Comparison of Land-Cover Classification Methods in the Brazilian Amazon Basin

Lu, Dengsheng; Mausel, Paul W.; Batistella, Mateus; Moran, Emílio F.

doi:10.14358/pers.70.6.723

Cited by 138 publications

(99 citation statements)

References 39 publications

(57 reference statements)

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“…Methods Thomas et al, 2003Lu et al, 2004Ma et al, 2017Maulik and Chakraborty, 2017Guisan and Thuiller, 2005Elith and Leathwick, 2009Franklin, 2010Li and Wang, 2013Morris et al, 2016Ashraf et al, 2017de Rivera and López-Quílez, 2017 can be approached from two perspectives: the data collection methods and the map production methods. Developments in data collection techniques in the last few decades have increased the types, amount and quality of data that can be collected for marine environmental characterization, particularly in terms of remotely sensed data (Brown et al, 2011;Kachelriess et al, 2014;Lecours et al, 2016b).…”

Section: Marine Habitat Mappingmentioning

confidence: 99%

On the Use of Maps and Models in Conservation and Resource Management (Warning: Results May Vary)

Lecours

2017

Front. Mar. Sci.

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Conservation planning and management typically require accurate and spatially explicit data at scales that are relevant for conservation objectives. In marine conservation, these data are often combined with spatial analytical techniques to produce marine habitat maps. While marine habitat mapping is increasingly used to inform conservation efforts, this field is still relatively young and its methods are rapidly evolving. Because conservation efforts do not always specify standards or guidelines for the production of habitat maps, results can vary dramatically. As representations of real environmental characteristics, habitat maps are highly sensitive to how they are produced. In this review paper, I present four concepts that are known to cause variation in spatial representation and prediction of habitats: the methodology used, the quality and scale of the data, and the choice of variables in regards to fitness for use. I then discuss the potential antinomy associated with the use of habitat maps in conservation: while habitat maps have become an invaluable tool to inform and assist decision-making, maps of the same area built using different methods and data may provide dissimilar representations, thus providing different information and possibly leading to different decisions. Exploring the theories and methods that have proved effective in terrestrial conservation and the spatial sciences, and how they can be integrated in marine habitat mapping practices, could help improve maps used to support marine conservation efforts and result in more reliable products to inform conservation decisions. Having a strong, consistent, transparent, repeatable, and science-based protocol for data collection and mapping is essential for effectively supporting decision-makers in developing conservation and management plans. The development of user-friendly tools to assist in the application of such protocol is crucial to a widespread improvement in practices. I discuss the potential to use interactive and collaborative Geographic Information Systems (GIS) to encourage the conservation and management community, from data collectors and mapmakers to decision-makers, to move toward a digital resilience and to develop such science-based protocol. Until standards and protocols are developed, habitat maps should always be interpreted with care, and the methods and metadata associated with their production should always be explicitly stated.

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Section: Marine Habitat Mappingmentioning

confidence: 99%

On the Use of Maps and Models in Conservation and Resource Management (Warning: Results May Vary)

Lecours

2017

Front. Mar. Sci.

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“…The MD algorithm is fast and one of the more commonly used algorithms because of its mathematic simplicity, only requiring the mean vectors for each band from the training data. This method does not consider class variability; thus, large differences in the variance of the classes often lead to misclassification (Lu et al 2004). …”

Section: Minimum Distance Classificationmentioning

confidence: 99%

Semiautomatic approach for land cover classification: a remote sensing study for arid climate in southeastern Tunisia

Bouaziz

Eisold

Guermazi

2017

Euro-Mediterr J Environ Integr

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The land use and land cover (LULC) classification has great potential to contribute to the monitoring of land degradation and climatic disasters. The purpose of this study was to assess the performance of parametric and nonparametric classification methods using remotely sensed Landsat satellite data of arid and semiarid areas, based on the computed producer's accuracy, user's accuracy, overall accuracy, and Cohen's kappa coefficient. Three LULC classes were identified, and supervised classifications were applied to Landsat 8 imagery. The results show that the support vector machines (SVM) classification method produced more accurate results, using two different kernel functions, compared with the maximum likelihood classification (MLC) and the minimum distance classification (MDC). The basis radial function affords the highest overall classification accuracy of 91.20% and a mean kappa coefficient of 0.87. This classification method is very well suited to accurately map LULC in arid and semiarid regions where the main vegetation type is oasis or steppes.

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“…Here, seedlings were defined as young trees or shrubs with a stem diameter smaller than 2 cm, saplings as young trees with a stem DBH greater than 2 cm and smaller than 10 cm, and trees as woody plants with a DBH greater than or equal to 10 cm. A detailed description of field data collection can be found in Batistella (2001) and Lu et al (2004).…”

Section: Field Vegetation Inventory and Biomass Estimationmentioning

confidence: 99%

Exploring TM image texture and its relationships with biomass estimation in Rondônia, Brazilian Amazon

Batistella

2005

Acta Amaz.

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153

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Many texture measures have been developed and used for improving land-cover classification accuracy, but rarely has research examined the role of textures in improving the performance of aboveground biomass estimations. The relationship between texture and biomass is poorly understood. This paper used Landsat Thematic Mapper (TM) data to explore relationships between TM image textures and aboveground biomass in Rondônia, Brazilian Amazon. Eight grey level co-occurrence matrix (GLCM) based texture measures (i.e., mean, variance, homogeneity, contrast, dissimilarity, entropy, second moment, and correlation), associated with seven different window sizes (5x5, 7x7, 9x9, 11x11, 15x15, 19x19, and 25x25), and five TM bands (TM 2, 3, 4, 5, and 7) were analyzed. Pearson's correlation coefficient was used to analyze texture and biomass relationships. This research indicates that most textures are weakly correlated with successional vegetation biomass, but some textures are significantly correlated with mature forest biomass. In contrast, TM spectral signatures are significantly correlated with successional vegetation biomass, but weakly correlated with mature forest biomass. Our findings imply that textures may be critical in improving mature forest biomass estimation, but relatively less important for successional vegetation biomass estimation. KEYWORDStexture, aboveground biomass, TM image, correlation, Amazon Explorando texturas de imagens TM e suas relações com estimativas de Biomassa em Rondônia. (i.e., média, variância, homogeneidade, contraste, dissimilaridade, entropia, segundo momento e correlação), associadas com sete diferentes tamanhos de janela (5x5, 7x7, 9x9, 11x11, 15x15, 19x19 e 25x25) e cinco bandas TM (TM 2, 3, 4, 5 RESUMO Muitas medidas de textura têm sido desenvolvidas e utilizadas para melhorar a acurácia de classificações de cobertura das terras, mas raramente têm-se avaliado a importância dessas medidas em estimativas de biomassa. Este trabalho utilizou dados Landsat TM para explorar as relações entre texturas de imagens TM e biomassa em Rondônia, Amazônia. Foram analisadas oito medidas de textura baseadas em matrizes de co-ocorrência de tons de cinza

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Comparison of Land-Cover Classification Methods in the Brazilian Amazon Basin

Cited by 138 publications

References 39 publications

On the Use of Maps and Models in Conservation and Resource Management (Warning: Results May Vary)

On the Use of Maps and Models in Conservation and Resource Management (Warning: Results May Vary)

Semiautomatic approach for land cover classification: a remote sensing study for arid climate in southeastern Tunisia

Exploring TM image texture and its relationships with biomass estimation in Rondônia, Brazilian Amazon

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