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opencc-by-4.0Oct 2024View details →

GLOBMAP FTC China: an annual fractional tree cover dataset in China from 2000 to 2022

We generated an annual fractional tree cover dataset (GLOBMAP) in China from 2000 to 2022 with a resolution of 250 m using the MODIS land surface reflectance products. The dataset is generated using a feedforward neural network. A total of 2000 samples are automatically extracted from the very high spatial resolution imagery on Google Earth through an auto-segmentation algorithm to calibrate and validate the model, with 80% for training and the remaining 20% for validation. Seven highly discriminative input features are extracted from the annual series of MODIS data, including values in the red (B01) and short wavelength infra-red 2.1 (SWIR 2.1: B07) bands for three key time phases, which are the day of year (DOY) of the maximum NDVI during the starting month (near 136~167), the medium month (near 197~228) and the ending month (near 259~289) of the growing season, and the yearly minimum NDVI. The root mean square error (RMSE) and Mean Absolute Error (MAE) of the dataset are 11.78% and 7.39%, respectively.   The dataset provides fractional tree cover maps in China from 2000 to 2022. The data are projected by WGS 1984 and are stored with GeoTIFF format. The files are designated with the naming structure “GLOBMAP_YYYY_TreeCover_China.tif”, where “YYYY” represents the year of the data. The valid range of tree cover value is 0-100, with a scale factor of 1.0 and the unit of percentage (%).

opencc-by-4.0Oct 2024View details →

Urban Tree Canopy Cover, environmental and socioeconomic data to São Paulo city, Brazil

Urban Tree Canopy Cover from 2017, elevation and roughness from 2011 and socioeconomic data from 2010, to São Paulo city, Brazil. These data are analyzed in: <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.ufug.2024.128497" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.ufug.2024.128497</a>

opencc-by-4.0Oct 2024View details →

Data from: Using Landsat time-series to investigate nearly 50 years of tree canopy cover change across an urban-rural landscape in southern Ontario

Paper Abstract: Canadian urban and adjacent landscapes have been dynamic over the last 50 years due to land management, land cover alternations, climate change, and disturbances. Remote sensing, particularly the Landsat archive, provides the only means to spatially quantify these long-term dynamics locally. Here, we explore the utility of Landsat, including the often-forgotten MSS sensor, for investigating percent tree canopy cover (TCC) change between 1972 and 2020 in a Canadian urban-rural context. We build a TCC time-series by training random forest models using visually interpreted TCC from high-resolution imagery. Predictors include topographic and yearly LandsatLinkr-harmonized and LandTrendr-fitted tasseled cap indices. Yearly binary TCC maps are built to mask consistently treeless areas and limit noise. To increase confidence in observed TCC change without historical reference imagery, we investigate multiple temporal validation options. Our TCC time-series (R2: 0.89, RMSE: 10.7%), quantifies TCC dynamics while limiting erroneous change and predictor space extrapolation. We explore TCC changes across landscapes, revealing periods of gain and loss associated with agricultural reforestation (1978-1996), housing development (on-going), drought (late 1990s), emerald ash borer (2010s), an ice storm (2013), and other drivers. Results demonstrate how long-term Landsat time-series can be used to better understand historical tree canopy change at local-regional scales.    Dataset details: See paper.  <ul> <li>cc_72to20.tif: Yearly tree CC predictions (1972-2020)</li> <li>always_nonforest10_nowater.tif: continuous-non-canopy mask</li> <li>water.tif: water mask</li> <li>Yearly.zip: Annual predictors (including CC10) and asc outputs</li> </ul>   See code on GitHub: <a href="https://github.com/ZZMitch/PredictTreeCC_Landsat_1972to2020">ZZMitch/PredictTreeCC_Landsat_1972to2020: Code from the portion of my PhD about using Landsat time-series to predict tree canopy cover from 1972 - 2020. Code will be released as papers are published. (github.com)</a>

