Find research datasets worth reusing

opencc-by-4.0Dec 2020View details →

Data from: Using model analysis to unveil hidden patterns in tropical forest structures

Data set of the article entitled: Using model analysis to unveil hidden patterns in tropical forest structures This data set gives the following structural attributes for 133 forest plots at 9 sites in the tropics: <ul> <li>tree density (ha-1)</li> <li>basal area (m2 ha-1)</li> <li>mean diametere (cm)</li> <li>equivalent diameter (cm)</li> <li>density of trees in the dbh class 10-30 cm (ha-1)</li> <li>density of trees in the dbh class 30-60 cm (ha-1)</li> <li>density of trees with dbh ≥ 60 cm (ha-1)</li> <li>aboveground dry biomass (Mg ha-1)</li> <li>fraction of the biomass of trees with dbh ≥ 60 cm</li> <li>weighted mean wood density (g cm-3)</li> <li>density of trees in the dbh class 10-20 cm (ha-1)</li> <li>density of trees in the dbh class 20-30 cm (ha-1)</li> <li>density of trees in the dbh class 30-40 cm (ha-1)</li> <li>density of trees in the dbh class 40-50 cm (ha-1)</li> <li>density of trees in the dbh class 50-60 cm (ha-1)</li> <li>density of trees in the dbh class 60-70 cm (ha-1)</li> <li>density of trees in the dbh class 70-80 cm (ha-1)</li> <li>density of trees in the dbh class 80-90 cm (ha-1)</li> <li>density of trees in the dbh class 90-100 cm (ha-1)</li> <li>density of trees in the dbh class 100-110 cm (ha-1)</li> <li>density of trees in the dbh class 110-120 cm (ha-1)</li> <li>density of trees in the dbh class 120-130 cm (ha-1)</li> <li>density of trees with dbh ≥ 130 cm (ha-1)</li> </ul>

opencc-by-4.0Mar 2022View details →

Data for "Random forest-based modeling of stream nutrients at national level in a data-scarce region"

The aim of the study was to model annual total nitrogen (TN) and total phosphorus (TP) concentrations at national level using an ML approach. We used water quality data originating from the Environmental Monitoring Database KESE to train RF models for nutrient concentration prediction in 242 catchments across Estonia. A total of 82 environmental variables were used as predictors in the models. In order to yield the best results, a feature selection strategy along with hyperparameter optimization was performed when building the models. The models are applicable for predicting nutrient loads on an annual level, e.g. for the purpose of reporting national level water quality statistics in regional projects, such as HELCOM. The results showed that this relatively basic RF modeling approach can have a performance similar to process-based models. Moreover, these models are easier to reuse and apply on a larger scale, since the required inputs can be derived from freely available datasets (e.g. satellite imagery) This repository contains the input data used for building the RF models and the files describing the modeling results. The description of the files is given in the README.txt file. Virro, H., Kmoch, A., Vainu, M. and Uuemaa, E., 2022. Random forest-based modeling of stream nutrients at national level in a data-scarce region. Science of The Total Environment, 840, p.156613. <a href="https://doi.org/10.1016/j.scitotenv.2022.156613">https://doi.org/10.1016/j.scitotenv.2022.156613</a>

opencc-by-4.0Dec 2021View details →

Dataset - Identification of early abandonment in cropland through radar-based coherence data and application of a Random-Forest model

