Multi-Index Image Differencing Method (MINDED) for Flood Extent Estimations

Oliveira, Elise Botteselle de; Disperati, Leonardo; Cenci, Luca; Pereira, L. Gomes; Alves, Fátima L.

doi:10.3390/rs11111305

Cited by 20 publications

(31 citation statements)

References 81 publications

(135 reference statements)

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“…The method developed in this study consists of an adaptation of the Multi-INDEx Differencing (MINDED) method [34], which was originally developed to estimate flood extents. For this work, we have incorporated the same methodological principles, yet with a different purpose: the detection of burned areas.…”

Section: Methodsmentioning

confidence: 99%

“…One of the main objectives of this study is to develop and implement a method based on satellite RS imagery and GIS techniques, using an integrated multi-index imagedifferencing approach aimed at determining burned areas, representing an advancement in respect to the applications of the single indices listed in Table 1. The method develops from the work of [34], and it consists of the analysis of the frequency distribution of a set of index differencing statistics, as well as the combination of different single-index classifications by means of a spatial majority analysis. The method was implemented in a fully automatic procedure and tested for a study area in northwestern Portugal.…”

Section: Namementioning

confidence: 99%

“…7) [29] BAIMs BAI Ms = 1 (0.05−N IR) 2 +(0.2−SW IRs) 2 (8) [30] BAIMl BAI Ml = 1 (0.05−N IR) 2 +(0.2−SW IRl) 2 (9) [30] The extraction of thematic information from spectral indices is usually carried out using one or more thresholds, which may be used to define two or more classes (density slicing) [31][32][33]. However, as with any spectral index, finding optimal thresholds is often a difficult task, since they are usually scene-dependent [34,35]. Additionally, the characteristics and spectral reflectance of burned areas may also be highly variable, depending on fire severity or the density of pre-fire vegetation [29,36].…”

Section: Introductionmentioning

confidence: 99%

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A New Method (MINDED-BA) for Automatic Detection of Burned Areas Using Remote Sensing

2021

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This work presents a change detection method (MINDED-BA) for determining burned extents from multispectral remote sensing imagery. It consists of a development of a previous model (MINDED), originally created to estimate flood extents, combining a multi-index image-differencing approach and the analysis of magnitudes of the image-differencing statistics. The method was implemented, using Landsat and Sentinel-2 data, to estimate yearly burn extents within a study area located in northwest central Portugal, from 2000–2019. The modelling workflow includes several innovations, such as preprocessing steps to address some of the most important sources of error mentioned in the literature, and an optimal bin number selection procedure, the latter being the basis for the threshold selection for the classification of burn-related changes. The results of the model have been compared to an official yearly-burn-extent database and allow verifying the significant improvements introduced by both the pre-processing procedures and the multi-index approach. The high overall accuracies of the model (ca. 97%) and its levels of automatization (through open-source software) indicate potential for being a reliable method for systematic unsupervised classification of burned areas.

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Section: Methodsmentioning

confidence: 99%

Section: Namementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A New Method (MINDED-BA) for Automatic Detection of Burned Areas Using Remote Sensing

2021

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“…This technique is used for environmental change detection [33] e.g. deforestation and land use change [34][35][36], identification of fire [35,37], droughts [38,39] or floods [40,41]. Three studies used shorttime image differencing to detect polar bears from space [10,24,26].…”

Section: Satellite Remote Sensing Has Recently Emerged As a New Viablmentioning

confidence: 99%

Using very high-resolution satellite imagery and deep learning to detect and count African elephants in heterogeneous landscapes