dryad40/100

Data from: Determinants of tree cover in tropical floodplains

In uplands, water availability and fire limit tree cover, differentiating between tropical forest, savanna, and grassland biomes. In contrast, we know less about tropical floodplain tree cover determinants, although these habitats similarly include near-treeless grasslands, moderately treed savannas, and forests. Using GIS and remotely sensed data from 13 large tropical and sub-tropical floodplain ecosystems on five continents, we show that floodplain tree cover increases with climatic water balance (= rainfall - potential evapotranspiration) and decreases with frequent fire, as in uplands. Additionally, in floodplains, higher elevations and steeper slopes have higher tree cover, likely because these areas escape the worst inundation. Moreover, the effects of fire and topography are stronger in wetter regions. More prolonged and frequent flooding also reduce tree cover in climatically wetter floodplains but increase it in drier ones, although the effects are surprisingly weak compared to those of water balance, fire, and topography. Opposite to uplands, sandier soils have lower tree cover, likely because the benefits presented by deep tree roots in uplands are less important in floodplains with abundant water. While outright wetland conversion proceeds globally, floodplain tree cover faces other anthropogenic threats, like changing fire frequency and dam construction, especially in wet climates.

opencc-zeroOct 2019View details →

opencc-by-4.0Dec 2022View details →

MODIS tree cover change of North American boreal forests 2000-2019

The published files are two maps of North American boreal forest tree cover trends between 2000 and 2019. Pixel values are annual trends in tree cover expressed as % change per year. The trends are based on annual tree cover estimates from the MODIS Vegetation Continuous Field version 6 product. We quantified tree cover trends per pixel through Theil-Sen's slope estimation using the 'zyp' package in R. We followed the Yue-Pilon pre-whitening method to account for temporal autocorrelation. We created a trend map for all data points within the boreal biome boundary following Gauthier et al. 2015, Science (<a href="https://doi.org/10.1126/science.aaa9092">DOI: 10.1126/science.aaa9092</a>) and added a 120km buffer around it (tcchange_all_points_clipped.tif). We also produced a map where we masked out non-significant trends based on a Mann-Kendall-test (tcchange_significant_points.tif). Both maps have a spatial resolution of around 1,000m. The map forms the key results in our manuscript: Rotbarth et al. 2023. 'North American boreal forests: Northern expansion is not compensating for southern declines'. Nature Communications

dryad40/100

Data from: Determinants of tree cover in tropical floodplains

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publicOct 2019View details →

opencc-zeroMay 2020View details →

Forest cover and fruit crop size differentially influence frugivory of select rainforest tree species in Western Ghats, India (Part II)

Forest fragmentation and habitat loss are major disruptors of plant–frugivore interactions, affecting seed dispersal and altering recruitment patterns of tree species dependent on vertebrate dispersers. In a heterogeneous production landscape (primarily tea and coffee plantations) in the southern Western Ghats, India, we examined effects of surrounding forest cover and fruit crop size on frugivory of four rainforest bird-dispersed tree species (N = 131 trees, ≥ 30 trees per species, observed for 623 h). Frugivore composition differed among the four tree species with the large-seeded Canarium strictum and Myristica dactyloides exclusively dependent on large-bodied avian frugivores, whereas, medium-seeded Persea macrantha and Heynea trijuga were predominantlyvisited by small-bodied and large-bodied avian frugivores, respectively. Using the seed-dispersal-effectiveness framework, we identified effective frugivores and examined their response to forest cover and fruit crop size. Results were idiosyncratic and governed by plant and frugivore traits. Visitations to medium-seeded Persea had a positive relationship with forest cover but the relationship was negative for the large-seeded Myristica. In addition, two of the three effective frugivores for Persea responded to the interactive effect of forest cover and fruit crop size. Frugivore visitations to Hyenea were not related to forest cover or fruit crop and there were too few visitations to Canarium to discern any trends. These results highlight the context-specific response of plant-frugivore interactions to forest cover and fruit crop size influenced by the plant and frugivore traits.

opencc-zeroJun 2020View details →

Forest cover and fruit crop size differentially influence frugivory of select rainforest tree species in Western Ghats, India (Part I)

