ashes on forest growth. *A proposal for environmental guidelines on the utilisation of ashes in construction. *A handbook for using non-coal fly ashes in unpaved roads. *Technical and environmental assessments of MSWI bottom ashes in road construction. *Development of the use of ashes with municipal wastewater sludge as a cover for landfills and mine tailings. *Use of ashes from bio-fuels in concrete and replacement of cement in stoop mining. *A method to classify those by-products from combustion that have mirror entries in the EWC as a hazardous or non-hazardous compound. The Ash Programme has also made it possible to increase knowledge on ashes as valuable materials, on quality assurance and on markets for recovered materials.

36 CFR 223.277 - Forest botanical products definition.

Code of Federal Regulations, 2013 CFR

2013-07-01

..., transplants, tree sap, and wildflowers. Forest botanical products are not animals, animal parts, Christmas... 36 Parks, Forests, and Public Property 2 2013-07-01 2013-07-01 false Forest botanical products definition. 223.277 Section 223.277 Parks, Forests, and Public Property FOREST SERVICE, DEPARTMENT OF...

36 CFR 223.277 - Forest botanical products definition.

Code of Federal Regulations, 2012 CFR

2012-07-01

..., transplants, tree sap, and wildflowers. Forest botanical products are not animals, animal parts, Christmas... 36 Parks, Forests, and Public Property 2 2012-07-01 2012-07-01 false Forest botanical products definition. 223.277 Section 223.277 Parks, Forests, and Public Property FOREST SERVICE, DEPARTMENT OF...

36 CFR 223.277 - Forest botanical products definition.

Code of Federal Regulations, 2014 CFR

2014-07-01

..., transplants, tree sap, and wildflowers. Forest botanical products are not animals, animal parts, Christmas... 36 Parks, Forests, and Public Property 2 2014-07-01 2014-07-01 false Forest botanical products definition. 223.277 Section 223.277 Parks, Forests, and Public Property FOREST SERVICE, DEPARTMENT OF...

Potential for forest products in interior Alaska.

Treesearch

George R. Sampson; Willem W.S. van Hees; Theodore S. Setzer; Richard C. Smith

1988-01-01

Future opportunities for producing Alaska forest products were examined from the perspective of timber supply as reported in timber inventory reports and past studies of forest products industry potential. The best prospects for increasing industrial production of forest products in interior Alaska are for softwood lumber. Current softwood lumber production in the...

Using Remote Sensing and Synthetic Controls to Understand Deforestation Drivers and their Moderation by Forest Use in Kalimantan, Indonesia

NASA Astrophysics Data System (ADS)

Gray, J. M.; Sills, E. O.; Amanatides, M. M.

2017-12-01

Tropical forests offer valuable ecosystem services at multiple scales, from the local hydrological cycle to the global carbon cycle. This has motivated significant international attention and funding for efforts to reduce emissions from deforestation and forest degradation (REDD+), especially where they account for most greenhouse gas emissions, as in Indonesia. Indonesia holds 39% of Southeast Asian forest, experiences the second highest rate of deforestation after Brazil, and has the potential to earn high profits both from logging native forests and from clearing forests for oil palm and pulp plantations. In Indonesia, REDD+ initiatives have taken a wide variety of forms, with some interventions focused on encouraging sustainable forest management and others focused on reducing demand for cleared land. Evaluating the efficacy of these interventions is critical but challenging because exogenous factors may affect both placement of the interventions and deforestation trends. Overcoming this limitation requires an in-depth understanding of the drivers of deforestation and how they vary with context. One barrier to improved understanding has been that existing deforestation datasets are largely binary (e.g. forested/deforested). Recent developments in mapping land-use change from time series of remotely sensed images may offer a path towards obtaining longer times series with more detail on land use. Such data would enable use of the synthetic control method (SCM), which allows for heterogenous impacts across units and over time. Here, we use this approach to answer the question: How has the designation and active use of logging concessions affected deforestation rates in East Kalimantan province, Indonesia since 2000? That is, we ask whether, where, and how using forests for timber production affects the probability of deforestation. We used an image time-series approach (YATSM/CCDC) to classify Landsat imagery from 2000 to 2017 for East Kalimantan, and SCM to evaluate the effect of allocating forest to logging concessions, controlling for a large variety of covariates such as proximity to pulp and palm oil mills and topography to construct our synthetic controls. By mapping land use in previously forested areas, we are able to interrogate the primary drivers of deforestation in different contexts.

Recommendations for sustainable development of non-timber forest products

Treesearch

Gina H. Mohammed

2001-01-01

Non-timber forest products--or NTFPs--are considered here to be botanical products harvested or originating from forest-based species, but excluding primary timber products, industrial boards and composites, and paper products. A recent study of non-timber forest products in Ontario, Canada, identified at least 50 types of NTFPs and hundreds of specific products used...

25 CFR 163.26 - Forest product harvesting permits.

Code of Federal Regulations, 2010 CFR

2010-04-01

... 25 Indians 1 2010-04-01 2010-04-01 false Forest product harvesting permits. 163.26 Section 163.26... Forest Management and Operations § 163.26 Forest product harvesting permits. (a) Except as provided in §§ 163.13 and 163.27 of this part, removal of forest products that are not under formal contract...

Managing forest products for community benefit

Treesearch

Susan Charnley; Jonathan W. Long

2014-01-01

Forest products harvesting and use from national forest lands remain important to local residents and communities in some parts of the Sierra Nevada science synthesis area. Managing national forests for the sustainable production of timber, biomass, nontimber forest products, and forage for livestock can help support forestbased livelihoods in parts of the region where...

Forest Tree Species Distribution Mapping Using Landsat Satellite Imagery and Topographic Variables with the Maximum Entropy Method in Mongolia

NASA Astrophysics Data System (ADS)

Hao Chiang, Shou; Valdez, Miguel; Chen, Chi-Farn

2016-06-01

Forest is a very important ecosystem and natural resource for living things. Based on forest inventories, government is able to make decisions to converse, improve and manage forests in a sustainable way. Field work for forestry investigation is difficult and time consuming, because it needs intensive physical labor and the costs are high, especially surveying in remote mountainous regions. A reliable forest inventory can give us a more accurate and timely information to develop new and efficient approaches of forest management. The remote sensing technology has been recently used for forest investigation at a large scale. To produce an informative forest inventory, forest attributes, including tree species are unavoidably required to be considered. In this study the aim is to classify forest tree species in Erdenebulgan County, Huwsgul province in Mongolia, using Maximum Entropy method. The study area is covered by a dense forest which is almost 70% of total territorial extension of Erdenebulgan County and is located in a high mountain region in northern Mongolia. For this study, Landsat satellite imagery and a Digital Elevation Model (DEM) were acquired to perform tree species mapping. The forest tree species inventory map was collected from the Forest Division of the Mongolian Ministry of Nature and Environment as training data and also used as ground truth to perform the accuracy assessment of the tree species classification. Landsat images and DEM were processed for maximum entropy modeling, and this study applied the model with two experiments. The first one is to use Landsat surface reflectance for tree species classification; and the second experiment incorporates terrain variables in addition to the Landsat surface reflectance to perform the tree species classification. All experimental results were compared with the tree species inventory to assess the classification accuracy. Results show that the second one which uses Landsat surface reflectance coupled with terrain variables produced better result, with the higher overall accuracy and kappa coefficient than first experiment. The results indicate that the Maximum Entropy method is an applicable, and to classify tree species using satellite imagery data coupled with terrain information can improve the classification of tree species in the study area.

Developing New Coastal Forest Restoration Products Based on Landsat, ASTER, and MODIS Data

NASA Technical Reports Server (NTRS)

Spruce, Joseph P.; Graham, William; Smoot, James

2009-01-01

This paper discusses an ongoing effort to develop new geospatial information products for aiding coastal forest restoration and conservation efforts in coastal Louisiana and Mississippi. This project employs Landsat, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), and Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data in conjunction with airborne elevation data to compute coastal forest cover type maps and change detection products. Improved forest mapping products are needed to aid coastal forest restoration and management efforts of State and Federal agencies in the Northern Gulf of Mexico (NGOM) region. In particular, such products may aid coastal forest land acquisition and conservation easement procurements. This region's forests are often disturbed and subjected to multiple biotic and abiotic threats, including subsidence, salt water intrusion, hurricanes, sea-level rise, insect-induced defoliation and mortality, altered hydrology, wildfire, and conversion to non-forest land use. In some cases, such forest disturbance has led to forest loss or loss of regeneration capacity. In response, a case study was conducted to assess and demonstrate the potential of satellite remote sensing products for improving forest type maps and for assessing forest change over the last 25 years. Change detection products are needed for assessing risks for specific priority coastal forest types, such as live oak and baldcypress-dominated forest. Preliminary results indicate Landsat time series data are capable of generating the needed forest type and change detection products. Useful classifications were obtained using 2 strategies: 1) general forest classification based on use of 3 seasons of Landsat data from the same year; and 2) classification of specific forest types of concern using a single date of Landsat data in which a given targeted type is spectrally distinct compared to adjacent forested cover. When available, ASTER data was useful as a complement to Landsat data. Elevation data helped to define areas in which targeted forest types occur, such as live oak forests on natural levees. MODIS Normalized Difference Vegetation Index time series data aided visual assessments of coastal forest damage and recovery from hurricanes. Landsat change detection products enabled change to be identified at the stand level and at 10- year intervals with the earliest date preceding available change detection products from the National Oceanic and Atmospheric Administration and from the U.S. Geological Survey. Additional work is being done in collaboration with State and Federal agency partners in a follow-on NASA ROSES project to refine and validate these new, promising products. The products from the ROSES project will be available for aiding NGOM coastal forest restoration and conservation.

Modelling the ecological consequences of whole tree harvest for bioenergy production

NASA Astrophysics Data System (ADS)

Skår, Silje; Lange, Holger; Sogn, Trine

2013-04-01

There is an increasing demand for energy from biomass as a substitute to fossil fuels worldwide, and the Norwegian government plans to double the production of bioenergy to 9% of the national energy production or to 28 TWh per year by 2020. A large part of this increase may come from forests, which have a great potential with respect to biomass supply as forest growth increasingly has exceeded harvest in the last decades. One feasible option is the utilization of forest residues (needles, twigs and branches) in addition to stems, known as Whole Tree Harvest (WTH). As opposed to WTH, the residues are traditionally left in the forest with Conventional Timber Harvesting (CH). However, the residues contain a large share of the treés nutrients, indicating that WTH may possibly alter the supply of nutrients and organic matter to the soil and the forest ecosystem. This may potentially lead to reduced tree growth. Other implications can be nutrient imbalance, loss of carbon from the soil and changes in species composition and diversity. This study aims to identify key factors and appropriate strategies for ecologically sustainable WTH in Norway spruce (Picea abies) and Scots pine (Pinus sylvestris) forest stands in Norway. We focus on identifying key factors driving soil organic matter, nutrients, biomass, biodiversity etc. Simulations of the effect on the carbon and nitrogen budget with the two harvesting methods will also be conducted. Data from field trials and long-term manipulation experiments are used to obtain a first overview of key variables. The relationships between the variables are hitherto unknown, but it is by no means obvious that they could be assumed as linear; thus, an ordinary multiple linear regression approach is expected to be insufficient. Here we apply two advanced and highly flexible modelling frameworks which hardly have been used in the context of tree growth, nutrient balances and biomass removal so far: Generalized Additive Models (GAMs) and Random Forests. Results obtained for GAMs so far show that there are differences between WTH and CH in two directions: both the significance of drivers and the shape of the response functions differ. GAMs turn out to be a flexible and powerful alternative to multivariate linear regression. The restriction to linear relationships seems to be unjustified in the present case. We use Random Forests as a highly efficient classifier which gives reliable estimates for the importance of each driver variable in determining the diameter growth for the two different harvesting treatments. Based on the final results of these two modelling approaches, the study contributes to find appropriate strategies and suitable regions (in Norway) where WTH may be sustainable performed.

Forest statistics for the Northern coastal plain of South Carolina, 1992

Treesearch

Michael T. Thompson; Raymond M. Sheffield

1993-01-01

Since 1988, area of timberland in the Northern Coastal Plain of South Carolina increased by 3 percent to 4.7 million acres. Nonindustrial private forest landowners control 67 percent of the region's timberland. Area classified as a pine type remained stable at 1.9 million acres. More than 116,000 acres were harvested annually, while 177,000 acres were regenerated...

Landscape Builder: software for the creation of initial landscapes for LANDIS from FIA data

Treesearch

William Dijak

2013-01-01

I developed Landscape Builder to create spatially explicit landscapes as starting conditions for LANDIS Pro 7.0 and LANDIS II landscape forest simulation models from classified satellite imagery and Forest Inventory and Analysis (FIA) data collected over multiple years. LANDIS Pro and LANDIS II models project future landscapes by simulating tree growth, tree species...

Changes in landscape patterns and associated forest succession on the western slope of the Rocky Mountains, Colorado

Treesearch

Daniel J. Manier; Richard D. Laven

2001-01-01

Using repeat photography, we conducted a qualitative and quantitative analysis of changes in forest cover on the western slope of the Rocky Mountains in Colorado. For the quantitative analysis, both images in a pair were classified using remote sensing and geographic information system (GIS) technologies. Comparisons were made using three landscape metrics: total...

Forest statistics for the Southern Coastal Plain of South Carolina

Treesearch

Benjamin L. Koontz; Raymond M. Sheffield

1993-01-01

Since 1987, area of timberland in the Southern Coastal Plain of South Carolina increased by 3 percent to 3.3 million acres. Nonindustrial private forest landowners control nearly three-fourths of the region's timberland. The area classified as pine increased by 14 percent, while hardwood acreage dropped by 12 percent. The area harvested annually fell to 87.000...

Exploring Google Earth Engine platform for big data processing: classification of multi-temporal satellite imagery for crop mapping

NASA Astrophysics Data System (ADS)

Shelestov, Andrii; Lavreniuk, Mykola; Kussul, Nataliia; Novikov, Alexei; Skakun, Sergii

2017-02-01

Many applied problems arising in agricultural monitoring and food security require reliable crop maps at national or global scale. Large scale crop mapping requires processing and management of large amount of heterogeneous satellite imagery acquired by various sensors that consequently leads to a “Big Data” problem. The main objective of this study is to explore efficiency of using the Google Earth Engine (GEE) platform when classifying multi-temporal satellite imagery with potential to apply the platform for a larger scale (e.g. country level) and multiple sensors (e.g. Landsat-8 and Sentinel-2). In particular, multiple state-of-the-art classifiers available in the GEE platform are compared to produce a high resolution (30 m) crop classification map for a large territory ( 28,100 km2 and 1.0 M ha of cropland). Though this study does not involve large volumes of data, it does address efficiency of the GEE platform to effectively execute complex workflows of satellite data processing required with large scale applications such as crop mapping. The study discusses strengths and weaknesses of classifiers, assesses accuracies that can be achieved with different classifiers for the Ukrainian landscape, and compares them to the benchmark classifier using a neural network approach that was developed in our previous studies. The study is carried out for the Joint Experiment of Crop Assessment and Monitoring (JECAM) test site in Ukraine covering the Kyiv region (North of Ukraine) in 2013. We found that Google Earth Engine (GEE) provides very good performance in terms of enabling access to the remote sensing products through the cloud platform and providing pre-processing; however, in terms of classification accuracy, the neural network based approach outperformed support vector machine (SVM), decision tree and random forest classifiers available in GEE.

Optimizing classification performance in an object-based very-high-resolution land use-land cover urban application

NASA Astrophysics Data System (ADS)

Georganos, Stefanos; Grippa, Tais; Vanhuysse, Sabine; Lennert, Moritz; Shimoni, Michal; Wolff, Eléonore

2017-10-01

This study evaluates the impact of three Feature Selection (FS) algorithms in an Object Based Image Analysis (OBIA) framework for Very-High-Resolution (VHR) Land Use-Land Cover (LULC) classification. The three selected FS algorithms, Correlation Based Selection (CFS), Mean Decrease in Accuracy (MDA) and Random Forest (RF) based Recursive Feature Elimination (RFE), were tested on Support Vector Machine (SVM), K-Nearest Neighbor, and Random Forest (RF) classifiers. The results demonstrate that the accuracy of SVM and KNN classifiers are the most sensitive to FS. The RF appeared to be more robust to high dimensionality, although a significant increase in accuracy was found by using the RFE method. In terms of classification accuracy, SVM performed the best using FS, followed by RF and KNN. Finally, only a small number of features is needed to achieve the highest performance using each classifier. This study emphasizes the benefits of rigorous FS for maximizing performance, as well as for minimizing model complexity and interpretation.

Toward Improving Electrocardiogram (ECG) Biometric Verification using Mobile Sensors: A Two-Stage Classifier Approach

PubMed Central

Tan, Robin; Perkowski, Marek

2017-01-01

Electrocardiogram (ECG) signals sensed from mobile devices pertain the potential for biometric identity recognition applicable in remote access control systems where enhanced data security is demanding. In this study, we propose a new algorithm that consists of a two-stage classifier combining random forest and wavelet distance measure through a probabilistic threshold schema, to improve the effectiveness and robustness of a biometric recognition system using ECG data acquired from a biosensor integrated into mobile devices. The proposed algorithm is evaluated using a mixed dataset from 184 subjects under different health conditions. The proposed two-stage classifier achieves a total of 99.52% subject verification accuracy, better than the 98.33% accuracy from random forest alone and 96.31% accuracy from wavelet distance measure algorithm alone. These results demonstrate the superiority of the proposed algorithm for biometric identification, hence supporting its practicality in areas such as cloud data security, cyber-security or remote healthcare systems. PMID:28230745

Toward Improving Electrocardiogram (ECG) Biometric Verification using Mobile Sensors: A Two-Stage Classifier Approach.

