Changes
On June 25, 2024 at 10:03:01 AM UTC, Alessio Bulckaen:
-
Updated description of resource Global Vegetation Carbon Stock in 2015 in Vegetation Carbon Stock 2001-2020 to
The map provides the value of the tonnes of vegetation carbon stock per hectare (t/ha), each grid has an approximate resolution of 300m. _[The values in the map are expressed using an intensive quantity (intensive physical property)]_
f | 1 | { | f | 1 | { |
2 | "author": "Alessio Bulckaen", | 2 | "author": "Alessio Bulckaen", | ||
3 | "author_email": "alessio.bulckaen@bc3research.org", | 3 | "author_email": "alessio.bulckaen@bc3research.org", | ||
4 | "creator_user_id": "c074a22a-ce93-493a-bfff-f2f9ec56cd95", | 4 | "creator_user_id": "c074a22a-ce93-493a-bfff-f2f9ec56cd95", | ||
5 | "extras": [ | 5 | "extras": [ | ||
6 | { | 6 | { | ||
7 | "key": "Map content:", | 7 | "key": "Map content:", | ||
8 | "value": "Vegetation carbon stock" | 8 | "value": "Vegetation carbon stock" | ||
9 | }, | 9 | }, | ||
10 | { | 10 | { | ||
11 | "key": "Spatial Resolution:", | 11 | "key": "Spatial Resolution:", | ||
12 | "value": "300m" | 12 | "value": "300m" | ||
13 | }, | 13 | }, | ||
14 | { | 14 | { | ||
15 | "key": "Unit of measure:", | 15 | "key": "Unit of measure:", | ||
16 | "value": "Tonnes per hectare (t/ha)" | 16 | "value": "Tonnes per hectare (t/ha)" | ||
17 | } | 17 | } | ||
18 | ], | 18 | ], | ||
19 | "groups": [ | 19 | "groups": [ | ||
20 | { | 20 | { | ||
21 | "description": "#__ Annual carbon vegetation stock all over the | 21 | "description": "#__ Annual carbon vegetation stock all over the | ||
22 | world to identify global trends, comparing results across geographic | 22 | world to identify global trends, comparing results across geographic | ||
23 | areas and over time__\r\n\r\nThe maps are computed based on the | 23 | areas and over time__\r\n\r\nThe maps are computed based on the | ||
24 | original methodology by Gibbs and Ruesch (2008) and using IPCC 2006 | 24 | original methodology by Gibbs and Ruesch (2008) and using IPCC 2006 | ||
25 | default factors to estimate the value of __above-ground__ and | 25 | default factors to estimate the value of __above-ground__ and | ||
26 | __below-ground vegetation carbon stock__. \r\nFor each basic spatial | 26 | __below-ground vegetation carbon stock__. \r\nFor each basic spatial | ||
27 | unit (an area of 9 hectares) in the map, the model considers:\r\n*The | 27 | unit (an area of 9 hectares) in the map, the model considers:\r\n*The | ||
28 | landcover class, according to the ESA-CCI dataset,\r\n*The ecological | 28 | landcover class, according to the ESA-CCI dataset,\r\n*The ecological | ||
29 | area, from the FAO's Ecofloristic region and geographical zones | 29 | area, from the FAO's Ecofloristic region and geographical zones | ||
30 | dataset,\r\n*The geographical location, to capture regional/ | 30 | dataset,\r\n*The geographical location, to capture regional/ | ||
31 | continental specific characteristics,\r\n*in the case of a forest, the | 31 | continental specific characteristics,\r\n*in the case of a forest, the | ||
32 | carbon content also depends on how pristine, old and intact the forest | 32 | carbon content also depends on how pristine, old and intact the forest | ||
33 | is. So-called primary forest stores a higher quantity of | 33 | is. So-called primary forest stores a higher quantity of | ||
34 | carbon,\r\n*areas burned by a fire assume a significantly lower or | 34 | carbon,\r\n*areas burned by a fire assume a significantly lower or | ||
35 | absolutely no vegetation carbon stock for that year. \r\n\r\nThe | 35 | absolutely no vegetation carbon stock for that year. \r\n\r\nThe | ||
36 | vegetation carbon stock includes values for above-ground and | 36 | vegetation carbon stock includes values for above-ground and | ||
37 | below-ground vegetation carbon stock, but do not consider any | 37 | below-ground vegetation carbon stock, but do not consider any | ||
38 | contribution of the soil organic carbon stock.\r\n\r\n##Datasets used | 38 | contribution of the soil organic carbon stock.\r\n\r\n##Datasets used | ||
