diff --git a/404.html b/404.html index e5d39f6..e18cb66 100644 --- a/404.html +++ b/404.html @@ -33,11 +33,11 @@ BrightSpace - - + + -

404

There's nothing here.
Take me home
- +

404

How did we get here?
Take me home
+ diff --git a/assets/404.html-B7tEw--O.js b/assets/404.html-Ckh1DWnP.js similarity index 83% rename from assets/404.html-B7tEw--O.js rename to assets/404.html-Ckh1DWnP.js index eceeb52..6d3fe6d 100644 --- a/assets/404.html-B7tEw--O.js +++ b/assets/404.html-Ckh1DWnP.js @@ -1 +1 @@ -import{_ as e,c as o,a,o as n}from"./app-DgqI7hgw.js";const l={};function r(s,t){return n(),o("div",null,t[0]||(t[0]=[a("p",null,"404 Not Found",-1)]))}const _=e(l,[["render",r],["__file","404.html.vue"]]),i=JSON.parse('{"path":"/404.html","title":"","lang":"en-US","frontmatter":{"layout":"NotFound"},"headers":[],"git":{},"filePathRelative":null}');export{_ as comp,i as data}; +import{_ as e,c as o,a,o as n}from"./app-Ze_EQsYO.js";const l={};function r(s,t){return n(),o("div",null,t[0]||(t[0]=[a("p",null,"404 Not Found",-1)]))}const _=e(l,[["render",r],["__file","404.html.vue"]]),i=JSON.parse('{"path":"/404.html","title":"","lang":"en-US","frontmatter":{"layout":"NotFound"},"headers":[],"git":{},"filePathRelative":null}');export{_ as comp,i as data}; diff --git a/assets/Footer-DwMjjcWz.js b/assets/Footer-DOWb5vsQ.js similarity index 88% rename from assets/Footer-DwMjjcWz.js rename to assets/Footer-DOWb5vsQ.js index ab636eb..ec1a4f7 100644 --- a/assets/Footer-DwMjjcWz.js +++ b/assets/Footer-DOWb5vsQ.js @@ -1 +1 @@ -import{_ as o,g as r,o as s,c as a,a as e}from"./app-DgqI7hgw.js";const n="/BirghtSpace_DMS/images/EN-Funded%20by%20the%20EU-POS.jpg",c=r({name:"Footer"}),p={class:"vp-footer"};function i(_,t,d,f,g,l){return s(),a("footer",p,t[0]||(t[0]=[e("div",{class:"footer-content"},[e("img",{src:n,alt:"Footer Logo",style:{height:"40px",float:"right","margin-right":"100px"}}),e("p",null,"© 2024 BrightSpace Grant agreement ID 101060075")],-1)]))}const h=o(c,[["render",i],["__scopeId","data-v-1086d96e"],["__file","Footer.vue"]]);export{h as default}; +import{_ as o,g as r,o as s,c as a,a as e}from"./app-Ze_EQsYO.js";const n="/BirghtSpace_DMS/images/EN-Funded%20by%20the%20EU-POS.jpg",c=r({name:"Footer"}),p={class:"vp-footer"};function i(_,t,d,f,g,l){return s(),a("footer",p,t[0]||(t[0]=[e("div",{class:"footer-content"},[e("img",{src:n,alt:"Footer Logo",style:{height:"40px",float:"right","margin-right":"100px"}}),e("p",null,"© 2024 BrightSpace Grant agreement ID 101060075")],-1)]))}const h=o(c,[["render",i],["__scopeId","data-v-1086d96e"],["__file","Footer.vue"]]);export{h as default}; diff --git a/assets/app-DgqI7hgw.js b/assets/app-Ze_EQsYO.js similarity index 99% rename from assets/app-DgqI7hgw.js rename to assets/app-Ze_EQsYO.js index 33990a2..c0a24ef 100644 --- a/assets/app-DgqI7hgw.js +++ b/assets/app-Ze_EQsYO.js @@ -30,7 +30,7 @@ Server rendered element contains fewer child nodes than client vdom.