Mapping Selective Logging in Mixed Deciduous Forest

Lippitt, Christopher D.; Rogan, John; Li, Zhe; Eastman, J. Ronald; Jones, Trevor G.

doi:10.14358/pers.74.10.1201

Cited by 56Remote-Sensing Image ClassificationRemote Sensing in AgricultureRemote Sensing and Land Use

Mapping Selective Logging in Mixed Deciduous Forest

Open access

Lippitt, Christopher D., Rogan, John, Li, Zhe, Eastman, J. Ronald, Jones, Trevor G.·2008·Photogrammetric Engineering & Remote Sensing

DOI 10.14358/pers.74.10.1201

Open this paper in CoChat — free account

Abstract

This study assesses the performance of five Machine Learning Algorithms (MLAS) in a chronically modified mixed deciduous forest in Massachusetts (USA) in terms of their ability to detect selective timber logging and to cope with deficient reference datasets. Multitemporal Landsat Enhanced Thematic Mapper-plus (ETM+) imagery is used to assess the performance of three Anificial Neural Networks - Multi-Layer Perceptron, ARTMAP, Self-Organizing Map, and two Classification Tree splitting algorithms: gini and entropy rules, MLA performance evaluations are based on susceptibility to reduced training set size, noise, and variations in the training set, as well as the operability/transparency of the classification process. Classification trees produced the most accurate selective logging maps (gini and entropy rule decision tree mean overall map accuracy = 94 percent and mean per-class kappa of 0.59 and 0.60, respectively). Classification trees are shown to be more robust and accurate when faced with deficient training data, regardless of splitting rule. Of the neural network algorithms, self-organizing maps were least sensitive to the introduction of noise and variations in training data. Given their robust classification capabilities and transparency of the class-selection process, classification trees are preferable algorithms for mapping selective logging and have potential in other forest monitoring applications. © 2008 American Society for Photogrammetry and Remote Sensing.

Integrity signals

Registry

Crossref · 10.14358/pers.74.10.1201

Cites

88 of 88 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

ML can be used with remote sensing imagery to pinpoint changes in forest cover, or proxies for deforestation such as smoke from fires set to clear vegetation, as well as to differentiate selective cutting from clearcutting.
“ML can be used with remote sensing imagery to pinpoint changes in forest cover [241,329,369], or proxies for deforestation such as smoke from fires set to clear vegetation [505], as well as to differentiate selective cutting from clearcutting [342,487].”
From Tackling climate change with machine learning (2023) · cites this paper as [487]