Forest fragmentation and habitat loss are major disruptors of plant–frugivore interactions, affecting seed dispersal and altering recruitment patterns of tree species dependent on vertebrate dispersers. In a heterogeneous production landscape (primarily tea and coffee plantations) in the southern Western Ghats, India, we examined effects of surrounding forest cover and fruit crop size on frugivory of four rainforest bird-dispersed tree species (N = 131 trees, ≥ 30 trees per species, observed for 623 h). Frugivore composition differed among the four tree species with the large-seeded Canarium strictum and Myristica dactyloides exclusively dependent on large-bodied avian frugivores, whereas, medium-seeded Persea macrantha and Heynea trijuga were predominantly visited by small-bodied and large-bodied avian frugivores, respectively. Using the seed-dispersal-effectiveness framework, we identified effective frugivores and examined their response to forest cover and fruit crop size. Results were idiosyncratic and governed by plant and frugivore traits. Visitations to medium-seeded Persea had a positive relationship with forest cover but the relationship was negativefor the large-seeded Myristica. In addition, two of the three effective frugivores for Persea responded to the interactive effect of forest cover and fruit crop size. Frugivore visitations to Hyenea were not related to forest cover or fruit crop and there were too few visitations to Canarium to discern any trends. These results highlight the context-specific response of plant-frugivore interactions to forest cover and fruit crop size influenced by the plant and frugivore traits.

opencc-zeroDec 2017View details →

Data from: Divergent rates of change between tree cover types in a tropical pastoral region

Context: Forest cover change analyses have revealed net forest gain in many tropical regions. While most analyses have focused solely on forest cover, trees outside forests are vital components of landscape integrity. Quantifying regional-scale patterns of tree cover change, including non-forest trees, could benefit forest and landscape restoration (FLR) efforts. Objectives: We analyzed tree cover change in Southwestern Panama to quantify: 1) patterns of change from 1998-2014, 2) differences in rates of change between forest and non-forest classes, and 3) the relative importance of social-ecological predictors of tree cover change between classes. Methods: We digitized tree cover classes, including dispersed trees, live fences, riparian forest, and forest, in very high resolution images from 1998-2014. We then applied hurdle models to relate social-ecological predictors to the probability and amount of tree cover gain. Results: All tree cover classes increased in extent, but gains were highly variable between classes. Non-forest tree cover accounted for 21% of tree cover gains, while riparian trees constituted 31% of forest cover gains. Drivers of tree cover change varied widely between classes, with opposite impacts of some social-ecological predictors on non-forest and forest cover. Conclusions: We demonstrate that key drivers of forest cover change, including topography, road distance and historical forest cover, do not explain rates of non-forest tree cover change. Consequently, predictions from medium-resolution forest cover change analyses may not apply to finer-scale patterns of tree cover. We highlight the opportunity for FLR projects to target tree cover classes adapted to local social and ecological conditions.

opencc-zeroNov 2019View details →

Data from: Spatial transitions in tree cover are associated with soil hydrology, but not with grass biomass, fire frequency, or herbivore biomass in Serengeti savannahs

1. Although there is a well-known association between tree cover and soil texture in savannahs, the hydrological drivers of tree cover variation have not been systematically explored, particularly in parallel with factors such as fire, herbivory, and tree-grass interactions. The relationship between hydrological factors and tree cover is important for resolving the relative contribution of bottom-up vs. top-down factors in structuring savannah vegetation. 2. We quantified soil moisture dynamics across eight 1-km transects spanning tree cover gradients from open to woody savannah in Serengeti National Park in Tanzania using soil moisture sensors coupled with dataloggers. We mapped tree cover at two spatial scales through supervised classification of high-resolution satellite imagery. We simultaneously produced water retention curves in open and woody habitats within each transect to compare soil hydrological properties and to convert volumetric water content (θ) from dataloggers to plant-available water over the course of an annual cycle. We also quantified grass biomass at 100 locations per transect, estimated fire frequency from MODIS satellite data, and quantified herbivore occupancy with paired camera traps situated in open and woody habitats within each transect. 3. We found a positive relationship between tree cover and soil moisture drainage rate, and found that open habitats had more negative water potentials than woody habitats for a given value of θ. In contrast, we found no evidence for a consistent relationship between grass biomass or fire frequency and tree cover. We found evidence for higher browser occupancy in woody than open habitats, but no habitat effects on herbivores as a group (browsers plus grazers), suggesting that herbivory is unlikely to be the dominant factor explaining variation in tree cover. 4. Our results suggest that variation in tree cover is partly driven by hydrological (edaphic) factors unrelated to fire, herbivory, tree-grass interactions or mean annual precipitation at these spatial scales in Serengeti. We contrast our findings with previous work attributing tree cover shifts in Serengeti to precipitation gradients.