This dataset accompanies the manuscript titled "Identification of early abandonment in cropland through radar-based coherence data and application of a Random-Forest model", submitted by co-authors to the journal Global Change Biology (GCB) Bioenergy. Wouter Meijninger1, Berien Elbersen1, Michiel van Eupen1, Stephan Mantel2, Pilar Ciria Ciria3, Andrea Parenti4, Marina Sanz Gallego3 and Paloma Perez Ortiz3, Marco Acciai4,and Andrea Monti4 Institutes: 1) Wageningen University & Research, 2) ISRIC, 3) CIEMAT, 4) Bologna University, Abstract (Manuscript) In the context of increased pressures on land for food and non-food production it is relevant to understand better, which land resources have become unused and abandoned and where these lands are. Data on where these lands are and what their extend is are not collected in regular statistics. In this paper we present an approach to detect signs of abandonment in cropping land using radar coherence data. The methodology was tested in the Spanish regions of Albacete and Soria where agricultural land abandonment is a common process. The results show that land abandonment detection using radar coherence data works well for the region of Albacete in arable lands. The radar-based analysis is a relatively simple method to detect land abandonment in an early to longer-term state and can therefore be applied once developed and tested further in other regions to larger areas of the EU where land abandonment is serious and needs monitoring and policy response. The applicability of the method to Soria and Emilia Romagna (Italy) regions show that there are still challenges to overcome to make the method more widely applicable for detecting land abandonment in other environmental zones of Europe. Lack of reliable training and validation data, like LPIS data, in regions is one of the challenges in this respect. Readme data files Coherence_quarterly_statisitcs_2017_to_2020.zip Radar coherence quarterly statistics - Albacete (Spain) Radar coherence data is based on Sentinel-1B Period: 2017 to 2020 File naming (.tif files) per year (YYYY): <ul> <li>Mean coherence: mean_YYYY_1to4.tif</li> <li>Standard deviation coherence: std_YYYY_1to4.tif</li> <li>Range coherence: range_YYYY_1to4.tif</li> <li>Mean delta coherence: mean_delta_YYYY_1to4.tif</li> <li>Standard deviation delta coherence: std_delta_YYYY_1to4.tif</li> <li>Maximum delta coherence: max_delta_YYYY_1to4.tif</li> </ul> Each file consists of 4 bands: <ul> <li>band 1: 1st quarter [Jan-Feb-March]</li> <li>band 2: 2nd quarter [April-May-June]</li> <li>band 3: 3rd quarter [July-Aug-Sept]</li> <li>band 4: 4th quarter [Oct-Nov-Dec]</li> </ul> Statistics are based on radar coherence data, which is scaled between >0 and 1 No data: 0-values Projection: EPSG:32630 - WGS 84 / UTM zone 30N Pixel size: 20m SIGPAC_data_Albacete_2018_to_2020.zip <ul> <li>More than 5 year fallow (20m raster files)</li> <li>Land Use Land Cover LULC (20m raster files)</li> </ul> More than 5 year fallow (according to SIGPAC) Period: 2018 to 2020 File naming (ENVI files): <ul> <li>Albacete_SIGPAC_MoreThan5YrsFallowAreas_2018_20m.dat (+ Albacete_SIGPAC_MoreThan5YrsFallowAreas_2018_20m.hdr)</li> <li>Albacete_SIGPAC_MoreThan5YrsFallowAreas_2019_20m.dat (+ Albacete_SIGPAC_MoreThan5YrsFallowAreas_2019_20m.hdr)</li> <li>Albacete_SIGPAC_MoreThan5YrsFallowAreas_2020_20m.dat (+ Albacete_SIGPAC_MoreThan5YrsFallowAreas_2020_20m.hdr)</li> </ul> Pixel values: 0: Not fallow 1: Fallow more than 5 years Projection: EPSG:32630 - WGS 84 / UTM zone 30N Pixel size: 20m Land Use Land Cover LULC (according to SIGPAC) Period: 2018 to 2020 File naming (ENVI files): <ul> <li>LULC_SIGPAC_Albacete_2018_20m.dat (+ LULC_SIGPAC_Albacete_2018_20m.hdr)</li> <li>LULC_SIGPAC_Albacete_2019_20m.dat (+ LULC_SIGPAC_Albacete_2019_20m.hdr)</li> <li>LULC_SIGPAC_Albacete_2020_20m.dat (+ LULC_SIGPAC_Albacete_2020_20m.hdr)</li> </ul> Pixel values: <ul> <li>0 - Nan</li> <li>1 - Arable land</li> <li>2 - Vineyards</li> <li>3 - Olives</li> <li>4 - Fruits</li> <li>5 - Nuts</li> <li>6 - Citrus</li> <li>7 - Permanent grassland</li> <li>8 - Forest</li> <li>9 - Rest, small elements</li> <li>10 - Built-up areas</li> <li>11 - Water</li> <li>12 - Roads</li> <li>13 - Unproductive land</li> </ul> Projection: EPSG:32630 - WGS 84 / UTM zone 30N Pixel size: 20m Annual_unused_used_land_maps_Albacete_2017_to_2020.zip Derived annual unused/used land maps - Albacete (Spain), based on Random-Forest model Period: 2017-2020 File naming (ENVI files): <ul> <li>predict_RF_Albacete_2017_quarterly_stats_LU1_v181920.dat (+ predict_RF_Albacete_2017_quarterly_stats_LU1_v181920.hdr)</li> <li>predict_RF_Albacete_2018_quarterly_stats_LU1_v181920.dat (+ predict_RF_Albacete_2018_quarterly_stats_LU1_v181920.hdr)</li> <li>predict_RF_Albacete_2019_quarterly_stats_LU1_v181920.dat (+ predict_RF_Albacete_2019_quarterly_stats_LU1_v181920.hdr)</li> <li>predict_RF_Albacete_2020_quarterly_stats_LU1_v181920.dat (+ predict_RF_Albacete_2020_quarterly_stats_LU1_v181920.hdr)</li> </ul> Pixel values: 0 - Used (and/or Nan) 1 - Unused Projection: EPSG:32630 - WGS 84 / UTM zone 30N Pixel size: 20m Four_year_abandoned_land_Albacete_2017_to_2020.zip Four-year abandonment map is based on the 4 annual unused/used land maps File naming (ENVI): <ul> <li>Four_year_abandoned_land_Albacete_2017_to_2020.dat (+ Four_year_abandoned_land_Albacete_2017_to_2020.hdr)</li> </ul> Pixel values: 0 - (Nan) 1 - Used (1 year unused in period 2017 - 2020) 2 - Used (2 year unused in a row in period 2017 - 2020) 3 - Abandoned (3 year unused in a row in period 2017 - 2020) 4 - Abandoned (4 year unused in a row in period 2017 - 2020) Projection: EPSG:32630 - WGS 84 / UTM zone 30N Pixel size: 20m