39 | in the analysis\r\n*ESA-CCI land cover data used to retrieve a | 39 | in the analysis\r\n*ESA-CCI land cover data used to retrieve a | ||
40 | complete time series of land cover classes from 1992 to 2020. Land | 40 | complete time series of land cover classes from 1992 to 2020. Land | ||
41 | cover classes are used to assign IPCC values of carbon stock. It | 41 | cover classes are used to assign IPCC values of carbon stock. It | ||
42 | should be noted that some classes, such as water bodies and glaciers, | 42 | should be noted that some classes, such as water bodies and glaciers, | ||
43 | are excluded from the computation in this first set of results, while | 43 | are excluded from the computation in this first set of results, while | ||
44 | others, like bare areas, only contribute modestly to carbon stock. | 44 | others, like bare areas, only contribute modestly to carbon stock. | ||
45 | \r\n*Ecofloristic region and geographical zones. The zones are assumed | 45 | \r\n*Ecofloristic region and geographical zones. The zones are assumed | ||
46 | to be static and are used to select default IPCC values and to produce | 46 | to be static and are used to select default IPCC values and to produce | ||
47 | results at different spatial scales.\r\nSpatially explicit layers on | 47 | results at different spatial scales.\r\nSpatially explicit layers on | ||
48 | intact/primary forests. The layers provide information on the presence | 48 | intact/primary forests. The layers provide information on the presence | ||
49 | of intact forests, which are used as a proxy for primary forests. The | 49 | of intact forests, which are used as a proxy for primary forests. The | ||
50 | information is available for the years 2000, 2013, 2016, and 2020. The | 50 | information is available for the years 2000, 2013, 2016, and 2020. The | ||
51 | location of intact and non-intact forests allows for a proxy of the | 51 | location of intact and non-intact forests allows for a proxy of the | ||
52 | degree of forest degradation and other anthropogenic effects that | 52 | degree of forest degradation and other anthropogenic effects that | ||
53 | affect the carbon stock in the vegetation on non-intact areas. | 53 | affect the carbon stock in the vegetation on non-intact areas. | ||
54 | \r\n*Occurrence of fires (annually available from 2000 to 2020). The | 54 | \r\n*Occurrence of fires (annually available from 2000 to 2020). The | ||
55 | information is needed to incorporate in the model the effect of | 55 | information is needed to incorporate in the model the effect of | ||
56 | wildfires on the carbon budget of the ecosystem. \r\n\r\n##_Main | 56 | wildfires on the carbon budget of the ecosystem. \r\n\r\n##_Main | ||
57 | drivers of change_\r\nIt is important to emphasize that changes in | 57 | drivers of change_\r\nIt is important to emphasize that changes in | ||
58 | these results are mainly driven by land cover changes, and potentially | 58 | these results are mainly driven by land cover changes, and potentially | ||
59 | by land cover re-classification, which do not always match with the | 59 | by land cover re-classification, which do not always match with the | ||
60 | real, but rather the observed ecological | 60 | real, but rather the observed ecological | ||
61 | information\r\n\r\n##__Results interpretation__\r\n\r\n", | 61 | information\r\n\r\n##__Results interpretation__\r\n\r\n", | ||
62 | "display_name": "Vegetation Carbon Stock 2001-2020", | 62 | "display_name": "Vegetation Carbon Stock 2001-2020", | ||
63 | "id": "9d9a2abf-a4b5-45b6-aa11-57c8e287c89d", | 63 | "id": "9d9a2abf-a4b5-45b6-aa11-57c8e287c89d", | ||
64 | "image_display_url": "", | 64 | "image_display_url": "", | ||
65 | "name": "vegetation-carbon-stock-2001-2020", | 65 | "name": "vegetation-carbon-stock-2001-2020", | ||
66 | "title": "Vegetation Carbon Stock 2001-2020" | 66 | "title": "Vegetation Carbon Stock 2001-2020" | ||
67 | } | 67 | } | ||
68 | ], | 68 | ], | ||
69 | "id": "b6900b3f-c345-4cae-90a2-809104b0ed76", | 69 | "id": "b6900b3f-c345-4cae-90a2-809104b0ed76", | ||
70 | "isopen": true, | 70 | "isopen": true, | ||
71 | "license_id": "cc-by", | 71 | "license_id": "cc-by", | ||
