BackgroundThe quantification and spatially explicit mapping of carbon stocks in terrestrial ecosystems is important to better understand the global carbon cycle and to monitor and report change processes, especially in the context of international policy mechanisms such as REDD+ or the implementation of Nationally Determined Contributions (NDCs) and the UN Sustainable Development Goals (SDGs). Especially in heterogeneous ecosystems, such as Savannas, accurate carbon quantifications are still lacking, where highly variable vegetation densities occur and a strong seasonality hinders consistent data acquisition. In order to account for these challenges we analyzed the potential of land surface phenological metrics derived from gap-filled 8-day Landsat time series for carbon mapping. We selected three areas located in different subregions in the central Brazil region, which is a prominent example of a Savanna with significant carbon stocks that has been undergoing extensive land cover conversions. Here phenological metrics from the season 2014/2015 were combined with aboveground carbon field samples of cerrado sensu stricto vegetation using Random Forest regression models to map the regional carbon distribution and to analyze the relation between phenological metrics and aboveground carbon.ResultsThe gap filling approach enabled to accurately approximate the original Landsat ETM+ and OLI EVI values and the subsequent derivation of annual phenological metrics. Random Forest model performances varied between the three study areas with RMSE values of 1.64 t/ha (mean relative RMSE 30%), 2.35 t/ha (46%) and 2.18 t/ha (45%). Comparable relationships between remote sensing based land surface phenological metrics and aboveground carbon were observed in all study areas. Aboveground carbon distributions could be mapped and revealed comprehensible spatial patterns.ConclusionPhenological metrics were derived from 8-day Landsat time series with a spatial resolution that is sufficient to capture gradual changes in carbon stocks of heterogeneous Savanna ecosystems. These metrics revealed the relationship between aboveground carbon and the phenology of the observed vegetation. Our results suggest that metrics relating to the seasonal minimum and maximum values were the most influential variables and bear potential to improve spatially explicit mapping approaches in heterogeneous ecosystems, where both spatial and temporal resolutions are critical.Electronic supplementary materialThe online version of this article (10.1186/s13021-018-0097-1) contains supplementary material, which is available to authorized users.
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.
Resprouting is an important trait that allows plants to persist after fire and is considered a key functional trait in woody plants. While resprouting is well documented in fire-prone biomes, information is scarce in non-fire-prone ecosystems, such as New Zealand (NZ) forests. Our objective was to investigate patterns of post-fire resprouting in NZ by identifying the ability of species to resprout and quantifying the resprouting rates within the local plant community. Fire occurrence is likely to increase in NZ as a consequence of climate change, and this investigation addresses an important knowledge gap needed for planning restoration actions in fire-susceptible regions. The study was conducted in two phases: (1) A detailed review of the resprouting ability of the NZ woody flora, and (2) a field study where the post-fire responses of plants were quantified. The field study was undertaken in the eastern South Island, where woody plants (>5 cm diameter at 30 cm height) were sampled in 10 plots (10x10 m), five- and 10-months post-fire. The research synthesized the resprouting ability of 73 woody species and is the first to provide extensive quantitative data on resprouting in NZ. Most of the canopy dominant species were non-resprouters, but many smaller trees and shrubs were capable of resprouting, despite their evolution in an environment with low fire frequency. Species composition and abundance were important predictors of resprouting patterns among plots, with similar communities resulting in similar resprouting responses. Resprouting capacity provides species with a competitive advantage in the post-fire recovery. We suggest that it is possible to engineer more fire resilient restoration plantings by planting higher proportions of the resprouters identified in this study. The incorporation of resprouting as a trait in restoration plans is likely to be relevant not just in NZ, but also in other non-fire-prone regions facing increases in fire frequency.
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