Classifying regenerating forest stages in Amazônia using remotely sensed images and a neural network

Kuplich, Tatiana Mora

doi:10.1016/j.foreco.2006.05.066

Cited by 51 publications

(29 citation statements)

References 33 publications

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“…In order to make use of the available reflectance and backscatter spectra of optical and SAR, class distributions should be derived from multivariate time series. Using all available Landsat bands and other metrics (e.g., Landsat Band 5 or band 7, normalized burn ratio, tasseled cap wetness, texture), for example, showed improved F and NF class distinction [21,44,80,81].…”

Section: Discussionmentioning

confidence: 99%

A Bayesian Approach to Combine Landsat and ALOS PALSAR Time Series for Near Real-Time Deforestation Detection

et al. 2015

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Abstract:To address the need for timely information on newly deforested areas at medium resolution scale, we introduce a Bayesian approach to combine SAR and optical time series for near real-time deforestation detection. Once a new image of either of the input time series is available, the conditional probability of deforestation is computed using Bayesian updating, and deforestation events are indicated. Future observations are used to update the conditional probability of deforestation and, thus, to confirm or reject an indicated deforestation event. A proof of concept was demonstrated using Landsat NDVI and ALOS PALSAR time series acquired at an evergreen forest plantation in Fiji. We emulated a near real-time scenario and assessed the deforestation detection accuracies using three-monthly reference data covering the entire study site. Spatial and temporal accuracies for the fused Landsat-PALSAR case (overall accuracy = 87.4%; mean time lag of detected deforestation = 1.3 months) were consistently higher than those of the Landsat-and PALSAR-only cases. The improvement maintained even for increasing missing data in the Landsat time series.

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Section: Discussionmentioning

confidence: 99%

A Bayesian Approach to Combine Landsat and ALOS PALSAR Time Series for Near Real-Time Deforestation Detection

et al. 2015

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“…Forest age has been modeled from Synthetic Aperture Radar (SAR) backscatter texture [9], but in many cases researchers can only resolve recently disturbed and old growth stands [10,11] and confidence intervals increase with stand age [12]. Backscatter temporal variation has also been used to model stand age [13,14], but this approach requires multiple acquisitions.…”

Section: Motivationmentioning

confidence: 99%

Using InSAR Coherence to Map Stand Age in a Boreal Forest

2012

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Abstract:The interferometric coherence parameter γ estimates the degree of correlation between two Synthetic Aperture Radar (SAR) images and can be influenced by vegetation structure. Here, we investigate the use of repeat-pass interferometric coherence γ to map stand age, an important parameter for the study of carbon stocks and forest regeneration. In August 2009 NASA's L-band airborne sensor UAVSAR (Uninhabited Aerial Vehicle Synthetic Aperture Radar) acquired zero-baseline data over Quebec with temporal separation ranging between 45 min and 9 days. Our analysis focuses on a 66 km 2 managed boreal forest and addresses three questions: (i) Can coherence from L-band systems be used to model forest age? (ii) Are models sensitive to weather events and temporal baseline? and (iii) How is model accuracy impacted by the spatial scale of analysis? Linear regression models with 2-day baseline showed the best results and indicated an inverse relationship between γ and stand age. Model accuracy improved at 5 ha scale (R 2 = 0.75, RMSE = 5.3) as compared to 1 ha (R 2 = 0.67, RMSE = 5.8). Our results indicate that coherence measurements from L-band repeat-pass systems can estimate forest age accurately and with no saturation. However, empirical model relationships and their accuracy are sensitive to weather events, temporal baseline, and spatial scale of analysis.

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“…A relatively low correspondence between regenerative age (class) and spectral information has usually been reported (Sader et al 1989, Lucas et al 2000, Lu 2005 and references therein, Kuplich 2006). Recently, hyperspectral images (Thenkabail et al 2004), Landsat ETM+ images (Vieira et al 2003) and radar images combined with optical images (Kuplich 2006) have provided promising results in discriminating between secondary forest classes. Vieira (2003) and Kuplich (2006) also linked information on dominant tree species to the forest classes distinguished in the remotely sensed data.…”

Section: Vegetation Classesmentioning

confidence: 99%

“…Recently, hyperspectral images (Thenkabail et al 2004), Landsat ETM+ images (Vieira et al 2003) and radar images combined with optical images (Kuplich 2006) have provided promising results in discriminating between secondary forest classes. Vieira (2003) and Kuplich (2006) also linked information on dominant tree species to the forest classes distinguished in the remotely sensed data. Some forest types traditionally recognised by indigenous peoples have also been classified from satellite images (Shepard et al 2004, HernandezStefanoni et al 2006).…”

Section: Vegetation Classesmentioning

confidence: 99%

Remote sensing of floristic patterns in the lowland rain forest landscape

Thessler¹

2008

Diss. For.

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Land use and conservation planning urgently need information on floristic variation over large rain forest areas. Floristic variation can not be inventoried in every location and of all the flora, thus inventory is limited in sample sites of a group(s) of indicator species and modelled to predict the floristic composition of non-inventoried sites using spatially continuous information on the environment. Modelling is, however, practicable only if the dimensions of species data can be drastically reduced to a surrogate of floristic composition. The aim was to explore whether remote sensing can be applied to study and map the spatial variation of surrogates in lowland old-growth rain forest.I studied three surrogates: 1) number of species in ecological categories, 2) vegetation / forest type classification, and 3) species composition, summarized as the scores of three ordination axes. The understorey Melastomataceae and pteridophytes, and tree and palm species were used as indicator species. Landsat TM or ETM+ -satellite images and the SRTM digital elevation model were used as a proxy of environmental variation. The prediction methods included a k nearest neighbour method and linear discriminant analysis. The study areas were located in eastern Ecuador, in north-eastern Peru and northern Costa Rica.The main finding was that floristic patterns in lowland rain forest, expressed as vegetation classes, ordination axis scores or the number of species in ecological categories, can be predicted on the basis of remotely sensed data and field observations. The accuracy of the predictions depended on feature selection and weighting and on spatial resolution. The k-nn method proved to be a promising method in predicting floristic variation when it was expressed as a continuous variable, such as ordination axis scores or number of species. It also performed better than linear discriminant analysis in distinguishing forest classes using satellite image data.

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Classifying regenerating forest stages in Amazônia using remotely sensed images and a neural network

Cited by 51 publications

References 33 publications

A Bayesian Approach to Combine Landsat and ALOS PALSAR Time Series for Near Real-Time Deforestation Detection

A Bayesian Approach to Combine Landsat and ALOS PALSAR Time Series for Near Real-Time Deforestation Detection

Using InSAR Coherence to Map Stand Age in a Boreal Forest

Remote sensing of floristic patterns in the lowland rain forest landscape

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