A Coefficient of Agreement for Nominal Scales
Cohen, J. (1960) · Educational and Psychological Measurement · doi:10.1177/001316446002000104 · Cited by 42,398 · see citing works
The Elements of Statistical Learning
Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970 · see citing works
The Elements of Statistical Learning
Hastie, T., Friedman, J., & Tibshirani, R. (2001) · Springer Series in Statistics · doi:10.1007/978-0-387-21606-5 · Cited by 24,162 · see citing works
Parallel Distributed Processing: Explorations in the Microstructure of Cognition: Foundations
Rumelhart, D. E., McClelland, J. L., & AU (1986) · doi:10.7551/mitpress/5236.001.0001 · Cited by 15,686 · see citing works
Self-Organization and Associative Memory
Kohonen, T. (1989) · Springer Series in Information Sciences · doi:10.1007/978-3-642-88163-3 · Cited by 8,740 · see citing works
The self-organizing map
Kohonen, T. (1990) · Proceedings of the IEEE · doi:10.1109/5.58325 · Cited by 8,414 · see citing works
Modern Applied Statistics with S-Plus.
Venables, W. N., & Ripley, B. D. (1996) · Biometrics · doi:10.2307/2532871 · Cited by 3,012 · see citing works
Learning internal representations by error propagation
RUMELHART, D. E., HINTON, G. E., & WILLIAMS, R. J. (1988) · Readings in Cognitive Science · doi:10.1016/b978-1-4832-1446-7.50035-2 · Cited by 3,002 · see citing works
Modern Applied Statistics with S Fourth edition
Venables, W. N., & Ripley, B. D. (2002) · Cited by 2,538 · see citing works
Fuzzy ARTMAP: A neural network architecture for incremental supervised learning of analog multidimensional maps
Carpenter, G. A., Grossberg, S., Markuzon, N., Reynolds, J. H., & Rosen, D. B. (1992) · IEEE Transactions on Neural Networks · doi:10.1109/72.159059 · Cited by 1,977 · see citing works
Image-Based Atmospheric Corrections - Revisited and Improved
Chavez, P. S. (1996) · Photogrammetric Engineering & Remote Sensing · Cited by 1,889 · see citing works
Decision tree classification of land cover from remotely sensed data
Friedl, M. A., & Brodley, C. E. (1997) · Remote Sensing of Environment · doi:10.1016/s0034-4257(97)00049-7 · Cited by 1,543 · see citing works
The tasselled cap - A graphic description of the spectral-temporal development of agricultural crops as seen by Landsat
Kauth, R. J., & Thomas, G. (1976) · Purdue e-Pubs (Purdue University System) · Cited by 1,406 · see citing works
An assessment of the effectiveness of decision tree methods for land cover classification
Pal, M., & Mather, P. M. (2003) · Remote Sensing of Environment · doi:10.1016/s0034-4257(03)00132-9 · Cited by 1,147 · see citing works
Computer processing of remotely‐sensed images — An introduction
Mather, P. M. (1987) · Geocarto International · doi:10.1080/10106048709354125 · Cited by 1,092 · see citing works
Modern Applied Statistics with S-PLUS
Venables, W. N., & Ripley, B. D. (1999) · Statisctics and computing/Statistics and computing · doi:10.1007/978-1-4757-3121-7 · Cited by 986 · see citing works
Exchange of Carbon Dioxide by a Deciduous Forest: Response to Interannual Climate Variability
Goulden, M. L., Munger, J. W., Fan, S., Daube, B. C., & Wofsy, S. C. (1996) · Science · doi:10.1126/science.271.5255.1576 · Cited by 720 · see citing works
Classification Methods for Remotely Sensed Data
Mather, P. M., & Tso, B. (2001) · CRC Press eBooks · doi:10.1201/b12554 · Cited by 719 · see citing works
The 'neural' phonetic typewriter
Kohonen, T. (1988) · Computer · doi:10.1109/2.28 · Cited by 571 · see citing works
Remote Sensing for Sustainable Forest Management
Franklin, S. E. (2001) · CRC Press eBooks · doi:10.1201/9781420032857 · Cited by 515 · see citing works
The use of backpropagating artificial neural networks in land cover classification
Kavzoğlu, T., & Mather, P. M. (2003) · International Journal of Remote Sensing · doi:10.1080/0143116031000114851 · Cited by 459 · see citing works
The use of prior probabilities in maximum likelihood classification of remotely sensed data
Strahler, A. H. (1980) · Remote Sensing of Environment · doi:10.1016/0034-4257(80)90011-5 · Cited by 454 · see citing works
A review and analysis of backpropagation neural networks for classification of remotely-sensed multi-spectral imagery
Paola, J. D., & Schowengerdt, R. A. (1995) · International Journal of Remote Sensing · doi:10.1080/01431169508954607 · Cited by 443 · see citing works
Classification trees: an alternative to traditional land cover classifiers
Hansen, M. C., Dubayah, R., & DeFries, R. (1996) · International Journal of Remote Sensing · doi:10.1080/01431169608949069 · Cited by 434 · see citing works
A comparison of methods for monitoring multitemporal vegetation change using Thematic Mapper imagery
Rogan, J. S., Franklin, J., & Roberts, D. A. (2002) · Remote Sensing of Environment · doi:10.1016/s0034-4257(01)00296-6 · Cited by 402 · see citing works
Self-citation
Patterns of Forest Damage Resulting from Catastrophic Wind in Central New England, USA
Foster, D. R., & Boose, E. R. (1992) · Journal of Ecology · doi:10.2307/2261065 · Cited by 385 · see citing works
Strategies and best practice for neural network image classification
Kanellopoulos, I., & Wilkinson, G. G. (1997) · International Journal of Remote Sensing · doi:10.1080/014311697218719 · Cited by 383 · see citing works
Combining spectral and spatial information to map canopy damage from selective logging and forest fires
Júnior, C. M. D. S., Roberts, D. A., & Cochrane, M. A. (2005) · Remote Sensing of Environment · doi:10.1016/j.rse.2005.07.013 · Cited by 382 · see citing works
Optical remote-sensing techniques for the assessment of forest inventory and biophysical parameters
Wulder, M. (1998) · Progress in Physical Geography Earth and Environment · doi:10.1177/030913339802200402 · Cited by 373 · see citing works
Classification Methods for Remotely Sensed Data
Tso, B., & Mather, P. M. (2001) · Taylor & Francis eBooks · doi:10.4324/9780203303566 · Cited by 343 · see citing works
Computer processing of remotely-sensed images: An introduction
RICHARDS, J. (1990) · Earth-Science Reviews · doi:10.1016/0012-8252(90)90075-7 · Cited by 335 · see citing works
Rule-Based Classification Systems Using Classification and Regression Tree (CART) Analysis
Lawrence, R. L. (2001) · Photogrammetric Engineering & Remote Sensing · Cited by 311 · see citing works