`),w=!0),cn( * @vue/runtime-dom 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b/assets/bar.html-CzvkehHC.js similarity index 90% rename from assets/bar.html-Ca8xNHwx.js rename to assets/bar.html-CzvkehHC.js index b2cbb32..5f4c8fb 100644 --- a/assets/bar.html-Ca8xNHwx.js +++ b/assets/bar.html-CzvkehHC.js @@ -1 +1 @@ -import{_ as n,c as t,a as e,o as r}from"./app-DgqI7hgw.js";const i={};function s(l,a){return r(),t("div",null,a[0]||(a[0]=[e("h2",{id:"bar-in-sub1",tabindex:"-1"},[e("a",{class:"header-anchor",href:"#bar-in-sub1"},[e("span",null,"Bar in sub1")])],-1)]))}const b=n(i,[["render",s],["__file","bar.html.vue"]]),c=JSON.parse('{"path":"/sub1/bar.html","title":"","lang":"en-US","frontmatter":{},"headers":[{"level":2,"title":"Bar in sub1","slug":"bar-in-sub1","link":"#bar-in-sub1","children":[]}],"git":{"updatedTime":1729773561000,"contributors":[{"name":"Xinxin Yang","email":"xinxin.yang@thuenen.de","commits":1}]},"filePathRelative":"sub1/bar.md"}');export{b as comp,c as data}; +import{_ as n,c as t,a as e,o as r}from"./app-Ze_EQsYO.js";const i={};function s(l,a){return r(),t("div",null,a[0]||(a[0]=[e("h2",{id:"bar-in-sub1",tabindex:"-1"},[e("a",{class:"header-anchor",href:"#bar-in-sub1"},[e("span",null,"Bar in sub1")])],-1)]))}const b=n(i,[["render",s],["__file","bar.html.vue"]]),c=JSON.parse('{"path":"/sub1/bar.html","title":"","lang":"en-US","frontmatter":{},"headers":[{"level":2,"title":"Bar in sub1","slug":"bar-in-sub1","link":"#bar-in-sub1","children":[]}],"git":{"updatedTime":1729773561000,"contributors":[{"name":"Xinxin Yang","email":"xinxin.yang@thuenen.de","commits":1}]},"filePathRelative":"sub1/bar.md"}');export{b as comp,c as data}; diff --git a/assets/bar.html-B6igAdcC.js b/assets/bar.html-Zl6eNlKS.js similarity index 90% rename from assets/bar.html-B6igAdcC.js rename to assets/bar.html-Zl6eNlKS.js index 3956d1e..3a4dd01 100644 --- a/assets/bar.html-B6igAdcC.js +++ b/assets/bar.html-Zl6eNlKS.js @@ -1 +1 @@ -import{_ as n,c as t,a as e,o as r}from"./app-DgqI7hgw.js";const i={};function s(l,a){return r(),t("div",null,a[0]||(a[0]=[e("h2",{id:"bar-in-sub2",tabindex:"-1"},[e("a",{class:"header-anchor",href:"#bar-in-sub2"},[e("span",null,"bar in sub2")])],-1)]))}const o=n(i,[["render",s],["__file","bar.html.vue"]]),c=JSON.parse('{"path":"/sub2/bar.html","title":"","lang":"en-US","frontmatter":{},"headers":[{"level":2,"title":"bar in sub2","slug":"bar-in-sub2","link":"#bar-in-sub2","children":[]}],"git":{"updatedTime":1729773561000,"contributors":[{"name":"Xinxin Yang","email":"xinxin.yang@thuenen.de","commits":1}]},"filePathRelative":"sub2/bar.md"}');export{o as comp,c as data}; +import{_ as n,c as t,a as e,o as r}from"./app-Ze_EQsYO.js";const i={};function s(l,a){return r(),t("div",null,a[0]||(a[0]=[e("h2",{id:"bar-in-sub2",tabindex:"-1"},[e("a",{class:"header-anchor",href:"#bar-in-sub2"},[e("span",null,"bar in sub2")])],-1)]))}const o=n(i,[["render",s],["__file","bar.html.vue"]]),c=JSON.parse('{"path":"/sub2/bar.html","title":"","lang":"en-US","frontmatter":{},"headers":[{"level":2,"title":"bar in sub2","slug":"bar-in-sub2","link":"#bar-in-sub2","children":[]}],"git":{"updatedTime":1729773561000,"contributors":[{"name":"Xinxin Yang","email":"xinxin.yang@thuenen.de","commits":1}]},"filePathRelative":"sub2/bar.md"}');export{o as comp,c as data}; diff --git a/assets/examples.html-DAaaoBiX.js b/assets/examples.html-Yvp4Z-OL.js similarity index 99% rename from assets/examples.html-DAaaoBiX.js rename to assets/examples.html-Yvp4Z-OL.js index b8e1bf8..97a05de 100644 --- a/assets/examples.html-DAaaoBiX.js +++ b/assets/examples.html-Yvp4Z-OL.js @@ -1,4 +1,4 @@ -import{_ as n,c as a,e,o as l}from"./app-DgqI7hgw.js";const i={};function p(t,s){return l(),a("div",null,s[0]||(s[0]=[e(`

Markdown Extension Examples

This page demonstrates some of the built-in markdown extensions provided by VitePress.

