Tree cover and carbon mapping of Argentine savannas: Scaling from field to region

González‐Roglich, Mariano; Swenson, Jennifer J.

doi:10.1016/j.rse.2015.11.021

Cited by 32 publications

(20 citation statements)

References 43 publications

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“…To test model performance, we used spatially independent testing data, which provided a better estimate of the true mapping accuracy (Gonz alez-Roglich & Swenson, 2016;Schwantes et al, 2016), by conducting leave-one-out cross validation for each orthophoto, independently for each of the six modeling zones (see supporting information for a complete description). As is the case for most remote sensing studies, we were only mapping aerial cover, defined as the uppermost layer of canopy (Fehmi, 2010); we were not mapping overlapping canopy cover.…”

Section: Creating Training and Testing Datamentioning

confidence: 99%

Measuring canopy loss and climatic thresholds from an extreme drought along a fivefold precipitation gradient across Texas

Schwantes

Swenson

González‐Roglich

et al. 2017

Global Change Biology

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Globally, trees are increasingly dying from extreme drought, a trend that is expected to increase with climate change. Loss of trees has significant ecological, biophysical, and biogeochemical consequences. In 2011, a record drought caused widespread tree mortality in Texas. Using remotely sensed imagery, we quantified canopy loss during and after the drought across the state at 30-m spatial resolution, from the eastern pine/hardwood forests to the western shrublands, a region that includes the boundaries of many species ranges. Canopy loss observations in ~200 multitemporal fine-scale orthophotos (1-m) were used to train coarser Landsat imagery (30-m) to create 30-m binary statewide canopy loss maps. We found that canopy loss occurred across all major ecoregions of Texas, with an average loss of 9.5%. The drought had the highest impact in post oak woodlands, pinyon-juniper shrublands and Ashe juniper woodlands. Focusing on a 100-km by ~1,000-km transect spanning the State's fivefold east-west precipitation gradient (~1,500 to ~300 mm), we compared spatially explicit 2011 climatic anomalies to our canopy loss maps. Much of the canopy loss occurred in areas that passed specific climatic thresholds: warm season anomalies in mean temperature (+1.6°C) and vapor pressure deficit (VPD, +0.66 kPa), annual percent deviation in precipitation (-38%), and 2011 difference between precipitation and potential evapotranspiration (-1,206 mm). Although similarly low precipitation occurred during the landmark 1950s drought, the VPD and temperature anomalies observed in 2011 were even greater. Furthermore, future climate data under the representative concentration pathway 8.5 trajectory project that average values will surpass the 2011 VPD anomaly during the 2070-2099 period and the temperature anomaly during the 2040-2099 period. Identifying vulnerable ecological systems to drought stress and climate thresholds associated with canopy loss will aid in predicting how forests will respond to a changing climate and how ecological landscapes will change in the near term.

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Section: Creating Training and Testing Datamentioning

confidence: 99%

Measuring canopy loss and climatic thresholds from an extreme drought along a fivefold precipitation gradient across Texas

Schwantes

Swenson

González‐Roglich

et al. 2017

Global Change Biology

Self Cite

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“…Forest canopy cover (CC), which is defined as the proportion of the forest floor that is covered by the vertical projection of the tree crown [1], plays a major role in understanding the structure and health condition of forest ecosystems [2]. It is also a useful measure for evaluating the leaf area index (LAI), carbon stocks, tree density, and wildlife microhabitat [3][4][5][6]. The growth and diversity of understory vegetation is also related to CC [7].…”

Section: Introductionmentioning

confidence: 99%

“…Optical instruments, such as digital hemispherical photographs (DHPs), LAI-2200 plant canopy analyzers, and AccuPAR Ceptometers are widely adopted for CC acquisition due to their non-destructive nature [13]. However, these methods suffer from many shortcomings; for example, they are time-consuming, labor-intensive, and difficult to apply for large areas [5,14]. Thus, a method is needed that can be used to easily extract forest CC over a large area.…”