A comparison of the IGBP DISCover and University of Maryland 1 km global land cover products
Hansen, M. C., & Reed, B. C. (2000) · International Journal of Remote Sensing · doi:10.1080/014311600210218 · Cited by 309 · see citing works
Data mining with decision trees and decision rules
Apte, C. V., & Weiss, S. M. (1997) · Future Generation Computer Systems · doi:10.1016/s0167-739x(97)00021-6 · Cited by 307 · see citing works
An evaluation of some factors affecting the accuracy of classification by an artificial neural network
Foody, G. M., & Arora, M. (1997) · International Journal of Remote Sensing · doi:10.1080/014311697218764 · Cited by 304 · see citing works
Data Mining for Scientific and Engineering Applications
(2001) · Massive computing · doi:10.1007/978-1-4615-1733-7 · Cited by 280 · see citing works
Mapping land-cover modifications over large areas: A comparison of machine learning algorithms
Rogan, J. S., Franklin, J., Stow, D. A., Miller, J. A., Woodcock, C. E., & Roberts, D. A. (2008) · Remote Sensing of Environment · doi:10.1016/j.rse.2007.10.004 · Cited by 268 · see citing works
Self-citation
Three-dimensional neural net for learning visuomotor coordination of a robot arm
Martinetz, T., Ritter, H. J., & Schulten, K. J. (1990) · IEEE Transactions on Neural Networks · doi:10.1109/72.80212 · Cited by 261 · see citing works
Multiple Criteria for Evaluating Machine Learning Algorithms for Land Cover Classification from Satellite Data
DeFries, R. (2000) · Remote Sensing of Environment · doi:10.1016/s0034-4257(00)00142-5 · Cited by 255 · see citing works
A Strategy for Estimating the Rates of Recent United States Land-Cover Changes
Loveland, T. R., Sohl, T. L., Stehman, S. V., Gallant, A. L., Sayler, K. L., & Napton, D. E. (2002) · Photogrammetric Engineering & Remote Sensing · Cited by 251 · see citing works
Comparison of maximum likelihood classification method with supervised artificial neural network algorithms for land use activities
Erbek, F. S., Özkan, C., & Taberner, M. (2004) · International Journal of Remote Sensing · doi:10.1080/0143116031000150077 · Cited by 250 · see citing works
Remote sensing of forest change using artificial neural networks
Gopal, S., & Woodcock, C. E. (1996) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/36.485117 · Cited by 241 · see citing works
Vector quantization of images based upon the Kohonen self-organizing feature maps
Nasrabadi, & Feng (1988) · IEEE International Conference on Neural Networks/IEEE ... International Conference on Neural Networks · doi:10.1109/icnn.1988.23837 · Cited by 239 · see citing works
Forest canopy damage and recovery in reduced-impact and conventional selective logging in eastern Para, Brazil
Pereira, R. M., Zweede, J., Asner, G. P., & Keller, M. (2002) · Forest Ecology and Management · doi:10.1016/s0378-1127(01)00732-0 · Cited by 235 · see citing works
CANOPY DAMAGE AND RECOVERY AFTER SELECTIVE LOGGING IN AMAZONIA: FIELD AND SATELLITE STUDIES
Asner, G. P., Keller, M., Pereira, R. M., Zweede, J., & Silva, J. N. M. (2004) · Ecological Applications · doi:10.1890/01-6019 · Cited by 223 · see citing works
Remote sensing of selective logging in Amazonia
Asner, G. P., Keller, M., Pereira, R. M., & Zweede, J. (2002) · Remote Sensing of Environment · doi:10.1016/s0034-4257(01)00326-1 · Cited by 214 · see citing works
Land-Cover Change Monitoring with Classification Trees Using Landsat TM and Ancillary Data
Rogan, J. S., Miller, J. A., Stow, D. A., Franklin, J., Levien, L. M., & Fischer, C. S. (2003) · Photogrammetric Engineering & Remote Sensing · doi:10.14358/pers.69.7.793 · Cited by 210 · see citing works
Self-citation
The use of decision tree and multiscale texture for classification of JERS-1 SAR data over tropical forest
Simard, M., Saatchi, S. S., & de Grandi, G. D. (2000) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/36.868888 · Cited by 194 · see citing works
Monitoring forest succession with multitemporal landsat images: factors of uncertainty
Conghe Song, A., & Woodcock, C. E. (2003) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2003.818367 · Cited by 184 · see citing works
Decision-tree and rule-induction approach to integration of remotely sensed and GIS data in mapping vegetation in disturbed or hilly environments
Lees, B. G., & Ritman, K. T. (1991) · Environmental Management · doi:10.1007/bf02394820 · Cited by 168 · see citing works
Neural maps in remote sensing image analysis
Villmann, T., Merényi, E., & Hammer, B. (2003) · Neural Networks · doi:10.1016/s0893-6080(03)00021-2 · Cited by 168 · see citing works
Detecting the nature of change in an urban environment : A comparison of machine learning algorithms
Chan, J. C.-W., Chan, K.-P., & Yeh, A. G. O. (2001) · The HKU Scholars Hub (University of Hong Kong) · Cited by 154 · see citing works
Distributed ARTMAP: a neural network for fast distributed supervised learning
Carpenter, G. A., Milenova, B. L., & Noeske, B. W. (1998) · Neural Networks · doi:10.1016/s0893-6080(98)00019-7 · Cited by 150 · see citing works
Dynamic forest mosaics
Spies, T. A., & Turner, M. G. (1999) · Cambridge University Press eBooks · doi:10.1017/cbo9780511613029.006 · Cited by 149 · see citing works
Urban change detection based on an artificial neural network
Liu, X., & Lathrop, R. G. (2002) · International Journal of Remote Sensing · doi:10.1080/01431160110097240 · Cited by 148 · see citing works
Forest mapping with a generalized classifier and Landsat TM data
Pax-Lenney, M., Woodcock, C. E., Macomber, S. A., Gopal, S., & Conghe Song, A. (2001) · Remote Sensing of Environment · doi:10.1016/s0034-4257(01)00208-5 · Cited by 143 · see citing works
Application of neural networks to radar image classification
Hara, Y., Atkins, R. G., Yueh, S. H., Shin, R. T., & Kong, J. A. (1994) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/36.285193 · Cited by 142 · see citing works
Fuzzy Neural Network Classification of Global Land Cover from a 1° AVHRR Data Set
Gopal, S., Woodcock, C. E., & Strahler, A. H. (1999) · Remote Sensing of Environment · doi:10.1016/s0034-4257(98)00088-1 · Cited by 138 · see citing works
Comparison of non-linear mixture models: sub-pixel classification
Liu, W., & Wu, E. Y. (2004) · Remote Sensing of Environment · doi:10.1016/j.rse.2004.09.004 · Cited by 132 · see citing works