opencc-by-4.0Apr 2024View details →

CATCD: The First 30m China Annual Tree Cover Dataset from 1985 to 2024 (Part Ⅵ: 2019-2024)

China Annual Tree Cover Dataset (CATCD) is the first long-term, high spatial resolution annual tree cover dataset for China, generated through the integration of time series of Landsat imagery and ensemble learning techniques based on random forests. The reliability and accuracy of CATCD have been validated using multi-source reference data (correlation: 0.70-0.96, RMSE: 5.6%-25.2%). The current version of CATCD (v 0.0.1) includes 30m resolution annual tree cover datasets for China from 1985 to 2024. Each year's data is stored in a GeoTIFF file with 'TreeCover' and 'Uncertainty' bands. TreeCover data ranges from 0 to 100, representing the percentage of tree cover in each pixel, while Uncertainty represents the uncertainty estimation of tree cover derived through 5-fold cross-validation, expressed as the standard deviation of predictions from five models. In addition to providing annual tree cover information for China, users can extract forest dynamic ranges by setting tree cover thresholds using CATCD.  In addition to Zenodo, users can access, download, and reanalyze CATCD on the Google Earth Engine platform via the following link: <a href="https://code.earthengine.google.com/46a2b5b773d373899545566bd05ec8cb">https://code.earthengine.google.com/46a2b5b773d373899545566bd05ec8cb</a>. CATCD is expected to be updated annually at the end of each year on both the Zenodo and GEE platform. Please note that while Zenodo contains annual tree cover products, the uncertainty estimates pertaining to tree cover are not available within its repository. However, these estimates have been furnished within the Google Earth Engine (GEE) platform. <a target="_blank">The dataset has now been updated to include data up to 2024.</a> Data citation: Yaotong Cai, Xiaocong Xu, Sheng Nie, Cheng Wang, Peng Zhu, Yujiu Xiong, and Xiaoping Liu (2024). Unveiling Spatiotemporal Tree Cover Patterns in China: The First 30m Annual Tree Cover Mapping from 1985 to 2023. ISPRS Journal of Photogrammetry and Remote Sensing, 216: 240-258. DOI:10.1016/j.isprsjprs.2024.08.001. For data-related inquiries, please contact Dr. Yaotong Cai (<a target="_new">caiyt33@mail2.sysu.edu.cn</a>).

opencc-zeroOct 2021View details →

Genotype data of 1970 Pedunculate oak trees (Quercus robur L.) in 13 European countries at 381 gene loci covering the nuclear and organelle genome

The data set is the result of genetic inventory on 1970 Pedunculate oak trees from 197 locations in Europe. The samples are from the countries: Belarus, Bosnia, Bulgaria, Croatia, Finland, France, Germany, Hungary, Italy, Latvia, Poland, Russia and Ukraine. At each location ten individual trees were collected. The data set includes the location ID and geographic coordinates of each sampled tree (longitude and latitude in decimal degrees) and the genotype data. All samples were screened with a targeted sequencing approach on a set of 381 polymorphic loci (356 nuclear SNPs, 3 nuclear InDels, 17 chloroplast SNPs and five mitochondrial SNPs). The genotype of each individual is one row in the csv-file "genotypes". The genotypes at the nuclear markers are diploid and represented by two columns per gene marker. The genetic information at the organelle genome is haploid. For each of these gene markers one column is used. Genotypes are coded by Arabic numbers. The meaning of the numbers is explained in the table "coding genotypes" in a second csv-file.

opencc-zeroMar 2022View details →

Genotype data of 970 Pedunculate oak trees (Quercus robur L.) in Russia and neighbouring countries at 385 gene loci covering the nuclear and organelle genome

The data set is the result of genetic inventory on 970 Pedunculate oak trees from 97 locations in Russia, Belarus, Ukraine and Latvia representing the eastern part of the natural species range. The data set includes the sample IDs, geographic coordinates of each sampled tree (longitude and latitude in decimal degrees) and the genotype data. All samples were screened with a set of 385 gene markers covering the nuclear (n=364) and the organelle genomes (chloroplast: n=14, mitochondria: n=7) by use of a genotyping by sequencing approach. With exception of three INDELs (Insert and Deletion mutations) all gene markers are SNPs (Single nucleotide Polymorphism).