opencc-by-4.0Sep 2021View details →

Data collection for article "Quantifying Local Ecosystem Service Outcomes by Modelling Their Supply, Demand and Flow in Myanmar's Forest Frontier Landscape"

This dataset contains the nine ecosystem service models (in .neta format) underlying the publication "Quantifying Local Ecosystem Service Outcomes by Modelling Their Supply, Demand and Flow in Myanmar’s Forest Frontier Landscape". The ecosystem models were implemented using the commercial software Netica (version 6.05) for constructing and analysing Bayesian Networks.

dryad40/100

The evolution, complexity and diversity of models of long-term forest dynamics

1. To assess the impacts of climate change on vegetation from stand to global scales, models of forest dynamics that include tree demography are needed. Such models are now available for 50 years, but the currently existing diversity of model formulations and its evolution over time are poorly documented. This hampers systematic assessments of structural uncertainties in model-based studies. 2. We conducted a meta-analysis of 28 models, focusing on models that were used in the past five years for climate change studies. We defined 52 model attributes in five groups (basic assumptions, growth, regeneration, mortality and soil moisture) and characterized each model according to these attributes. Analyses of model complexity and diversity included hierarchical cluster analysis and redundancy analysis. 3. Model complexity evolved considerably over the past 50 years. Increases in complexity were largest for growth processes, while complexity of modelled establishment processes increased only moderately. Model diversity was lowest at the global scale, and highest at the landscape scale. We identified five distinct clusters of models, ranging from very simple models to models where specific attribute groups are rendered in a complex manner and models that feature high complexity across all attributes. 4. Most models in use today are not balanced in the level of complexity with which they represent different processes. This is the result of different model purposes, but also reflects legacies in model code, modelers' preferences, and the 'prevailing spirit of the epoch'. The lack of firm theories, laws and 'first principles' in ecology provides high degrees of freedom in model development, but also results in high responsibilities for model developers and the need for rigorous model evaluation. 5. Synthesis. The currently available model diversity is beneficial: convergence in simulations of structurally different models indicates robust projections, while convergence of similar models may convey a false sense of certainty. The existing model diversity – with the exception of global models – can be exploited for improved projections based on multiple models. We strongly recommend balanced further developments of forest models that should particularly focus on establishment and mortality processes, in order to provide robust information for decisions in ecosystem management and policymaking.