Land-use classification of remotely sensed data using Kohonen Self-Organizing Feature Map neural networks.
Ji, C.-H. (2000) · Photogrammetric Engineering & Remote Sensing · Cited by 119 · see citing works
Land cover classification by an artificial neural network with ancillary information
Foody, G. M. (1995) · International Journal of Geographical Information Systems · doi:10.1080/02693799508902054 · Cited by 114 · see citing works
Change Detection Using Adaptive Fuzzy Neural Networks
Abuelgasim, A. A., Ross, W. D., Gopal, S., & Woodcock, C. E. (1999) · Remote Sensing of Environment · doi:10.1016/s0034-4257(99)00039-5 · Cited by 104 · see citing works
Analyzing the effect of structural variability and canopy gaps on forest BRDF using a geometric-optical model
Gerard, F., & North, P. R. J. (1997) · Remote Sensing of Environment · doi:10.1016/s0034-4257(97)00070-9 · Cited by 103 · see citing works
Uncertainty and Confidence in Land Cover Classification Using a Hybrid Classifier Approach
Liu, W., Gopal, S., & Woodcock, C. E. (2004) · Photogrammetric Engineering & Remote Sensing · doi:10.14358/pers.70.8.963 · Cited by 96 · see citing works
Timber harvesting as ongoing disturbance in a landscape of diverse ownership
Kittredge, D. B., Finley, A. O., & Foster, D. R. (2003) · Forest Ecology and Management · doi:10.1016/s0378-1127(02)00561-3 · Cited by 89 · see citing works
Early Detection of Douglas-Fir Beetle Infestation with Subcanopy Resolution Hyperspectral Imagery
Lawrence, R. L., & Labus, M. P. (2003) · Western Journal of Applied Forestry · doi:10.1093/wjaf/18.3.202 · Cited by 88 · see citing works
Effect of Alternative Splitting Rules on Image Processing Using Classification Tree Analysis
Zambon, M., Lawrence, R. L., Bunn, A. G., & Powell, S. L. (2006) · Photogrammetric Engineering & Remote Sensing · doi:10.14358/pers.72.1.25 · Cited by 82 · see citing works
Is inductive machine learning just another wild goose (or might it lay the golden egg)?
GAHEGAN, M. N. (2003) · International Journal of Geographical Information Systems · doi:10.1080/713811742 · Cited by 79 · see citing works
Changes in satellite-measured reflectances caused by thinning cuttings in Boreal forest
Olsson, H. (1994) · Remote Sensing of Environment · doi:10.1016/0034-4257(94)90072-8 · Cited by 67 · see citing works
Fuzzy ARTMAP supervised classification of multi-spectral remotely-sensed images
Mannan, B., Roy, J. R., & Ray, A. K. (1998) · International Journal of Remote Sensing · doi:10.1080/014311698215991 · Cited by 60 · see citing works
Interpretation and Classification of Partially Harvested Forest Stands in the Fundy Model Forest Using Multitemporal Landsat TM Digital Data
Franklin, S. E., Moskal, L. M., Lavigne, M. B., & Pugh, K. (2000) · Canadian Journal of Remote Sensing · doi:10.1080/07038992.2000.10874783 · Cited by 58 · see citing works
Ecology and Conservation in the Cultural Landscape of New England: Lessons from Nature's History
Foster, D. R., & Motzkin, G. (1998) · Northeastern Naturalist · doi:10.2307/3858582 · Cited by 54 · see citing works
Pixel- and site-based calibration and validation methods for evaluating supervised classification of remotely sensed data
Muchoney, D. M., & Strahler, A. H. (2002) · Remote Sensing of Environment · doi:10.1016/s0034-4257(02)00006-8 · Cited by 52 · see citing works
Learning in Single Hidden‐Layer Feedforward Network Models: Backpropagation in a Spatial Interaction Modeling Context
Gopal, S., & Fischer, M. M. (1996) · Geographical Analysis · doi:10.1111/j.1538-4632.1996.tb00920.x · Cited by 49 · see citing works
Integrating GIS and Remotely Sensed Data for Mapping Forest Disturbance and Change
Rogan, J. S., & Miller, J. A. (2006) · CRC Press eBooks · doi:10.1201/9781420005189.ch6 · Cited by 39 · see citing works
Self-citation
Uncertainty, knowledge discovery and data mining in GIS
Foody, G. M. (2003) · Progress in Physical Geography Earth and Environment · doi:10.1191/0309133303pp345pr · Cited by 38 · see citing works
The Nature and Classification of Unlabelled Neurons in the Use of Kohonen's Self-Organizing Map for Supervised Classification
Li, Z., & Eastman, J. R. (2006) · Transactions in GIS · doi:10.1111/j.1467-9671.2006.01014.x · Cited by 33 · see citing works
Self-citation
Integrating GIS and Remotely Sensed Data for Mapping Forest Disturbance and Change
Wulder, M. A., & Franklin, S. E. (2006) · CRC Press eBooks · doi:10.1201/9781420005189-10 · Cited by 25 · see citing works
Application of multilayer feedforward neural networks to precipitation cell-top altitude estimation
Spina, M. S., Schwartz, M., Staelin, D. H., & Gaisewski, A. J. (1998) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/36.655325 · Cited by 23 · see citing works
Soil survey of Franklin County, Massachusetts.
Mott, J., & Fuller, D. (1967) · Cited by 20 · see citing works
Forest statistics for Massachusetts
Dickson, D. R., McAfee, C. L., & Station, N. F. E. (1988) · Cited by 20 · see citing works
Selecting and Interpreting High-Resolution Images
Green, K. (2000) · Journal of Forestry · doi:10.1093/jof/98.6.37 · Cited by 19 · see citing works
Forest statistics for Massachusetts: 1985 and 1998
Alerich, C. L., & Alerich, C. L. (2000) · doi:10.2737/ne-rb-148 · Cited by 19 · see citing works
History and Importance of Land Use and Protection in the North Quabbin Region of Massachusetts (USA)
Golodetz, A. D., & Foster, D. R. (1997) · Conservation Biology · doi:10.1046/j.1523-1739.1997.95315.x · Cited by 16 · see citing works
Fuzzy ARTMAP Based Neurocomputational Spatial Uncertainty Measures
Li, Z. (2008) · Photogrammetric Engineering & Remote Sensing · doi:10.14358/pers.74.12.1573 · Cited by 14 · see citing works
Self-citation
Application of multilayer feedforward neural networks to precipitation cell-top altitude estimation
Spina, M. S., Schwartz, M., Staelin, D. H., & Gasiewski, A. J. (2002) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss.1994.399597 · Cited by 11 · see citing works
Forest statistics for Massachusetts : 1985 and 1998 /
Alerich, C. L., & States., U. (2000) · doi:10.5962/bhl.title.69918 · Cited by 5 · see citing works
THE USE OF SOM-ENCODED TEXTURE SPECTRUM FOR CHANGE DETECTION
Chan, J. C.-W., & CHAN, K.-P. (2002) · doi:10.1142/9789812777249_0014 · Cited by 2 · see citing works