72 | "license_title": "Creative Commons Attribution", | 72 | "license_title": "Creative Commons Attribution", | ||
73 | "license_url": "http://www.opendefinition.org/licenses/cc-by", | 73 | "license_url": "http://www.opendefinition.org/licenses/cc-by", | ||
74 | "maintainer": "Ruben Crespo", | 74 | "maintainer": "Ruben Crespo", | ||
75 | "maintainer_email": "ruben.crespo@bc3research.org", | 75 | "maintainer_email": "ruben.crespo@bc3research.org", | ||
76 | "metadata_created": "2023-03-24T16:35:00.363856", | 76 | "metadata_created": "2023-03-24T16:35:00.363856", | ||
n | 77 | "metadata_modified": "2024-06-25T10:03:00.864438", | n | 77 | "metadata_modified": "2024-06-25T10:03:01.465137", |
78 | "name": "global-vegetation-carbon-stock-2001-2020", | 78 | "name": "global-vegetation-carbon-stock-2001-2020", | ||
79 | "notes": "#__Annual global vegetation carbon stocks to identify | 79 | "notes": "#__Annual global vegetation carbon stocks to identify | ||
80 | global spatiotemporal trends__\r\nThese annual global maps are | 80 | global spatiotemporal trends__\r\nThese annual global maps are | ||
81 | computed based on the original methodology by Gibbs and Ruesch (2008) | 81 | computed based on the original methodology by Gibbs and Ruesch (2008) | ||
82 | [1] and using IPCC 2006 [2] default factors to estimate the value of | 82 | [1] and using IPCC 2006 [2] default factors to estimate the value of | ||
83 | __above-ground__ and __below-ground vegetation carbon stock__. The | 83 | __above-ground__ and __below-ground vegetation carbon stock__. The | ||
84 | maps are provided at 300 m spatial resolution, the model to estimate | 84 | maps are provided at 300 m spatial resolution, the model to estimate | ||
85 | these stock considers:\r\n\r\n* the land cover class of the area | 85 | these stock considers:\r\n\r\n* the land cover class of the area | ||
86 | observed, according to the European Space Agency - Climate Change | 86 | observed, according to the European Space Agency - Climate Change | ||
87 | Initiative (ESA-CCI) dataset,\r\n* the ecological area, from the Food | 87 | Initiative (ESA-CCI) dataset,\r\n* the ecological area, from the Food | ||
88 | and Agriculture Organization of the United Nations (FAO)'s | 88 | and Agriculture Organization of the United Nations (FAO)'s | ||
89 | Ecofloristic region and\r\n* the geographical zones to capture | 89 | Ecofloristic region and\r\n* the geographical zones to capture | ||
90 | regional/ continental specific characteristics.\r\n\r\nFor forests, | 90 | regional/ continental specific characteristics.\r\n\r\nFor forests, | ||
91 | the carbon content additionally depends on how intact the forest is, | 91 | the carbon content additionally depends on how intact the forest is, | ||
92 | with so-called primary forest storing a higher quantity of carbon. | 92 | with so-called primary forest storing a higher quantity of carbon. | ||
93 | Moreover, areas burned by fire are modeled to have a significantly | 93 | Moreover, areas burned by fire are modeled to have a significantly | ||
94 | lower to no vegetation carbon stock.\r\n\r\nResults include both | 94 | lower to no vegetation carbon stock.\r\n\r\nResults include both | ||
95 | above-ground and below-ground vegetation carbon but do not consider | 95 | above-ground and below-ground vegetation carbon but do not consider | ||
96 | any contribution of the coarse woody debris or soil organic carbon | 96 | any contribution of the coarse woody debris or soil organic carbon | ||
97 | stocks. The results represent the worldwide terrestrial carbon stocks | 97 | stocks. The results represent the worldwide terrestrial carbon stocks | ||
98 | in vegetation that are modeled based on yearly spatially explicit | 98 | in vegetation that are modeled based on yearly spatially explicit | ||
99 | information on the above-noted inputs, particularly dynamic land | 99 | information on the above-noted inputs, particularly dynamic land | ||
100 | cover, primary forest, and burned area data.\r\n\r\n\r\n##Datasets | 100 | cover, primary forest, and burned area data.\r\n\r\n\r\n##Datasets | ||