Syntax Highlighting

VitePress provides Syntax Highlighting powered by Shiki, with additional features like line-highlighting:

Input

\`\`\`js
+import{_ as n,c as a,e,o as l}from"./app-Ze_EQsYO.js";const i={};function p(t,s){return l(),a("div",null,s[0]||(s[0]=[e(`
Markdown Extension Examples
This page demonstrates some of the built-in markdown extensions provided by VitePress.
Syntax Highlighting
VitePress provides Syntax Highlighting powered by Shiki, with additional features like line-highlighting:
Input
\`\`\`js
 export default {
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diff --git a/assets/foo.html-C7AlTZfc.js b/assets/foo.html-DrCuU4uG.js
similarity index 90%
rename from assets/foo.html-C7AlTZfc.js
rename to assets/foo.html-DrCuU4uG.js
index f5cac58..1105ae3 100644
--- a/assets/foo.html-C7AlTZfc.js
+++ b/assets/foo.html-DrCuU4uG.js
@@ -1 +1 @@
-import{_ as n,c as t,a as e,o as a}from"./app-DgqI7hgw.js";const i={};function s(l,o){return a(),t("div",null,o[0]||(o[0]=[e("h2",{id:"foo-in-sub1",tabindex:"-1"},[e("a",{class:"header-anchor",href:"#foo-in-sub1"},[e("span",null,"Foo in sub1")])],-1)]))}const c=n(i,[["render",s],["__file","foo.html.vue"]]),f=JSON.parse('{"path":"/sub1/foo.html","title":"","lang":"en-US","frontmatter":{},"headers":[{"level":2,"title":"Foo in sub1","slug":"foo-in-sub1","link":"#foo-in-sub1","children":[]}],"git":{"updatedTime":1729773561000,"contributors":[{"name":"Xinxin Yang","email":"xinxin.yang@thuenen.de","commits":1}]},"filePathRelative":"sub1/foo.md"}');export{c as comp,f as data};
+import{_ as n,c as t,a as e,o as a}from"./app-Ze_EQsYO.js";const i={};function s(l,o){return a(),t("div",null,o[0]||(o[0]=[e("h2",{id:"foo-in-sub1",tabindex:"-1"},[e("a",{class:"header-anchor",href:"#foo-in-sub1"},[e("span",null,"Foo in sub1")])],-1)]))}const c=n(i,[["render",s],["__file","foo.html.vue"]]),f=JSON.parse('{"path":"/sub1/foo.html","title":"","lang":"en-US","frontmatter":{},"headers":[{"level":2,"title":"Foo in sub1","slug":"foo-in-sub1","link":"#foo-in-sub1","children":[]}],"git":{"updatedTime":1729773561000,"contributors":[{"name":"Xinxin Yang","email":"xinxin.yang@thuenen.de","commits":1}]},"filePathRelative":"sub1/foo.md"}');export{c as comp,f as data};
diff --git a/assets/foo.html-IbrMCbvx.js b/assets/foo.html-gf-ohbwd.js
similarity index 89%
rename from assets/foo.html-IbrMCbvx.js
rename to assets/foo.html-gf-ohbwd.js
index 6661e5f..cd92e23 100644
--- a/assets/foo.html-IbrMCbvx.js
+++ b/assets/foo.html-gf-ohbwd.js
@@ -1 +1 @@
-import{_ as n,c as t,a as e,o as a}from"./app-DgqI7hgw.js";const s={};function i(r,o){return a(),t("div",null,o[0]||(o[0]=[e("h1",{id:"foo-in-sub2",tabindex:"-1"},[e("a",{class:"header-anchor",href:"#foo-in-sub2"},[e("span",null,"Foo in sub2")])],-1)]))}const c=n(s,[["render",i],["__file","foo.html.vue"]]),f=JSON.parse('{"path":"/sub2/foo.html","title":"Foo in sub2","lang":"en-US","frontmatter":{},"headers":[],"git":{"updatedTime":1729773561000,"contributors":[{"name":"Xinxin Yang","email":"xinxin.yang@thuenen.de","commits":1}]},"filePathRelative":"sub2/foo.md"}');export{c as comp,f as data};