Flagged first, then by citations

1 of the 89 references couldn’t be matched to a known paper.

Sorted by citations

An assessment of the effectiveness of a random forest classifier for land-cover classification
Rodriguez-Galiano, V. F., Ghimire, B., Rogan, J. S., Chica‐Olmo, M., & Rigol-Sánchez, J. P. (2011) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2011.11.002 · Cited by 2,962 · see citing works
Self-citation
Implementation of machine-learning classification in remote sensing: an applied review
Maxwell, A. E., Warner, T. A., & Fang, F. (2018) · International Journal of Remote Sensing · doi:10.1080/01431161.2018.1433343 · Cited by 2,152 · see citing works
Machine learning predictive models for mineral prospectivity: An evaluation of neural networks, random forest, regression trees and support vector machines
Rodriguez-Galiano, V., Sanchez-Castillo, M., Chica-Olmo, M., & Chica-Rivas, M. (2015) · Ore Geology Reviews · doi:10.1016/j.oregeorev.2015.01.001 · Cited by 1,576 · see citing works
Change detection from remotely sensed images: From pixel-based to object-based approaches
Hussain, M., Chen, D., Cheng, A., Wei, H., & Stanley, D. (2013) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2013.03.006 · Cited by 1,515 · see citing works
Monitoring land cover change in urban and peri-urban areas using dense time stacks of Landsat satellite data and a data mining approach
Schneider, A. (2012) · Remote Sensing of Environment · doi:10.1016/j.rse.2012.06.006 · Cited by 446 · see citing works
Mapping rubber tree growth in mainland Southeast Asia using time-series MODIS 250 m NDVI and statistical data
Li, Z., & Fox, J. (2011) · Applied Geography · doi:10.1016/j.apgeog.2011.06.018 · Cited by 306 · see citing works
Self-citation
Exploring issues of training data imbalance and mislabelling on random forest performance for large area land cover classification using the ensemble margin
Mellor, A., Boukir, S., Haywood, A., & Jones, S. (2015) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2015.03.014 · Cited by 312 · see citing works
Crop classification of upland fields using Random forest of time-series Landsat 7 ETM+ data
Tatsumi, K., Yamashiki, Y., Torres, M. A. C., & Taipe, C. L. R. (2015) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2015.05.001 · Cited by 277 · see citing works
An Evaluation of Bagging, Boosting, and Random Forests for Land-Cover Classification in Cape Cod, Massachusetts, USA
Ghimire, B., Rogan, J. S., Rodriguez-Galiano, V. F., Panday, P. K., & Neeti, N. (2012) · GIScience & Remote Sensing · doi:10.2747/1548-1603.49.5.623 · Cited by 246 · see citing works
Self-citation
Contextual land-cover classification: incorporating spatial dependence in land-cover classification models using random forests and the Getis statistic
Ghimire, B., Rogan, J. S., & Miller, J. A. (2010) · Remote Sensing Letters · doi:10.1080/01431160903252327 · Cited by 245 · see citing works
Self-citation
Tackling climate change with machine learning
Rolnick, D., Donti, P. L., Kaack, L. H., Kochanski, K., Lacoste, A., Sankaran, K., Ross, A. S., Milojevic-Dupont, N., Jaques, N., Waldman-Brown, A., et al. (2023) · ACM Computing Surveys · doi:10.1145/3485128 · Cited by 336 · see citing works
Integrating OpenStreetMap crowdsourced data and Landsat time-series imagery for rapid land use/land cover (LULC) mapping: Case study of the Laguna de Bay area of the Philippines
Johnson, B. A., & Iizuka, K. (2016) · Applied Geography · doi:10.1016/j.apgeog.2015.12.006 · Cited by 154 · see citing works
Long-Term Satellite Image Time-Series for Land Use/Land Cover Change Detection Using Refined Open Source Data in a Rural Region
Viana, C. M., Girão, I., & Rocha, J. (2019) · Remote Sensing · doi:10.3390/rs11091104 · Cited by 141 · see citing works
Ecological Variability and Carbon Stock Estimates of Mangrove Ecosystems in Northwestern Madagascar
Jones, T., Ratsimba, H. R., Ravaoarinorotsihoarana, L. A., Cripps, G., & Bey, A. (2014) · Forests · doi:10.3390/f5010177 · Cited by 128 · see citing works
Self-citation
Detecting Himalayan glacial lake outburst floods from Landsat time series
Veh, G., Korup, O., Roessner, S., & Walz, A. (2018) · Remote Sensing of Environment · doi:10.1016/j.rse.2017.12.025 · Cited by 127 · see citing works
Land cover mapping based on random forest classification of multitemporal spectral and thermal images
Eisavi, V., Homayouni, S., Yazdi, A. M., & Alimohammadi, A. (2015) · Environmental Monitoring and Assessment · doi:10.1007/s10661-015-4489-3 · Cited by 120 · see citing works
Assessing land cover and soil quality by remote sensing and geographical information systems (GIS)
Obade, V. D. P., & Lal, R. (2012) · CATENA · doi:10.1016/j.catena.2012.10.014 · Cited by 108 · see citing works
3D Mineral Prospectivity Mapping with Random Forests: A Case Study of Tongling, Anhui, China
Xiang, J., Xiao, K., Carranza, E. J., Chen, J.-P., & Li, S. (2019) · Natural Resources Research · doi:10.1007/s11053-019-09578-2 · Cited by 95 · see citing works
Automatic Extraction of Water Bodies from Landsat Imagery Using Perceptron Model
Mishra, K., & Prasad, P. R. C. (2015) · Journal of Computational Environmental Sciences · doi:10.1155/2015/903465 · Cited by 100 · see citing works
Explaining the Principles to Practices Gap in AI
Schiff, D. S., Rakova, B., Ayesh, A., Fanti, A., & Lennon, M. (2021) · IEEE Technology and Society Magazine · doi:10.1109/mts.2021.3056286 · Cited by 93 · see citing works
Characterizing tree canopy loss using multi-source GIS data in Central Massachusetts, USA
Hostetler, A. E., Rogan, J. S., Martin, D., DeLauer, V., & O’Neil-Dunne, J. P. M. (2013) · Remote Sensing Letters · doi:10.1080/2150704x.2013.852704 · Cited by 58 · see citing works
Self-citation
Image interpretation-guided supervised classification using nested segmentation
Egorov, A., Hansen, M. C., Roy, D. P., Kommareddy, A. R., & Potapov, P. V. (2015) · Remote Sensing of Environment · doi:10.1016/j.rse.2015.04.022 · Cited by 44 · see citing works
Rubber Tree Distribution Mapping in Northeast Thailand
Li, Z., & Fox, J. (2011) · International Journal of Geosciences · doi:10.4236/ijg.2011.24060 · Cited by 43 · see citing works
Self-citation
Dynamic Changes and Driving Forces of Alpine Wetlands on the Qinghai–Tibetan Plateau Based on Long-Term Time Series Satellite Data: A Case Study in the Gansu Maqu Wetlands
Zhang, B., Niu, Z., Zhang, D., & Huo, X. (2022) · Remote Sensing · doi:10.3390/rs14174147 · Cited by 35 · see citing works
Mapping land-cover change in a Haitian watershed using a combined spectral mixture analysis and classification tree procedure
Versluis, A., & Rogan, J. S. (2009) · Geocarto International · doi:10.1080/10106040902977584 · Cited by 31 · see citing works
Self-citation
Using remote sensing data for geological mapping in semi-arid environment: a machine learning approach
Fels, A. E. A. E., & Ghorfi, M. E. (2022) · Earth Science Informatics · doi:10.1007/s12145-021-00744-w · Cited by 28 · see citing works
Pixel-based crop classification in Peru from Landsat 7 ETM+ images using a Random Forest model
Tatsumi, K., Yamashiki, Y., MORANTE, A. K. M., Ramos-Fernández, L., & Nalvarte, R. A. (2016) · Journal of Agricultural Meteorology · doi:10.2480/agrmet.d-15-00010 · Cited by 24 · see citing works