101 | used in the analysis\r\n1. ESA-CCI land cover data [3] provide a | 101 | used in the analysis\r\n1. ESA-CCI land cover data [3] provide a | ||
102 | complete time series of land cover from 2001 to 2020. Land cover | 102 | complete time series of land cover from 2001 to 2020. Land cover | ||
103 | classes are used to assign IPCC 2006 values of carbon stock. Some | 103 | classes are used to assign IPCC 2006 values of carbon stock. Some | ||
104 | classes, such as water bodies and glaciers, provide no vegetation | 104 | classes, such as water bodies and glaciers, provide no vegetation | ||
105 | carbon storage, while others, like bare areas and lichen and moss, | 105 | carbon storage, while others, like bare areas and lichen and moss, | ||
106 | contribute negligibly to carbon stock.\r\n2. Ecofloristic region [4] | 106 | contribute negligibly to carbon stock.\r\n2. Ecofloristic region [4] | ||
107 | and geographical zones [5]. The zones are assumed to be static and are | 107 | and geographical zones [5]. The zones are assumed to be static and are | ||
108 | used to select default IPCC 2006 values and to produce results at | 108 | used to select default IPCC 2006 values and to produce results at | ||
109 | different spatial scales.\r\n3. Spatially explicit layers on | 109 | different spatial scales.\r\n3. Spatially explicit layers on | ||
110 | intact/primary forests [6]. The layers provide information on the | 110 | intact/primary forests [6]. The layers provide information on the | ||
111 | presence of intact forests, which are used as a proxy for primary | 111 | presence of intact forests, which are used as a proxy for primary | ||
112 | forests. The information is available for the years 2000, 2013, 2016, | 112 | forests. The information is available for the years 2000, 2013, 2016, | ||
113 | and 2020. Whenever data for a year is not available, the model uses | 113 | and 2020. Whenever data for a year is not available, the model uses | ||
114 | the latest observation for that variable in the time series ( e.g. the | 114 | the latest observation for that variable in the time series ( e.g. the | ||
115 | information in 2000 has been used to estimate values from 2001 to 2012 | 115 | information in 2000 has been used to estimate values from 2001 to 2012 | ||
116 | included).\r\nThe location of intact and non-intact forests allows for | 116 | included).\r\nThe location of intact and non-intact forests allows for | ||
117 | a proxy of the degree of forest degradation and other anthropogenic | 117 | a proxy of the degree of forest degradation and other anthropogenic | ||
118 | effects that affect vegetation carbon stocks in non-intact areas. | 118 | effects that affect vegetation carbon stocks in non-intact areas. | ||
119 | \r\n4. Occurrence of fires [7] (annually available from 2001 to 2020). | 119 | \r\n4. Occurrence of fires [7] (annually available from 2001 to 2020). | ||
120 | The information is required to incorporate the effect of wildfires on | 120 | The information is required to incorporate the effect of wildfires on | ||
121 | vegetation carbon storage.\r\n\r\n##__Interpretation of | 121 | vegetation carbon storage.\r\n\r\n##__Interpretation of | ||
122 | results__\r\nThe results represent the worldwide terrestrial | 122 | results__\r\nThe results represent the worldwide terrestrial | ||
123 | vegetation carbon stocks based on inputs described in the previous | 123 | vegetation carbon stocks based on inputs described in the previous | ||
124 | section. These inputs ensure consistent global results by using time | 124 | section. These inputs ensure consistent global results by using time | ||
125 | series global data sets and an approach aligned with the 2006 IPCC | 125 | series global data sets and an approach aligned with the 2006 IPCC | ||
126 | guidelines. Data may not align with country-level estimates produced | 126 | guidelines. Data may not align with country-level estimates produced | ||
127 | using different data, methods, or spatiotemporal scales, and do not | 127 | using different data, methods, or spatiotemporal scales, and do not | ||
