Datasets
This page contains all data and information generated in the course of the RESTORE+ project.
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Potential growth of forest restoration options in Indonesia
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Further details
Potential growth of secondary forest and tree species typology were assessed using biophysical productivity model developed by IIASA's Agriculture, Forestry, and Ecosystems Services Group. The methodology involved the integration of random forest algorithm, ground data, remote sensing products, soil properties, and literature on yield tables (more detailed methodology publication in preparation). Publication Date: July, 2023 Original Data: https://zenodo.org/record/8117522 |
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Yield and carbon sequestration of main tree crops in Indonesia
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FURTHER DETAILS
The dataset cover current practices and potential management improvements of tree crops cultivation that can increase the benefits to people’s livelihood while contributing to climate change mitigation and biodiversity. Biophysical productivity of production systems involving oil palm, rubber, cacao, coffee, sago and pineapple on mineral soil and peatland are modelled using various approaches. Publication Date: May, 2023 Original Data: https://zenodo.org/record/7937085 |
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Forest and Native Vegetation Restoration in Brazil Considering Forest Code and Climate Change Impacts
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Further Details
This application presents land use changes scenarios related to forest and native vegetation restoration in Brazil. Built upon the IDCImperfect2 scenario, the scenarios evaluate the implementation of Brazilian Forest Code considering climate change impacts. The scenarios are different regarding deforestation control and restoration targets. The table below shows the key features of the different scenarios. Publication Date: November, 2022 Data Visualization platform: https://brazilsforestcode.publishdata.com.br/ |
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Rice productivity along waterlogging gradient in Indonesian peatlands (EPIC-IIASA model outputs)
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Further Details
Description: The spatially explicit modelled water table depth data were produced for a total of ten waterlogging intensity scenarios (WetScen) in Indonesian peatlands using the Environmental Policy Integrated Climate-based modelling framework EPIC-IIASA. This data brings harmonized groundwater regime data in large-scale agricultural modelling to allow assessing the impact of peatland rewetting on rice productivity in Indonesia. Further, the spatially explicit datasets of annual rice yield and monthly root zone soil water content were simulated at 0.25×0.25° spatial resolution following the waterlogging scenarios described above. Publication Date: November, 24, 2022 Original Data: https://doi.org/10.5281/zenodo.7355372 |
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The Harmonized peatland dataset for Indonesia v.0.1
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Further Details
Description: The primary purpose of the harmonized data on peatlands of Indonesia, version 0.1, for the bio-physical modelling in Indonesia is to provide bio-physical models (EPIC-IIASA, G4M, and WaNuLCAS) with consistent input data on peat and mineral soil distribution in Indonesia and with the peat parameters necessary for proper representation of peat hydrology in simulations. Publication Date: August, 16, 2022 Original Data: https://doi.org/10.5281/zenodo.6998052 |
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The spatial simulation infrastructure for Indonesia v.0.1
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Further Details
Description: The primary purpose of the dataset on spatial simulation units (SimU) for Indonesia, version 0.1 (IND_SimU_v01) is to provide spatially allocated inputs for bio-physical models (EPIC-IIASA, G4M, and WaNuLCAS), and economic optimization models (GLOBIOM) with minimum data content necessary for running the models, including the data on terrain, soil, and administrative units attributed to the spatial simulation units. Publication Date: August, 16, 2022 Original Data: https://doi.org/10.5281/zenodo.6997968 |
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A national-scale land cover reference dataset from local crowdsourcing initiatives in Indonesia
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Further Details
Description: This collection represents geographically diverse, temporally consistent, and nationally relevant land cover (LC) reference data collected by visual interpretation of very high spatial resolution imagery, in a national-scale crowdsourcing campaign (targeting seven generic LC classes) and a series of expert workshops (targeting seventeen detailed LC classes) in Indonesia. Publication Date: July, 09, 2022 Original Data: https://doi.org/10.6084/m9.figshare.20278341.v1 Related publication: https://www.nature.com/articles/s41597-022-01689-5#citeas |
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Calculation for national allocation of global greenhouse gas emissions and removals targets
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Further Details
Description: The open access data contain results and detailed calculations for the national allocation of global greenhouse gas emissions and removals targets. Publication Date: June, 30, 2020 Original Data: https://doi.org/10.5281/zenodo.5045402 Related publication: https://doi.org/10.1016/j.nbsj.2022.100048 |
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Land use and land cover maps for Amazon biome in Brazil for 2001-2019 derived from MODIS time series
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Further Details
Description: This dataset contains the yearly maps of land use and land cover classification for Amazon biome, Brazil, from 2000 to 2019 at 250 meters of spatial resolution. We used image time series from MOD13Q1 product from MODIS (collection 6), with four bands (NDVI, EVI, near-infrared, and mid-infrared) as data input. A deep learning classification MLP network consisting of 4 hidden layers with 512 units was trained using a set of 33,052 time series of 12 known classes from both natural and anthropic land covers. Publication Date: 2020 Original Data: https://doi.pangaea.de/10.1594/PANGAEA.911560 |
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Determining a Carbon Reference Level for a high-forest-low-deforestation country
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Further Details
Description: The model’s rationale builds on demand for agricultural products that needs to be satisfied by a matching supply. The model provides results in five year intervals from 2000 until 2030 for each of the five agro-ecological zones of Cameroon and the study area in Southern Cameroon, and in terms of the contributions of the 15 most prevalent agricultural crops in the country. The model comprises six computation steps, each of which is parameterized using available data for Cameroon: 1) population, 2) food & feed consumption, 3) trade within Cameroon and with the rest of the world, 4) agricultural production, 5) cultivated area, and 6) resulting forest cover change. Publication Date: 2019 Original Data: http://pure.iiasa.ac.at/id/eprint/17566/ Related publication: https://doi.org/10.3390/f10121095 |