Disaggregating conventional soil maps with limited descriptive data: A knowledge-based approach in Serra Gaúcha, Brazil
Sarmento, E. C., Giasson, É., Weber, E. J., Flores, C. A., & Hasenack, H. (2016) · Geoderma Regional · doi:10.1016/j.geodrs.2016.12.004 · Cited by 24 · see citing works
Consideration of Landsat-8 Spectral Band Combination in Typical Mediterranean Forest Classification in Halkidiki, Greece
Elhag, M. (2017) · Open Geosciences · doi:10.1515/geo-2017-0036 · Cited by 24 · see citing works
Using self-organizing maps in the visualization and analysis of forest inventory
Klobučar, D., & Subašić, M. (2012) · iForest - Biogeosciences and Forestry · doi:10.3832/ifor0629-005 · Cited by 23 · see citing works
Predictive Modeling for Site Detection Using Remotely Sensed Phenological Data
Kirk, S. D., Thompson, A. E., & Lippitt, C. D. (2016) · Advances in Archaeological Practice · doi:10.7183/2326-3768.4.1.87 · Cited by 19 · see citing works
Self-citation
Wetlands Mapping and Monitoring with Long-Term Time Series Satellite Data Based on Google Earth Engine, Random Forest, and Feature Optimization: A Case Study in Gansu Province, China
Zhang, J., Liu, X., Qin, Y., Fan, Y., & Cheng, S. (2024) · Land · doi:10.3390/land13091527 · Cited by 17 · see citing works
Training Algorithm Performance for Image Classification by Neural Networks
Zhou, L., & Yang, X. (2010) · Photogrammetric Engineering & Remote Sensing · doi:10.14358/pers.76.8.945 · Cited by 17 · see citing works
Prediction of soil orders with high spatial resolution: response of different classifiers to sampling density
Sarmento, E. C., Giasson, É., Weber, E. J., Flores, C. A., & Hasenack, H. (2012) · Pesquisa Agropecuária Brasileira · doi:10.1590/s0100-204x2012000900025 · Cited by 15 · see citing works
Efficiency of Extreme Gradient Boosting for Imbalanced Land Cover Classification Using an Extended Margin and Disagreement Performance
Sun, F., Wang, R., Wan, B., Su, Y., Guo, Q., Huang, Y., & Wu, X. (2019) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi8070315 · Cited by 14 · see citing works
Using ensemble margin to explore issues of training data imbalance and mislabeling on large area land cover classification
Mellor, A., Boukir, S., Haywood, A., & Jones, S. (2014) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2014.7026026 · Cited by 13 · see citing works
Trustworthy Predictive Algorithms for Complex Forest System Decision-Making
Rana, P., & Varshney, L. R. (2021) · Frontiers in Forests and Global Change · doi:10.3389/ffgc.2020.587178 · Cited by 14 · see citing works
Spatial and seasonal modeling of the land surface temperature using random forest
Pouyan, S., Rahmanian, S., Amindin, A., & Pourghasemi, H. R. (2021) · Elsevier eBooks · doi:10.1016/b978-0-323-89861-4.00035-x · Cited by 12 · see citing works
Ormancılıkta makine öğrenmesi kullanımı
Eker, R., Alkiş, K. C., Uçar, Z., & Aydın, A. (2023) · Turkish Journal of Forestry | Türkiye Ormancılık Dergisi · doi:10.18182/tjf.1282768 · Cited by 12 · see citing works
Evaluating Satellite Image Classification: Exploring Methods and Techniques
Yasin, E. H. E., & Czimber, K. (2024) · IntechOpen eBooks · doi:10.5772/intechopen.1003196 · Cited by 12 · see citing works
Monitoring selective logging with Landsat satellite imagery reveals that protected forests in Western Siberia experience greater harvest than non-protected forests
Shchur, A., Bragina, E., Sieber, A., Pidgeon, A. M., & Radeloff, V. C. (2017) · Environmental Conservation · doi:10.1017/s0376892916000576 · Cited by 12 · see citing works
Unsupervised Fuzzy ARTMAP Classification of Hyperspectral Hyperion Data for Savanna and Agriculture Discrimination in the Brazilian Cerrado
Filippi, A. M., Brannstrom, C., Dobreva, I. D., Cairns, D. M., & Kim, D. (2009) · GIScience & Remote Sensing · doi:10.2747/1548-1603.46.1.1 · Cited by 12 · see citing works
An Impartial Semi-Supervised Learning Strategy for Imbalanced Classification on VHR Images
Sun, F., Fang, F., Wang, R., Wan, B., Guo, Q., Li, H., & Wu, X. (2020) · Sensors · doi:10.3390/s20226699 · Cited by 11 · see citing works
Automated Pixel Purification for Delineating Pervious and Impervious Surfaces in a City Using Advanced Hyperspectral Imagery Techniques
Sajadi, P., Gholamnia, M., Bonafoni, S., Mills, G. M., Sang, Y., Li, Z., Khan, S., Han, J., & Pilla, F. (2024) · IEEE Access · doi:10.1109/access.2024.3408805 · Cited by 7 · see citing works
Self-citation
Performance evaluation of classification trees for building detection from aerial images and LiDAR data: a comparison of classification trees models
Salah, M., Trinder, J. C., & Shaker, A. (2011) · International Journal of Remote Sensing · doi:10.1080/01431161.2010.507678 · Cited by 7 · see citing works
Modelling forest canopy gaps using LiDAR-derived variables
Lombard, L., Ismail, R., & Poona, N. (2017) · Geocarto International · doi:10.1080/10106049.2017.1377775 · Cited by 7 · see citing works
Machine learning approach for optimizing usability of healthcare websites
Kaur, A., Singh, J., & Kaur, S. (2025) · Scientific Reports · doi:10.1038/s41598-025-99449-z · Cited by 6 · see citing works
Integration of the pixel and object domain for the mapping of new urban landscapes in the Mediterranean with a focus on outdoor water consumption
Wolf, N., & Hof, A. (2013) · doi:10.12760/01-2013-1-07 · Cited by 4 · see citing works
Identificação de áreas prioritárias para recuperação florestal com o uso de rede neural de mapas auto-organizáveis
Arcoverde, G. F. B., de Almeida, C. M., Ximenes, A. C., Maeda, E. E., & de Araujo, L. S. (2011) · Boletim de Ciências Geodésicas · doi:10.1590/s1982-21702011000300004 · Cited by 3 · see citing works
Improving Remote Sensing Multiple Classification by Data and Ensemble Selection
Boukir, S., Guo, L., & Chehata, N. (2021) · Photogrammetric Engineering & Remote Sensing · doi:10.14358/pers.20-00071r3 · Cited by 2 · see citing works
Change detection using bi-temporal aerial photographs and registration at the stand level
Hyvönen, P., Heinonen, J., & Haara, A. (2010) · European Journal of Forest Research · doi:10.1007/s10342-010-0455-3 · Cited by 2 · see citing works
Optical imagery and digital spaces in the era of machine learning for better geospatial information and services
Caraballo-Vega, J. A., Blanco-Rojas, M., Montesano, P., Carroll, M., Frost, M. J., Burke, A. L., Spradlin, C. S., Li, J., Gill, R. L., Wooten, M., et al. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-29216-3.00008-4 · Cited by 1 · see citing works
An Overview and Recent Advances in Fuzzy ARTMAP Classifier Usage for Mapping Purposes Using Remotely Sensed Data
Prado, P. F., & Duarte, I. C. S. (2020) · Journal of Environmental Informatics Letters · doi:10.3808/jeil.202000032 · Cited by 1 · see citing works
Comparação entre quatro algoritmos de aprendizagem de máquina no mapeamento digital de solos no Vale dos Vinhedos, RS, Brasil
Sarmento, E. C. (2010) · Americanae (AECID Library)
Quantifying post-fire recovery of forest canopy structure and its environmental drivers using satellite image time-series
Khanal, S. (2014) · EGUGA
Investigating the utility of LiDAR for modelling forest canopy gaps and species classification
Lombard, L. (2018) · SUNScholar (Stellenbosch University)

