Using spectral and textural features from RapidEye images to estimate age and structural parameters of Cerrado vegetation

Gomes, Marília Ferreira; Maillard, Philippe

doi:10.1080/01431161.2015.1055604

Cited by 8 publications

(17 citation statements)

References 33 publications

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“…The use of textural information was acknowledged as a strong attribute for differentiating vegetation classes by several authors [52,[92][93][94][95][96][97]. Sette and Maillard [94] classified vegetation successional stages of a Dense Ombrophilous Forest in the Atlantic Rain Forest with FORMOSAT-2 imagery and obtained an overall accuracy of 60.5% based only on visible bands, which rose to 91% when the classification also relied on textural attributes.…”

Section: Discussionmentioning

confidence: 99%

Evaluating Sentinel-2 and Landsat-8 Data to Map Sucessional Forest Stages in a Subtropical Forest in Southern Brazil

et al. 2017

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Studies designed to discriminate different successional forest stages play a strategic role in forest management, forest policy and environmental conservation in tropical environments. The discrimination of different successional forest stages is still a challenge due to the spectral similarity among the concerned classes. Considering this, the objective of this paper was to investigate the performance of Sentinel-2 and Landsat-8 data for discriminating different successional forest stages of a patch located in a subtropical portion of the Atlantic Rain Forest in Southern Brazil with the aid of two machine learning algorithms and relying on the use of spectral reflectance data selected over two seasons and attributes thereof derived. Random Forest (RF) and Support Vector Machine (SVM) were used as classifiers with different subsets of predictor variables (multitemporal spectral reflectance, textural metrics and vegetation indices). All the experiments reached satisfactory results, with Kappa indices varying between 0.9, with Landsat-8 spectral reflectance alone and the SVM algorithm, and 0.98, with Sentinel-2 spectral reflectance alone also associated with the SVM algorithm. The Landsat-8 data had a significant increase in accuracy with the inclusion of other predictor variables in the classification process besides the pure spectral reflectance bands. The classification methods SVM and RF had similar performances in general. As to the RF method, the texture mean of the red-edge and SWIR bands were considered the most important ranked attributes for the classification of Sentinel-2 data, while attributes resulting from multitemporal bands, textural metrics of SWIR bands and vegetation indices were the most important ones in the Landsat-8 data classification.

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Section: Discussionmentioning

confidence: 99%

Evaluating Sentinel-2 and Landsat-8 Data to Map Sucessional Forest Stages in a Subtropical Forest in Southern Brazil

et al. 2017

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“…Savanna areas used for eucalyptus plantation in the past have a more open canopy, shorter trees, lower basal area, and slightly less trees and shrubs per hectare than old growth savanna (Maillard & Costa‐Pereira , Gomes & Maillard ), whereas the variation in vegetation structure within former eucalyptus areas is subtle and is likely to be better explained by fire history and other local conditions than regeneration age (Maillard & Costa‐Pereira ). We did not have accurate information on vegetation structure of mixed‐use areas, however, a lower NDVI value in portions of southern VPSP (Gomes ) and the general appearance of the vegetation allow us to infer that vegetation structure in sites that we classified as mixed‐use is more similar to areas used for eucalyptus.…”

Section: Methodsmentioning

confidence: 99%

“…, Whitfeld et al . , Gomes & Maillard ). Cerrado regeneration generally follows a path from open to dense vegetation, with regeneration typified by an increase in tree density and height, and a decrease in herbaceous cover (Durigan & Ratter , Maillard & Costa‐Pereira ).…”

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confidence: 98%

“…, Greene & Johnson , Zardo & Henriques ). (3) Occupancy would be higher in secondary savanna for herbivores that feed predominantly on the grass layer and for species that favor more open habitats (such as Pampas deer Ozotoceros bezoarticus and giant anteater Myrmecophaga tridactyla ), as secondary vegetation in VPSP tend to have a more open canopy (Gomes & Maillard ).…”