opencc-zeroAug 2022View details →

dryad40/100

Random forest climatic modeling of agricultural insurance loss across the inland Pacific Northwest region of the United States

We compared climatic relationships to insurance loss across the inland Pacific Northwest region of the United States, using a design matrix methodology, to identify optimum temporal windows for climate variables by county in relationship to wheat insurance loss due to drought. The results of our temporal window construction for water availability variables (precipitation, temperature, evapotranspiration, and the Palmer drought severity index [PDSI]) identified spatial patterns across the study area that aligned with regional climate patterns, particularly with regards to drought-prone counties of eastern Washington. Using these optimum time-lagged correlational relationships between insurance loss and individual climate variables, along with commodity pricing, we constructed a regression-based random forest model for insurance loss prediction and evaluation of climatic feature importance. Our cross-validated model results indicated that PDSI was the most important factor in predicting total seasonal wheat/drought insurance loss, with wheat pricing and potential evapotranspiration having noted contributions. Our overall regional model had a R2 of 0.49 and a RMSE of $30.8 million. Model performance typically underestimated annual losses, with moderate spatial variability in terms of performance between counties.

opencc-zeroOct 2022View details →

opencc-by-4.0May 2024View details →

Forest segmentation of multi-source national forest inventory biomass rasters and canopy height model from 2021

The dataset is produced at Natural Resources Institute Finland (Luke) and the study is funded by the European Union's Horizon 2020 research and innovation programme (Holisoils, grant agreement No 101000289). Source data (multi-source National Forest Inventory, MS-NFI and peatland fertility map of Finland) of varying resolution (10m -16m) was reprojected to 10mx10m resolution from which stand polygons were formulated based on automatic segmentation and regional minimum size limit for a stand. The dataset is a file geodatabase with 5 regional layers, all including the polygons of stands with stand attributes based on MS-NFI 2021 information on the site type, fertility class, dominant height, basal area, diameter, age, volume as total and per tree species, total and aboveground biomass as total and per tree species. Coordinate system: ETRS-TM35FIN (EPSG:3067)

Figure 6 in Forest yield prediction under different climate change scenarios using data intelligent models in Pakistan

Figure 6. Empirical cumulative distribution function (ECDF) of the Predicted error |PE| (cft) in testing period for the RF and KRR models between the predicted and observed yields of Blue pine and Silver fir species.

Figure 4 in Forest yield prediction under different climate change scenarios using data intelligent models in Pakistan

Figure 4. Box-plots of the Predicted error | PE| (cft) in testing period (1996-2016) for the RF and KRR models between the predicted and observed yields of Blue pine and Silver fir species.

Figure 7 in Forest yield prediction under different climate change scenarios using data intelligent models in Pakistan

Figure 7. Taylor diagram showing the correlation coefficient between the predicted and observed yields (Blue pine and Silver fir) (cft) and standard deviation for the RF and KRR models.

Figure 5 in Forest yield prediction under different climate change scenarios using data intelligent models in Pakistan

Figure 5. Polar plots show the Predicted error |PE|(cft) in testing period (1996-2016) for the RF and KRR models between the predicted and observed yields of Blue pine and Silver fir species.

FCH and FS Datasets for the paper "Integrating Multi-Source Remote Sensing Data for Mapping Boreal Forest Canopy Height and Species in interior Alaska in Support of Radar Modeling"

This dataset provides forest canopy height and forest species in Delta Junction, interior Alaska in 2017. This dataset was produced based on the multi-source remote sensing datasets (AirMOSS, UAVSAR, Sentinel-1, Sentinel-2, topography), using a XGBoost approach.

