Mapping Brazilian savanna vegetation gradients with Landsat time series

Schwieder, Marcel; Leitão, Pedro J.; Bustamante, Mercedes M. C.; Ferreira, Laerte Guimarães; Rabe, Andreas; Hostert, Patrick

doi:10.1016/j.jag.2016.06.019

Cited by 86 publications

(99 citation statements)

References 34 publications

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“…Since modularity may increase with habitat complexity (Macfadyen, Gibson, Symondson, & Memmott, ; Pimm & Lawton, ; Rezende, Albert, Fortuna, & Bascompte, ), we also expected an increase of modularity in the warm‐wet season. More specifically, we expected that with the biomass increasing of the vegetation during the rainy season (Schwieder et al, ), new microhabitats would be available for groups of individuals to exploit their resources, generating modules. Therefore, we also expected a positive relationship between habitat structure related to vegetation density and modularity considering the four distinct populations studied in both cool‐dry and warm‐wet seasons.…”

Section: Introductionmentioning

confidence: 99%

“…This is the case of the highly seasonal Neotropical savanna-the Cerrado, which presents well-defined cool-dry and warm-wet seasons (Eiten, 1972). Therefore, food resources availability can vary between seasons (Gouveia & Felfili, 1998;Pinheiro, Diniz, Coelho, & Bandeira, 2002;Silva, Frizzas, & Oliveira, 2011), as well as the microhabitat structure (e.g., herbaceous and canopy cover) due to the expansion and the retraction of the vegetation biomass (Schwieder et al, 2016). In this Neotropical savanna, several mammal species present betweenseason differences in both diet and space use (Camargo, Ribeiro, Camargo, & Vieira, 2014a, 2014bHannibal & Caceres, 2010;Lessa & da Costa, 2010;Ribeiro, 2011;Vieira, 2003).…”

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confidence: 99%

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Availability of food resources and habitat structure shape the individual‐resource network of a Neotropical marsupial

Camargo

Oliveira

Ribeiro

et al. 2019

Ecology and Evolution

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Spatial and temporal variation in networks has been reported in different studies. However, the many effects of habitat structure and food resource availability variation on network structures have remained poorly investigated, especially in individual‐based networks. This approach can shed light on individual specialization of resource use and how habitat variations shape trophic interactions. To test hypotheses related to habitat variability on trophic interactions, we investigated seasonal and spatial variation in network structure of four populations of the marsupial Gracilinanus agilis in the highly seasonal tropical savannas of the Brazilian Cerrado. We evaluated such variation with network nestedness and modularity considering both cool‐dry and warm‐wet seasons, and related such variations with food resource availability and habitat structure (considered in the present study as environmental variation) in four sites of savanna woodland forest. Network analyses showed that modularity (but not nestedness) was consistently lower during the cool‐dry season in all G. agilis populations. Our results indicated that nestedness is related to habitat structure, showing that this metric increases in sites with thick and spaced trees. On the other hand, modularity was positively related to diversity of arthropods and abundance of fruits. We propose that the relationship between nestedness and habitat structure is an outcome of individual variation in the vertical space and food resource use by G. agilis in sites with thick and spaced trees. Moreover, individual specialization in resource‐rich and population‐dense periods possibly increased the network modularity of G. agilis . Therefore, our study reveals that environment variability considering spatial and temporal components is important for shaping network structure of populations.

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Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

Availability of food resources and habitat structure shape the individual‐resource network of a Neotropical marsupial

Camargo

Oliveira

Ribeiro

et al. 2019

Ecology and Evolution

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“…Indeed, pioneer studies have made use of spectral indices derived from hyperspectral Hyperion data (Pearlman et al 2001) for modeling aboveground biomass of both woody and non-woody vegetation (Psomas et al 2011, Zandler et al 2015, or estimating forest structure and diversity parameters (Kalacska et al 2007). We focus on three study sites in the Brazilian savanna (Cerrado), a highly heterogeneous system, which consists on a mosaic of different vegetation physiognomies (Schwieder et al 2016). We focus on three study sites in the Brazilian savanna (Cerrado), a highly heterogeneous system, which consists on a mosaic of different vegetation physiognomies (Schwieder et al 2016).…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2018

Self Cite

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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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“…Major techniques used for the detection, classification, and mapping of vegetation using remote sensing imagery are vegetation indices [20,21], spectral mixture analysis [22], temporal image-fusion [23,24], texture based measures [25], and supervised classification using machine learning classifiers such as maximum likelihood [26], random forests [27,28], decision trees [29], support vector machines [30], fuzzy learning [31], and neural networks [32][33][34]. Nevertheless, performance of existing large-scale land cover maps is limited to the discrimination of vegetation physiognomic types, which is still a challenging field [35].…”

Section: Introductionmentioning

confidence: 99%

High-Resolution Vegetation Mapping in Japan by Combining Sentinel-2 and Landsat 8 Based Multi-Temporal Datasets through Machine Learning and Cross-Validation Approach

Sharma

Hara

Tateishi

2017

Land

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This paper presents an evaluation of the multi-source satellite datasets such as Sentinel-2, Landsat-8, and Moderate Resolution Imaging Spectroradiometer (MODIS) with different spatial and temporal resolutions for nationwide vegetation mapping. The random forests based machine learning and cross-validation approach was applied for evaluating the performance of different datasets. Cross-validation with the rich-feature datasets-with a sample size of 390-showed that the MODIS datasets provided highest classification accuracy (Overall accuracy = 0.80, Kappa coefficient = 0.77) compared with Landsat 8 (Overall accuracy = 0.77, Kappa coefficient = 0.74) and Sentinel-2 (Overall accuracy = 0.66, Kappa coefficient = 0.61) datasets. As a result, temporally rich datasets were found to be crucial for the vegetation physiognomic classification. However, in the case of Landsat 8 or Sentinel-2 datasets, sample size could be increased excessively as around 9800 ground truth points could be prepared within 390 MODIS pixel-sized polygons. The increase in the sample size significantly enhanced the classification using Landsat-8 datasets (Overall accuracy = 0.86, Kappa coefficient = 0.84). However, Sentinel-2 datasets (Overall accuracy = 0.77, Kappa coefficient = 0.74) could not perform as much as the Landsat-8 datasets, possibly because of temporally limited datasets covered by the Sentinel-2 satellites so far. A combination of the Landsat-8 and Sentinel-2 datasets slightly improved the classification (Overall accuracy = 0.89, Kappa coefficient = 0.87) than using the Landsat 8 datasets separately. Regardless of the fact that Landsat 8 and Sentinel-2 datasets have lower temporal resolutions than MODIS datasets, they could enhance the classification of otherwise challenging vegetation physiognomic types due to possibility of training a wider variation of physiognomic types at 30 m resolution. Based on these findings, an up-to-date 30 m resolution vegetation map was generated by using Landsat 8 and Sentinel-2 datasets, which showed better accuracy than the existing map in Japan.

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Mapping Brazilian savanna vegetation gradients with Landsat time series

Cited by 86 publications

References 34 publications

Availability of food resources and habitat structure shape the individual‐resource network of a Neotropical marsupial

Availability of food resources and habitat structure shape the individual‐resource network of a Neotropical marsupial

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

High-Resolution Vegetation Mapping in Japan by Combining Sentinel-2 and Landsat 8 Based Multi-Temporal Datasets through Machine Learning and Cross-Validation Approach

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