+import{_ as n,c as t,a as e,o as a}from"./app-Ze_EQsYO.js";const s={};function i(r,o){return a(),t("div",null,o[0]||(o[0]=[e("h1",{id:"foo-in-sub2",tabindex:"-1"},[e("a",{class:"header-anchor",href:"#foo-in-sub2"},[e("span",null,"Foo in sub2")])],-1)]))}const c=n(s,[["render",i],["__file","foo.html.vue"]]),f=JSON.parse('{"path":"/sub2/foo.html","title":"Foo in sub2","lang":"en-US","frontmatter":{},"headers":[],"git":{"updatedTime":1729773561000,"contributors":[{"name":"Xinxin Yang","email":"xinxin.yang@thuenen.de","commits":1}]},"filePathRelative":"sub2/foo.md"}');export{c as comp,f as data};
diff --git a/assets/get-started.html-Ck4lVK9u.js b/assets/get-started.html-B4OCRtFC.js
similarity index 96%
rename from assets/get-started.html-Ck4lVK9u.js
rename to assets/get-started.html-B4OCRtFC.js
index b85adfc..32e2004 100644
--- a/assets/get-started.html-Ck4lVK9u.js
+++ b/assets/get-started.html-B4OCRtFC.js
@@ -1 +1 @@
-import{_ as i,c as r,e as s,a as t,b as a,d as o,f as d,r as l,o as c}from"./app-DgqI7hgw.js";const h={},m={class:"hint-container tip"};function p(u,e){const n=l("RouteLink");return c(),r("div",null,[e[4]||(e[4]=s('
Overview
Indicator
Data
  • aaa
  • jjjj
  • EU and FAO-WHO Codex Alimentarius dataset on pesticides downloaded and screened for further use in econometric models. The UNCTAD-TRAINS NTMs database has been matched with bilateral trade data (UN ComTrade) at HS6 digits (720 products), for a panel between 2010-2020. RASFF database has been downloaded and we are in the process of matching with HS6 nomenclature.
',4)),t("div",m,[e[3]||(e[3]=t("p",{class:"hint-container-title"},"Tips",-1)),t("p",null,[e[1]||(e[1]=a("Theme components are under the same ")),o(n,{to:"/guide/using-vue.html#browser-api-access-restrictions"},{default:d(()=>e[0]||(e[0]=[a("browser API access restrictions")])),_:1}),e[2]||(e[2]=a("."))])])])}const f=i(h,[["render",p],["__file","get-started.html.vue"]]),g=JSON.parse('{"path":"/get-started.html","title":"","lang":"en-US","frontmatter":{},"headers":[{"level":2,"title":"Overview","slug":"overview","link":"#overview","children":[]},{"level":2,"title":"Indicator","slug":"indicator","link":"#indicator","children":[]},{"level":2,"title":"Data","slug":"data","link":"#data","children":[]}],"git":{"updatedTime":1730293346000,"contributors":[{"name":"Xinxin Yang","email":"xinxin.yang@thuenen.de","commits":4}]},"filePathRelative":"get-started.md"}');export{f as comp,g as data};
+import{_ as i,c as r,e as s,a as t,b as a,d as o,f as d,r as l,o as c}from"./app-Ze_EQsYO.js";const h={},m={class:"hint-container tip"};function p(u,e){const n=l("RouteLink");return c(),r("div",null,[e[4]||(e[4]=s('
Overview
Indicator
Data
  • aaa
  • jjjj
  • EU and FAO-WHO Codex Alimentarius dataset on pesticides downloaded and screened for further use in econometric models. The UNCTAD-TRAINS NTMs database has been matched with bilateral trade data (UN ComTrade) at HS6 digits (720 products), for a panel between 2010-2020. RASFF database has been downloaded and we are in the process of matching with HS6 nomenclature.