opencc-zeroJun 2022View details →

Short-term effects of continuous cover forestry on forest biomass production and biodiversity: Applying single-tree selection in forests dominated by Picea abies

AbstractThe rotation forestry system provides high biomass production, but could also have a negative impact on species sensitive to disturbance. Continuous cover forestry (CCF) could contribute to solving these conflicting goals, but its feasibility in nutrient limited boreal forests is yet unresolved. In a unique experiment, we simultaneously assessed the short-term effect of single-tree selection on both biomass production and biodiversity (vascular plants, bryophytes, wood-inhabiting fungi), and tested fertilization as a way to mediate growth-biodiversity trade-offs. We found that unharvested stands and stands subjected to single-tree selection had a similar species assemblage of vascular plants, bryophytes, and wood-inhabiting fungi. Fertilization increased growth by 37% and induced shifts in two understory species (favoring the grass Avenella flexuosa and disfavoring the bryophyte Hylocomium splendens). We conclude that single-tree selection may become a useful tool to enhance biodiversity in managed forests.

opencc-by-4.0Sep 2022View details →

Global Forest Watch data: Global Tree Cover loss, Carbon data and Primary Forest loss

Both datasets are available on the Global Forest Watch website on their Dashboard. The Datasets on their Dashboard are updated from time to time and can differ from the ones uploaded here. The datasets provided here are used for supporting analysis of a journal article about REDD+ in countries with high forest and low deforestation rates (HFLD). References: Hansen M C et al 2013 High-Resolution Global Maps of 21st-Century Forest Cover Change Science 342 850–3, https://doi.org/10.1126/science.1239552 Harris N L et al 2021 Global maps of twenty-first century forest carbon fluxes Nature Clim Change 11 234–40, https://doi.org/10.1038/s41558-020-00976-6 Turubanova S, Potapov P V, Tyukavina A and Hansen M C 2018 Ongoing primary forest loss in Brazil, Democratic Republic of the Congo, and Indonesia Environ. Res. Lett. 13 74028, https://doi.org/10.1088/1748-9326/aacd1c University of Maryland and World Resources Institute "Global Primary Forest Loss". Accessed through Global Forest Watch, www.globalforestwatch.org (accessed 12 Sep 2022)

opencc-by-4.0Jun 2024View details →

Dataset_Social-ecological heterogeneity drove contrasting tree cover restoration in South China Karst

This is the dataset for manuscript "Social-ecological heterogeneity drove contrasting tree cover restoration in South China Karst".

opencc-by-4.0Jun 2024View details →

The distribution and drivers of tree cover in savannas and forests across India

The dataset provided contain the source data to create the three main figures in the publication "The distribution and drivers of tree cover in savannas and forests across India". The data associated with Figures 1 & 3 are in csvs and the source data for Figure 2 are rasters. Each file is named with the Figure number.  For Figure 1- the data includes information about the tree cover, maximum climate water deficit (MCWD) and whether the data point has been sampled for this study or is from Nerleker et al. ,2022 (the data for which has been published with the respective publication).  For Figure 2- there are two rasters showing the two subfigures. The rasters are in GeoTiff format (32bit Float) in WGS 1984 CRS (no projection) and at a resolution of 0.002 degrees. The raster titled climatic_max_tc_250m_resolution_desert_excluded_REVISED has a range of 13.11 to 100.43% and the raster PTCSNA_250m_resolution_desert_excluded_REVISED has a range of 0-1 (unitless). Please refer to the manuscript for additional details of the meaning of the pixel values. For Figure 3- there are two csvs provided- data for the histograms of each driver analysed and shown in each subplot and the model outputs (from generalized additive  modeling).

opencc-by-4.0Apr 2024View details →

CATCD: The First 30m China Annual Tree Cover Dataset from 1985 to 2024 (Part Ⅳ: 2006-2012)

