Remotely sensed estimation of forest canopy density: A comparison of the performance of four methods

Joshi, C.M.; Leeuw, Jan de; Skidmore, Andrew K.; Duren, I.C. van; Oosten, Henk van

doi:10.1016/j.jag.2005.08.004

Cited by 87 publications

(55 citation statements)

References 31 publications

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“…Typically, the confusion matrix [81][82][83] is used to display class membership of observations according to the map and according to field observations. The diagonal of the confusion matrix lists the correct classifications while off diagonal cells list errors.…”

Section: Quantitative Validation Of the Flood Hazard Index With Pgis mentioning

confidence: 99%

Modeling Flood Hazard Zones at the Sub-District Level with the Rational Model Integrated with GIS and Remote Sensing Approaches

Asare-Kyei

Forkuor

Venus

2015

Water

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Robust risk assessment requires accurate flood intensity area mapping to allow for the identification of populations and elements at risk. However, available flood maps in West Africa lack spatial variability while global datasets have resolutions too coarse to be relevant for local scale risk assessment. Consequently, local disaster managers are forced to use traditional methods such as watermarks on buildings and media reports to identify flood hazard areas. In this study, remote sensing and Geographic Information System (GIS) techniques were combined with hydrological and statistical models to delineate the spatial limits of flood hazard zones in selected communities in Ghana, Burkina Faso and Benin. The approach involves estimating peak runoff concentrations at different elevations and then applying statistical methods to develop a Flood Hazard Index (FHI). Results show that about half of the study areas fall into high intensity flood zones. Empirical validation using statistical confusion matrix and the principles of Participatory GIS show that flood hazard areas could be mapped at an accuracy ranging from 77% to 81%. This was supported with local expert knowledge which accurately classified 79% of communities deemed to be highly OPEN ACCESSWater 2015, 7 3532 susceptible to flood hazard. The results will assist disaster managers to reduce the risk to flood disasters at the community level where risk outcomes are first materialized.

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Section: Quantitative Validation Of the Flood Hazard Index With Pgis mentioning

confidence: 99%

Modeling Flood Hazard Zones at the Sub-District Level with the Rational Model Integrated with GIS and Remote Sensing Approaches

Asare-Kyei

Forkuor

Venus

2015

Water

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“…Although many neural-network models have been developed, the multilayer perceptron (MLP) feedforward neural network is the most frequently used. The MLP has been applied in land-use/land-cover classifications (Kavzoglu and Mather 2003;Zhang and Foody 2001;Joshi et al, 2006), impervious surface estimation (Weng et al, 2008), and change detection (Li and Yeh 2002;Nemmour and Chibani, 2006). The LANDSAT data archive has played an important role across many disciplines, being used as a tool to achieve improved understanding of the Earth's land surfaces and human impacts on the environment.…”

Section: Introductionmentioning

confidence: 99%

Multi-Temporal Land Use Analysis of an Ephemeral River Area Using an Artificial Neural Network Approach on Landsat Imagery

Aquilino

Tarantino

Fratino

2014

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:This paper proposes a change detection analysis method based on multitemporal LANDSAT satellite data, presenting a study performed on the Lama San Giorgio (Bari, Italy) river basin area. Based on its geological and hydrological characteristics, as well as on the number of recent and remote flooding events already occurred, this area seems to be naturally prone to flooding. The historical archive of LANDSAT imagery dating back to the launch of ERTS in 1972 provides a comprehensive and permanent data source for tracking change on the planet"s land surface. In this study case the imagery acquisition dates of 1987, 2002 and 2011 were selected to cover a time trend of 24 years. Land cover categories were based on classes outlined by the Curve Number method with the aim of characterizing land use according to the level of surface imperviousness. After comparing two land use classification methods, i.e. Maximum Likelihood Classifier (MLC) and Multi-Layer Perceptron (MLP) neural network, the Artificial Neural Networks (ANN) approach was found the best reliable and efficient method in the absence of ground reference data. The ANN approach has a distinct advantage over statistical classification methods in that it is non-parametric and requires little or no a priori knowledge on the distribution model of input data. The results quantify land cover change patterns in the river basin area under study and demonstrate the potential of multitemporal LANDSAT data to provide an accurate and costeffective means to map and analyse land cover changes over time that can be used as input in land management and policy decisionmaking.

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“…The association of thermal and optical data has already been realized in other automatic classifications with outlines extractions contexts: glaciology (Raciviteanu et al 2008), the canopy (Joshi et al, 2006), agriculture (Kasdan, 1979;Saito et al, 2001). For similar spectral resolution data, the error matrix results are comparable to those obtained by our methodology.…”

Section: Discussionmentioning

confidence: 99%

Automatic Mapping of the Lava Flows at Piton de la Fournaise Volcano, by Combining Thermal Data in Near and Visible Infrared

Servadio¹,

Villeneuve²,

Bachèlery³

2012

Remote Sensing - Applications

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Remotely sensed estimation of forest canopy density: A comparison of the performance of four methods

Cited by 87 publications

References 31 publications

Modeling Flood Hazard Zones at the Sub-District Level with the Rational Model Integrated with GIS and Remote Sensing Approaches

Modeling Flood Hazard Zones at the Sub-District Level with the Rational Model Integrated with GIS and Remote Sensing Approaches

Multi-Temporal Land Use Analysis of an Ephemeral River Area Using an Artificial Neural Network Approach on Landsat Imagery

Automatic Mapping of the Lava Flows at Piton de la Fournaise Volcano, by Combining Thermal Data in Near and Visible Infrared

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