PubMed

Tan, Robin; Perkowski, Marek

2017-02-20

Electrocardiogram (ECG) signals sensed from mobile devices pertain the potential for biometric identity recognition applicable in remote access control systems where enhanced data security is demanding. In this study, we propose a new algorithm that consists of a two-stage classifier combining random forest and wavelet distance measure through a probabilistic threshold schema, to improve the effectiveness and robustness of a biometric recognition system using ECG data acquired from a biosensor integrated into mobile devices. The proposed algorithm is evaluated using a mixed dataset from 184 subjects under different health conditions. The proposed two-stage classifier achieves a total of 99.52% subject verification accuracy, better than the 98.33% accuracy from random forest alone and 96.31% accuracy from wavelet distance measure algorithm alone. These results demonstrate the superiority of the proposed algorithm for biometric identification, hence supporting its practicality in areas such as cloud data security, cyber-security or remote healthcare systems.

25 CFR 163.16 - Forest product sales without advertisement.

Code of Federal Regulations, 2010 CFR

2010-04-01

... 25 Indians 1 2010-04-01 2010-04-01 false Forest product sales without advertisement. 163.16 Section 163.16 Indians BUREAU OF INDIAN AFFAIRS, DEPARTMENT OF THE INTERIOR LAND AND WATER GENERAL FORESTRY REGULATIONS Forest Management and Operations § 163.16 Forest product sales without advertisement. (a) Sales of forest products may be made without...

25 CFR 163.19 - Contracts for the sale of forest products.

Code of Federal Regulations, 2010 CFR

2010-04-01

... 25 Indians 1 2010-04-01 2010-04-01 false Contracts for the sale of forest products. 163.19 Section... REGULATIONS Forest Management and Operations § 163.19 Contracts for the sale of forest products. (a) In sales of forest products with an appraised stumpage value exceeding $15,000, the contract forms approved by...

Preliminary classification of forest vegetation of the Kenai Peninsula, Alaska.

Treesearch

K.M. Reynolds

1990-01-01

A total of 5,597 photo points was systematically located on 1:60,000-scale high altitude photographs of the Kenai Peninsula, Alaska; photo interpretation was used to classify the vegetation at each grid position. Of the total grid points, 12.3 percent were classified as timberland; 129 photo points within the timberland class were randomly selected for field survey....

Interpreting forest biome productivity and cover utilizing nested scales of image resolution and biogeographical analysis

NASA Technical Reports Server (NTRS)

Iverson, Louis R.; Cook, Elizabeth A.; Graham, Robin L.; Olson, Jerry S.; Frank, Thomas D.; Ying, KE

1988-01-01

The objective was to relate spectral imagery of varying resolution with ground-based data on forest productivity and cover, and to create models to predict regional estimates of forest productivity and cover with a quantifiable degree of accuracy. A three stage approach was outlined. In the first stage, a model was developed relating forest cover or productivity to TM surface reflectance values (TM/FOREST models). The TM/FOREST models were more accurate when biogeographic information regarding the landscape was either used to stratigy the landscape into more homogeneous units or incorporated directly into the TM/FOREST model. In the second stage, AVHRR/FOREST models that predicted forest cover and productivity on the basis of AVHRR band values were developed. The AVHRR/FOREST models had statistical properties similar to or better than those of the TM/FOREST models. In the third stage, the regional predictions were compared with the independent U.S. Forest Service (USFS) data. To do this regional forest cover and forest productivity maps were created using AVHRR scenes and the AVHRR/FOREST models. From the maps the county values of forest productivity and cover were calculated. It is apparent that the landscape has a strong influence on the success of the approach. An approach of using nested scales of imagery in conjunction with ground-based data can be successful in generating regional estimates of variables that are functionally related to some variable a sensor can detect.

Random forests for classification in ecology

USGS Publications Warehouse

Cutler, D.R.; Edwards, T.C.; Beard, K.H.; Cutler, A.; Hess, K.T.; Gibson, J.; Lawler, J.J.

2007-01-01

Classification procedures are some of the most widely used statistical methods in ecology. Random forests (RF) is a new and powerful statistical classifier that is well established in other disciplines but is relatively unknown in ecology. Advantages of RF compared to other statistical classifiers include (1) very high classification accuracy; (2) a novel method of determining variable importance; (3) ability to model complex interactions among predictor variables; (4) flexibility to perform several types of statistical data analysis, including regression, classification, survival analysis, and unsupervised learning; and (5) an algorithm for imputing missing values. We compared the accuracies of RF and four other commonly used statistical classifiers using data on invasive plant species presence in Lava Beds National Monument, California, USA, rare lichen species presence in the Pacific Northwest, USA, and nest sites for cavity nesting birds in the Uinta Mountains, Utah, USA. We observed high classification accuracy in all applications as measured by cross-validation and, in the case of the lichen data, by independent test data, when comparing RF to other common classification methods. We also observed that the variables that RF identified as most important for classifying invasive plant species coincided with expectations based on the literature. ?? 2007 by the Ecological Society of America.

An efficient ensemble learning method for gene microarray classification.

PubMed

Osareh, Alireza; Shadgar, Bita

2013-01-01

The gene microarray analysis and classification have demonstrated an effective way for the effective diagnosis of diseases and cancers. However, it has been also revealed that the basic classification techniques have intrinsic drawbacks in achieving accurate gene classification and cancer diagnosis. On the other hand, classifier ensembles have received increasing attention in various applications. Here, we address the gene classification issue using RotBoost ensemble methodology. This method is a combination of Rotation Forest and AdaBoost techniques which in turn preserve both desirable features of an ensemble architecture, that is, accuracy and diversity. To select a concise subset of informative genes, 5 different feature selection algorithms are considered. To assess the efficiency of the RotBoost, other nonensemble/ensemble techniques including Decision Trees, Support Vector Machines, Rotation Forest, AdaBoost, and Bagging are also deployed. Experimental results have revealed that the combination of the fast correlation-based feature selection method with ICA-based RotBoost ensemble is highly effective for gene classification. In fact, the proposed method can create ensemble classifiers which outperform not only the classifiers produced by the conventional machine learning but also the classifiers generated by two widely used conventional ensemble learning methods, that is, Bagging and AdaBoost.

Evaluation of Semi-supervised Learning for Classification of Protein Crystallization Imagery

PubMed Central

Sigdel, Madhav; Dinç, İmren; Dinç, Semih; Sigdel, Madhu S.; Pusey, Marc L.; Aygün, Ramazan S.

2015-01-01

In this paper, we investigate the performance of two wrapper methods for semi-supervised learning algorithms for classification of protein crystallization images with limited labeled images. Firstly, we evaluate the performance of semi-supervised approach using self-training with naïve Bayesian (NB) and sequential minimum optimization (SMO) as the base classifiers. The confidence values returned by these classifiers are used to select high confident predictions to be used for self-training. Secondly, we analyze the performance of Yet Another Two Stage Idea (YATSI) semi-supervised learning using NB, SMO, multilayer perceptron (MLP), J48 and random forest (RF) classifiers. These results are compared with the basic supervised learning using the same training sets. We perform our experiments on a dataset consisting of 2250 protein crystallization images for different proportions of training and test data. Our results indicate that NB and SMO using both self-training and YATSI semi-supervised approaches improve accuracies with respect to supervised learning. On the other hand, MLP, J48 and RF perform better using basic supervised learning. Overall, random forest classifier yields the best accuracy with supervised learning for our dataset. PMID:25914518

SVM feature selection based rotation forest ensemble classifiers to improve computer-aided diagnosis of Parkinson disease.

PubMed

Ozcift, Akin

2012-08-01

Parkinson disease (PD) is an age-related deterioration of certain nerve systems, which affects movement, balance, and muscle control of clients. PD is one of the common diseases which affect 1% of people older than 60 years. A new classification scheme based on support vector machine (SVM) selected features to train rotation forest (RF) ensemble classifiers is presented for improving diagnosis of PD. The dataset contains records of voice measurements from 31 people, 23 with PD and each record in the dataset is defined with 22 features. The diagnosis model first makes use of a linear SVM to select ten most relevant features from 22. As a second step of the classification model, six different classifiers are trained with the subset of features. Subsequently, at the third step, the accuracies of classifiers are improved by the utilization of RF ensemble classification strategy. The results of the experiments are evaluated using three metrics; classification accuracy (ACC), Kappa Error (KE) and Area under the Receiver Operating Characteristic (ROC) Curve (AUC). Performance measures of two base classifiers, i.e. KStar and IBk, demonstrated an apparent increase in PD diagnosis accuracy compared to similar studies in literature. After all, application of RF ensemble classification scheme improved PD diagnosis in 5 of 6 classifiers significantly. We, numerically, obtained about 97% accuracy in RF ensemble of IBk (a K-Nearest Neighbor variant) algorithm, which is a quite high performance for Parkinson disease diagnosis.

Non-timber forest products: alternative multiple-uses for sustainable forest management

Treesearch

James L. Chamberlain; Mary Predny

2003-01-01

Forests of the southern United States are the source of a great diversity of flora, much of which is gathered for non-timber forest products (NTFPs). These products are made from resources that grow under the forest canopy as trees, herbs, shrubs, vines, moss and even lichen. They occur naturally in forests or may be cultivated under the forest canopy or in...

Improving ensemble decision tree performance using Adaboost and Bagging

NASA Astrophysics Data System (ADS)

Hasan, Md. Rajib; Siraj, Fadzilah; Sainin, Mohd Shamrie

2015-12-01

Ensemble classifier systems are considered as one of the most promising in medical data classification and the performance of deceision tree classifier can be increased by the ensemble method as it is proven to be better than single classifiers. However, in a ensemble settings the performance depends on the selection of suitable base classifier. This research employed two prominent esemble s namely Adaboost and Bagging with base classifiers such as Random Forest, Random Tree, j48, j48grafts and Logistic Model Regression (LMT) that have been selected independently. The empirical study shows that the performance varries when different base classifiers are selected and even some places overfitting issue also been noted. The evidence shows that ensemble decision tree classfiers using Adaboost and Bagging improves the performance of selected medical data sets.

Rubber and Land-Cover Land-Use Change in Mainland Southeast Asia

NASA Astrophysics Data System (ADS)

Fox, J. M.; Hurni, K.

2017-12-01

Over the past half century, the five countries of Mainland Southeast Asia (MSEA) - Cambodia, Laos, Myanmar, Thailand, and Vietnam - have witnessed major shifts from predominantly subsistence agrarian economies to increasingly commercialized agriculture. Major drivers of change include policy initiatives that fostered regional economic integration and promoted among other changes rapid expansion of boom-crop plantations. Among the many types of commercial boom crops promoted and grown in MSEA are numerous tree-based products such as rubber, coffee, tree species for pulp and paper (particularly eucalyptus and acacia), cashews, and fruits such as oranges, lychees, and longans. The project proposal hypothesized that most (but not all) tree crops replaced swidden cultivation fields and hence are not necessarily accompanied by deforestation. We used MODIS EVI and SWIR time-series from 2001-2014 to classify changes in tree cover across MSEA; a total of 6849 sample points were used to train the classifier (75%) and verification (25%). The classification consists of 24 classes and 17 classes represent tree crops. Project results suggest that 4.4 m ha of rubber have been planted since 2003; 50% of rubber is planted on former evergreen forest land, 18% on deciduous forest land, and 32% on low vegetation area (former crop lands, bushes, scrub). Tree crops occupy about 8% of the landscape (half of that is rubber). Due to the differences in their political and economic histories these countries display different LCLUCs. In northern Laos, smallholder rubber plantations dominate and shifting cultivation is common in the upland. In southern Laos, large-scale plantations of rubber, coffee, eucalyptus, and sugarcane are widespread. In Thailand, vast areas are covered by annual agriculture; fruit trees and rubber are the prevailing tree crops and are mostly planted by smallholders. In Cambodia, large-scale rubber plantations have expanded in recent years on forest lands; smallholder plantations of cashews and rubber also occur. In Vietnam small holder tree crops (e.g. rubber, cashews, coffee) were already established before 2000, but since then have continued to expand. Contrary to our hypothesis, boom crops are planted primarily on forest lands and are a cause of deforestation in MSEA.

Criterion 2: Maintenance of productive capacity of forest ecosystems

Treesearch

Stephen R. Shifley; Francisco X. Aguilar; Nianfu Song; Susan I. Stewart; David J. Nowak; Dale D. Gormanson; W. Keith Moser; Sherri Wormstead; Eric J. Greenfield

2012-01-01

People rely on forests, directly and indirectly, for a wide range of goods and services. Measures of forest productive capacity are indicators of the ability of forests to sustainably supply goods and services over time. An ongoing emphasis on maintaining productive capacity of forests can help ensure that utilization of forest resources does not impair long term...

National measures of forest productivity for timber

Treesearch

Peter J. Ince; H. Edward Dickerhoof; H. Fred Kaiser

1989-01-01

This report presents national measures of forest productivity for timber. These measures reveal trends in the relationship between quantity of timber produced by forests and the quantity of forest resources employed in timber production. Timber production is measured by net annual growth of timber and annual timber removals. Measures of timber productivity include...

Under What Circumstances Do Wood Products from Native Forests Benefit Climate Change Mitigation?

PubMed

Keith, Heather; Lindenmayer, David; Macintosh, Andrew; Mackey, Brendan

2015-01-01

Climate change mitigation benefits from the land sector are not being fully realised because of uncertainty and controversy about the role of native forest management. The dominant policy view, as stated in the IPCC's Fifth Assessment Report, is that sustainable forest harvesting yielding wood products, generates the largest mitigation benefit. We demonstrate that changing native forest management from commercial harvesting to conservation can make an important contribution to mitigation. Conservation of native forests results in an immediate and substantial reduction in net emissions relative to a reference case of commercial harvesting. We calibrated models to simulate scenarios of native forest management for two Australian case studies: mixed-eucalypt in New South Wales and Mountain Ash in Victoria. Carbon stocks in the harvested forest included forest biomass, wood and paper products, waste in landfill, and bioenergy that substituted for fossil fuel energy. The conservation forest included forest biomass, and subtracted stocks for the foregone products that were substituted by non-wood products or plantation products. Total carbon stocks were lower in harvested forest than in conservation forest in both case studies over the 100-year simulation period. We tested a range of potential parameter values reported in the literature: none could increase the combined carbon stock in products, slash, landfill and substitution sufficiently to exceed the increase in carbon stock due to changing management of native forest to conservation. The key parameters determining carbon stock change under different forest management scenarios are those affecting accumulation of carbon in forest biomass, rather than parameters affecting transfers among wood products. This analysis helps prioritise mitigation activities to focus on maximising forest biomass. International forest-related policies, including negotiations under the UNFCCC, have failed to recognize fully the mitigation value of native forest conservation. Our analyses provide evidence for decision-making about the circumstances under which forest management provides mitigation benefits.

Under What Circumstances Do Wood Products from Native Forests Benefit Climate Change Mitigation?

PubMed Central

Keith, Heather; Lindenmayer, David; Macintosh, Andrew; Mackey, Brendan

2015-01-01

Climate change mitigation benefits from the land sector are not being fully realised because of uncertainty and controversy about the role of native forest management. The dominant policy view, as stated in the IPCC’s Fifth Assessment Report, is that sustainable forest harvesting yielding wood products, generates the largest mitigation benefit. We demonstrate that changing native forest management from commercial harvesting to conservation can make an important contribution to mitigation. Conservation of native forests results in an immediate and substantial reduction in net emissions relative to a reference case of commercial harvesting. We calibrated models to simulate scenarios of native forest management for two Australian case studies: mixed-eucalypt in New South Wales and Mountain Ash in Victoria. Carbon stocks in the harvested forest included forest biomass, wood and paper products, waste in landfill, and bioenergy that substituted for fossil fuel energy. The conservation forest included forest biomass, and subtracted stocks for the foregone products that were substituted by non-wood products or plantation products. Total carbon stocks were lower in harvested forest than in conservation forest in both case studies over the 100-year simulation period. We tested a range of potential parameter values reported in the literature: none could increase the combined carbon stock in products, slash, landfill and substitution sufficiently to exceed the increase in carbon stock due to changing management of native forest to conservation. The key parameters determining carbon stock change under different forest management scenarios are those affecting accumulation of carbon in forest biomass, rather than parameters affecting transfers among wood products. This analysis helps prioritise mitigation activities to focus on maximising forest biomass. International forest-related policies, including negotiations under the UNFCCC, have failed to recognize fully the mitigation value of native forest conservation. Our analyses provide evidence for decision-making about the circumstances under which forest management provides mitigation benefits. PMID:26436916

Non-timber forest products enterprises in the south: perceived distribution and implications for sustainable forest management

Treesearch

J.L. Chamberlain; M. Predny

2003-01-01

Forests of the southern United States are the source of a great diversity of flora, much of which is gathered to produce non-timber forest products (NTFPs). These products are made from resources that grow under the forest canopy as trees, herbs, shrubs, vines, moss and even lichen. They occur naturally in forests or may be cultivated under the forest canopy or in...

36 CFR 223.224 - Performance bonds and security.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 36 Parks, Forests, and Public Property 2 2011-07-01 2011-07-01 false Performance bonds and... AGRICULTURE SALE AND DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS Special Forest Products Contract and Permit Conditions and Provisions § 223.224 Performance bonds...

Lemur responses to edge effects in the Vohibola III classified forest, Madagascar.