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Assessing the conservation value of secondary savanna for large mammals in the Brazilian Cerrado

et al. 2017

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Debate about the conservation value of secondary habitats has tended to focus on tropical forests, increasingly recognizing the role of secondary forests for biodiversity conservation. However, there remains a lack of information about the conservation value of secondary savannas. Here, we conducted a camera trap survey to assess the effect of secondary vegetation on large mammals in a Brazilian Cerrado protected area, using a single‐season occupancy framework to investigate the response of individual species (species‐level models) and of all species combined (community‐level models). In addition, we investigated the cost effectiveness of different sampling designs to monitor globally threatened species in the study area. At the community level, savanna that regenerated from eucalyptus plantation had similar occupancy estimate as old growth areas. At the species level, none of the ten species individually assessed seemed to respond to succession stage, with greater support for the effect of other covariates on occupancy, such as distance from water and vegetation physiognomy. These results demonstrate that secondary vegetation does not appear to negatively impact large mammals in the study area and suggest that, given a favorable context, Cerrado mammals can recolonize and use secondary savannas that regenerated from clearcut. However, our study area should be considered a best‐case scenario, as it retained key ecological attributes of high‐value secondary habitats. Our simulations showed that a sampling design with 60 camera trap sites surveyed during nine occasions is appropriate to monitor most globally threatened species in the study area, and could be a useful starting point for new monitoring initiatives in other Cerrado areas.

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“…2; Tyc et al 2005). for characterizing vegetation condition and structure (Gitelson et al 1996, Gomes andMaillard 2015). 1.…”

Section: Remote Sensing Datamentioning

confidence: 99%

From sample to pixel: multi‐scale remote sensing data for upscaling aboveground carbon data in heterogeneous landscapes

et al. 2018

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In times of rapid global change, ecosystem monitoring is of utmost importance. Combined field and remote sensing data enable large-scale ecosystem assessments, while maintaining local relevance and accuracy. In heterogeneous landscapes, however, the integration of field-collected data with remote sensing image pixels is not a trivial matter. Indeed, much of the uncertainty in models that use remote sensing to map larger areas lies on the field data integration. In this study, we propose to use fine spatial resolution (5 9 5 m 2 ) remote sensing data as auxiliary data for upscaling field-sampled aboveground carbon data to target (meso-scale, i.e., 30 9 30 m 2 ) image pixels. In this process, we assess the effects of field data disaggregation and extrapolation, with and without the auxiliary data. We test this on three study sites in heterogeneous landscapes of the Brazilian savanna. We thus compare two methods that use auxiliary data-surface method, which uses a weighting layer, and regression method, which applies a regression model-with one method without auxiliary data-cartographic method. To evaluate our results, we compared observed vs. estimated aboveground carbon values (for known samples) at the pixel level. Additionally, we fitted a random forest regression model with the assigned carbon estimates and the target satellite imagery and assessed the influence of the fraction of extrapolated vs. sampled carbon values on model performance. We observed that, in heterogeneous landscapes, the use of fine spatial resolution remote sensing data improves the upscaling of field-based aboveground carbon data to coarser image pixels. We also show that a surface method is more suitable for spatial disaggregation, while a regression approach is preferable for extrapolating non-sampled pixel fractions. In our study, larger datasets, which included a higher proportion of estimated values, generally delivered better models of aboveground carbon than smaller datasets that are assumed to more reliably reflect reality. Our approach enables to link field and remote sensing data, which in turn enables the detailed mapping of aboveground carbon in heterogeneous landscapes over large areas through the optimized integration of field data and multi-scale remote sensing data.

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Using spectral and textural features from RapidEye images to estimate age and structural parameters of Cerrado vegetation

Cited by 8 publications

References 33 publications

Evaluating Sentinel-2 and Landsat-8 Data to Map Sucessional Forest Stages in a Subtropical Forest in Southern Brazil

Evaluating Sentinel-2 and Landsat-8 Data to Map Sucessional Forest Stages in a Subtropical Forest in Southern Brazil

Assessing the conservation value of secondary savanna for large mammals in the Brazilian Cerrado

From sample to pixel: multi‐scale remote sensing data for upscaling aboveground carbon data in heterogeneous landscapes

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