China Annual Tree Cover Dataset (CATCD) is the first long-term, high spatial resolution annual tree cover dataset for China, generated through the integration of time series of Landsat imagery and ensemble learning techniques based on random forests. The reliability and accuracy of CATCD have been validated using multi-source reference data (correlation: 0.70-0.96, RMSE: 5.6%-25.2%). The current version of CATCD (v 0.0.1) includes 30m resolution annual tree cover datasets for China from 1985 to 2024. Each year's data is stored in a GeoTIFF file with 'TreeCover' and 'Uncertainty' bands. TreeCover data ranges from 0 to 100, representing the percentage of tree cover in each pixel, while Uncertainty represents the uncertainty estimation of tree cover derived through 5-fold cross-validation, expressed as the standard deviation of predictions from five models. In addition to providing annual tree cover information for China, users can extract forest dynamic ranges by setting tree cover thresholds using CATCD.  In addition to Zenodo, users can access, download, and reanalyze CATCD on the Google Earth Engine platform via the following link: <a href="https://code.earthengine.google.com/a28dcf064bba1975f5a6c28400c41edb">https://code.earthengine.google.com/a28dcf064bba1975f5a6c28400c41edb</a>. CATCD is expected to be updated annually at the end of each year on both the Zenodo and GEE platform. Please note that while Zenodo contains annual tree cover products, the uncertainty estimates pertaining to tree cover are not available within its repository. However, these estimates have been furnished within the Google Earth Engine (GEE) platform. <a target="_blank">The dataset has now been updated to include data up to 2024.</a> Data citation: Yaotong Cai, Xiaocong Xu, Sheng Nie, Cheng Wang, Peng Zhu, Yujiu Xiong, and Xiaoping Liu (2024). Unveiling Spatiotemporal Tree Cover Patterns in China: The First 30m Annual Tree Cover Mapping from 1985 to 2023. ISPRS Journal of Photogrammetry and Remote Sensing, 216: 240-258. DOI:10.1016/j.isprsjprs.2024.08.001. For data-related inquiries, please contact Dr. Yaotong Cai (<a target="_new">caiyt33@mail2.sysu.edu.cn</a>).

opencc-by-4.0Mar 2024View details →

CATCD: The First 30m China Annual Tree Cover Dataset from 1985 to 2024 (Part Ⅲ: 1999-2005)

China Annual Tree Cover Dataset (CATCD) is the first long-term, high spatial resolution annual tree cover dataset for China, generated through the integration of time series of Landsat imagery and ensemble learning techniques based on random forests. The reliability and accuracy of CATCD have been validated using multi-source reference data (correlation: 0.70-0.96, RMSE: 5.6%-25.2%). The current version of CATCD (v 0.0.1) includes 30m resolution annual tree cover datasets for China from 1985 to 2024. Each year's data is stored in a GeoTIFF file with 'TreeCover' and 'Uncertainty' bands. TreeCover data ranges from 0 to 100, representing the percentage of tree cover in each pixel, while Uncertainty represents the uncertainty estimation of tree cover derived through 5-fold cross-validation, expressed as the standard deviation of predictions from five models. In addition to providing annual tree cover information for China, users can extract forest dynamic ranges by setting tree cover thresholds using CATCD.  In addition to Zenodo, users can access, download, and reanalyze CATCD on the Google Earth Engine platform via the following link: <a href="https://code.earthengine.google.com/a28dcf064bba1975f5a6c28400c41edb">https://code.earthengine.google.com/a28dcf064bba1975f5a6c28400c41edb</a>. CATCD is expected to be updated annually at the end of each year on both the Zenodo and GEE platform. Please note that while Zenodo contains annual tree cover products, the uncertainty estimates pertaining to tree cover are not available within its repository. However, these estimates have been furnished within the Google Earth Engine (GEE) platform. <a target="_blank">The dataset has now been updated to include data up to 2024.</a> Data citation: Yaotong Cai, Xiaocong Xu, Sheng Nie, Cheng Wang, Peng Zhu, Yujiu Xiong, and Xiaoping Liu (2024). Unveiling Spatiotemporal Tree Cover Patterns in China: The First 30m Annual Tree Cover Mapping from 1985 to 2023. ISPRS Journal of Photogrammetry and Remote Sensing, 216: 240-258. DOI:10.1016/j.isprsjprs.2024.08.001. For data-related inquiries, please contact Dr. Yaotong Cai (<a target="_new">caiyt33@mail2.sysu.edu.cn</a>).