PubMed

Lehman, Shawn M; Rajaonson, Andry; Day, Sabine

2006-03-01

Forest edges are dynamic zones characterized by the penetration (to varying depths and intensities) of conditions from the surrounding environment (matrix) into the forest interior. Although edge effects influence many tropical organisms, they have not been studied directly in primates. Edge effects are particularly relevant to lemurs because of the highly fragmented forest landscapes found in Madagascar. In this study, data are presented regarding how the densities of six lemur species (Avahi laniger, Cheirogaleus major, Eulemur rubriventer, Hapalemur griseus griseus, Microcebus rufus, and Propithecus diadema edwardsi) varied between six 500-m interior transects and six 500-m edge transects in the Vohibola III Classified Forest in SE Madagascar. Diurnal (n = 433) and nocturnal (n = 128) lemur surveys were conducted during June-October 2003 and May-November 2004. A. laniger, E. rubriventer, and H. g. griseus exhibited a neutral edge response (no differences in densities between habitats). M. rufus and P. d. edwardsi had a positive edge response (higher densities in edge habitats), which may be related to edge-related variations in food abundance and quality. Positive edge responses by M. rufus and P. d. edwardsi may ultimately be detrimental due to edge-related anthropogenic factors (e.g., hunting by local people). The negative edge response exhibited by C. major (lower densities in edge habitats) may result from heightened ambient temperatures that inhibit torpor in edge habitats.

Monitoring Regional Forest Disturbances across the US with Near Real Time MODIS NDVI Products included in the ForWarn Forest Threat Early Warning System

NASA Astrophysics Data System (ADS)

Spruce, J.; Hargrove, W. W.; Gasser, J.; Norman, S. P.

2013-12-01

Forest threats across the US have become increasingly evident in recent years. These include regionally extensive disturbances (e.g., from drought, bark beetle outbreaks, and wildfires) that can occur across multiyear durations and result in extensive forest mortality. In addition, forests can be subject to ephemeral, sometimes yearly defoliation from various insects and types of storm damage. After prolonged severe disturbance, signs of forest recovery can vary in terms of satellite-based Normalized Difference Vegetation Index (NDVI) values. The increased extent and threat of forest disturbances in part led to the enactment of the 2003 Healthy Forest Restoration Act, which mandated that a national forest threat Early Warning System (EWS) be deployed. In response, the US Forest Service collaborated with NASA, DOE Oak Ridge National Laboratory, and the USGS Eros Data Center to build the near real time ForWarn forest threat EWS for monitoring regionally evident forest disturbances, starting on-line operations in 2010. Given the diversity of disturbance types, severities, and durations, ForWarn employs multiple historical baselines used with current NDVI to derive a suite of six nationwide 'weekly' forest change products. ForWarn uses daily 232 meter MODIS Aqua and Terra satellite NDVI data, including MOD13 products for deriving historical baseline NDVIs and eMODIS products for compiling current NDVI. Separately pre-processing the current and historical NDVIs, the Time Series Product Tool and the Phenological Parameters Estimation Tool are used to temporally reduce noise, fuse, and aggregate MODIS NDVIs into 24 day composites refreshed every 8 days with 46 dates of forest change products per year. The 24 day compositing interval typically enables new disturbances to be detected, while minimizing the frequency of residual atmospheric contamination. ForWarn's three standard forest change products compare current NDVI to that from the previous year, previous 3 years, and all previous years since 2000. Other forest change products added in 2013 include one for quicker disturbance detection and two others that adjust for seasonal fluctuations in normal vegetation phenology. This product suite and ForWarn's geospatial data viewer allow end users to view and assess disturbance dynamics for many regionally evident biotic and abiotic forest disturbances throughout a given current year. ForWarn's change products are also being used for forest change trend analysis and for developing regional forest overstory mortality products. They are used to alert forest health specialists about new regional forest disturbances. Such alerts also typically consider available Landsat, aerial, and ground data as well as communications with forest health specialists and previous experience. ForWarn products have been used to detect and track many types of regional disturbances for multiple forest types, including defoliation from caterpillars and severe storms, as well as mortality from both biotic and abiotic agents (e.g., bark beetles, drought, fire, anthropogenic clearing). ForWarn provides forest change products that could be combined with other geospatial data on forest biomass to help assess forest disturbance carbon impacts within the conterminous US.

Vegetation survey in Amazonia using LANDSAT data. [Brazil

NASA Technical Reports Server (NTRS)

Parada, N. D. J. (Principal Investigator); Shimabukuro, Y. E.; Dossantos, J. R.; Deaquino, L. C. S.

1982-01-01

Automatic Image-100 analysis of LANDSAT data was performed using the MAXVER classification algorithm. In the pilot area, four vegetation units were mapped automatically in addition to the areas occupied for agricultural activities. The Image-100 classified results together with a soil map and information from RADAR images, permitted the establishment of the final legend with six classes: semi-deciduous tropical forest; low land evergreen tropical forest; secondary vegetation; tropical forest of humid areas, predominant pastureland and flood plains. Two water types were identified based on their sediments indicating different geological and geomorphological aspects.

Digital spatial data for predicted nitrate and arsenic concentrations in basin-fill aquifers of the Southwest Principal Aquifers study area

USGS Publications Warehouse

McKinney, Tim S.; Anning, David W.

2012-01-01

This product "Digital spatial data for predicted nitrate and arsenic concentrations in basin-fill aquifers of the Southwest Principal Aquifers study area" is a 1:250,000-scale vector spatial dataset developed as part of a regional Southwest Principal Aquifers (SWPA) study (Anning and others, 2012). The study examined the vulnerability of basin-fill aquifers in the southwestern United States to nitrate contamination and arsenic enrichment. Statistical models were developed by using the random forest classifier algorithm to predict concentrations of nitrate and arsenic across a model grid that represents local- and basin-scale measures of source, aquifer susceptibility, and geochemical conditions.

78 FR 62957 - National Forest Products Week, 2013

Federal Register 2010, 2011, 2012, 2013, 2014

2013-10-23

... National Forest Products Week, 2013 By the President of the United States of America A Proclamation Our.... During National Forest Products Week, we celebrate the sustainable uses of America's forests and the... forests will be vital to our progress in the years ahead. This week, we recommit to collaborating across...

Use of Current 2010 Forest Disturbance Monitoring Products for the Conterminous United States in Aiding a National Forest Threat Early Warning System

NASA Technical Reports Server (NTRS)

Spruce, Joseph P.; Hargrove, William; Gasser, J.; Smoot, J.; Kuper, P.

2010-01-01

This presentation discusses contributions of near real time (NRT) MODIS forest disturbance detection products for the conterminous United States to an emerging national forest threat early warning system (EWS). The latter is being developed by the USDA Forest Service s Eastern and Western Environmental Threat Centers with help from NASA Stennis Space Center and the Oak Ridge National Laboratory. Building off work done in 2009, this national and regional forest disturbance detection and viewing capability of the EWS employs NRT MODIS NDVI data from the USGS eMODIS group and historical NDVI data from standard MOD13 products. Disturbance detection products are being computed for 24 day composites that are refreshed every 8 days. Products for 2010 include 42 dates of the 24 day composites. For each compositing date, we computed % change in forest maximum NDVI products for 2010 with respect to each of three historical baselines of 2009, 2007-2009, and 2003-2009,. The three baselines enable one to view potential current, recent, and longer term forest disturbances. A rainbow color table was applied to each forest change product so that potential disturbances (NDVI drops) were identified in hot color tones and growth (NDVI gains) in cold color tones. Example products were provided to end-users responsible for forest health monitoring at the Federal and State levels. Large patches of potential forest disturbances were validated based on comparisons with available reference data, including Landsat and field survey data. Products were posted on two internet mapping systems for US Forest Service internal and collaborator use. MODIS forest disturbance detection products were computed and posted for use in as little as 1 day after the last input date of the compositing period. Such products were useful for aiding aerial disturbance detection surveys and for assessing disturbance persistence on both inter- and intra-annual scales. Multiple 2010 forest disturbance events were detected across the nation, including damage from ice storms, tornadoes, caterpillars, bark beetles, and wildfires. This effort enabled improved NRT forest disturbance monitoring capabilities for this nation-wide forest threat EWS.

Use of Current 2010 Forest Disturbance Monitoring Products for the Conterminous United States in Aiding a National Forest Threat Early Warning System

NASA Astrophysics Data System (ADS)

Spruce, J.; Hargrove, W. W.; Gasser, J.; Smoot, J.; Kuper, P.

2010-12-01

This presentation discusses contributions of near real time (NRT) MODIS forest disturbance detection products for the conterminous United States to an emerging national forest threat early warning system (EWS). The latter is being developed by the USDA Forest Service’s Eastern and Western Environmental Threat Centers with help from NASA Stennis Space Center and the Oak Ridge National Laboratory. Building off work done in 2009, this national and regional forest disturbance detection and viewing capability of the EWS employs NRT MODIS NDVI data from the USGS eMODIS group and historical NDVI data from standard MOD13 products. Disturbance detection products are being computed for 24 day composites that are refreshed every 8 days. Products for 2010 include 42 dates of the 24 day composites. For each compositing date, we computed % change in forest maximum NDVI products for 2010 with respect to each of three historical baselines of 2009, 2007-2009, and 2003-2009. The three baselines enable one to view potential current, recent, and longer term forest disturbances. A rainbow color table was applied to each forest change product so that potential disturbances (NDVI drops) were identified in hot color tones and growth (NDVI gains) in cold color tones. Example products were provided to end-users responsible for forest health monitoring at the Federal and State levels. Large patches of potential forest disturbances were validated based on comparisons with available reference data, including Landsat and field survey data. Products were posted on two internet mapping systems for US Forest Service internal and collaborator use. MODIS forest disturbance detection products were computed and posted for use in as little as 1 day after the last input date of the compositing period. Such products were useful for aiding aerial disturbance detection surveys and for assessing disturbance persistence on both inter- and intra-annual scales. Multiple 2010 forest disturbance events were detected across the nation, including damage from ice storms, tornados, caterpillars, bark beetles, and wildfires. This effort enabled improved NRT forest disturbance monitoring capabilities for this nation-wide forest threat EWS.

Mapping stand-age distribution of Russian forests from satellite data

NASA Astrophysics Data System (ADS)

Chen, D.; Loboda, T. V.; Hall, A.; Channan, S.; Weber, C. Y.

2013-12-01

Russian boreal forest is a critical component of the global boreal biome as approximately two thirds of the boreal forest is located in Russia. Numerous studies have shown that wildfire and logging have led to extensive modifications of forest cover in the region since 2000. Forest disturbance and subsequent regrowth influences carbon and energy budgets and, in turn, affect climate. Several global and regional satellite-based data products have been developed from coarse (>100m) and moderate (10-100m) resolution imagery to monitor forest cover change over the past decade, record of forest cover change pre-dating year 2000 is very fragmented. Although by using stacks of Landsat images, some information regarding the past disturbances can be obtained, the quantity and locations of such stacks with sufficient number of images are extremely limited, especially in Eastern Siberia. This paper describes a modified method which is built upon previous work to hindcast the disturbance history and map stand-age distribution in the Russian boreal forest. Utilizing data from both Landsat and the Moderate Resolution Imaging Spectroradiometer (MODIS), a wall-to-wall map indicating the estimated age of forest in the Russian boreal forest is created. Our previous work has shown that disturbances can be mapped successfully up to 30 years in the past as the spectral signature of regrowing forests is statistically significantly different from that of mature forests. The presented algorithm ingests 55 multi-temporal stacks of Landsat imagery available over Russian forest before 2001 and processes through a standardized and semi-automated approach to extract training and validation data samples. Landsat data, dating back to 1984, are used to generate maps of forest disturbance using temporal shifts in Disturbance Index through the multi-temporal stack of imagery in selected locations. These maps are then used as reference data to train a decision tree classifier on 50 MODIS-based indices. The resultant map provides an estimate of forest age based on the regrowth curves observed from Landsat imagery. The accuracy of the resultant map is assessed against three datasets: 1) subset of the disturbance maps developed within the algorithm, 2) independent disturbance maps created by the Northern Eurasia Land Dynamics Analysis (NELDA) project, and 3) field-based stand-age distribution from forestry inventory units. The current version of the product presents a considerable improvement on the previous version which used Landsat data samples at a set of randomly selected locations, resulting a strong bias of the training samples towards the Landsat-rich regions (e.g. European Russia) whereas regions such as Siberia were under-sampled. Aiming at improving accuracy, the current method significantly increases the number of training Landsat samples compared to the previous work. Aside from the previously used data, the current method uses all available Landsat data for the under-sampled regions in order to increase the representativeness of the total samples. The finial accuracy assessment is still ongoing, however, the initial results suggested an overall accuracy expressed in Kappa > 0.8. We plan to release both the training data and the final disturbance map of the Russian boreal forest to the public after the validation is completed.

Why do forest products become less available?A pan-tropical comparison of drivers of forest-resource degradation

NASA Astrophysics Data System (ADS)

Hermans-Neumann, Kathleen; Gerstner, Katharina; Geijzendorffer, Ilse R.; Herold, Martin; Seppelt, Ralf; Wunder, Sven

2016-12-01

Forest products provide an important source of income and wellbeing for rural smallholder communities across the tropics. Although tropical forest products frequently become over-exploited, only few studies explicitly address the dynamics of degradation in response to socio-economic drivers. Our study addresses this gap by analyzing the factors driving changes in tropical forest products in the perception of rural smallholder communities. Using the poverty and environment network global dataset, we studied recently perceived trends of forest product availability considering firewood, charcoal, timber, food, medicine, forage and other forest products. We looked at a pan-tropical sample of 233 villages with forest access. Our results show that 90% of the villages experienced declining availability of forest resources over the last five years according to the informants. Timber and fuelwood together with forest foods were featured as the most strongly affected, though with marked differences across continents. In contrast, availability of at least one main forest product was perceived to increase in only 39% of the villages. Furthermore, the growing local use of forest resources is seen as the main culprit for the decline. In villages with both growing forest resource use and immigration—vividly illustrating demographic pressures—the strongest forest resources degradation was observed. Conversely, villages with little or no population growth and a decreased use of forest resources were most likely to see significant forest-resource increases. Further, villages are less likely to perceive resource declines when local communities own a significant share of forest area. Our results thus suggest that perceived resource declines have only exceptionally triggered adaptations in local resource-use and management patterns that would effectively deal with scarcity. Hence, at the margin this supports neo-Malthusian over neo-Boserupian explanations of local resource-use dynamics.

36 CFR 223.225 - Term.

Code of Federal Regulations, 2014 CFR

2014-07-01

... 36 Parks, Forests, and Public Property 2 2014-07-01 2014-07-01 false Term. 223.225 Section 223.225 Parks, Forests, and Public Property FOREST SERVICE, DEPARTMENT OF AGRICULTURE SALE AND DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS Special Forest...

Adjustments to forest inventory and analysis estimates of 2001 saw-log volumes for Kentucky

Treesearch

Stanley J. Zarnoch; Jeffery A. Turner

2005-01-01

The 2001 Kentucky Forest Inventory and Analysis survey overestimated hardwood saw-log volume in tree grade 1. This occurred because 2001 field crews classified too many trees as grade 1 trees. Data collected by quality assurance crews were used to generate two types of adjustments, one based on the proportion of trees misclassified and the other on the proportion of...

Effects of intermediate-scale wind disturbance on composition, structure, and succession in Quercus stands: Implications for natural disturbance-based silviculture

Treesearch

M.M. Cowden; J.L. Hart; C.J. Schweitzer; D.C. Dey

2014-01-01

Forest disturbances are discrete events in space and time that disrupt the biophysical environment and impart lasting legacies on forest composition and structure. Disturbances are often classified along a gradient of spatial extent and magnitude that ranges from catastrophic events where most of the overstory is removed to gap-scale events that modify local...

Northern goshawk and its prey in the Black Hills: Habitat assessment

Treesearch

Russell T. Graham; Shelley Bayard de Volo; Richard T. Reynolds

2015-01-01

The northern goshawk is classified as a Sensitive Species in all USDA Forest Service regions, including on the Black Hills National Forest in western South Dakota and northeastern Wyoming. An assessment was conducted of the quality of northern goshawk nesting and foraging habitat, along with the habitat quality of 22 of the goshawk’s prey species. A Delphi (expert...

The Increasing Influence of Urban Environments on US Forest Management

Treesearch

David J. Nowak; Jeffrey T. Walton; John F. Dwyer; Latif G. Kaya; Soojeong Myeong; Soojeong Myeong

2005-01-01

The expansion of urban land promises to have an increasingly significant influence on US forest management in the coming decades. Percent of the coterminous United States classified as urban increased from 2.5% in 1990 to 3.1% in 2000, an area about the size of Vermont and New Hampshire combined. Patterns of urban expansion reveal that increased growth rates are likely...

Creation of a Digital Surface Model and Extraction of Coarse Woody Debris from Terrestrial Laser Scans in an Open Eucalypt Woodland

NASA Astrophysics Data System (ADS)

Muir, J.; Phinn, S. R.; Armston, J.; Scarth, P.; Eyre, T.

2014-12-01

Coarse woody debris (CWD) provides important habitat for many species and plays a vital role in nutrient cycling within an ecosystem. In addition, CWD makes an important contribution to forest biomass and fuel loads. Airborne or space based remote sensing instruments typically do not detect CWD beneath the forest canopy. Terrestrial laser scanning (TLS) provides a ground based method for three-dimensional (3-D) reconstruction of surface features and CWD. This research produced a 3-D reconstruction of the ground surface and automatically classified coarse woody debris from registered TLS scans. The outputs will be used to inform the development of a site-based index for the assessment of forest condition, and quantitative assessments of biomass and fuel loads. A survey grade terrestrial laser scanner (Riegl VZ400) was used to scan 13 positions, in an open eucalypt woodland site at Karawatha Forest Park, near Brisbane, Australia. Scans were registered, and a digital surface model (DSM) produced using an intensity threshold and an iterative morphological filter. The DSMs produced from single scans were compared to the registered multi-scan point cloud using standard error metrics including: Root Mean Squared Error (RMSE), Mean Squared Error (MSE), range, absolute error and signed error. In addition the DSM was compared to a Digital Elevation Model (DEM) produced from Airborne Laser Scanning (ALS). Coarse woody debris was subsequently classified from the DSM using laser pulse properties, including: width and amplitude, as well as point spatial relationships (e.g. nearest neighbour slope vectors). Validation of the coarse woody debris classification was completed using true-colour photographs co-registered to the TLS point cloud. The volume and length of the coarse woody debris was calculated from the classified point cloud. A representative network of TLS sites will allow for up-scaling to large area assessment using airborne or space based sensors to monitor forest condition, biomass and fuel loads.