Duporge

Isupova

Reece

et al. 2020

Preprint

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Satellites allow large-scale surveys to be conducted in short time periods with repeat surveys possible <24hrs. Very high-resolution satellite imagery has been successfully used to detect and count a number of wildlife species in open, homogeneous landscapes and seascapes where target animals have a strong contrast with their environment. However, no research to date has detected animals in complex heterogeneous environments or detected elephants from space using very high-resolution satellite imagery and deep learning.In this study we apply a Convolution Neural Network (CNN) model to automatically detect and count African elephants in a woodland savanna ecosystem in South Africa. We use WorldView-3 and 4 satellite data – the highest resolution satellite imagery commercially available. We train and test the model on eleven images from 2014-2019. We compare the performance accuracy of the CNN against human accuracy. Additionally, we apply the model on a coarser resolution satellite image (GeoEye-1) captured in Kenya to test if the algorithm can generalise to an elephant population outside of the training area.Our results show the CNN performs with high accuracy, comparable to human detection capabilities. The detection accuracy (i.e., F2 score) of the CNN models was 0.78 in heterogeneous areas and 0.73 in homogenous areas. This compares with the detection accuracy of the human labels with an averaged F2 score 0.77 in heterogeneous areas and 0.80 in homogenous areas. The CNN model can generalise to detect elephants in a different geographical location and from a lower resolution satellite.Our study demonstrates the feasibility of applying state-of-the-art satellite remote sensing and deep learning technologies for detecting and counting African elephants in heterogeneous landscapes. The study showcases the feasibility of using high resolution satellite imagery as a promising new wildlife surveying technique. Through creation of a customised training dataset and application of a Convolutional Neural Network, we have automated the detection of elephants in satellite imagery with as high accuracy as human detection capabilities. The success of the model to detect elephants outside of the training data site demonstrates the generalisability of the technique.

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“…This technique is used for environmental change detection (Lu et al., 2010) e.g. deforestation and land use change (Kiage et al., 2007; Kusimi, 2008; Meng & Meentemeyer, 2011), identification of fire (Carvalho Júnior et al., 2015; Meng & Meentemeyer, 2011), droughts (Buma & Lee, 2019; Rulinda et al., 2010) or floods (Oliveira et al., 2019; Thito et al., 2016). Three studies used short‐time image differencing to detect polar bears from space (LaRue & Stapleton, 2018; LaRue et al., 2015; Stapleton et al., 2014).…”

Section: Introductionmentioning

confidence: 99%

Using very‐high‐resolution satellite imagery and deep learning to detect and count African elephants in heterogeneous landscapes

Duporge

Isupova

Reece

et al. 2020

Remote Sens Ecol Conserv

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Satellites allow large‐scale surveys to be conducted in short time periods with repeat surveys possible at intervals of <24 h. Very‐high‐resolution satellite imagery has been successfully used to detect and count a number of wildlife species in open, homogeneous landscapes and seascapes where target animals have a strong contrast with their environment. However, no research to date has detected animals in complex heterogeneous environments or detected elephants from space using very‐high‐resolution satellite imagery and deep learning. In this study, we apply a Convolution Neural Network (CNN) model to automatically detect and count African elephants in a woodland savanna ecosystem in South Africa. We use WorldView‐3 and 4 satellite data –the highest resolution satellite imagery commercially available. We train and test the model on 11 images from 2014 to 2019. We compare the performance accuracy of the CNN against human accuracy. Additionally, we apply the model on a coarser resolution satellite image (GeoEye‐1) captured in Kenya, without any additional training data, to test if the algorithm can generalize to an elephant population outside of the training area. Our results show that the CNN performs with high accuracy, comparable to human detection capabilities. The detection accuracy (i.e., F2 score) of the CNN models was 0.78 in heterogeneous areas and 0.73 in homogenous areas. This compares with the detection accuracy of the human labels with an averaged F2 score 0.77 in heterogeneous areas and 0.80 in homogenous areas. The CNN model can generalize to detect elephants in a different geographical location and from a lower resolution satellite. Our study demonstrates the feasibility of applying state‐of‐the‐art satellite remote sensing and deep learning technologies for detecting and counting African elephants in heterogeneous landscapes. The study showcases the feasibility of using high resolution satellite imagery as a promising new wildlife surveying technique. Through creation of a customized training dataset and application of a Convolutional Neural Network, we have automated the detection of elephants in satellite imagery with accuracy as high as human detection capabilities. The success of the model to detect elephants outside of the training data site demonstrates the generalizability of the technique.

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Multi-Index Image Differencing Method (MINDED) for Flood Extent Estimations

Cited by 20 publications

References 81 publications

A New Method (MINDED-BA) for Automatic Detection of Burned Areas Using Remote Sensing

A New Method (MINDED-BA) for Automatic Detection of Burned Areas Using Remote Sensing

Using very high-resolution satellite imagery and deep learning to detect and count African elephants in heterogeneous landscapes

Using very‐high‐resolution satellite imagery and deep learning to detect and count African elephants in heterogeneous landscapes

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