Auxiliary data for Moustakis et al. 2024 "Temperature overshoot responses to ambitious forestation in an Earth System Model"

The netcdf file "Moustakis_et_al_2024_Data.nc" contains all the key variables presented in the figures of the manuscript of Moustakis et al. 2024: "Temperature overshoot responses to ambitious forestation in an Earth System Model". Please read the README.txt file for more information on the variables included. For any further queries please refer to the corresponding author, Yiannis Moustakis:  yiannis.moustakis@geographie.uni-muenchen.de

opencc-by-4.0Dec 2017View details →

Fig.1 in Preliminary Biophysical Assessment Of Forest Ecosystem Services: Two Model Area Examples

Fig.1. Ecosystem service class: biomass energy products. Indicator: potential energy wood supply within felling limits.

opencc-by-4.0Dec 2017View details →

Fig. 2 in Preliminary Biophysical Assessment Of Forest Ecosystem Services: Two Model Area Examples

Fig. 2. Ecosystem service class: global climate regulation by reduction of GHG concentration. Indicator: Estimated carbon stock in live above-ground tree biomass.

opencc-by-4.0Jul 2024View details →

UAS-SfM data from Unoccupied aerial system (UAS) Structure-from-Motion canopy fuel parameters: Multisite area-based modelling across forests in California, USA

Data for: Unoccupied aerial system (UAS) Structure-from-Motion canopy fuel parameters: Multisite area-based modelling across forests in California, USA Sean Reilly 1, Matthew L. Clark 2, Lika Loechler 2, Jack Spillane 2, Melina Kozanitas 3, Paris Krause 4, David Ackerly 3, Lisa Patrick Bentley 4, and Imma Oliveras Menor 1,5 1 Environmental Change Institute, University of Oxford, Oxford OX1 3QY, UK 2 Center for Interdisciplinary Geospatial Analysis, Department of Geography, Environment, and Planning, Sonoma State University, Rohnert Park, CA 94928, USA 3 Departments of Integrative Biology and Environmental Science, Policy, and Management, University of California, Berkeley, CA 94720, USA 4 Department of Biology, Sonoma State University, Rohnert Park, CA 94928, USA 5 AMAP (Botanique et Modélisation de l’Architecture des Plantes et des Végétations), CIRAD, CNRS, INRA, IRD, Université de Montpellier, Montpellier, France Study abstract: There is a pressing need for well-informed management to reduce wildfire hazard and restore fire’s beneficial ecological role in the Mediterranean- and temperate-climate forests of California, USA. These efforts rely upon the accessibility of high spatial and temporal resolution data on biomass and canopy fuel parameters such as canopy base height (CBH), mean canopy height, canopy bulk density (CBD), canopy cover, and leaf area index (LAI). Remote sensing using unoccupied aerial system Structure-from-Motion (UAS-SfM) presents a promising technology for this application due to its accessibility, relatively low cost, and possibility for high temporal cadence. However, to date, this method has not been studied in the complex mosaic of forest types found across California. In this study we examined the capacity of structural and multispectral information obtained from UAS-SfM, in conjunction with machine learning methods, to model aboveground biomass and forest canopy fuel structural parameters using an area-based approach across multiple sites representing a diversity of forest types in California. Based on correlations with field measurements, fuel parameters separated into vertical (biomass, CBH, and mean height) and horizontal (LAI, CBD, canopy cover) groups. UAS-SfM random forest models performed well for modelling the vertical structure canopy fuels parameters (R2 0.69 – 0.75). These models exhibited strong performance in comparison to ALS, as well as when transferred to a novel site. Vertical structure predictors were prominent in these models, and did not improve with the addition of spectral predictors. UAS-SfM random forest models of horizontal structure parameters mainly used raster-based spectral indices (primarily NDVI) and had relatively low performance (R2 0.49 – 0.59). In addition, these models underperformed ALS and had poor performance when applied to a novel site. When applied to a region with widespread UAS-SfM coverage, models from both groups successfully produced contiguous maps that could be used for modelling fire behavior or in management decision making and monitoring. These findings indicate that UAS-SfM, without the need for multispectral sensors, is well suited for mapping area-based vertical-structure canopy parameters across diverse landscapes supporting a wide range of forest types. In contrast, the identification of spectral mean variables for modelling horizontal structure canopy fuels suggests the potential of multi- or hyperspectral sensors or high-resolution satellite imagery for meeting management information needs.  Published in Remote Sensing of Environment Contents: This repository contains multispectral UAS-SfM data from four sites around California, USA: jcksn: Jackson Demonstration State Forest ltr: LaTour Demonstration State Forest ppwd: Pepperwood Preserve sdlmtn: Saddle Mountain Open Space Preserve Data were collected during a series of campaigns: c1: Pepperwood, 2019-09-01 to 2019-10-15 c3: Jackson, 2020-06-15 to 2020-07-02 c4: LaTour, 2020-07-07 to 2020-07-17 c6: Saddle Mountain, 2020-08-04 to 2020-08-09 c9: Jackson, 2021-07-08 to 2021-07-12 Data are included in three formats: raw: Raw outputs from Pix4D (spectral and las) reg_grnd, reg_cnpy: Las files with merged multispectral data and classified ground, registered to ALS using either ground points (grnd) or, in cases with insufficient ground points for registration, to the canopy (cnpy) hnrm: Height normalized las files, normalization performed using ALS terrain model File naming structure: site_campaign_flightzone_uas_processedstate See accompanying paper for methods on data collection and processing Data are grouped into zipped folder by product type Funding: Funding for this research was supported by CAL FIRE Forest Health and Forest Legacy (8GG18806) and California State University, Agricultural Research Institute (20-01-106) awards to L.P.B and M.L.C. S.R. was funded by the Rhodes Trust and through the University of Oxford Environmental Change Institute Small Grant Scheme. Pepperwood ground data collection was supported by funding from the Gordon and Betty Moore Foundation and National Science Foundation grants 1754475 and 1835086. Citation: <div> <div>Reilly, S., Clark, M.L., Loechler, L., Spillane, J., Kozanitas, M., Krause, P., Ackerly, D., Bentley, L.P., Menor, I.O., 2024. Unoccupied aerial system (UAS) Structure-from-Motion canopy fuel parameters: Multisite area-based modelling across forests in California, USA. Remote Sensing of Environment 312, 114310. <a href="https://doi.org/10.1016/j.rse.2024.114310">https://doi.org/10.1016/j.rse.2024.114310</a></div> </div>