Recent Trends in the Asian Forest Products Trade and Their Impact on Alaska

Treesearch

Joseph A. Roos; Daisuke Sasatani; Allen M Brackley; Valerie Barber

2010-01-01

This paper analyzes patterns of forest products trade between Asia and Alaska. Secondary data were collected and analyzed to identify Alaska forest product trading partners and the species used. Some of the many trends occurring in the Asian forest products industry include the shift from solid wood products to engineered wood products, the evolution of China as “the...

Sustainable production of wood and non-wood forest products

Treesearch

Ellen M. Donoghue; Gary L. Benson; James L. Chamberlain

2003-01-01

The International Union of Forest Research Organizations (IUFRO) All Divisions 5 Conference in Rotorua, New Zealand, March 11-15, 2003, focused on issues surrounding sustainable foest management and forest products research. As the conference title "Forest Products Research: Providing for Sustainable Choices" suggests, the purpose of the conference was to...

Ensemble of random forests One vs. Rest classifiers for MCI and AD prediction using ANOVA cortical and subcortical feature selection and partial least squares.

PubMed

Ramírez, J; Górriz, J M; Ortiz, A; Martínez-Murcia, F J; Segovia, F; Salas-Gonzalez, D; Castillo-Barnes, D; Illán, I A; Puntonet, C G

2018-05-15

Alzheimer's disease (AD) is the most common cause of dementia in the elderly and affects approximately 30 million individuals worldwide. Mild cognitive impairment (MCI) is very frequently a prodromal phase of AD, and existing studies have suggested that people with MCI tend to progress to AD at a rate of about 10-15% per year. However, the ability of clinicians and machine learning systems to predict AD based on MRI biomarkers at an early stage is still a challenging problem that can have a great impact in improving treatments. The proposed system, developed by the SiPBA-UGR team for this challenge, is based on feature standardization, ANOVA feature selection, partial least squares feature dimension reduction and an ensemble of One vs. Rest random forest classifiers. With the aim of improving its performance when discriminating healthy controls (HC) from MCI, a second binary classification level was introduced that reconsiders the HC and MCI predictions of the first level. The system was trained and evaluated on an ADNI datasets that consist of T1-weighted MRI morphological measurements from HC, stable MCI, converter MCI and AD subjects. The proposed system yields a 56.25% classification score on the test subset which consists of 160 real subjects. The classifier yielded the best performance when compared to: (i) One vs. One (OvO), One vs. Rest (OvR) and error correcting output codes (ECOC) as strategies for reducing the multiclass classification task to multiple binary classification problems, (ii) support vector machines, gradient boosting classifier and random forest as base binary classifiers, and (iii) bagging ensemble learning. A robust method has been proposed for the international challenge on MCI prediction based on MRI data. The system yielded the second best performance during the competition with an accuracy rate of 56.25% when evaluated on the real subjects of the test set. Copyright © 2017 Elsevier B.V. All rights reserved.

Does tree diversity increase wood production in pine forests?

PubMed

Vilà, Montserrat; Vayreda, Jordi; Gracia, Carles; Ibáñez, Joan Josep

2003-04-01

Recent experimental advances on the positive effect of species richness on ecosystem productivity highlight the need to explore this relationship in communities other than grasslands and using non-synthetic experiments. We investigated whether wood production in forests dominated by Aleppo pine (Pinus halepensis) and Pyrenean Scots pine (Pinus sylvestris) differed between monospecific and mixed forests (2-5 species) using the Ecological and Forest Inventory of Catalonia (IEFC) database which contains biotic and environmental characteristics for 10,644 field plots distributed within a 31,944 km(2) area in Catalonia (NE Spain). We found that in Pyrenean Scots pine forests wood production was not significantly different between monospecific and mixed plots. In contrast, in Aleppo pine forests wood production was greater in mixed plots than in monospecific plots. However, when climate, bedrock types, radiation and successional stage per plot were included in the analysis, species richness was no longer a significant factor. Aleppo pine forests had the highest productivity in plots located in humid climates and on marls and sandstone bedrocks. Climate did not influence wood production in Pyrenean Scots pine forests, but it was highest on sandstone and consolidated alluvial materials. For both pine forests wood production was negatively correlated with successional stage. Radiation did not influence wood production. Our analysis emphasizes the influence of macroenvironmental factors and temporal variation on tree productivity at the regional scale. Well-conducted forest surveys are an excellent source of data to test for the association between diversity and productivity driven by large-scale environmental factors.

Using Bayesian neural networks to classify forest scenes

NASA Astrophysics Data System (ADS)

Vehtari, Aki; Heikkonen, Jukka; Lampinen, Jouko; Juujarvi, Jouni

1998-10-01

We present results that compare the performance of Bayesian learning methods for neural networks on the task of classifying forest scenes into trees and background. Classification task is demanding due to the texture richness of the trees, occlusions of the forest scene objects and diverse lighting conditions under operation. This makes it difficult to determine which are optimal image features for the classification. A natural way to proceed is to extract many different types of potentially suitable features, and to evaluate their usefulness in later processing stages. One approach to cope with large number of features is to use Bayesian methods to control the model complexity. Bayesian learning uses a prior on model parameters, combines this with evidence from a training data, and the integrates over the resulting posterior to make predictions. With this method, we can use large networks and many features without fear of overfitting. For this classification task we compare two Bayesian learning methods for multi-layer perceptron (MLP) neural networks: (1) The evidence framework of MacKay uses a Gaussian approximation to the posterior weight distribution and maximizes with respect to hyperparameters. (2) In a Markov Chain Monte Carlo (MCMC) method due to Neal, the posterior distribution of the network parameters is numerically integrated using the MCMC method. As baseline classifiers for comparison we use (3) MLP early stop committee, (4) K-nearest-neighbor and (5) Classification And Regression Tree.

Managing national forests of the eastern United States for non-timber forest products

Treesearch

James L. Chamberlain; Robert J. Bush; A.L. Hammett; Philip A. Araman

2000-01-01

Over the last decade, there has been a growing interest in the economic and ecological potential of non-timber forest products. In the United States, much of this increased interest stems from drastic changes in forest practices and policies in the Pacific Northwest region, a region that produces many non-timber forest products. The forests of the eastern United States...

The mangement of national forests of eastern United States for non-timber forest products

Treesearch

James L. Chamberlain

2000-01-01

Many products are harvested fiom the forests of the United States in addition to timber. These non-timber forest products (NTFPs) are plants, parts of plants, or fungi that are harvested from within and on the edges of natural, disturbed or managed forests. Often, NTFPs are harvested from public forests for the socio-economic benefit they provide to rural collectors....

U.S. forest products module : a technical document supporting the Forest Service 2010 RPA Assessment

Treesearch

Peter J. Ince; Andrew D. Kramp; Kenneth E. Skog; Henry N. Spelter; David N. Wear

2011-01-01

The U.S. Forest Products Module (USFPM) is a partial market equilibrium model of the U.S. forest sector that operates within the Global Forest Products Model (GFPM) to provide long-range timber market projections in relation to global economic scenarios. USFPM was designed specifically for the 2010 RPA forest assessment, but it is being used also in other applications...

Latent information in fluency lists predicts functional decline in persons at risk for Alzheimer disease.

PubMed

Clark, D G; Kapur, P; Geldmacher, D S; Brockington, J C; Harrell, L; DeRamus, T P; Blanton, P D; Lokken, K; Nicholas, A P; Marson, D C

2014-06-01

We constructed random forest classifiers employing either the traditional method of scoring semantic fluency word lists or new methods. These classifiers were then compared in terms of their ability to diagnose Alzheimer disease (AD) or to prognosticate among individuals along the continuum from cognitively normal (CN) through mild cognitive impairment (MCI) to AD. Semantic fluency lists from 44 cognitively normal elderly individuals, 80 MCI patients, and 41 AD patients were transcribed into electronic text files and scored by four methods: traditional raw scores, clustering and switching scores, "generalized" versions of clustering and switching, and a method based on independent components analysis (ICA). Random forest classifiers based on raw scores were compared to "augmented" classifiers that incorporated newer scoring methods. Outcome variables included AD diagnosis at baseline, MCI conversion, increase in Clinical Dementia Rating-Sum of Boxes (CDR-SOB) score, or decrease in Financial Capacity Instrument (FCI) score. Receiver operating characteristic (ROC) curves were constructed for each classifier and the area under the curve (AUC) was calculated. We compared AUC between raw and augmented classifiers using Delong's test and assessed validity and reliability of the augmented classifier. Augmented classifiers outperformed classifiers based on raw scores for the outcome measures AD diagnosis (AUC .97 vs. .95), MCI conversion (AUC .91 vs. .77), CDR-SOB increase (AUC .90 vs. .79), and FCI decrease (AUC .89 vs. .72). Measures of validity and stability over time support the use of the method. Latent information in semantic fluency word lists is useful for predicting cognitive and functional decline among elderly individuals at increased risk for developing AD. Modern machine learning methods may incorporate latent information to enhance the diagnostic value of semantic fluency raw scores. These methods could yield information valuable for patient care and clinical trial design with a relatively small investment of time and money. Published by Elsevier Ltd.

Sustainable biomass production on Marginal Lands (SEEMLA)

NASA Astrophysics Data System (ADS)

Barbera, Federica; Baumgarten, Wibke; Pelikan, Vincent

2017-04-01

Sustainable biomass production on Marginal Lands (SEEMLA) The main objective of the H2020 funded EU project SEEMLA (acronym for Sustainable Exploitation of Biomass for Bioenergy from Marginal Lands in Europe) is the establishment of suitable innovative land-use strategies for a sustainable production of plant-based energy on marginal lands while improving general ecosystem services. The use of marginal lands (MagL) could contribute to the mitigation of the fast growing competition between traditional food production and production of renewable bio-resources on arable lands. SEEMLA focuses on the promotion of re-conversion of MagLs for the production of bioenergy through the direct involvement of farmers and forester, the strengthening of local small-scale supply chains, and the promotion of plantations of bioenergy plants on MagLs. Life cycle assessment is performed in order to analyse possible impacts on the environment. A soil quality rating tool is applied to define and classify MagL. Suitable perennial and woody bioenergy crops are selected to be grown in pilot areas in the partner countries Ukraine, Greece and Germany. SEEMLA is expected to contribute to an increasing demand of biomass for bioenergy production in order to meet the 2020 targets and beyond.

Potential for Expanding the Near Real Time ForWarn Regional Forest Monitoring System to Include Alaska

NASA Technical Reports Server (NTRS)

Spruce, Joseph P.; Gasser, Gerald; Hargrove, William; Smoot, James; Kuper, Philip D.

2014-01-01

The on-line near real time (NRT) ForWarn system is currently deployed to monitor regional forest disturbances within the conterminous United States (CONUS), using daily MODIS Aqua and Terra NDVI data to derive monitoring products. The Healthy Forest Restoration Act of 2003 mandated such a system. Work on ForWarn began in 2006 with development and validation of retrospective MODIS NDVI-based forest monitoring products. Subsequently, NRT forest disturbance monitoring products were demonstrated, leading to the actual system deployment in 2010. ForWarn provides new CONUS forest disturbance monitoring products every 8 days, using USGS eMODIS data for current NDVI. ForWarn currently does not cover Alaska, which includes extensive forest lands at risk to multiple biotic and abiotic threats. This poster discusses a case study using Alaska eMODIS Terra data to derive ForWarn like forest change products during the 2010 growing season. The eMODIS system provides current MODIS Terra NDVI products for Alaska. Resulting forest change products were assessed with ground, aerial, and Landsat reference data. When cloud and snow free, these preliminary products appeared to capture regional forest disturbances from insect defoliation and fires; however, more work is needed to mitigate cloud and snow contamination, including integration of eMODIS Aqua data.

Ligninolytic Activity at 0 °C of Fungi on Oak Leaves Under Snow Cover in a Mixed Forest in Japan.

PubMed

Miyamoto, Toshizumi; Koda, Keiichi; Kawaguchi, Arata; Uraki, Yasumitsu

2017-08-01

Despite the importance of litter decomposition under snow cover in boreal forests and tundra, very little is known regarding the characteristics and functions of litter-decomposing fungi adapted to the cold climate. We investigated the decomposition of oak leaves in a heavy snowfall forest region of Japan. The rate of litter weight loss reached 26.5% during the snow cover period for 7 months and accounted for 64.6% of the annual loss (41.1%). Although no statistically significant lignin loss was detected, decolourization portions of oak leaf litter, which was attributable to the activities of ligninolytic fungi, were observed during snow cover period. This suggests that fungi involved in litter decomposition can produce extracellular enzymes to degrade lignin that remain active at 0 °C. Fungi were isolated from oak leaves collected from the forest floor under the snow layer. One hundred and sixty-six strains were isolated and classified into 33 operational taxonomic units (OTUs) based on culture characteristics and nuclear rDNA internal transcribed spacer (ITS) region sequences. To test the ability to degrade lignin, the production of extracellular phenoloxidases by isolates was quantified at 0 °C. Ten OTUs (9 Ascomycota and 1 Basidiomycota) of fungi exhibited mycelial growth and ligninolytic activity. These results suggested that some litter-decomposing fungi that had the potential to degrade lignin at 0 °C significantly contribute to litter decomposition under snow cover.

Mississippi's forest products industry: performance and contribution to the State's economy, 1970 to 1980.

Treesearch

Con H Schallau; Wilbur R. Maki; Bennett B. Foster; Clair H. Redmond

1988-01-01

The forest products industry is one of Mississippi's basic industries, and in 1980, it accounted for about one of six basic jobs. Mississippi was one of the majority of Southern States in which the forest products industry improved its competitive position during the 1970's. Between 1972 and 1977, growth in productivity of Mississippi's forest products...

Productivity of Western forests: a forest products focus.

Treesearch

Constance A. Harrington; Stephen H. Schoenholtz

2005-01-01

In August 20-23, 2004, a conference was held in Kamilche, WA, with the title “Productivity of Western Forests: A Forest Products Focus.” The meeting brought together researchers and practitioners interested in discussing the economic and biological factors influencing wood production and value. One of the underlying assumptions of the meeting organizers was that...

A Dirichlet-Multinomial Bayes Classifier for Disease Diagnosis with Microbial Compositions.

PubMed

Gao, Xiang; Lin, Huaiying; Dong, Qunfeng

2017-01-01

Dysbiosis of microbial communities is associated with various human diseases, raising the possibility of using microbial compositions as biomarkers for disease diagnosis. We have developed a Bayes classifier by modeling microbial compositions with Dirichlet-multinomial distributions, which are widely used to model multicategorical count data with extra variation. The parameters of the Dirichlet-multinomial distributions are estimated from training microbiome data sets based on maximum likelihood. The posterior probability of a microbiome sample belonging to a disease or healthy category is calculated based on Bayes' theorem, using the likelihood values computed from the estimated Dirichlet-multinomial distribution, as well as a prior probability estimated from the training microbiome data set or previously published information on disease prevalence. When tested on real-world microbiome data sets, our method, called DMBC (for Dirichlet-multinomial Bayes classifier), shows better classification accuracy than the only existing Bayesian microbiome classifier based on a Dirichlet-multinomial mixture model and the popular random forest method. The advantage of DMBC is its built-in automatic feature selection, capable of identifying a subset of microbial taxa with the best classification accuracy between different classes of samples based on cross-validation. This unique ability enables DMBC to maintain and even improve its accuracy at modeling species-level taxa. The R package for DMBC is freely available at https://github.com/qunfengdong/DMBC. IMPORTANCE By incorporating prior information on disease prevalence, Bayes classifiers have the potential to estimate disease probability better than other common machine-learning methods. Thus, it is important to develop Bayes classifiers specifically tailored for microbiome data. Our method shows higher classification accuracy than the only existing Bayesian classifier and the popular random forest method, and thus provides an alternative option for using microbial compositions for disease diagnosis.

Use of Multi-Year MODIS Phenological Data Products to Detect and Monitor Forest Disturbances at Regional and National Scales

NASA Technical Reports Server (NTRS)

Spruce, Joseph; Hargrove, William W.; Gasser, Jerry; Smoot, James; Ross, Kenton

2010-01-01

This presentation discusses an effort to use select MODIS phenological products for forest disturbance monitoring at the regional and CONUS scales. Forests occur on 1/3 of the U.S. land base and include regionally prevalent forest disturbances that can threaten forest sustainability. Regional and CONUS forest disturbance monitoring is needed for a national forest threat early warning system being developed by the USDA Forest Service with help from NASA, ORNL, and USGS. MODIS NDVI phenology products are being used to develop forest disturbance monitoring capabilities of this EWS.

Monitoring 2009 Forest Disturbance Across the Conterminous United States, Based on Near-Real Time and Historical MODIS 250 Meter NDVI Products

NASA Technical Reports Server (NTRS)

Spruce, J.; Hargrove, W. W.; Gasser, G.; Smoot, J. C.; Kuper, P.

2009-01-01

This case study shows the promise of computing current season forest disturbance detection products at regional to CONUS scales. Use of the eMODIS expedited product enabled a NRT CONUS forest disturbance detection product, a requirement for an eventual, operational forest threat EWS. The 2009 classification product from this study can be used to quantify the areal extent of forest disturbance across CONUS, although a quantitative accuracy assessment still needs to be completed. However, the results would not include disturbances that occurred after July 27, such as the Station Fire. While not shown here, the project also produced maximum NDVI products for the June 10-July 27 period of each year of the 2000-2009 time frame. These products could be applied to compute forest change products on an annual basis. GIS could then be used to assess disturbance persistence. Such follow-on work could lead to attribution of year in which a disturbance occurred. These products (e.g., Figures 6 and 7) may also be useful for assessing forest change associated with climate change, such as carbon losses from bark beetle-induced forest mortality in the Western United States. Other MODIS phenological products are being assessed for aiding forest monitoring needs of the EWS, including cumulative NDVI products (Figure 10).