Section: Introductionmentioning

confidence: 99%

“…The NDVI, SR, and SAVI derived from SPOT 5 satellite data were used to estimate CC by Chasmer et al [25], their results indicated that the NDVI and SAVI are more comparable to CC. In addition, González-Roglich and Swenson [5] studied the tree cover and carbon of Argentine savannas using seven VIs that are based on the China Brazil Earth Resources Satellite series and Landsat images. Karlson et al [24] estimated the tree CC and aboveground biomass using six VIs based on the Landsat 8 OLI in a woodland landscape.…”

Section: Introductionmentioning

confidence: 99%

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Estimating Forest Canopy Cover in Black Locust (Robinia pseudoacacia L.) Plantations on the Loess Plateau Using Random Forest

Zhao

Wang

Zhao

et al. 2018

Forests

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The forest canopy is the medium for energy and mass exchange between forest ecosystems and the atmosphere. Remote sensing techniques are more efficient and appropriate for estimating forest canopy cover (CC) than traditional methods, especially at large scales. In this study, we evaluated the CC of black locust plantations on the Loess Plateau using random forest (RF) regression models. The models were established using the relationships between digital hemispherical photograph (DHP) field data and variables that were calculated from satellite images. Three types of variables were calculated from the satellite data: spectral variables calculated from a multispectral image, textural variables calculated from a panchromatic image (Tpan) with a 15 × 15 window size, and textural variables calculated from spectral variables (TB+VIs) with a 9 × 9 window size. We compared different mtry and ntree values to find the most suitable parameters for the RF models. The results indicated that the RF model of spectral variables explained 57% (root mean square error (RMSE) = 0.06) of the variability in the field CC data. The soil-adjusted vegetation index (SAVI) and enhanced vegetation index (EVI) were more important than other spectral variables. The RF model of Tpan obtained higher accuracy (R2 = 0.69, RMSE = 0.05) than the spectral variables, and the grey level co-occurrence matrix-based texture measure—Correlation (COR) was the most important variable for Tpan. The most accurate model was obtained from the TB+VIs (R2 = 0.79, RMSE = 0.05), which combined spectral and textural information, thus providing a significant improvement in estimating CC. This model provided an effective approach for detecting the CC of black locust plantations on the Loess Plateau.

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“…Trees affect terrestrial energy exchanges such as photosynthesis, transpiration, and net primary production [1], and play an important role within and outside forest ecosystems (e.g., on farms and pastures, and in urban areas) [2,3]. Tree cover is defined structurally as the proportion of ground by, vertically projected area of woody plants (including leaves, stem, branches, etc.)…”

Section: Introductionmentioning

confidence: 99%

The Spatiotemporal Variation of Tree Cover in the Loess Plateau of China after the ‘Grain for Green’ Project

et al. 2017

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Analyzing spatiotemporal variation of tree cover could enhance understanding of the environment and promote a sustainable resource use of ecosystems. This study investigated the variation in tree cover in the Loess Plateau after an ecological restoration effort called the 'Grain for Green Project' (GGP). The results show that the proportion of tree covered area in the Loess Plateau changed from 73% to 88%, with the cumulative tree cover fluctuating from approximately 7% to 11%, and the average annual tree cover increased from 10% in 2000 to 12% in 2014. Based on tree cover values over the course of 15 years, the study area was classified into five regions, which provide much more information for spatial assessment of tree cover change in the Loess Plateau spatially. The increase in tree cover value was mainly in the core part of Loess Plateau, the mountains, and the edge of the mountain areas; whereas the values were stable in 36.21% of the area, and a decrease was noted in 5.63% of the area, primarily located in the low plain areas. Approximately 26.36% of the Loess Plateau will show a sustained increase in tree cover in the future. The results of this study will facilitate us to understand the current conditions and development of the GGP's effects, and offer a valuable reference for future detection of tree cover change through geographic information system (GIS) and remote sensing (RS) tools.

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Tree cover and carbon mapping of Argentine savannas: Scaling from field to region

Cited by 32 publications

References 43 publications

Measuring canopy loss and climatic thresholds from an extreme drought along a fivefold precipitation gradient across Texas

Measuring canopy loss and climatic thresholds from an extreme drought along a fivefold precipitation gradient across Texas

Estimating Forest Canopy Cover in Black Locust (Robinia pseudoacacia L.) Plantations on the Loess Plateau Using Random Forest

The Spatiotemporal Variation of Tree Cover in the Loess Plateau of China after the ‘Grain for Green’ Project

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