Fig. 7 in Interpreting the condition of the forest environment with use of the SCP/MIB model of carabid communities (Coleoptera: Carabidae)

Fig. 7. The effect of fertilization, liming and soil acidification of the soil of the old coppices on Carabidae communities. Explanation of abbreviations please see in the text.

Fig. 4 in Interpreting the condition of the forest environment with use of the SCP/MIB model of carabid communities (Coleoptera: Carabidae)

Fig. 4. The effect of unevenaged forest stands fires on their Carabidae communities in Ostrow Mazowiecka

Fig. 5 in Interpreting the condition of the forest environment with use of the SCP/MIB model of carabid communities (Coleoptera: Carabidae)

Fig. 5. The effect of unevenaged forest stands fires on their Carabidae communities in Solec Kujawski

Fig. 3 in Interpreting the condition of the forest environment with use of the SCP/MIB model of carabid communities (Coleoptera: Carabidae)

Fig. 3. The SCP/MIB model presenting the development of the Carabidae communities as taking part in the course of the Scots pine forest stands production cycle, in the post arable ground (dotted line) and in the forest soil (solid line). Denotations I. Post arable grounds: A – arable ground, A1 - 2-6- year old forest cultures, A2 - 8-14 year old coppices, Pdc3 - 18-year old coppices, A4 - 25-year-old forest stands, A5 - 33-62-year old forest stands, A6 – old growth (98-year old) forest stands. II. Forest grounds: F1 - 2-3- year old forest cultures, F2 - 6-11- year old coppices, F3 - 18 22-year old forest stands, F4 - 25-42- year old forest stands, F5 - 62-98 year old forest stands.