Proceedings of the Alaska forest soil productivity workshop.

Treesearch

C.W. Slaughter; T. Gasbarro

1988-01-01

The Alaska Forest Soil Productivity Workshop addressed (1) the role of soil information for forest management in Alaska; (2) assessment, monitoring, and enhancement of soil productivity; and (3) Alaska research projects involved in studies of productivity of forests and soils. This proceedings includes 27 papers in five categories: agency objectives in monitoring and...

Forest area and distribution in the Mississippi alluvial valley: Implications for breeding bird conservation

USGS Publications Warehouse

Twedt, D.J.; Loesch, C.R.

1999-01-01

Knowing the current forest distribution and patch size characteristics is integral to the development of geographically defined, habitat-based conservation objectives for breeding birds. Towards this end, we classified 2.6 million ha of forest cover within the Mississippi Alluvial Valley using 1992 thematic mapper satellite imagery. Although historically this area, from southern Illinois to southern Louisiana, was dominated by forested wetlands, forest cover remains on less than 25% of the floodplain. Remaining forest cover is comprised of > 38,000 discrete forest patches > 2 ha. Mean patch area (64.1?5.2 ha; 0 ?SE) was highly skewed towards small fragment size. Larger patches had a higher proportion of more hydric forest cover classes than did smaller patches which had a higher proportion of less hydric forest cover classes. Public lands accounted for 16% of remaining forested wetlands. Fewer than 100 forest patches exceeded our hypothesized habitat objective (4000 ha minimum contiguous forest area) intended to support self-sustaining populations of forest breeding birds. To increase the number of forest patches exceeding 4000 ha contiguous area, and thereby increase the likelihood of successful forest bird conservation, we recommend afforestation adjoining existing forest fragments ?1012 ha and focused within designated Forest Bird Conservation Regions.

75 FR 64617 - National Forest Products Week, 2010

Federal Register 2010, 2011, 2012, 2013, 2014

2010-10-20

... National Forest Products Week, 2010 By the President of the United States of America A Proclamation Since... settings for contemplation. As we mark the 50th anniversary of National Forest Products Week, we recognize... our daily lives, from the houses we live in to the paper we write on. National Forest Products Week...

Effect of sample size on multi-parametric prediction of tissue outcome in acute ischemic stroke using a random forest classifier

NASA Astrophysics Data System (ADS)

Forkert, Nils Daniel; Fiehler, Jens

2015-03-01

The tissue outcome prediction in acute ischemic stroke patients is highly relevant for clinical and research purposes. It has been shown that the combined analysis of diffusion and perfusion MRI datasets using high-level machine learning techniques leads to an improved prediction of final infarction compared to single perfusion parameter thresholding. However, most high-level classifiers require a previous training and, until now, it is ambiguous how many subjects are required for this, which is the focus of this work. 23 MRI datasets of acute stroke patients with known tissue outcome were used in this work. Relative values of diffusion and perfusion parameters as well as the binary tissue outcome were extracted on a voxel-by- voxel level for all patients and used for training of a random forest classifier. The number of patients used for training set definition was iteratively and randomly reduced from using all 22 other patients to only one other patient. Thus, 22 tissue outcome predictions were generated for each patient using the trained random forest classifiers and compared to the known tissue outcome using the Dice coefficient. Overall, a logarithmic relation between the number of patients used for training set definition and tissue outcome prediction accuracy was found. Quantitatively, a mean Dice coefficient of 0.45 was found for the prediction using the training set consisting of the voxel information from only one other patient, which increases to 0.53 if using all other patients (n=22). Based on extrapolation, 50-100 patients appear to be a reasonable tradeoff between tissue outcome prediction accuracy and effort required for data acquisition and preparation.

A Hybrid Color Space for Skin Detection Using Genetic Algorithm Heuristic Search and Principal Component Analysis Technique

PubMed Central

2015-01-01

Color is one of the most prominent features of an image and used in many skin and face detection applications. Color space transformation is widely used by researchers to improve face and skin detection performance. Despite the substantial research efforts in this area, choosing a proper color space in terms of skin and face classification performance which can address issues like illumination variations, various camera characteristics and diversity in skin color tones has remained an open issue. This research proposes a new three-dimensional hybrid color space termed SKN by employing the Genetic Algorithm heuristic and Principal Component Analysis to find the optimal representation of human skin color in over seventeen existing color spaces. Genetic Algorithm heuristic is used to find the optimal color component combination setup in terms of skin detection accuracy while the Principal Component Analysis projects the optimal Genetic Algorithm solution to a less complex dimension. Pixel wise skin detection was used to evaluate the performance of the proposed color space. We have employed four classifiers including Random Forest, Naïve Bayes, Support Vector Machine and Multilayer Perceptron in order to generate the human skin color predictive model. The proposed color space was compared to some existing color spaces and shows superior results in terms of pixel-wise skin detection accuracy. Experimental results show that by using Random Forest classifier, the proposed SKN color space obtained an average F-score and True Positive Rate of 0.953 and False Positive Rate of 0.0482 which outperformed the existing color spaces in terms of pixel wise skin detection accuracy. The results also indicate that among the classifiers used in this study, Random Forest is the most suitable classifier for pixel wise skin detection applications. PMID:26267377

Revealing the z ~ 2.5 Cosmic Web with 3D Lyα Forest Tomography: a Deformation Tensor Approach

NASA Astrophysics Data System (ADS)

Lee, Khee-Gan; White, Martin

2016-11-01

Studies of cosmological objects should take into account their positions within the cosmic web of large-scale structure. Unfortunately, the cosmic web has only been extensively mapped at low redshifts (z\\lt 1), using galaxy redshifts as tracers of the underlying density field. At z\\gt 1, the required galaxy densities are inaccessible for the foreseeable future, but 3D reconstructions of Lyα forest absorption in closely separated background QSOs and star-forming galaxies already offer a detailed window into z˜ 2-3 large-scale structure. We quantify the utility of such maps for studying the cosmic web by using realistic z = 2.5 Lyα forest simulations matched to observational properties of upcoming surveys. A deformation tensor-based analysis is used to classify voids, sheets, filaments, and nodes in the flux, which are compared to those determined from the underlying dark matter (DM) field. We find an extremely good correspondence, with 70% of the volume in the flux maps correctly classified relative to the DM web, and 99% classified to within one eigenvalue. This compares favorably to the performance of galaxy-based classifiers with even the highest galaxy densities from low-redshift surveys. We find that narrow survey geometries can degrade the recovery of the cosmic web unless the survey is ≳ 60 {h}-1 {Mpc} or ≳ 1 deg on the sky. We also examine halo abundances as a function of the cosmic web, and find a clear dependence as a function of flux overdensity, but little explicit dependence on the cosmic web. These methods will provide a new window on cosmological environments of galaxies at this very special time in galaxy formation, “high noon,” and on overall properties of cosmological structures at this epoch.

Pool spacing, channel morphology, and the restoration of tidal forested wetlands of the Columbia River, U.S.A.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Diefenderfer, Heida L.; Montgomery, David R.

2008-10-09

Tidal forested wetlands have sustained substantial areal losses, and restoration practitioners lack a description of many ecosystem structures associated with these late-successional systems in which surface water is a significant controlling factor on the flora and fauna. The roles of large woody debris in terrestrial and riverine ecosystems have been well described compared to functions in tidal areas. This study documents the role of large wood in forcing channel morphology in Picea-sitchensis (Sitka spruce) dominated freshwater tidal wetlands in the floodplain of the Columbia River, U.S.A. near the Pacific coast. The average pool spacing documented in channel surveys of threemore » freshwater tidal forested wetlands near Grays Bay were 2.2 ± 1.3, 2.3 ± 1.2, and 2.5 ± 1.5. There were significantly greater numbers of pools on tidal forested wetland channels than on a nearby restoration site. On the basis of pool spacing and the observed sequences of log jams and pools, the tidal forested wetland channels were classified consistent with a forced step-pool class. Tidal systems, with bidirectional flow, have not previously been classified in this way. The classification provides a useful basis for restoration project design and planning in historically forested tidal freshwater areas, particularly in regard to the use of large wood in restoration actions and the development of pool habitats for aquatic species. Significant modifications by beaver on these sites warrant further investigation to explore the interactions between these animals and restoration actions affecting hydraulics and channel structure in tidal areas.« less

36 CFR 223.131 - Applicability.

Code of Federal Regulations, 2013 CFR

2013-07-01

... 36 Parks, Forests, and Public Property 2 2013-07-01 2013-07-01 false Applicability. 223.131 Section 223.131 Parks, Forests, and Public Property FOREST SERVICE, DEPARTMENT OF AGRICULTURE SALE AND DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS...

36 CFR 223.136 - Debarment.

Code of Federal Regulations, 2012 CFR

2012-07-01

... 36 Parks, Forests, and Public Property 2 2012-07-01 2012-07-01 false Debarment. 223.136 Section 223.136 Parks, Forests, and Public Property FOREST SERVICE, DEPARTMENT OF AGRICULTURE SALE AND DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS...

36 CFR 223.131 - Applicability.

Code of Federal Regulations, 2012 CFR

2012-07-01

... 36 Parks, Forests, and Public Property 2 2012-07-01 2012-07-01 false Applicability. 223.131 Section 223.131 Parks, Forests, and Public Property FOREST SERVICE, DEPARTMENT OF AGRICULTURE SALE AND DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS...

36 CFR 223.116 - Cancellation.

Code of Federal Regulations, 2013 CFR

2013-07-01

... 36 Parks, Forests, and Public Property 2 2013-07-01 2013-07-01 false Cancellation. 223.116 Section 223.116 Parks, Forests, and Public Property FOREST SERVICE, DEPARTMENT OF AGRICULTURE SALE AND DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS Timber...

36 CFR 223.163 - [Reserved

Code of Federal Regulations, 2011 CFR

2011-07-01

... 36 Parks, Forests, and Public Property 2 2011-07-01 2011-07-01 false [Reserved] 223.163 Section 223.163 Parks, Forests, and Public Property FOREST SERVICE, DEPARTMENT OF AGRICULTURE SALE AND DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS Timber...

36 CFR 223.141 - Suspension.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 36 Parks, Forests, and Public Property 2 2011-07-01 2011-07-01 false Suspension. 223.141 Section 223.141 Parks, Forests, and Public Property FOREST SERVICE, DEPARTMENT OF AGRICULTURE SALE AND DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS...

36 CFR 223.116 - Cancellation.

Code of Federal Regulations, 2012 CFR

2012-07-01

... 36 Parks, Forests, and Public Property 2 2012-07-01 2012-07-01 false Cancellation. 223.116 Section 223.116 Parks, Forests, and Public Property FOREST SERVICE, DEPARTMENT OF AGRICULTURE SALE AND DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS Timber...

36 CFR 223.161 - [Reserved

Code of Federal Regulations, 2011 CFR

2011-07-01

... 36 Parks, Forests, and Public Property 2 2011-07-01 2011-07-01 false [Reserved] 223.161 Section 223.161 Parks, Forests, and Public Property FOREST SERVICE, DEPARTMENT OF AGRICULTURE SALE AND DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS Timber...

36 CFR 223.13 - Compliance.

Code of Federal Regulations, 2014 CFR

2014-07-01

... 36 Parks, Forests, and Public Property 2 2014-07-01 2014-07-01 false Compliance. 223.13 Section 223.13 Parks, Forests, and Public Property FOREST SERVICE, DEPARTMENT OF AGRICULTURE SALE AND DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS General...

36 CFR 223.136 - Debarment.

Code of Federal Regulations, 2013 CFR

2013-07-01

... 36 Parks, Forests, and Public Property 2 2013-07-01 2013-07-01 false Debarment. 223.136 Section 223.136 Parks, Forests, and Public Property FOREST SERVICE, DEPARTMENT OF AGRICULTURE SALE AND DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS...

36 CFR 223.163 - [Reserved

Code of Federal Regulations, 2013 CFR

2013-07-01

... 36 Parks, Forests, and Public Property 2 2013-07-01 2013-07-01 false [Reserved] 223.163 Section 223.163 Parks, Forests, and Public Property FOREST SERVICE, DEPARTMENT OF AGRICULTURE SALE AND DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS Timber...

36 CFR 223.136 - Debarment.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 36 Parks, Forests, and Public Property 2 2011-07-01 2011-07-01 false Debarment. 223.136 Section 223.136 Parks, Forests, and Public Property FOREST SERVICE, DEPARTMENT OF AGRICULTURE SALE AND DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS...

36 CFR 223.161 - [Reserved

Code of Federal Regulations, 2013 CFR

2013-07-01

... 36 Parks, Forests, and Public Property 2 2013-07-01 2013-07-01 false [Reserved] 223.161 Section 223.161 Parks, Forests, and Public Property FOREST SERVICE, DEPARTMENT OF AGRICULTURE SALE AND DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS Timber...

36 CFR 223.141 - Suspension.

Code of Federal Regulations, 2012 CFR

2012-07-01

... 36 Parks, Forests, and Public Property 2 2012-07-01 2012-07-01 false Suspension. 223.141 Section 223.141 Parks, Forests, and Public Property FOREST SERVICE, DEPARTMENT OF AGRICULTURE SALE AND DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS...

36 CFR 223.163 - [Reserved

Code of Federal Regulations, 2014 CFR

2014-07-01

... 36 Parks, Forests, and Public Property 2 2014-07-01 2014-07-01 false [Reserved] 223.163 Section 223.163 Parks, Forests, and Public Property FOREST SERVICE, DEPARTMENT OF AGRICULTURE SALE AND DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS Timber...

36 CFR 223.136 - Debarment.

Code of Federal Regulations, 2014 CFR

2014-07-01

... 36 Parks, Forests, and Public Property 2 2014-07-01 2014-07-01 false Debarment. 223.136 Section 223.136 Parks, Forests, and Public Property FOREST SERVICE, DEPARTMENT OF AGRICULTURE SALE AND DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS...

36 CFR 223.13 - Compliance.

Code of Federal Regulations, 2012 CFR

2012-07-01

... 36 Parks, Forests, and Public Property 2 2012-07-01 2012-07-01 false Compliance. 223.13 Section 223.13 Parks, Forests, and Public Property FOREST SERVICE, DEPARTMENT OF AGRICULTURE SALE AND DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS General...

36 CFR 223.133 - Definitions.

Code of Federal Regulations, 2013 CFR

2013-07-01

... 36 Parks, Forests, and Public Property 2 2013-07-01 2013-07-01 false Definitions. 223.133 Section 223.133 Parks, Forests, and Public Property FOREST SERVICE, DEPARTMENT OF AGRICULTURE SALE AND DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS...

36 CFR 223.131 - Applicability.

Code of Federal Regulations, 2014 CFR

2014-07-01

... 36 Parks, Forests, and Public Property 2 2014-07-01 2014-07-01 false Applicability. 223.131 Section 223.131 Parks, Forests, and Public Property FOREST SERVICE, DEPARTMENT OF AGRICULTURE SALE AND DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS...

36 CFR 223.163 - [Reserved

Code of Federal Regulations, 2012 CFR

2012-07-01

... 36 Parks, Forests, and Public Property 2 2012-07-01 2012-07-01 false [Reserved] 223.163 Section 223.163 Parks, Forests, and Public Property FOREST SERVICE, DEPARTMENT OF AGRICULTURE SALE AND DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS Timber...

36 CFR 223.161 - [Reserved

Code of Federal Regulations, 2012 CFR

2012-07-01

... 36 Parks, Forests, and Public Property 2 2012-07-01 2012-07-01 false [Reserved] 223.161 Section 223.161 Parks, Forests, and Public Property FOREST SERVICE, DEPARTMENT OF AGRICULTURE SALE AND DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS Timber...

36 CFR 223.116 - Cancellation.

Code of Federal Regulations, 2014 CFR

2014-07-01

... 36 Parks, Forests, and Public Property 2 2014-07-01 2014-07-01 false Cancellation. 223.116 Section 223.116 Parks, Forests, and Public Property FOREST SERVICE, DEPARTMENT OF AGRICULTURE SALE AND DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS Timber...

36 CFR 223.13 - Compliance.

Code of Federal Regulations, 2013 CFR

2013-07-01

... 36 Parks, Forests, and Public Property 2 2013-07-01 2013-07-01 false Compliance. 223.13 Section 223.13 Parks, Forests, and Public Property FOREST SERVICE, DEPARTMENT OF AGRICULTURE SALE AND DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS General...

36 CFR 223.141 - Suspension.

Code of Federal Regulations, 2013 CFR

2013-07-01

... 36 Parks, Forests, and Public Property 2 2013-07-01 2013-07-01 false Suspension. 223.141 Section 223.141 Parks, Forests, and Public Property FOREST SERVICE, DEPARTMENT OF AGRICULTURE SALE AND DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS...

36 CFR 223.160 - Definitions.

Code of Federal Regulations, 2012 CFR

2012-07-01

... 36 Parks, Forests, and Public Property 2 2012-07-01 2012-07-01 false Definitions. 223.160 Section 223.160 Parks, Forests, and Public Property FOREST SERVICE, DEPARTMENT OF AGRICULTURE SALE AND DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS Timber...

36 CFR 223.160 - Definitions.

Code of Federal Regulations, 2013 CFR

2013-07-01

... 36 Parks, Forests, and Public Property 2 2013-07-01 2013-07-01 false Definitions. 223.160 Section 223.160 Parks, Forests, and Public Property FOREST SERVICE, DEPARTMENT OF AGRICULTURE SALE AND DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS Timber...