128 | provide a full view of carbon fluxes from lands or other, non-CO2, | 128 | provide a full view of carbon fluxes from lands or other, non-CO2, | ||
129 | greenhouse gas emissions. It is important to emphasize that changes in | 129 | greenhouse gas emissions. It is important to emphasize that changes in | ||
130 | these results are mainly driven by land cover changes, and potentially | 130 | these results are mainly driven by land cover changes, and potentially | ||
131 | by land cover re-classification, which do not always match with the | 131 | by land cover re-classification, which do not always match with the | ||
132 | real, but rather the observed ecological information. Forests\u2019 | 132 | real, but rather the observed ecological information. Forests\u2019 | ||
133 | results can also be affected significantly by the occurrence of | 133 | results can also be affected significantly by the occurrence of | ||
134 | fire.\r\n\r\nThe average global contribution of each aggregated land | 134 | fire.\r\n\r\nThe average global contribution of each aggregated land | ||
135 | cover class from 2001-2020 (expressed in %) to vegetation carbon | 135 | cover class from 2001-2020 (expressed in %) to vegetation carbon | ||
136 | stock:\r\n\r\n* Forest 70,50% | 136 | stock:\r\n\r\n* Forest 70,50% | ||
137 | ...................................................................... | 137 | ...................................................................... | ||
138 | (ESA-CCI LC 50 - 90)\r\n* Shrub and Herbaceous vegetation | 138 | (ESA-CCI LC 50 - 90)\r\n* Shrub and Herbaceous vegetation | ||
139 | 14,18%.......................................... (ESA-CCI LC 100 - | 139 | 14,18%.......................................... (ESA-CCI LC 100 - | ||
140 | 130)\r\n* Agricultural vegetation | 140 | 130)\r\n* Agricultural vegetation | ||
141 | 9,29%............................................................... | 141 | 9,29%............................................................... | ||
142 | (ESA-CCI LC 10 - 40)\r\n* Wetland 5,31% | 142 | (ESA-CCI LC 10 - 40)\r\n* Wetland 5,31% | ||
143 | ...................................................................... | 143 | ...................................................................... | ||
144 | (ESA-CCI LC 160 - 180)\r\n* Bare area 0,40% | 144 | (ESA-CCI LC 160 - 180)\r\n* Bare area 0,40% | ||
145 | ...................................................................... | 145 | ...................................................................... | ||
146 | (ESA-CCI LC 140, 200 - 202)\r\n* Sparse vegetation 0,33% | 146 | (ESA-CCI LC 140, 200 - 202)\r\n* Sparse vegetation 0,33% | ||
147 | .................................................................... | 147 | .................................................................... | ||
148 | (ESA-CCI LC 150 - 153)\r\n* Artificial Surface, Waterbody, and | 148 | (ESA-CCI LC 150 - 153)\r\n* Artificial Surface, Waterbody, and | ||
149 | Permanent snow and ice 0,00%..... (ESA-CCI LC 190, 210 and | 149 | Permanent snow and ice 0,00%..... (ESA-CCI LC 190, 210 and | ||
150 | 220)\r\n\r\n###Funding and acknowledgment\r\nThis work was produced by | 150 | 220)\r\n\r\n###Funding and acknowledgment\r\nThis work was produced by | ||
151 | the ARIES team at BC3 and funded by the World Bank under the GPS and | 151 | the ARIES team at BC3 and funded by the World Bank under the GPS and | ||
152 | PROGREEN programs.\r\n\r\nARIES (ARtificial Intelligence for | 152 | PROGREEN programs.\r\n\r\nARIES (ARtificial Intelligence for | ||
153 | Environment and Sustainability) - | 153 | Environment and Sustainability) - | ||
154 | https://aries.integratedmodelling.org/technology\r\n\r\nBC3 (Basque | 154 | https://aries.integratedmodelling.org/technology\r\n\r\nBC3 (Basque | ||
155 | Center for Climate Change) - | 155 | Center for Climate Change) - | ||
156 | https://www.bc3research.org/index.php\r\n\r\nGPS (Global Program for | 156 | https://www.bc3research.org/index.php\r\n\r\nGPS (Global Program for | ||
157 | Sustainability) - | 157 | Sustainability) - | ||
158 | dbank.org/en/programs/global-program-on-sustainability\r\n\r\nPROGREEN | 158 | dbank.org/en/programs/global-program-on-sustainability\r\n\r\nPROGREEN | ||
