Estimating deforestation in tropical humid and dry forests in Madagascar from 2000 to 2010 using multi-date Landsat satellite images and the random forests classifier

Grinand, Clovis; Rakotomalala, Fety A.; Gond, Valéry; Vaudry, Romuald; Bernoux, Martial; Vieilledent, Ghislain

doi:10.1016/j.rse.2013.07.008

Cited by 136 publications

(108 citation statements)

References 41 publications

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“…Multi-temporal satellite images are effective for reducing the uncertainty associated with land cover change [63]. The traditional classification algorithms (such as maximum likelihood) may not be appropriate for the classification of combined multi-date images because of the heterogeneous spectral signature of land-cover categories over large areas.…”

Section: Classification Algorithm Using Rf and Validationmentioning

confidence: 99%

Mapping Deforestation in North Korea Using Phenology-Based Multi-Index and Random Forest

et al. 2016

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Abstract:Phenology-based multi-index with the random forest (RF) algorithm can be used to overcome the shortcomings of traditional deforestation mapping that involves pixel-based classification, such as ISODATA or decision trees, and single images. The purpose of this study was to investigate methods to identify specific types of deforestation in North Korea, and to increase the accuracy of classification, using phenological characteristics extracted with multi-index and random forest algorithms. The mapping of deforestation area based on RF was carried out by merging phenology-based multi-indices (i.e., normalized difference vegetation index (NDVI), normalized difference water index (NDWI), and normalized difference soil index (NDSI)) derived from MODIS (Moderate Resolution Imaging Spectroradiometer) products and topographical variables. Our results showed overall classification accuracy of 89.38%, with corresponding kappa coefficients of 0.87. In particular, for forest and farm land categories with similar phenological characteristic (e.g., paddy, plateau vegetation, unstocked forest, hillside field), this approach improved the classification accuracy in comparison with pixel-based methods and other classes. The deforestation types were identified by incorporating point data from high-resolution imagery, outcomes of image classification, and slope data. Our study demonstrated that the proposed methodology could be used for deciding on the restoration priority and monitoring the expansion of deforestation areas.

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Section: Classification Algorithm Using Rf and Validationmentioning

confidence: 99%

Mapping Deforestation in North Korea Using Phenology-Based Multi-Index and Random Forest

et al. 2016

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“…Data time series are necessary to detect deforestation. Landsat data have been primarily used in monitoring forest disturbance (Griffiths et al 2012;Schroeder et al 2011;Zhu et al 2012;Grinand et al 2013;Huang et al 2010;Gorsevski et al 2012;Renó et al 2011;Goodwin and Collett 2014), mainly due to the long archive, spectral and spatial resolution properties, and the free availability of data. Tasseled Cap Transformation (TCT) indices from Landsat near-annual time series, evaluated under trajectory-based change detection methods, resulted in identifying forest disturbances within 22 years with overall accuracy (OA) 95.72% (Griffiths et al 2012).…”

Section: Degradation / Deforestationmentioning

confidence: 99%

Remote sensing for biodiversity monitoring: a review of methods for biodiversity indicator extraction and assessment of progress towards international targets

2015

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Recognizing the imperative need for biodiversity protection, the Convention on Biological Diversity (CBD) has recently established new targets towards 2020, the so-called Aichi targets, and updated proposed sets of indicators to quantitatively monitor the progress towards these targets. Remote sensing has been increasingly contributing to timely, accurate, and cost-effective assessment of biodiversity-related characteristics and functions during the last years. However, most relevant studies constitute individual research efforts, rarely related with the extraction of widely adopted CBD biodiversity indicators. Furthermore, systematic operational use of remote sensing data by managing authorities has still been limited. In this study, the Aichi targets and the related CBD indicators whose monitoring can be facilitated by remote sensing are identified. For each headline indicator a number of recent remote sensing approaches able for the extraction of related properties are reviewed. Methods cover a wide range of fields, including: habitat extent and condition monitoring; species distribution; pressures from unsustainable management, pollution and climate change; ecosystem service monitoring; and conservation status assessment of protected areas. The advantages and limitations of different remote sensing data and algorithms are discussed. Sorting of the methods based on their reported accuracies is attempted, when possible. The extensive literature survey aims at reviewing highly performing methods that can be used for large-area, effective, and timely biodiversity assessment, to encourage the more systematic use of remote sensing solutions in monitoring progress towards the Aichi targets, and to decrease the gaps between the remote sensing and management communities.