36 CFR 223.160 - Definitions.

Code of Federal Regulations, 2014 CFR

2014-07-01

... 36 Parks, Forests, and Public Property 2 2014-07-01 2014-07-01 false Definitions. 223.160 Section 223.160 Parks, Forests, and Public Property FOREST SERVICE, DEPARTMENT OF AGRICULTURE SALE AND DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS Timber...

36 CFR 223.160 - Definitions.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 36 Parks, Forests, and Public Property 2 2011-07-01 2011-07-01 false Definitions. 223.160 Section 223.160 Parks, Forests, and Public Property FOREST SERVICE, DEPARTMENT OF AGRICULTURE SALE AND DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS Timber...

Nationwide forestry applications program: Ten-Ecosystem Study (TES) site 5 report, Kershaw County, South Carolina, report 4

NASA Technical Reports Server (NTRS)

Dillman, R. D. (Principal Investigator)

1978-01-01

The author has identified the following significant results. The Kershaw County site, South Carolina, was selected to be representative of both the oak-pine ecosystem and the southeastern pine ecosystem. The following processing results have concluded that: (1) early spring LANDSAT data provide the best contrast between forest features; (2) level 2 forest features (softwood, hardwood, grassland, and water) can be classified with an accuracy of 70% + or - 5.7% at the 90% confidence level; (3) level 3 species classification was inconclusive; (4) temporal data did not provide a significant increase in classification accuracy of level 2 features, over single date classification to warrant the additional processing; and (5) training fields from only 10% of the site can be used to classify the entire site.

An Effort to Map and Monitor Baldcypress Forest Areas in Coastal Louisiana, Using Landsat, MODIS, and ASTER Satellite Data

NASA Technical Reports Server (NTRS)

Spruce, Joseph P.; Sader, Steve; Smoot, James

2012-01-01

This presentation discusses a collaborative project to develop, test, and demonstrate baldcypress forest mapping and monitoring products for aiding forest conservation and restoration in coastal Louisiana. Low lying coastal forests in the region are being negatively impacted by multiple factors, including subsidence, salt water intrusion, sea level rise, persistent flooding, hydrologic modification, annual insect-induced forest defoliation, timber harvesting, and conversion to urban land uses. Coastal baldcypress forests provide invaluable ecological services in terms of wildlife habitat, forest products, storm buffers, and water quality benefits. Before this project, current maps of baldcypress forest concentrations and change did not exist or were out of date. In response, this project was initiated to produce: 1) current maps showing the extent and location of baldcypress dominated forests; and 2) wetland forest change maps showing temporary and persistent disturbance and loss since the early 1970s. Project products are being developed collaboratively with multiple state and federal agencies. Products are being validated using available reference data from aerial, satellite, and field survey data. Results include Landsat TM- based classifications of baldcypress in terms of cover type and percent canopy cover. Landsat MSS data was employed to compute a circa 1972 classification of swamp and bottomland hardwood forest types. Landsat data for 1972-2010 was used to compute wetland forest change products. MODIS-based change products were applied to view and assess insect-induced swamp forest defoliation. MODIS, Landsat, and ASTER satellite data products were used to help assess hurricane and flood impacts to coastal wetland forests in the region.

Does nitrogen and sulfur deposition affect forest productivity?

Treesearch

Brittany A. Johnson; Kathryn B. Piatek; Mary Beth Adams; John R. Brooks

2010-01-01

We studied the effects of atmospheric nitrogen and sulfur deposition on forest productivity in a 10-year-old, aggrading forest stand at the Fernow Experimental Forest in Tucker County, WV. Forest productivity was expressed as total aboveground wood biomass, which included stem and branch weight of standing live trees. Ten years after stand regeneration and treatment...

National workshop on forest productivity & technology: cooperative research to support a sustainable & competitive future - progress and strategy

Treesearch

Eric D. Vance

2010-01-01

The Agenda 2020 Program is a partnership among government agencies, the forest products industry, and academia to develop technology capable of enhancing forest productivity, sustaining environmental values, increasing energy efficiency, and improving the economic competitiveness of the United States forest sector. In November 2006, the USDA Forest Service, in...

Model-experiment synthesis at two FACE sites in the southeastern US. Forest ecosystem responses to elevated CO[2]. (Invited)

NASA Astrophysics Data System (ADS)

Walker, A. P.; Zaehle, S.; De Kauwe, M. G.; Medlyn, B. E.; Dietze, M.; Hickler, T.; Iversen, C. M.; Jain, A. K.; Luo, Y.; McCarthy, H. R.; Parton, W. J.; Prentice, C.; Thornton, P. E.; Wang, S.; Wang, Y.; Warlind, D.; Warren, J.; Weng, E.; Hanson, P. J.; Oren, R.; Norby, R. J.

2013-12-01

Ecosystem observations from two long-term Free-Air CO[2] Enrichment (FACE) experiments (Duke forest and Oak Ridge forest) were used to evaluate the assumptions of 11 terrestrial ecosystem models and the consequences of those assumptions for the responses of ecosystem water, carbon (C) and nitrogen (N) fluxes to elevated CO[2] (eCO[2]). Nitrogen dynamics were the main constraint on simulated productivity responses to eCO[2]. At Oak Ridge some models reproduced the declining response of C and N fluxes, while at Duke none of the models were able to maintain the observed sustained responses. C and N cycles are coupled through a number of complex interactions, which causes uncertainty in model simulations in multiple ways. Nonetheless, the major difference between models and experiments was a larger than observed increase in N-use efficiency and lower than observed response of N uptake. The results indicate that at Duke there were mechanisms by which trees accessed additional N in response to eCO[2] that were not represented in the ecosystem models, and which did not operate with the same efficiency at Oak Ridge. Sequestration of the additional productivity under eCO[2] into forest biomass depended largely on C allocation. Allocation assumptions were classified into three main categories--fixed partitioning coefficients, functional relationships and a partial (leaf allocation only) optimisation. The assumption which best constrained model results was a functional relationship between leaf area and sapwood area (pipe-model) and increased root allocation when nitrogen or water were limiting. Both, productivity and allocation responses to eCO[2] determined the ecosystem-level response of LAI, which together with the response of stomatal conductance (and hence water-use efficiency; WUE) determined the ecosystem response of transpiration. Differences in the WUE response across models were related to the representation of the relationship of stomatal conductance to CO[2] and the relative importance of the combined boundary and aerodynamic resistances in the total resistance to leaf-atmosphere water transport.

Extrapolating carbon dynamics of tropical dry forests into future climates: improving simulation models with empirical observations

NASA Astrophysics Data System (ADS)

Medvigy, David; Waring, Bonnie; Vargas, German; Xu, Xiangtao; Smith, Christina; Becknell, Justin; Trierweiler, Annette; Brodribb, Timothy; Powers, Jennifer

2017-04-01

Tropical dry forests occur in areas with warm temperatures and a pronounced dry season with little to no rainfall that lasts 3 to 7 months. The potential area covered by this biome is vast: globally, 47% of all forest occurs in tropical and subtropical latitudes, and of all tropical forests approximately 42% are classified as dry forests. Throughout the last several centuries, the area covered by tropical dry forests has been dramatically reduced through conversion to grazing and croplands, and they are now considered the most threatened tropical biome. However, in many regions, tropical dry forests are now growing back. There is growing concern that this recovery process will be strongly impacted by climate variability and change. Observations show that climate is changing in the seasonal tropics, and climate models forecast that neotropical dry forests will receive significantly less rainfall in the 21st century than in the 20th century. Rates of nitrogen deposition are also changing rapidly in this sector, and the fertility of some soils may still be recovering from past land use. We are engaged in several efforts to understand how water and nutrients limit the productivity of these forests, including manipulative experiments, modeling, and investigation of responses to natural climate variability. In 2015, at a well-characterized site in Guanacaste, Costa Rica, we established a full-factorial fertilization experiment with N and P in diverse mature forest stands. Initial responses highlight stronger ecosystem sensitivity to P addition than to N addition. Intriguingly, pre-experiment numerical simulations with a mechanistic ecosystem model had indicated the reverse. Work is ongoing to use field observations to better represent critical processes in the model, and ultimately to improve the model's sensitivity to nutrients and water. In addition, in 2016, we established a full factorial nutrient addition and drought experiment in plantations. Thus far, soil moisture has been successfully reduced in the drought treatments. Finally, we are investigating the impact of an extreme climatic event, the 2015 drought, on the productivity of this forest. The fingerprint of the drought on tree mortality is very strong. We found that plot-level mortality rates were two to three times higher during the drought than before the drought, and varied from 0 to >50% among species. In contrast to observations at moist tropical forests, tree size had little influence on mortality. In terms of functional groups, mortality rates of evergreen oaks growing on nutrient-poor soils particularly increased during drought. However, elevated mortality rates were not clearly correlated with commonly-measured traits like wood density or specific leaf area. In addition, trees that died during the drought tended to have smaller relative growth rate prior to the drought than trees that survived the drought. Mechanistic models are able to simulate stand-level mortality following the drought, and model-data comparison highlights different tree hydraulic strategies that can mitigate drought effects.

Adapting an IPCC-Compliant Full Forest Carbon Accounting Model to Determine the Effects of Different Forest Management Strategies in California

NASA Astrophysics Data System (ADS)

Starrs, C.; Stewart, W.; Potts, M. D.

2016-12-01

As California experiences increasing rates of disturbance events such as wildfire, drought, and insect outbreaks, understanding how different management strategies affect long-term forest carbon stock changes in the forest and in harvested wood products used by society will be key to determining strategies to best maximize forest-related carbon sequestration in the future. California's forest area is roughly evenly split across three ownership types: private timberlands, National Forest timberlands, and reserved forests. Forest management strategies in California generally vary by these ownerships; management in reserved lands sequesters carbon within the forest (i.e. leaves wood in the forest), while on private and National Forest timberlands a significant amount of wood is removed from the forest and converted to harvested wood products. The Carbon Budget Model of the Canadian Forest Sector (CBM-CFS3) is an IPCC-compliant full forest carbon accounting model developed for use in Canada that has been adapted for use in other countries. Changes in natural disturbances in the forest and technological innovation in the use of harvested wood products could substantially alter future carbon trajectories of forests under different management regimes. A key advantage of the CBM-CFS3 model is that in addition to tracking live tree, dead tree, and dead organic matter (DOM) carbon pools in the forest, it also tracks carbon stock changes in harvested wood products. We calibrated the CBM-CFS3 model with US Forest Service Forest Inventory and Analysis (FIA) data for seven forest types across three ownership types to predict carbon stock changes under different natural disturbance and harvested wood product utilization futures. Our results illustrate the importance of using a tractable model that can integrate future changes in forest carbon cycling to keep pace with our changing climate and usage of wood products.

Productivity of forests of the United States and its relation to soil and site factors and management practices: a review.

Treesearch

C.C. Grier; K.M. Lee; N.M. [and others] Nadkarni

1989-01-01

Data on net primary biological productivity of United States forests are summarized by geographic region. Site factors influencing productivity are reviewed. This paper is a review of existing literature in the productivity of various forest regions of the United States, the influence of site factors on forest productivity, and the impact of various...

Who, what, and why: the products, their use, and issues about management of non-timber forest products in the United States

Treesearch

Susan J. Alexander

2001-01-01

Non-timber forest products in the United States include floral greens, Christmas ornamentals, wild edibles, medicinals, crafts, and transplants. Non-timber forest products are important to many people for many reasons. People harvest products from forests for personal use, cultural practices, and sale. The tremendous variety of species harvested for the many markets...

Contribution of Near Real Time MODIS-Based Forest Disturbance Detection Products to a National Forest Threat Early Warning System

NASA Astrophysics Data System (ADS)

Spruce, J.; Hargrove, W. W.; Gasser, J.; Smoot, J.; Kuper, P.

2011-12-01

This presentation discusses an effort to compute and post weekly MODIS forest change products for the conterminous US (CONUS), as part of a web-based national forest threat early warning system (EWS) known as the U.S. Forest Change Assessment Viewer (FCAV). The US Forest Service, NASA, USGS, and ORNL are working collaboratively to contribute weekly change products to this EWS. Large acreages of the nation's forests are being disturbed by a growing multitude of biotic and abiotic threats that can act either singularly or in combination. When common at regional scales, such disturbances can pose hazards and threats to floral and faunal bio-diversity, ecosystem sustainability, ecosystem services, and human settlements across the conterminous US. Regionally evident forest disturbances range from ephemeral periodic canopy defoliation to stand replacement mortality events due to insects, disease, fire, hurricanes, tornadoes, ice, hail, and drought. Mandated by the Healthy Forest Restoration Act of 2003, this forest threat EWS has been actively developed since 2006 and on-line since 2010. The FCAV system employs 250-meter MODIS NDVI-based forest change products as a key element of the system, providing regional and CONUS scale products in near real time every 8 days. Each of our forest change products in FCAV is based on current versus historical 24 day composites of NDVI data gridded at 231.66 meter resolution. Current NDVI is derived from USGS eMODIS expedited products. MOD13 NDVI is used for constructing historical baselines. CONUS change products are computed for all forests as % change in the current versus historical NDVI for a given 24 day period. Change products are computed according to previous year, previous 3 year and previous 8 year historical baselines. The use of multiple baselines enables apparent forest disturbance anomalies to be more fully assessed. CONUS forest change products are posted each week on the FCAV, a web mapping service constructed and maintained by the National Environmental Modeling and Analysis Center. The FCAV EWS has been used to aid multiple Federal and State agency forest management activities, including aerial disturbance detection surveys, as well as rapid response preliminary assessments of timber loss due to tornadoes, regional drought studies, and fire damage assessments. The FCAV allows end-users to assess the context of apparent forest vegetation change with respect to ancillary data, such as land cover, topography, hydrology, climate variables, and administrative boundaries. Such change products are being evaluated through case studies involving comparison with higher spatial resolution satellite, aerial, and field data. The presentation will include multiple examples in which regionally evident forest disturbances were successfully detected and monitored with the MODIS-based change products, as part of the FCAV. FCAV's MODIS forest change products enable end-users (e.g., resource managers) to view and monitor forest hazards at regional scales throughout the year and across the nation.

36 CFR 223.130 - Scope.

Code of Federal Regulations, 2013 CFR

2013-07-01

... 36 Parks, Forests, and Public Property 2 2013-07-01 2013-07-01 false Scope. 223.130 Section 223.130 Parks, Forests, and Public Property FOREST SERVICE, DEPARTMENT OF AGRICULTURE SALE AND DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS Suspension and...

36 CFR 223.115 - Contract extensions.

Code of Federal Regulations, 2012 CFR

2012-07-01

... 36 Parks, Forests, and Public Property 2 2012-07-01 2012-07-01 false Contract extensions. 223.115 Section 223.115 Parks, Forests, and Public Property FOREST SERVICE, DEPARTMENT OF AGRICULTURE SALE AND DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS Timber...

36 CFR 223.35 - Performance bond.

Code of Federal Regulations, 2012 CFR

2012-07-01

... 36 Parks, Forests, and Public Property 2 2012-07-01 2012-07-01 false Performance bond. 223.35 Section 223.35 Parks, Forests, and Public Property FOREST SERVICE, DEPARTMENT OF AGRICULTURE SALE AND DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS Timber...

36 CFR 223.34 - Advance payment.

Code of Federal Regulations, 2012 CFR

2012-07-01

... 36 Parks, Forests, and Public Property 2 2012-07-01 2012-07-01 false Advance payment. 223.34 Section 223.34 Parks, Forests, and Public Property FOREST SERVICE, DEPARTMENT OF AGRICULTURE SALE AND DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS Timber...

36 CFR 223.39 - [Reserved

Code of Federal Regulations, 2014 CFR

2014-07-01

... 36 Parks, Forests, and Public Property 2 2014-07-01 2014-07-01 false [Reserved] 223.39 Section 223.39 Parks, Forests, and Public Property FOREST SERVICE, DEPARTMENT OF AGRICULTURE SALE AND DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS Timber Sale Contracts...

36 CFR 223.51 - Bid monitoring.

Code of Federal Regulations, 2014 CFR

2014-07-01

... 36 Parks, Forests, and Public Property 2 2014-07-01 2014-07-01 false Bid monitoring. 223.51 Section 223.51 Parks, Forests, and Public Property FOREST SERVICE, DEPARTMENT OF AGRICULTURE SALE AND DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS Timber...

36 CFR 223.130 - Scope.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 36 Parks, Forests, and Public Property 2 2011-07-01 2011-07-01 false Scope. 223.130 Section 223.130 Parks, Forests, and Public Property FOREST SERVICE, DEPARTMENT OF AGRICULTURE SALE AND DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS Suspension and...

36 CFR 223.115 - Contract extensions.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 36 Parks, Forests, and Public Property 2 2011-07-01 2011-07-01 false Contract extensions. 223.115 Section 223.115 Parks, Forests, and Public Property FOREST SERVICE, DEPARTMENT OF AGRICULTURE SALE AND DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS Timber...

36 CFR 223.51 - Bid monitoring.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 36 Parks, Forests, and Public Property 2 2011-07-01 2011-07-01 false Bid monitoring. 223.51 Section 223.51 Parks, Forests, and Public Property FOREST SERVICE, DEPARTMENT OF AGRICULTURE SALE AND DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS Timber...

36 CFR 223.51 - Bid monitoring.

Code of Federal Regulations, 2013 CFR

2013-07-01

... 36 Parks, Forests, and Public Property 2 2013-07-01 2013-07-01 false Bid monitoring. 223.51 Section 223.51 Parks, Forests, and Public Property FOREST SERVICE, DEPARTMENT OF AGRICULTURE SALE AND DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS Timber...

36 CFR 223.66 - [Reserved

Code of Federal Regulations, 2011 CFR

2011-07-01

... 36 Parks, Forests, and Public Property 2 2011-07-01 2011-07-01 false [Reserved] 223.66 Section 223.66 Parks, Forests, and Public Property FOREST SERVICE, DEPARTMENT OF AGRICULTURE SALE AND DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS Timber Sale Contracts...

