An Analysis of Fast Learning Methods for Classifying Forest Cover Types

The need for a current, regional perspective of the forest of Labrador was identified. Mapping of forest cover types, peat-lands, recent burns and clearcut disturbances was accomplished through visual interpretation of 1:1,000,000 scale Landsat Thematic mapper colour composite transparencies and the transfer of interpreted polygons to a geographic information system. The mapping and verification process is described in this paper. The end product, a forest resource map, provides the most up-to-date and detailed information on Labrador's forest cover types and disturbances available on a single map. The digital format of the map facilities area summaries, viewing and printing.

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Monitoring Land Cover Changes in the Disaster-Prone Area: A Case Study of Cangkringan Sub-District, the Flanks of Mount Merapi, Indonesia

Forum Geografi ◽

10.23917/forgeo.v31i2.5324 ◽

2017 ◽

Vol 31 (2) ◽

pp. 209-219 ◽

Cited By ~ 2

Author(s):

Ronggo Sadono ◽

Hartono Hartono ◽

Mochammad Maksum Machfoedz ◽

Setiaji Setiaji

Keyword(s):

Land Cover ◽

Forest Cover ◽

Land Cover Changes ◽

Annual Crops ◽

The North ◽

Detection Analysis ◽

Prone Area ◽

Cover Class ◽

Forest Cover Types

Volcanic eruption is one of the natural factors that affect land cover changes. This study aimed to monitor land cover changes using a remote sensing approach in Cangkringan Sub-district, Yogyakarta, Indonesia, one of the areas most vulnerable to Mount Merapi eruption. Three satellite images, dating from 2001, 2006 and 2011, were used as main data for land cover classification based on a supervised classification approach. The land cover detection analysis was undertaken by overlaying the classification results from those images. The results show that the dominant land cover class is annual crops, covering 40% of the study area, while the remaining 60% consists of forest cover types, dryland farming, paddy fields, settlements, and bare land. The forests were distributed in the north, and the annual crops in the middle of the study area, while the villages and the rice fields were generally located in the south. In the 2001–2011 period, forests were the most increased land cover type, while annual crops decreased the most, as a result of the eruption of Mount Merapi in 2010. Such data and information are important for the local government or related institutions to formulate Detailed Spatial Plans (RDTR) in the Disaster-Prone Areas (KRB).

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Estimates of Down Woody Materials on Fort A.P. Hill, Virginia

Southern Journal of Applied Forestry ◽

10.1093/sjaf/32.2.53 ◽

2008 ◽

Vol 32 (2) ◽

pp. 53-59 ◽

Cited By ~ 1

Author(s):

Jason R. Applegate

Keyword(s):

Coarse Woody Debris ◽

Forest Inventory ◽

Forest Cover ◽

Forest Type ◽

Wildland Fire ◽

Woody Debris ◽

Forest Cover Type ◽

Cover Type ◽

Forest Cover Types ◽

Structure Class

Abstract An inventory of down woody materials (DWM) was conducted on Fort A.P. Hill, Virginia, to develop a baseline of DWM abundance and distribution to assist in wildland fire management. Estimates of DWM are necessary to develop accurate assessments of wildfire hazard, model wildland fire behavior, and establish thresholds for retaining DWM, specifically CWD (coarse woody debris), as a structural component of forest ecosystems. DWM were sampled by forest type and structure class using US Forest Service, Forest Inventory and Analysis (FIA) field procedures. DWM averaged 12–16 tn/ac depending on forest cover type and structure class. Coarse woody debris (CWD) averaged 2.7–13.0 tn/ac depending on forest cover type and structure class. CWD comprised more than 70% of DWM across all forest cover types and structure classes. Fine woody debris (FWD) averaged 0.05–3.2 tn/ac depending on fuel hour class, forest cover type, and structure class. DWM was consistently higher in mature (sawtimber) forests than in young (poletimber) forests across all forest cover types, attributed to an increased CWD component of DWM. The variability associated with DWM suggests that obtaining robust estimates of CWD biomass will require a higher sampling intensity than FWD because of its nonuniform distribution in forest systems. FIA field procedures for tallying and quantifying DWM were practical, efficient, and, subsequently, included as permanent metrics in Fort A.P. Hill's Continuous Forest Inventory program.