',4)),t("div",m,[e[3]||(e[3]=t("p",{class:"hint-container-title"},"Tips",-1)),t("p",null,[e[1]||(e[1]=a("Theme components are under the same ")),o(n,{to:"/guide/using-vue.html#browser-api-access-restrictions"},{default:d(()=>e[0]||(e[0]=[a("browser API access restrictions")])),_:1}),e[2]||(e[2]=a("."))])])])}const f=i(h,[["render",p],["__file","get-started.html.vue"]]),g=JSON.parse('{"path":"/get-started.html","title":"","lang":"en-US","frontmatter":{},"headers":[{"level":2,"title":"Overview","slug":"overview","link":"#overview","children":[]},{"level":2,"title":"Indicator","slug":"indicator","link":"#indicator","children":[]},{"level":2,"title":"Data","slug":"data","link":"#data","children":[]}],"git":{"updatedTime":1730293346000,"contributors":[{"name":"Xinxin Yang","email":"xinxin.yang@thuenen.de","commits":4}]},"filePathRelative":"get-started.md"}');export{f as comp,g as data};
diff --git a/assets/index.html-DgNliqFX.js b/assets/index.html-DMJSz-pr.js
similarity index 95%
rename from assets/index.html-DgNliqFX.js
rename to assets/index.html-DMJSz-pr.js
index 02f2f6a..fa22925 100644
--- a/assets/index.html-DgNliqFX.js
+++ b/assets/index.html-DMJSz-pr.js
@@ -1 +1 @@
-import{_ as o,c as r,a as n,b as t,d as i,o as s,r as l}from"./app-DgqI7hgw.js";const d={};function m(p,e){const a=l("Footer");return s(),r("div",null,[e[0]||(e[0]=n("p",null,"Designing a Roadmap for Effective and Sustainable Strategies for Assessing and Addressing the Challenges of EU Agriculture to Navigate within a Safe and Just Operating Space.",-1)),e[1]||(e[1]=n("p",null,[t("Funded by "),n("a",{href:"https://european-union.europa.eu/index_de",target:"_blank",rel:"noopener noreferrer"},"the european union"),t(". Views and opinions expressed are those of the authors only and do not necessarily reflect those of the European Union or the European Commission. Neither the European Union nor the granting authority can be held responsible for them.")],-1)),i(a)])}const h=o(d,[["render",m],["__file","index.html.vue"]]),c=JSON.parse('{"path":"/","title":"BrightSpace","lang":"en-US","frontmatter":{"home":true,"title":"BrightSpace","heroImage":"/images/brightspace logo horizontal color 300CMYK.png","actions":[{"text":"Database and Monitoring System","link":"/guide/overview.html","type":"primary"}]},"headers":[],"git":{"updatedTime":1731659664000,"contributors":[{"name":"xxThu","email":"xinxin.yang@thuenen.de","commits":4},{"name":"Xinxin Yang","email":"xinxin.yang@thuenen.de","commits":3},{"name":"sebastianneu","email":"sebastian.neuenfeldt@thuenen.de","commits":3}]},"filePathRelative":"README.md"}');export{h as comp,c as data};
+import{_ as o,c as r,a as n,b as t,d as i,o as s,r as l}from"./app-Ze_EQsYO.js";const d={};function m(p,e){const a=l("Footer");return s(),r("div",null,[e[0]||(e[0]=n("p",null,"Designing a Roadmap for Effective and Sustainable Strategies for Assessing and Addressing the Challenges of EU Agriculture to Navigate within a Safe and Just Operating Space.",-1)),e[1]||(e[1]=n("p",null,[t("Funded by "),n("a",{href:"https://european-union.europa.eu/index_de",target:"_blank",rel:"noopener noreferrer"},"the european union"),t(". Views and opinions expressed are those of the authors only and do not necessarily reflect those of the European Union or the European Commission. Neither the European Union nor the granting authority can be held responsible for them.")],-1)),i(a)])}const