36 CFR 223.34 - Advance payment.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 36 Parks, Forests, and Public Property 2 2011-07-01 2011-07-01 false Advance payment. 223.34 Section 223.34 Parks, Forests, and Public Property FOREST SERVICE, DEPARTMENT OF AGRICULTURE SALE AND DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS Timber...

36 CFR 223.39 - [Reserved

Code of Federal Regulations, 2012 CFR

2012-07-01

... 36 Parks, Forests, and Public Property 2 2012-07-01 2012-07-01 false [Reserved] 223.39 Section 223.39 Parks, Forests, and Public Property FOREST SERVICE, DEPARTMENT OF AGRICULTURE SALE AND DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS Timber Sale Contracts...

36 CFR 223.66 - [Reserved

Code of Federal Regulations, 2014 CFR

2014-07-01

... 36 Parks, Forests, and Public Property 2 2014-07-01 2014-07-01 false [Reserved] 223.66 Section 223.66 Parks, Forests, and Public Property FOREST SERVICE, DEPARTMENT OF AGRICULTURE SALE AND DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS Timber Sale Contracts...

36 CFR 223.223 - Advance payment.

Code of Federal Regulations, 2014 CFR

2014-07-01

... 36 Parks, Forests, and Public Property 2 2014-07-01 2014-07-01 false Advance payment. 223.223 Section 223.223 Parks, Forests, and Public Property FOREST SERVICE, DEPARTMENT OF AGRICULTURE SALE AND DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS Special...

36 CFR 223.132 - Policy.

Code of Federal Regulations, 2013 CFR

2013-07-01

... 36 Parks, Forests, and Public Property 2 2013-07-01 2013-07-01 false Policy. 223.132 Section 223.132 Parks, Forests, and Public Property FOREST SERVICE, DEPARTMENT OF AGRICULTURE SALE AND DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS Suspension and...

36 CFR 223.115 - Contract extensions.

Code of Federal Regulations, 2013 CFR

2013-07-01

... 36 Parks, Forests, and Public Property 2 2013-07-01 2013-07-01 false Contract extensions. 223.115 Section 223.115 Parks, Forests, and Public Property FOREST SERVICE, DEPARTMENT OF AGRICULTURE SALE AND DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS Timber...

36 CFR 223.227 - Sale advertisement.

Code of Federal Regulations, 2014 CFR

2014-07-01

... 36 Parks, Forests, and Public Property 2 2014-07-01 2014-07-01 false Sale advertisement. 223.227 Section 223.227 Parks, Forests, and Public Property FOREST SERVICE, DEPARTMENT OF AGRICULTURE SALE AND DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS Special...

36 CFR 223.130 - Scope.

Code of Federal Regulations, 2012 CFR

2012-07-01

... 36 Parks, Forests, and Public Property 2 2012-07-01 2012-07-01 false Scope. 223.130 Section 223.130 Parks, Forests, and Public Property FOREST SERVICE, DEPARTMENT OF AGRICULTURE SALE AND DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS Suspension and...

36 CFR 223.51 - Bid monitoring.

Code of Federal Regulations, 2012 CFR

2012-07-01

... 36 Parks, Forests, and Public Property 2 2012-07-01 2012-07-01 false Bid monitoring. 223.51 Section 223.51 Parks, Forests, and Public Property FOREST SERVICE, DEPARTMENT OF AGRICULTURE SALE AND DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS Timber...

36 CFR 223.34 - Advance payment.

Code of Federal Regulations, 2014 CFR

2014-07-01

... 36 Parks, Forests, and Public Property 2 2014-07-01 2014-07-01 false Advance payment. 223.34 Section 223.34 Parks, Forests, and Public Property FOREST SERVICE, DEPARTMENT OF AGRICULTURE SALE AND DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS Timber...

36 CFR 223.66 - [Reserved

Code of Federal Regulations, 2013 CFR

2013-07-01

... 36 Parks, Forests, and Public Property 2 2013-07-01 2013-07-01 false [Reserved] 223.66 Section 223.66 Parks, Forests, and Public Property FOREST SERVICE, DEPARTMENT OF AGRICULTURE SALE AND DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS Timber Sale Contracts...

36 CFR 223.130 - Scope.

Code of Federal Regulations, 2014 CFR

2014-07-01

... 36 Parks, Forests, and Public Property 2 2014-07-01 2014-07-01 false Scope. 223.130 Section 223.130 Parks, Forests, and Public Property FOREST SERVICE, DEPARTMENT OF AGRICULTURE SALE AND DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS Suspension and...

36 CFR 223.39 - [Reserved

Code of Federal Regulations, 2011 CFR

2011-07-01

... 36 Parks, Forests, and Public Property 2 2011-07-01 2011-07-01 false [Reserved] 223.39 Section 223.39 Parks, Forests, and Public Property FOREST SERVICE, DEPARTMENT OF AGRICULTURE SALE AND DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS Timber Sale Contracts...

36 CFR 223.115 - Contract extensions.

Code of Federal Regulations, 2014 CFR

2014-07-01

... 36 Parks, Forests, and Public Property 2 2014-07-01 2014-07-01 false Contract extensions. 223.115 Section 223.115 Parks, Forests, and Public Property FOREST SERVICE, DEPARTMENT OF AGRICULTURE SALE AND DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS Timber...

36 CFR 223.39 - [Reserved

Code of Federal Regulations, 2013 CFR

2013-07-01

... 36 Parks, Forests, and Public Property 2 2013-07-01 2013-07-01 false [Reserved] 223.39 Section 223.39 Parks, Forests, and Public Property FOREST SERVICE, DEPARTMENT OF AGRICULTURE SALE AND DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS Timber Sale Contracts...

36 CFR 223.132 - Policy.

Code of Federal Regulations, 2014 CFR

2014-07-01

... 36 Parks, Forests, and Public Property 2 2014-07-01 2014-07-01 false Policy. 223.132 Section 223.132 Parks, Forests, and Public Property FOREST SERVICE, DEPARTMENT OF AGRICULTURE SALE AND DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS Suspension and...

36 CFR 223.34 - Advance payment.

Code of Federal Regulations, 2013 CFR

2013-07-01

... 36 Parks, Forests, and Public Property 2 2013-07-01 2013-07-01 false Advance payment. 223.34 Section 223.34 Parks, Forests, and Public Property FOREST SERVICE, DEPARTMENT OF AGRICULTURE SALE AND DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS Timber...

36 CFR 223.66 - [Reserved

Code of Federal Regulations, 2012 CFR

2012-07-01

... 36 Parks, Forests, and Public Property 2 2012-07-01 2012-07-01 false [Reserved] 223.66 Section 223.66 Parks, Forests, and Public Property FOREST SERVICE, DEPARTMENT OF AGRICULTURE SALE AND DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS Timber Sale Contracts...

36 CFR 223.50 - Periodic payments.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 36 Parks, Forests, and Public Property 2 2011-07-01 2011-07-01 false Periodic payments. 223.50 Section 223.50 Parks, Forests, and Public Property FOREST SERVICE, DEPARTMENT OF AGRICULTURE SALE AND DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS Timber...

36 CFR 223.49 - Downpayments.

Code of Federal Regulations, 2014 CFR

2014-07-01

... 36 Parks, Forests, and Public Property 2 2014-07-01 2014-07-01 false Downpayments. 223.49 Section 223.49 Parks, Forests, and Public Property FOREST SERVICE, DEPARTMENT OF AGRICULTURE SALE AND DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS Timber Sale...

36 CFR 223.50 - Periodic payments.

Code of Federal Regulations, 2013 CFR

2013-07-01

... 36 Parks, Forests, and Public Property 2 2013-07-01 2013-07-01 false Periodic payments. 223.50 Section 223.50 Parks, Forests, and Public Property FOREST SERVICE, DEPARTMENT OF AGRICULTURE SALE AND DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS Timber...

36 CFR 223.49 - Downpayments.

Code of Federal Regulations, 2012 CFR

2012-07-01

... 36 Parks, Forests, and Public Property 2 2012-07-01 2012-07-01 false Downpayments. 223.49 Section 223.49 Parks, Forests, and Public Property FOREST SERVICE, DEPARTMENT OF AGRICULTURE SALE AND DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS Timber Sale...

36 CFR 223.49 - Downpayments.

Code of Federal Regulations, 2013 CFR

2013-07-01

... 36 Parks, Forests, and Public Property 2 2013-07-01 2013-07-01 false Downpayments. 223.49 Section 223.49 Parks, Forests, and Public Property FOREST SERVICE, DEPARTMENT OF AGRICULTURE SALE AND DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS Timber Sale...

36 CFR 223.50 - Periodic payments.

Code of Federal Regulations, 2014 CFR

2014-07-01

... 36 Parks, Forests, and Public Property 2 2014-07-01 2014-07-01 false Periodic payments. 223.50 Section 223.50 Parks, Forests, and Public Property FOREST SERVICE, DEPARTMENT OF AGRICULTURE SALE AND DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS Timber...

36 CFR 223.50 - Periodic payments.

Code of Federal Regulations, 2012 CFR

2012-07-01

... 36 Parks, Forests, and Public Property 2 2012-07-01 2012-07-01 false Periodic payments. 223.50 Section 223.50 Parks, Forests, and Public Property FOREST SERVICE, DEPARTMENT OF AGRICULTURE SALE AND DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS Timber...

Forecasting long-term acorn production with and without oak decline using forest inventory data

Treesearch

Cathryn H. Greenberg; Chad E. Keyser; Leah C. Rathburn; Anita K. Rose; Todd M. Fearer; Henry W. McNab

2013-01-01

Acorns are important as wildlife food and for oak regeneration, but production is highly variable, posing a challenge to forest managers targeting acorn production levels. Forest managers need tools to predict acorn production capability tailored to individual landscapes and forest management scenarios, adjusting for oak mortality and stand development over time. We...

Non-timber forest products: alternatives for landowners

Treesearch

James L. Chamberlain; A.L. Hammett

2002-01-01

Recently a great deal of attention has been given to forest products that are plant-based but do not come from timber. These "alternative" products are found growing under the forest canopy as herbs, shrubs, vines, moss and even lichen. Although they have been gathered for generations, non-timber forest products have had less attention than "more...

Techniques in Experimental Mechanics Applicable to Forest Products Research

Treesearch

Leslie H. Groom; Audrey G. Zink

1994-01-01

The title of this publication-Techniques in Experimental Mechanics Applicable to Forest Products Research-is the theme of this plenary session from the 1994 Annual Meeting of the Forest Products Society (FPS). Although this session focused on experimental techniques that can be of assistance to researchers in the field of forest products, it is hoped that the...

Forest Management Guidance Package: How to Conduct Forest Product Sales and Handle Forest Management Service Contracts,

DTIC Science & Technology

1982-03-01

state the conditions under which forest products will be sold. They describe the products for sale and the location of the sales area , as well as the...price. Forest products are sold (1) by species or groups of species, ") by designated logging area or areas , or (3) by product, i.e. sawtimber, poles...Lump Sum Sale (Appendix J). Designated trees or entire sale areas may be sold with this method. Individual trees are marked for sale in some manner

Sustainable Forest Management Support Based on the Spatial Distribution of Fuels for Fire Management

Treesearch

José Germán Flores Garnica; Juan de Dios Benavides Solorio; David Arturo Moreno Gonzalez

2006-01-01

Fire behavior simulation is based mainly on the fuel model-concept. However, there are great difficulties to develop the corresponding maps, therefore it is suggested the generation of four fuel maps (1-hour, 10-hours, 100-hours and alive). These maps will allow a better definition of the spatial variation of forest fuels, even within a zone classified as a given fuel...

Time Series of Images to Improve Tree Species Classification

NASA Astrophysics Data System (ADS)

Miyoshi, G. T.; Imai, N. N.; de Moraes, M. V. A.; Tommaselli, A. M. G.; Näsi, R.

2017-10-01

Tree species classification provides valuable information to forest monitoring and management. The high floristic variation of the tree species appears as a challenging issue in the tree species classification because the vegetation characteristics changes according to the season. To help to monitor this complex environment, the imaging spectroscopy has been largely applied since the development of miniaturized sensors attached to Unmanned Aerial Vehicles (UAV). Considering the seasonal changes in forests and the higher spectral and spatial resolution acquired with sensors attached to UAV, we present the use of time series of images to classify four tree species. The study area is an Atlantic Forest area located in the western part of São Paulo State. Images were acquired in August 2015 and August 2016, generating three data sets of images: only with the image spectra of 2015; only with the image spectra of 2016; with the layer stacking of images from 2015 and 2016. Four tree species were classified using Spectral angle mapper (SAM), Spectral information divergence (SID) and Random Forest (RF). The results showed that SAM and SID caused an overfitting of the data whereas RF showed better results and the use of the layer stacking improved the classification achieving a kappa coefficient of 18.26 %.

Application of airborne hyperspectral remote sensing for the retrieval of forest inventory parameters

NASA Astrophysics Data System (ADS)

Dmitriev, Yegor V.; Kozoderov, Vladimir V.; Sokolov, Anton A.

2016-04-01

Collecting and updating forest inventory data play an important part in the forest management. The data can be obtained directly by using exact enough but low efficient ground based methods as well as from the remote sensing measurements. We present applications of airborne hyperspectral remote sensing for the retrieval of such important inventory parameters as the forest species and age composition. The hyperspectral images of the test region were obtained from the airplane equipped by the produced in Russia light-weight airborne video-spectrometer of visible and near infrared spectral range and high resolution photo-camera on the same gyro-stabilized platform. The quality of the thematic processing depends on many factors such as the atmospheric conditions, characteristics of measuring instruments, corrections and preprocessing methods, etc. An important role plays the construction of the classifier together with methods of the reduction of the feature space. The performance of different spectral classification methods is analyzed for the problem of hyperspectral remote sensing of soil and vegetation. For the reduction of the feature space we used the earlier proposed stable feature selection method. The results of the classification of hyperspectral airborne images by using the Multiclass Support Vector Machine method with Gaussian kernel and the parametric Bayesian classifier based on the Gaussian mixture model and their comparative analysis are demonstrated.

Learning in data-limited multimodal scenarios: Scandent decision forests and tree-based features.

PubMed

Hor, Soheil; Moradi, Mehdi

2016-12-01

Incomplete and inconsistent datasets often pose difficulties in multimodal studies. We introduce the concept of scandent decision trees to tackle these difficulties. Scandent trees are decision trees that optimally mimic the partitioning of the data determined by another decision tree, and crucially, use only a subset of the feature set. We show how scandent trees can be used to enhance the performance of decision forests trained on a small number of multimodal samples when we have access to larger datasets with vastly incomplete feature sets. Additionally, we introduce the concept of tree-based feature transforms in the decision forest paradigm. When combined with scandent trees, the tree-based feature transforms enable us to train a classifier on a rich multimodal dataset, and use it to classify samples with only a subset of features of the training data. Using this methodology, we build a model trained on MRI and PET images of the ADNI dataset, and then test it on cases with only MRI data. We show that this is significantly more effective in staging of cognitive impairments compared to a similar decision forest model trained and tested on MRI only, or one that uses other kinds of feature transform applied to the MRI data. Copyright © 2016. Published by Elsevier B.V.

Land cover and land use mapping of the iSimangaliso Wetland Park, South Africa: comparison of oblique and orthogonal random forest algorithms

NASA Astrophysics Data System (ADS)

Bassa, Zaakirah; Bob, Urmilla; Szantoi, Zoltan; Ismail, Riyad

2016-01-01

In recent years, the popularity of tree-based ensemble methods for land cover classification has increased significantly. Using WorldView-2 image data, we evaluate the potential of the oblique random forest algorithm (oRF) to classify a highly heterogeneous protected area. In contrast to the random forest (RF) algorithm, the oRF algorithm builds multivariate trees by learning the optimal split using a supervised model. The oRF binary algorithm is adapted to a multiclass land cover and land use application using both the "one-against-one" and "one-against-all" combination approaches. Results show that the oRF algorithms are capable of achieving high classification accuracies (>80%). However, there was no statistical difference in classification accuracies obtained by the oRF algorithms and the more popular RF algorithm. For all the algorithms, user accuracies (UAs) and producer accuracies (PAs) >80% were recorded for most of the classes. Both the RF and oRF algorithms poorly classified the indigenous forest class as indicated by the low UAs and PAs. Finally, the results from this study advocate and support the utility of the oRF algorithm for land cover and land use mapping of protected areas using WorldView-2 image data.

Monitoring Regional Forest Disturbances across the US with Near Real Time MODIS NDVI Products included in the ForWarn Forest Threat Early Warning System

NASA Technical Reports Server (NTRS)

Spruce, Joseph; Hargrove, William W.; Gasser, Gerald; Norman, Steve

2013-01-01

U.S. forests occupy approx.1/3 of total land area (approx. 304 million ha). Since 2000, a growing number of regionally evident forest disturbances have occurred due to abiotic and biotic agents. Regional forest disturbances can threaten human life and property, bio-diversity and water supplies. Timely regional forest disturbance monitoring products are needed to aid forest health management work. Near Real Time (NRT) twice daily MODIS NDVI data provide a means to monitor U.S. regional forest disturbances every 8 days. Since 2010, these NRT forest change products have been produced and posted on the US Forest Service ForWarn Early Warning System for Forest Threats.