159 | (Global Partnership for Sustainable and Resilient Landscapes) - | 159 | (Global Partnership for Sustainable and Resilient Landscapes) - | ||
160 | https://www.progreen.info/about_page\r\n\r\n###References and | 160 | https://www.progreen.info/about_page\r\n\r\n###References and | ||
161 | citations\r\n1. Gibbs and Ruesch (2008) - New IPCC Tier-1 Global | 161 | citations\r\n1. Gibbs and Ruesch (2008) - New IPCC Tier-1 Global | ||
162 | Biomass Carbon Map for the Year 2000 (dataset): | 162 | Biomass Carbon Map for the Year 2000 (dataset): | ||
163 | https://www.osti.gov/biblio/1463800\r\n2. IPCC 2006 - | 163 | https://www.osti.gov/biblio/1463800\r\n2. IPCC 2006 - | ||
164 | rt/2006-ipcc-guidelines-for-national-greenhouse-gas-inventories/\r\n3. | 164 | rt/2006-ipcc-guidelines-for-national-greenhouse-gas-inventories/\r\n3. | ||
165 | ESA-Climate Change Initiative (CCI) dataset: | 165 | ESA-Climate Change Initiative (CCI) dataset: | ||
166 | https://doi.org/10.24381/cds.006f2c9a\r\n4. (FAO)'s Ecofloristic | 166 | https://doi.org/10.24381/cds.006f2c9a\r\n4. (FAO)'s Ecofloristic | ||
167 | region: | 167 | region: | ||
168 | https://databasin.org/datasets/dc4f6efd1fa84ea99df61ae9c5b3b763/\r\n5. | 168 | https://databasin.org/datasets/dc4f6efd1fa84ea99df61ae9c5b3b763/\r\n5. | ||
169 | Geographic (continental) areas: | 169 | Geographic (continental) areas: | ||
170 | cdiac.ess-dive.lbl.gov/ftp/global_carbon/continental_regions.zip\r\n6. | 170 | cdiac.ess-dive.lbl.gov/ftp/global_carbon/continental_regions.zip\r\n6. | ||
171 | Intact Forest Landscapes data: | 171 | Intact Forest Landscapes data: | ||
172 | https://intactforests.org/data.ifl.html.\r\n7. MODIS burned land maps: | 172 | https://intactforests.org/data.ifl.html.\r\n7. MODIS burned land maps: | ||
173 | https://modis-fire.umd.edu/ba.html, product MCD64A1", | 173 | https://modis-fire.umd.edu/ba.html, product MCD64A1", | ||
174 | "num_resources": 23, | 174 | "num_resources": 23, | ||
175 | "num_tags": 0, | 175 | "num_tags": 0, | ||
176 | "organization": { | 176 | "organization": { | ||
177 | "approval_status": "approved", | 177 | "approval_status": "approved", | ||
178 | "created": "2023-02-03T16:35:39.850142", | 178 | "created": "2023-02-03T16:35:39.850142", | ||
179 | "description": "Scientists in the past collected data in | 179 | "description": "Scientists in the past collected data in | ||
180 | notebooks. In the digital age, we need scientific data and models to | 180 | notebooks. In the digital age, we need scientific data and models to | ||
181 | be Findable, Accessible, Interoperable, and Reusable, helping | 181 | be Findable, Accessible, Interoperable, and Reusable, helping | ||
182 | individuals, businesses, and governments make better informed | 182 | individuals, businesses, and governments make better informed | ||
183 | decisions.\r\nA fully connected information landscape using open, | 183 | decisions.\r\nA fully connected information landscape using open, | ||
184 | safe, accurate, \u201cWikipedia-like\u201d sharing and linking of | 184 | safe, accurate, \u201cWikipedia-like\u201d sharing and linking of | ||
185 | models can enable data-intensive science for decision making on a | 185 | models can enable data-intensive science for decision making on a | ||
186 | scale yet unimagined.\r\nWe want to share the methods and technologies | 186 | scale yet unimagined.\r\nWe want to share the methods and technologies | ||
187 | we have built to achieve this vision. Join us to reach it faster.", | 187 | we have built to achieve this vision. Join us to reach it faster.", | ||
188 | "id": "e7a09a40-27a3-42b0-b265-8e034fe1454a", | 188 | "id": "e7a09a40-27a3-42b0-b265-8e034fe1454a", | ||
189 | "image_url": | 189 | "image_url": | ||
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191 | "is_organization": true, | 191 | "is_organization": true, | ||
192 | "name": "integrated-modelling-parnership", | 192 | "name": "integrated-modelling-parnership", | ||
193 | "state": "active", | 193 | "state": "active", | ||
194 | "title": "Integrated Modelling Partnership", | 194 | "title": "Integrated Modelling Partnership", | ||