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Forest Cover Types Classification Based on Online Machine Learning on Distributed Cloud Computing Platforms of Storm and SAMOA

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.955-959.3803 ◽

2014 ◽

Vol 955-959 ◽

pp. 3803-3812

Author(s):

Guang Di Li ◽

Guo Yin Wang ◽

Xue Rui Zhang ◽

Wei Hui Deng ◽

Fan Zhang

Keyword(s):

Machine Learning ◽

Forest Cover ◽

Stream Processing ◽

Processing Technique ◽

Machine Learning Algorithms ◽

Learning Tasks ◽

Hoeffding Tree ◽

Computing Platforms ◽

Processing Platform ◽

Forest Cover Types

Storm is the most popular realtime stream processing platform, which can be used to deal with online machine learning. Similar to how Hadoop provides a set of general primitives for doing batch processing, Storm provides a set of general primitives for doing realtime computation. SAMOA includes distributed algorithms for the most common machine learning tasks like Mahout for Hadoop. SAMOA is both a platform and a library. In this paper, Forest cover types, a large benchmaking dataset available at the UCI KDD Archive is used as the data stream source. Vertical Hoeffding Tree, a parallelizing streaming decision tree induction for distributed enviroment, which is incorporated in SAMOA API is applied on Storm platform. This study compared stream prcessing technique for predicting forest cover types from cartographic variables with traditional classic machine learning algorithms applied on this dataset. The test then train method used in this system is totally different from the traditional train then test. The results of the stream processing technique indicated that it’s output is aymptotically nearly identical to that of a conventional learner, but the model derived from this system is totally scalable, real-time, capable of dealing with evolving streams and insensitive to stream ordering.

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A comparison of forest resource inventory, provincial land cover maps and field surveys for wildlife habitat analysis in the Great Lakes – St. Lawrence forest

The Forestry Chronicle ◽

10.5558/tfc86077-1 ◽

2010 ◽

Vol 86 (1) ◽

pp. 77-86 ◽

Cited By ~ 17

Author(s):

Andrea J. Maxie ◽

Karen F. Hussey ◽

Stacey J. Lowe ◽

Kevin R. Middel ◽

Bruce A. Pond ◽

...

Keyword(s):

Land Cover ◽

Forest Cover ◽

Satellite Image ◽

Wildlife Habitat ◽

Forest Resource ◽

Habitat Analysis ◽

Resource Inventory ◽

Forest Resource Inventory ◽

Land Cover Maps ◽

Forest Cover Types

In a portion of central Ontario, Canada we assessed the classification agreement between field-based estimates of forest stand composition and each of two mapped data sources used in wildlife habitat studies, the Forest Resource Inventory (FRI) and satellite-image derived Provincial Land Cover (PLC). At two study areas, Algonquin Provincial Park (APP) and Wildlife Management Unit 49 (WMU49), we surveyed 119 forest stands and 40 water and wetland stands. Correspondence levels between FRI and field classifications were 48% in APP and 44% in WMU49 when assessing six forest cover types. With only four simplified forest cover types, levels improved to 77% in APP and 63% in WMU49. Correspondence between PLC and field classifications for three forested stand types was approximately 63% in APP and 55% in WMU49. Because of the poor to moderate level of correspondence we detected between map and field classifications, we recommend that care be exercised when FRI or PLC maps are used in forest and wildlife research and management planning. Key words: forest resource inventory, FRI, provincial land cover, PLC, Landsat Thematic Mapper, map accuracy, map correspondence, map agreement, Ontario, wildlife habitat

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Evaluation of vegetation greenness metrics for characterizing tropical forest cover types–a case study using NOAA AVHRR datasets

Geocarto International ◽

10.1080/10106040701204917 ◽

2007 ◽

Vol 22 (1) ◽

pp. 29-48 ◽

Cited By ~ 3

Author(s):

K. Vadrevu ◽

K. V. S. Badarinath ◽

E. Anuradha

Keyword(s):

Tropical Forest ◽

Forest Cover ◽

Vegetation Greenness ◽

Noaa Avhrr ◽

Forest Cover Types

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Validation of an Index System for Estimating Fireline Production With Handtools

The Forestry Chronicle ◽

10.5558/tfc65190-3 ◽

1989 ◽

Vol 65 (3) ◽

pp. 190-193 ◽

Cited By ~ 2

Author(s):

Peter J. Murphy ◽

Dennis Quintilio ◽

Paul M. Woodard

Keyword(s):