h=o(d,[["render",m],["__file","index.html.vue"]]),c=JSON.parse('{"path":"/","title":"BrightSpace","lang":"en-US","frontmatter":{"home":true,"title":"BrightSpace","heroImage":"/images/brightspace logo horizontal color 300CMYK.png","actions":[{"text":"Database and Monitoring System","link":"/guide/overview.html","type":"primary"}]},"headers":[],"git":{"updatedTime":1731659664000,"contributors":[{"name":"xxThu","email":"xinxin.yang@thuenen.de","commits":4},{"name":"Xinxin Yang","email":"xinxin.yang@thuenen.de","commits":3},{"name":"sebastianneu","email":"sebastian.neuenfeldt@thuenen.de","commits":3}]},"filePathRelative":"README.md"}');export{h as comp,c as data};
diff --git a/assets/index.html-B6nrabYv.js b/assets/index.html-DkR247OZ.js
similarity index 93%
rename from assets/index.html-B6nrabYv.js
rename to assets/index.html-DkR247OZ.js
index 025dad5..54da7fd 100644
--- a/assets/index.html-B6nrabYv.js
+++ b/assets/index.html-DkR247OZ.js
@@ -1 +1 @@
-import{_ as t,c as a,e as s,o as n}from"./app-DgqI7hgw.js";const i={};function l(r,e){return n(),a("div",null,e[0]||(e[0]=[s('
Test 0
Test 1
Test 1.1
',3)]))}const d=t(i,[["render",l],["__file","index.html.vue"]]),h=JSON.parse('{"path":"/baz/","title":"","lang":"en-US","frontmatter":{},"headers":[{"level":2,"title":"Test 0","slug":"test-0","link":"#test-0","children":[]},{"level":2,"title":"Test 1","slug":"test-1","link":"#test-1","children":[{"level":3,"title":"Test 1.1","slug":"test-1-1","link":"#test-1-1","children":[]}]}],"git":{"updatedTime":1729781272000,"contributors":[{"name":"Xinxin Yang","email":"xinxin.yang@thuenen.de","commits":1}]},"filePathRelative":"baz/README.md"}');export{d as comp,h as data};
+import{_ as t,c as a,e as s,o as n}from"./app-Ze_EQsYO.js";const i={};function l(r,e){return n(),a("div",null,e[0]||(e[0]=[s('
Test 0
Test 1
Test 1.1
',3)]))}const d=t(i,[["render",l],["__file","index.html.vue"]]),h=JSON.parse('{"path":"/baz/","title":"","lang":"en-US","frontmatter":{},"headers":[{"level":2,"title":"Test 0","slug":"test-0","link":"#test-0","children":[]},{"level":2,"title":"Test 1","slug":"test-1","link":"#test-1","children":[{"level":3,"title":"Test 1.1","slug":"test-1-1","link":"#test-1-1","children":[]}]}],"git":{"updatedTime":1729781272000,"contributors":[{"name":"Xinxin Yang","email":"xinxin.yang@thuenen.de","commits":1}]},"filePathRelative":"baz/README.md"}');export{d as comp,h as data};
diff --git a/assets/indicator.html-DDR3NupH.js b/assets/indicator.html-DDR3NupH.js
deleted file mode 100644
index b944528..0000000
--- a/assets/indicator.html-DDR3NupH.js
+++ /dev/null
@@ -1 +0,0 @@
-import{_ as o,c as s,a as e,d,f as i,e as r,r as c,o as l,b as n}from"./app-DgqI7hgw.js";const m={},h={id:"safe-operating-spaces-thematic-areas-and-indicator-domains",tabindex:"-1"},p={class:"header-anchor",href:"#safe-operating-spaces-thematic-areas-and-indicator-domains"},u={id:"just-operating-spaces-thematic-areas-and-indicator-domains",tabindex:"-1"},f={class:"header-anchor",href:"#just-operating-spaces-thematic-areas-and-indicator-domains"};function g(y,t){const a=c("RouteLink");return l(),s("div",null,[t[2]||(t[2]=e("h1",{id:"aligning-sjos-to-thematic-areas-and-indicator-domains",tabindex:"-1"},[e("a",{class:"header-anchor",href:"#aligning-sjos-to-thematic-areas-and-indicator-domains"},[e("span",null,"Aligning SJOS to thematic areas and indicator domains")])],-1)),t[3]||(t[3]=e("p",null,"Based on a review of the Safe Operating Spaces (SOS) and Just Operating Spaces (JOS) concepts, a comparison and alignment with EU policy targets, and expert consultations, a