Building rooftop classification using random forests for large-scale PV deployment

NASA Astrophysics Data System (ADS)

Assouline, Dan; Mohajeri, Nahid; Scartezzini, Jean-Louis

2017-10-01

Large scale solar Photovoltaic (PV) deployment on existing building rooftops has proven to be one of the most efficient and viable sources of renewable energy in urban areas. As it usually requires a potential analysis over the area of interest, a crucial step is to estimate the geometric characteristics of the building rooftops. In this paper, we introduce a multi-layer machine learning methodology to classify 6 roof types, 9 aspect (azimuth) classes and 5 slope (tilt) classes for all building rooftops in Switzerland, using GIS processing. We train Random Forests (RF), an ensemble learning algorithm, to build the classifiers. We use (2 × 2) [m2 ] LiDAR data (considering buildings and vegetation) to extract several rooftop features, and a generalised footprint polygon data to localize buildings. The roof classifier is trained and tested with 1252 labeled roofs from three different urban areas, namely Baden, Luzern, and Winterthur. The results for roof type classification show an average accuracy of 67%. The aspect and slope classifiers are trained and tested with 11449 labeled roofs in the Zurich periphery area. The results for aspect and slope classification show different accuracies depending on the classes: while some classes are well identified, other under-represented classes remain challenging to detect.

A new approach to human microRNA target prediction using ensemble pruning and rotation forest.

PubMed

Mousavi, Reza; Eftekhari, Mahdi; Haghighi, Mehdi Ghezelbash

2015-12-01

MicroRNAs (miRNAs) are small non-coding RNAs that have important functions in gene regulation. Since finding miRNA target experimentally is costly and needs spending much time, the use of machine learning methods is a growing research area for miRNA target prediction. In this paper, a new approach is proposed by using two popular ensemble strategies, i.e. Ensemble Pruning and Rotation Forest (EP-RTF), to predict human miRNA target. For EP, the approach utilizes Genetic Algorithm (GA). In other words, a subset of classifiers from the heterogeneous ensemble is first selected by GA. Next, the selected classifiers are trained based on the RTF method and then are combined using weighted majority voting. In addition to seeking a better subset of classifiers, the parameter of RTF is also optimized by GA. Findings of the present study confirm that the newly developed EP-RTF outperforms (in terms of classification accuracy, sensitivity, and specificity) the previously applied methods over four datasets in the field of human miRNA target. Diversity-error diagrams reveal that the proposed ensemble approach constructs individual classifiers which are more accurate and usually diverse than the other ensemble approaches. Given these experimental results, we highly recommend EP-RTF for improving the performance of miRNA target prediction.

Applying under-sampling techniques and cost-sensitive learning methods on risk assessment of breast cancer.

PubMed

Hsu, Jia-Lien; Hung, Ping-Cheng; Lin, Hung-Yen; Hsieh, Chung-Ho

2015-04-01

Breast cancer is one of the most common cause of cancer mortality. Early detection through mammography screening could significantly reduce mortality from breast cancer. However, most of screening methods may consume large amount of resources. We propose a computational model, which is solely based on personal health information, for breast cancer risk assessment. Our model can be served as a pre-screening program in the low-cost setting. In our study, the data set, consisting of 3976 records, is collected from Taipei City Hospital starting from 2008.1.1 to 2008.12.31. Based on the dataset, we first apply the sampling techniques and dimension reduction method to preprocess the testing data. Then, we construct various kinds of classifiers (including basic classifiers, ensemble methods, and cost-sensitive methods) to predict the risk. The cost-sensitive method with random forest classifier is able to achieve recall (or sensitivity) as 100 %. At the recall of 100 %, the precision (positive predictive value, PPV), and specificity of cost-sensitive method with random forest classifier was 2.9 % and 14.87 %, respectively. In our study, we build a breast cancer risk assessment model by using the data mining techniques. Our model has the potential to be served as an assisting tool in the breast cancer screening.

36 CFR 223.137 - Causes for debarment.

Code of Federal Regulations, 2012 CFR

2012-07-01

... 36 Parks, Forests, and Public Property 2 2012-07-01 2012-07-01 false Causes for debarment. 223.137 Section 223.137 Parks, Forests, and Public Property FOREST SERVICE, DEPARTMENT OF AGRICULTURE SALE AND DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS...

36 CFR 223.139 - Period of debarment.

Code of Federal Regulations, 2012 CFR

2012-07-01

... 36 Parks, Forests, and Public Property 2 2012-07-01 2012-07-01 false Period of debarment. 223.139 Section 223.139 Parks, Forests, and Public Property FOREST SERVICE, DEPARTMENT OF AGRICULTURE SALE AND DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS...

36 CFR 223.135 - Effect of listing.

Code of Federal Regulations, 2013 CFR

2013-07-01

... 36 Parks, Forests, and Public Property 2 2013-07-01 2013-07-01 false Effect of listing. 223.135 Section 223.135 Parks, Forests, and Public Property FOREST SERVICE, DEPARTMENT OF AGRICULTURE SALE AND DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS...

36 CFR 223.138 - Procedures for debarment.

Code of Federal Regulations, 2013 CFR

2013-07-01

... 36 Parks, Forests, and Public Property 2 2013-07-01 2013-07-01 false Procedures for debarment. 223.138 Section 223.138 Parks, Forests, and Public Property FOREST SERVICE, DEPARTMENT OF AGRICULTURE SALE AND DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS...

36 CFR 223.140 - Scope of debarment.

Code of Federal Regulations, 2012 CFR

2012-07-01

... 36 Parks, Forests, and Public Property 2 2012-07-01 2012-07-01 false Scope of debarment. 223.140 Section 223.140 Parks, Forests, and Public Property FOREST SERVICE, DEPARTMENT OF AGRICULTURE SALE AND DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS...

36 CFR 223.142 - Causes for suspension.

Code of Federal Regulations, 2013 CFR

2013-07-01

... 36 Parks, Forests, and Public Property 2 2013-07-01 2013-07-01 false Causes for suspension. 223.142 Section 223.142 Parks, Forests, and Public Property FOREST SERVICE, DEPARTMENT OF AGRICULTURE SALE AND DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS...

36 CFR 223.140 - Scope of debarment.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 36 Parks, Forests, and Public Property 2 2011-07-01 2011-07-01 false Scope of debarment. 223.140 Section 223.140 Parks, Forests, and Public Property FOREST SERVICE, DEPARTMENT OF AGRICULTURE SALE AND DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS...

36 CFR 223.31 - Duration of contracts.

Code of Federal Regulations, 2014 CFR

2014-07-01

... 36 Parks, Forests, and Public Property 2 2014-07-01 2014-07-01 false Duration of contracts. 223.31 Section 223.31 Parks, Forests, and Public Property FOREST SERVICE, DEPARTMENT OF AGRICULTURE SALE AND DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS Timber...

36 CFR 223.159 - Scope and applicability.

Code of Federal Regulations, 2014 CFR

2014-07-01

... 36 Parks, Forests, and Public Property 2 2014-07-01 2014-07-01 false Scope and applicability. 223.159 Section 223.159 Parks, Forests, and Public Property FOREST SERVICE, DEPARTMENT OF AGRICULTURE SALE AND DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS...

36 CFR 223.83 - Contents of prospectus.

Code of Federal Regulations, 2014 CFR

2014-07-01

... 36 Parks, Forests, and Public Property 2 2014-07-01 2014-07-01 false Contents of prospectus. 223.83 Section 223.83 Parks, Forests, and Public Property FOREST SERVICE, DEPARTMENT OF AGRICULTURE SALE AND DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS...

36 CFR 223.145 - Scope of suspension.

Code of Federal Regulations, 2014 CFR

2014-07-01

... 36 Parks, Forests, and Public Property 2 2014-07-01 2014-07-01 false Scope of suspension. 223.145 Section 223.145 Parks, Forests, and Public Property FOREST SERVICE, DEPARTMENT OF AGRICULTURE SALE AND DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS...

36 CFR 223.135 - Effect of listing.

Code of Federal Regulations, 2014 CFR

2014-07-01

... 36 Parks, Forests, and Public Property 2 2014-07-01 2014-07-01 false Effect of listing. 223.135 Section 223.135 Parks, Forests, and Public Property FOREST SERVICE, DEPARTMENT OF AGRICULTURE SALE AND DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS...

36 CFR 223.159 - Scope and applicability.

Code of Federal Regulations, 2012 CFR

2012-07-01

... 36 Parks, Forests, and Public Property 2 2012-07-01 2012-07-01 false Scope and applicability. 223.159 Section 223.159 Parks, Forests, and Public Property FOREST SERVICE, DEPARTMENT OF AGRICULTURE SALE AND DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS...

36 CFR 223.145 - Scope of suspension.

Code of Federal Regulations, 2012 CFR

2012-07-01

... 36 Parks, Forests, and Public Property 2 2012-07-01 2012-07-01 false Scope of suspension. 223.145 Section 223.145 Parks, Forests, and Public Property FOREST SERVICE, DEPARTMENT OF AGRICULTURE SALE AND DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS...

36 CFR 223.140 - Scope of debarment.

Code of Federal Regulations, 2013 CFR

2013-07-01

... 36 Parks, Forests, and Public Property 2 2013-07-01 2013-07-01 false Scope of debarment. 223.140 Section 223.140 Parks, Forests, and Public Property FOREST SERVICE, DEPARTMENT OF AGRICULTURE SALE AND DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS...

36 CFR 223.83 - Contents of prospectus.

Code of Federal Regulations, 2013 CFR

2013-07-01

... 36 Parks, Forests, and Public Property 2 2013-07-01 2013-07-01 false Contents of prospectus. 223.83 Section 223.83 Parks, Forests, and Public Property FOREST SERVICE, DEPARTMENT OF AGRICULTURE SALE AND DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS...

36 CFR 223.138 - Procedures for debarment.

Code of Federal Regulations, 2012 CFR

2012-07-01

... 36 Parks, Forests, and Public Property 2 2012-07-01 2012-07-01 false Procedures for debarment. 223.138 Section 223.138 Parks, Forests, and Public Property FOREST SERVICE, DEPARTMENT OF AGRICULTURE SALE AND DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS...

36 CFR 223.145 - Scope of suspension.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 36 Parks, Forests, and Public Property 2 2011-07-01 2011-07-01 false Scope of suspension. 223.145 Section 223.145 Parks, Forests, and Public Property FOREST SERVICE, DEPARTMENT OF AGRICULTURE SALE AND DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS...

36 CFR 223.139 - Period of debarment.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 36 Parks, Forests, and Public Property 2 2011-07-01 2011-07-01 false Period of debarment. 223.139 Section 223.139 Parks, Forests, and Public Property FOREST SERVICE, DEPARTMENT OF AGRICULTURE SALE AND DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS...

36 CFR 223.83 - Contents of prospectus.

Code of Federal Regulations, 2012 CFR

2012-07-01

... 36 Parks, Forests, and Public Property 2 2012-07-01 2012-07-01 false Contents of prospectus. 223.83 Section 223.83 Parks, Forests, and Public Property FOREST SERVICE, DEPARTMENT OF AGRICULTURE SALE AND DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS...

36 CFR 223.142 - Causes for suspension.

Code of Federal Regulations, 2012 CFR

2012-07-01

... 36 Parks, Forests, and Public Property 2 2012-07-01 2012-07-01 false Causes for suspension. 223.142 Section 223.142 Parks, Forests, and Public Property FOREST SERVICE, DEPARTMENT OF AGRICULTURE SALE AND DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS...

36 CFR 223.137 - Causes for debarment.

Code of Federal Regulations, 2013 CFR

2013-07-01

... 36 Parks, Forests, and Public Property 2 2013-07-01 2013-07-01 false Causes for debarment. 223.137 Section 223.137 Parks, Forests, and Public Property FOREST SERVICE, DEPARTMENT OF AGRICULTURE SALE AND DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS...

36 CFR 223.159 - Scope and applicability.

Code of Federal Regulations, 2013 CFR

2013-07-01

... 36 Parks, Forests, and Public Property 2 2013-07-01 2013-07-01 false Scope and applicability. 223.159 Section 223.159 Parks, Forests, and Public Property FOREST SERVICE, DEPARTMENT OF AGRICULTURE SALE AND DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS...

36 CFR 223.137 - Causes for debarment.

Code of Federal Regulations, 2014 CFR

2014-07-01

... 36 Parks, Forests, and Public Property 2 2014-07-01 2014-07-01 false Causes for debarment. 223.137 Section 223.137 Parks, Forests, and Public Property FOREST SERVICE, DEPARTMENT OF AGRICULTURE SALE AND DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS...

36 CFR 223.31 - Duration of contracts.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 36 Parks, Forests, and Public Property 2 2011-07-01 2011-07-01 false Duration of contracts. 223.31 Section 223.31 Parks, Forests, and Public Property FOREST SERVICE, DEPARTMENT OF AGRICULTURE SALE AND DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS Timber...

36 CFR 223.142 - Causes for suspension.

Code of Federal Regulations, 2014 CFR

2014-07-01

... 36 Parks, Forests, and Public Property 2 2014-07-01 2014-07-01 false Causes for suspension. 223.142 Section 223.142 Parks, Forests, and Public Property FOREST SERVICE, DEPARTMENT OF AGRICULTURE SALE AND DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS...

36 CFR 223.145 - Scope of suspension.

Code of Federal Regulations, 2013 CFR

2013-07-01

... 36 Parks, Forests, and Public Property 2 2013-07-01 2013-07-01 false Scope of suspension. 223.145 Section 223.145 Parks, Forests, and Public Property FOREST SERVICE, DEPARTMENT OF AGRICULTURE SALE AND DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS...

36 CFR 223.138 - Procedures for debarment.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 36 Parks, Forests, and Public Property 2 2011-07-01 2011-07-01 false Procedures for debarment. 223.138 Section 223.138 Parks, Forests, and Public Property FOREST SERVICE, DEPARTMENT OF AGRICULTURE SALE AND DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS...

36 CFR 223.144 - Period of suspension.

Code of Federal Regulations, 2013 CFR

2013-07-01

... 36 Parks, Forests, and Public Property 2 2013-07-01 2013-07-01 false Period of suspension. 223.144 Section 223.144 Parks, Forests, and Public Property FOREST SERVICE, DEPARTMENT OF AGRICULTURE SALE AND DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS...

36 CFR 223.31 - Duration of contracts.

Code of Federal Regulations, 2012 CFR

2012-07-01

... 36 Parks, Forests, and Public Property 2 2012-07-01 2012-07-01 false Duration of contracts. 223.31 Section 223.31 Parks, Forests, and Public Property FOREST SERVICE, DEPARTMENT OF AGRICULTURE SALE AND DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS Timber...

36 CFR 223.142 - Causes for suspension.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 36 Parks, Forests, and Public Property 2 2011-07-01 2011-07-01 false Causes for suspension. 223.142 Section 223.142 Parks, Forests, and Public Property FOREST SERVICE, DEPARTMENT OF AGRICULTURE SALE AND DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS...

36 CFR 223.138 - Procedures for debarment.

Code of Federal Regulations, 2014 CFR

2014-07-01

... 36 Parks, Forests, and Public Property 2 2014-07-01 2014-07-01 false Procedures for debarment. 223.138 Section 223.138 Parks, Forests, and Public Property FOREST SERVICE, DEPARTMENT OF AGRICULTURE SALE AND DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS...

36 CFR 223.159 - Scope and applicability.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 36 Parks, Forests, and Public Property 2 2011-07-01 2011-07-01 false Scope and applicability. 223.159 Section 223.159 Parks, Forests, and Public Property FOREST SERVICE, DEPARTMENT OF AGRICULTURE SALE AND DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS...

36 CFR 223.135 - Effect of listing.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 36 Parks, Forests, and Public Property 2 2011-07-01 2011-07-01 false Effect of listing. 223.135 Section 223.135 Parks, Forests, and Public Property FOREST SERVICE, DEPARTMENT OF AGRICULTURE SALE AND DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS...

36 CFR 223.135 - Effect of listing.

Code of Federal Regulations, 2012 CFR

2012-07-01

... 36 Parks, Forests, and Public Property 2 2012-07-01 2012-07-01 false Effect of listing. 223.135 Section 223.135 Parks, Forests, and Public Property FOREST SERVICE, DEPARTMENT OF AGRICULTURE SALE AND DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS...

36 CFR 223.144 - Period of suspension.

Code of Federal Regulations, 2012 CFR

2012-07-01

... 36 Parks, Forests, and Public Property 2 2012-07-01 2012-07-01 false Period of suspension. 223.144 Section 223.144 Parks, Forests, and Public Property FOREST SERVICE, DEPARTMENT OF AGRICULTURE SALE AND DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS...

36 CFR 223.144 - Period of suspension.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 36 Parks, Forests, and Public Property 2 2011-07-01 2011-07-01 false Period of suspension. 223.144 Section 223.144 Parks, Forests, and Public Property FOREST SERVICE, DEPARTMENT OF AGRICULTURE SALE AND DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS...

36 CFR 223.31 - Duration of contracts.

Code of Federal Regulations, 2013 CFR

2013-07-01

... 36 Parks, Forests, and Public Property 2 2013-07-01 2013-07-01 false Duration of contracts. 223.31 Section 223.31 Parks, Forests, and Public Property FOREST SERVICE, DEPARTMENT OF AGRICULTURE SALE AND DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS Timber...

36 CFR 223.139 - Period of debarment.

Code of Federal Regulations, 2013 CFR

2013-07-01

... 36 Parks, Forests, and Public Property 2 2013-07-01 2013-07-01 false Period of debarment. 223.139 Section 223.139 Parks, Forests, and Public Property FOREST SERVICE, DEPARTMENT OF AGRICULTURE SALE AND DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS...

36 CFR 223.229 - Contents of prospectus.

Code of Federal Regulations, 2014 CFR

2014-07-01

... 36 Parks, Forests, and Public Property 2 2014-07-01 2014-07-01 false Contents of prospectus. 223.229 Section 223.229 Parks, Forests, and Public Property FOREST SERVICE, DEPARTMENT OF AGRICULTURE SALE AND DISPOSAL OF NATIONAL FOREST SYSTEM TIMBER, SPECIAL FOREST PRODUCTS, AND FOREST BOTANICAL PRODUCTS...