A machine learning approach to map tropical selective logging
Hethcoat, M. G., Edwards, D. P., Carreiras, J. M. B., Bryant, R. G., França, F. M., & Quegan, S. (2019) · Remote Sensing of Environment · doi:10.1016/j.rse.2018.11.044 · Cited by 91 · see citing works
A Comparative Assessment of Machine-Learning Techniques for Forest Degradation Caused by Selective Logging in an Amazon Region Using Multitemporal X-Band SAR Images
Kuck, T. N., Sano, E. E., Bispo, P. D. C., Shiguemori, E. H., Filho, P. F. F. S., & Matricardi, E. A. T. (2021) · Remote Sensing · doi:10.3390/rs13173341 · Cited by 2 · see citing works
Selective Logging-Related Land-Cover Class Discrimination in the Brazilian Amazon with Landsat-8 and Sentinel-2 Products
de Oliveira, M. A. F., Reis, M. S., Sant’Anna, S. J. S., & Escada, M. I. S. (2026) · Land · doi:10.3390/land15071130
Remote sensing of tropical forest degradation from selective logging
Hethcoat, M. G. (2019) · White Rose eTheses Online (University of Leeds, The University of Sheffield, University of York) · Cited by 1 · see citing works
Mapping low-intensity selective logging across the Peruvian Amazon
Bousfield, C. G., Edwards, D. P., Hethcoat, M. G., Zumaeta, L. E. C., Mañuico, E. A., Minhuey, A., & Bryant, R. G. (2026) · Environmental Research Letters · doi:10.1088/1748-9326/ae3787 · Cited by 2 · see citing works