195 | "type": "organization" | 195 | "type": "organization" | ||
196 | }, | 196 | }, | ||
197 | "owner_org": "e7a09a40-27a3-42b0-b265-8e034fe1454a", | 197 | "owner_org": "e7a09a40-27a3-42b0-b265-8e034fe1454a", | ||
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199 | "relationships_as_object": [], | 199 | "relationships_as_object": [], | ||
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201 | "resources": [ | 201 | "resources": [ | ||
202 | { | 202 | { | ||
203 | "cache_last_updated": null, | 203 | "cache_last_updated": null, | ||
204 | "cache_url": null, | 204 | "cache_url": null, | ||
205 | "created": "2023-06-20T10:47:39.561951", | 205 | "created": "2023-06-20T10:47:39.561951", | ||
206 | "datastore_active": false, | 206 | "datastore_active": false, | ||
207 | "description": "The map provides the value of the tonnes of | 207 | "description": "The map provides the value of the tonnes of | ||
208 | vegetation carbon stock per hectare (t/ha), each grid has an | 208 | vegetation carbon stock per hectare (t/ha), each grid has an | ||
209 | approximate resolution of 300m.\r\n_[The values in the map are | 209 | approximate resolution of 300m.\r\n_[The values in the map are | ||
210 | expressed using an intensive quantity (intensive physical | 210 | expressed using an intensive quantity (intensive physical | ||
211 | property)]_", | 211 | property)]_", | ||
212 | "format": "WCS", | 212 | "format": "WCS", | ||
213 | "hash": "", | 213 | "hash": "", | ||
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215 | "last_modified": null, | 215 | "last_modified": null, | ||
216 | "metadata_modified": "2024-06-25T09:59:25.740167", | 216 | "metadata_modified": "2024-06-25T09:59:25.740167", | ||
217 | "mimetype": null, | 217 | "mimetype": null, | ||
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742 | carbon stored by landcover class, for each year in time series | 745 | carbon stored by landcover class, for each year in time series | ||
743 | (2001-2020), for each country", | 746 | (2001-2020), for each country", | ||
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750 | "mimetype_inner": null, | 753 | "mimetype_inner": null, | ||
751 | "name": "Vegetation Carbon Stock summary table", | 754 | "name": "Vegetation Carbon Stock summary table", | ||
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759 | "url_type": "upload" | 762 | "url_type": "upload" | ||
760 | }, | 763 | }, | ||
761 | { | 764 | { | ||
762 | "cache_last_updated": null, | 765 | "cache_last_updated": null, | ||
763 | "cache_url": null, | 766 | "cache_url": null, | ||
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765 | "datastore_active": false, | 768 | "datastore_active": false, | ||
766 | "description": "__Vegetation carbon stock legend__\r\nThis table | 769 | "description": "__Vegetation carbon stock legend__\r\nThis table | ||
767 | summarizes the aggregation of the original classes in the ESA-CCI | 770 | summarizes the aggregation of the original classes in the ESA-CCI | ||
768 | classification to the _aggregated classes_ used in the report and the | 771 | classification to the _aggregated classes_ used in the report and the | ||
769 | crosswalk to the _ARIES semantic concepts_", | 772 | crosswalk to the _ARIES semantic concepts_", | ||
770 | "format": "CSV", | 773 | "format": "CSV", | ||
771 | "hash": "", | 774 | "hash": "", | ||
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782 | "state": "active", | 785 | "state": "active", | ||
783 | "url": | 786 | "url": | ||
784 | 8-4b8d-892f-ff641a52b5d0/download/vegetation_carbon_stock_legend.csv", | 787 | 8-4b8d-892f-ff641a52b5d0/download/vegetation_carbon_stock_legend.csv", | ||
785 | "url_type": "upload" | 788 | "url_type": "upload" | ||
786 | } | 789 | } | ||
787 | ], | 790 | ], | ||
788 | "state": "active", | 791 | "state": "active", | ||
789 | "tags": [], | 792 | "tags": [], | ||
790 | "title": "Vegetation Carbon Stock 2001-2020", | 793 | "title": "Vegetation Carbon Stock 2001-2020", | ||
791 | "type": "dataset", | 794 | "type": "dataset", | ||
792 | "url": "", | 795 | "url": "", | ||
793 | "version": "" | 796 | "version": "" | ||
794 | } | 797 | } |