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“…Random forest (RF; Breiman 2001) is increasingly used in a range of applications including digital soil mapping (Grimm et al 2008), forest biomass mapping (Baccini et al 2012), species distribution modeling (Evans and Cushman 2009) and others given its often superior performance compared to other methods (Evans et al 2011). RF is also gaining prominence in land-use classification (e.g., Aide et al 2013;Grinand et al 2013), where it outperforms classification and regression trees (CART; RodriguezGaliano et al 2012) and maximum likelihood classifiers (Schneider 2012). Nonparametric procedures like RF are particularly effective at identifying complex multivariate associations, such as those that affect patterns of forest loss.…”

Section: Introductionmentioning

confidence: 99%

“…Nonparametric procedures like RF are particularly effective at identifying complex multivariate associations, such as those that affect patterns of forest loss. Few analyses of forest loss have employed random forest (but see Aide et al 2013;Grinand et al 2013), and none to our knowledge quantitatively compare the performance of random forest to other commonly used modelling approaches, such as logistic regression, or utilize multi-scale optimization (sensu McGarigal et al 2016).…”

Section: Introductionmentioning

confidence: 99%

Multiple-scale prediction of forest loss risk across Borneo

et al. 2017

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Context The forests of Borneo have among the highest biodiversity and also the highest forest loss rates on the planet. Objectives Our objectives were to: (1) compare multiple modelling approaches, (2) evaluate the utility of landscape composition and configuration as predictors, (3) assess the influence of the ratio of forest loss and persistence points in the training sample, (4) identify the multiple-scale drivers of recent forest loss and (5) predict future forest loss risk across Borneo. Methods We compared random forest machine learning and logistic regression in a multi-scale approach to model forest loss risk between 2000 and 2010 as a function of topographical variables and landscape structure, and applied the highest performing model to predict the spatial pattern of forest loss risk between 2010 and 2020. We utilized a naïve model as a null comparison and used the total operating characteristic AUC to assess model performance. Results Our analysis produced five main results. We found that: (1) random forest consistently outperformed logistic regression and the naïve model; (2) including landscape structure variables substantially improved predictions; (3) a ratio of occurrence to nonoccurrence points in the training dataset that does not match the actual ratio in the landscape biases the predictions of both random forest and logistic regression; (4) forest loss risk differed between the three nations that comprise Borneo, with patterns in Kalimantan highly related to distance from the edge of the previous frontier of forest loss, while Malaysian Borneo showed a more diffuse pattern related to the structure of the landscape; (5) we predicted continuing 123Landscape Ecol (2017) 32:1581-1598 DOI 10.1007 very high rates of forest loss in the 2010-2020 period, and produced maps of the expected risk of forest loss across the full extent of Borneo. Conclusions These results confirm that multiplescale modelling using landscape metrics as predictors in a random forest modelling framework is a powerful approach to landscape change modelling. There is immense immanent risk to Borneo's forests, with clear spatial patterns of risk related to topography and landscape structure that differ between the three nations that comprise Borneo.

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Estimating deforestation in tropical humid and dry forests in Madagascar from 2000 to 2010 using multi-date Landsat satellite images and the random forests classifier

Cited by 136 publications

References 41 publications

Mapping Deforestation in North Korea Using Phenology-Based Multi-Index and Random Forest

Mapping Deforestation in North Korea Using Phenology-Based Multi-Index and Random Forest

Remote sensing for biodiversity monitoring: a review of methods for biodiversity indicator extraction and assessment of progress towards international targets

Multiple-scale prediction of forest loss risk across Borneo

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