Authors: Lippitt, C. D., Rogan, J., Li, Z., Eastman, J. R., & Jones, T. G.
Affiliations: Dakota State University (US)
ORCID: Lippitt, Christopher D.
Li, Zhe
Jones, Trevor G.
Venue: Photogrammetric Engineering & Remote Sensing
Publisher: American Society for Photogrammetry and Remote Sensing
Published: 2008-10-01
Volume: 74
Issue: 10
Pages: 1201-1211
ISSN: 0099-1112 (ISSN-L)
DOI: 10.14358/pers.74.10.1201
OpenAlex: W2171058578
Cited by: 56
References: 89
Citations by year: 2025: 2 · 2024: 3 · 2023: 1 · 2022: 4 · 2021: 4 · 2020: 2 · 2019: 3 · 2018: 3 · 2017: 3 · 2016: 4 · 2015: 6 · 2014: 3 · 2013: 2 · 2012: 5
FWCI: 9.23
Type: journal article
Language: en
Open access: hybrid
doi.org
Licence: cc-by-nc-nd
Locations: Photogrammetric Engineering & Remote Sensing (publishedVersion, cc-by-nc-nd)
Clark Digital Commons (Clark University) (submittedVersion, cc-by-nc-nd)
Topics: Physical Sciences › Engineering › Media Technology › Remote-Sensing Image Classification
Physical Sciences › Environmental Science › Ecology › Remote Sensing in Agriculture
Physical Sciences › Earth and Planetary Sciences › Atmospheric Science › Remote Sensing and Land Use
Keywords: selective logging, mixed deciduous forest, forest disturbance mapping, classification trees, artificial neural networks, self-organizing maps, multilayer perceptron, ARTMAP, Landsat ETM+ imagery, forest monitoring

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Mapping Selective Logging in Mixed Deciduous Forest

Abstract

Integrity signals

Claims the paper makes

Claims other papers make about this one

Cites

Cited by

Related

Record

About these checks