Forest Cover ◽

Correlation Coefficients ◽

Initial Attack ◽

Production Rates ◽

Local Conditions ◽

Production Index ◽

Behavioral Studies ◽

Operational Application ◽

Forest Cover Types ◽

Northern Alberta

Production of hand-constructed fireline was simulated in 32 forest cover types and three slash fuel types in the boreal forest of northern Alberta. A total of 47 double trials were conducted in these 35 fuel types. The first trial simulated an initial attack situation, and the second a sustained attack situation. The results were used to test the validity of the fireline production index developed by Murphy and Quintilio (1978). Correlation coefficients between predicted and measured fireline production rates were 0.93 for initial attack simulations and 0.95 for sustained attack simulations. However, the predicted rates were consistently lower than observed rates, largely because of the methodologies used in classifying resistance categories, and the "over-achieving" syndrome common to participants in behavioral studies. We conclude the index is useful in providing consistent and desirably conservative estimates of handline production rates. These estimates could be adapted for operational application in other regions if they are verified for local conditions. Predictions will be further strengthened if based on data collected from actual fires rather than simulation tests.

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Forest types of the North Cascades National Park Service Complex

Canadian Journal of Botany ◽

10.1139/b87-210 ◽

1987 ◽

Vol 65 (7) ◽

pp. 1520-1530 ◽

Cited By ~ 5

Author(s):

James K. Agee ◽

Jane Kertis

Keyword(s):

Ponderosa Pine ◽

National Park ◽

Forest Cover ◽

National Park Service ◽

Silver Fir ◽

North Cascades ◽

Cover Type ◽

The North ◽

Park Service ◽

Forest Cover Types

A forest cover type classification was developed for the North Cascades National Park Service Complex in north central Washington, U.S.A., based on 425 reconnaissance-level plots. Detrended correspondence analysis (DECORANA) was used to ordinate the data. Temperature and available moisture were identified as primary environmental gradients. Two-way indicator species analysis (TWINSPAN) was used to classify the data, resulting in eight forest cover types: ponderosa pine (Pinus ponderosa), Douglas-fir (Pseudotsuga menziesii), subalpine fir (Abies lasiocarpa), whitebark pine – subalpine larch (Pinus albicaulis – Larix lyallii), mountain hemlock (Tsuga mertensiana), Pacific silver fir (Abies amabilis), western hemlock (Tsuga heterophylla), and hardwood forest. The coniferous forest cover types, with the exception of ponderosa pine, were defined to have open and closed canopy components; each cover type includes a variety of plant associations. The cover types were integrated into a geographic information system used to create a cover type map that was 85% accurate. The forest cover types of the park complex are unique not so much for within-community diversity as for the close juxtaposition of cover types with interior and coastal climatic influences.

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THE IMPACT OF FOREST FIRE ON FOREST COVER TYPES IN MONGOLIA

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xliii-b3-2020-693-2020 ◽

2020 ◽

Vol XLIII-B3-2020 ◽

pp. 693-698

Author(s):

N.-E. Geserbaatar ◽

E. Nasanbat ◽

O. Lkhamjav

Keyword(s):

Forest Fire ◽

Forest Fires ◽

Forest Cover ◽

Forest Type ◽

Landsat Imagery ◽

Object Oriented ◽

Burnt Area ◽

Current Classification ◽

The Impact ◽

Forest Cover Types

Abstract. The objective of this study was the impact of forest fire on forest cover types. This study has identified non-forest and forest area that has seven forest class are included with cedar, pine, larch, birch, birch-pine mixed, birch-larch mixed and cedar-larch mixed, additionally, remote sensing imagery is applied. In contrast, Landsat imagery has been used several classification approaches. Moreover, the current classification has developments in segmentation and object-oriented techniques offer the suitable analysis to classify satellite data. In the object-oriented classification approach, images cluster to homogenous area as forest types by suitable parameters in some level. The accuracy analysis revealed that overall accuracy showed a good accuracy of determination (86.33 percent in 2000 and 93.75 percent in 2011) with regard to identify of the forest cover and type. Furthermore, these results suggest that the Landsat TM and ETM+ data can reliable detect the forest type based upon the segmentation and object-oriented techniques. In generally, our study area is high-risky region to forest fires. It is higher influence to forest cover and tree species and other ecosystems. Overall, wildfire of impact results showed that 25239 ha of forests were changed to burnt area and 52603 ha forests were changed to grassland.

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