set of 12 thematic areas and 34 indicator domains was developed. The SOS components closely align with concepts in the literature on planetary boundaries, with the notable exceptions of ocean acidification and ozone layer depletion. For JOS, significant overlap was found between the literature and EU policies, particularly in the areas of food and nutrition security and health.",-1)),t[4]||(t[4]=e("p",null,'Economy-related topics were divided into two thematic areas. The "General Economy" area addresses the relative position of agriculture compared to other sectors in terms of employment, value-added, wages, and input prices. Topics specific to the agricultural sector, such as farm incomes, economic viability, future prospects, and farm structure, are encompassed within the broader thematic area of "Farm resilience." The resulting thematic areas and indicator domains are detailed below.',-1)),e("h2",h,[e("a",p,[e("span",null,[d(a,{to:"/docs/guide/safe.html"},{default:i(()=>t[0]||(t[0]=[n("Safe Operating Spaces Thematic Areas and Indicator Domains")])),_:1})])])]),t[5]||(t[5]=r("
Thematic areaIndicator Domain
BiodiversityGenetic/Functional diversity (outcomes)
Land management (drivers)
Land useLand cover
Soil health
Water useWater use
Nutrient flowsNitrogen cycle
Phosphorus cycle
Chemical pollution (novel entities)Pesticide use
Antimicrobials
Aerosol loadingAir pollution (Particulates)
ClimateGHG emissions
",1)),e("h2",u,[e("a",f,[e("span",null,[d(a,{to:"/docs/guide/jos.html"},{default:i(()=>t[1]||(t[1]=[n("Just Operating Spaces Thematic Areas and Indicator Domains")])),_:1})])])]),t[6]||(t[6]=r("
Thematic areaIndicator Domain
Nutrition securityFood availability
... affordability
... stability
Food waste
Diet quality/food utilization
HealthLife expectancy
Obesity
Animal welfare
Access to clean water
EconomySectoral employment
Sectoral productivity
Sectoral wages
Market organisation
Trade
Energy access
Farm resilienceFarm income
Farm viability
Farm structure
Social equityIncome distribution (sectoral, spatial)
Education
Gender equality
Depending on the progress of partners within BrightSpace working on different thematic areas or indicators, information like data, code or article and reports will be listed.
",2))])}const S=o(m,[["render",g],["__file","indicator.html.vue"]]),v=JSON.parse('{"path":"/guide/indicator.html","title":"Aligning SJOS to thematic areas and indicator domains","lang":"en-US","frontmatter":{},"headers":[{"level":2,"title":"Safe Operating Spaces Thematic Areas and Indicator Domains","slug":"safe-operating-spaces-thematic-areas-and-indicator-domains","link":"#safe-operating-spaces-thematic-areas-and-indicator-domains","children":[]},{"level":2,"title":"Just Operating Spaces Thematic Areas and Indicator Domains","slug":"just-operating-spaces-thematic-areas-and-indicator-domains","link":"#just-operating-spaces-thematic-areas-and-indicator-domains","children":[]}],"git":{"updatedTime":1733400875000,"contributors":[{"name":"neuenfeldt","email":"sebastian.neuenfeldt@thuenen.de","commits":2},{"name":"sebastianneu","email":"sebastian.neuenfeldt@thuenen.de","commits":2},{"name":"xxThu","email":"83697529+xxThu@users.noreply.github.com","commits":1},{"name":"xxThu","email":"xinxin.yang@thuenen.de","commits":1}]},"filePathRelative":"guide/indicator.md"}');export{S as comp,v as data};
diff --git a/assets/indicator.html-sgvtXqSr.js b/assets/indicator.html-sgvtXqSr.js
new file mode 100644
index 0000000..8a6681e
--- /dev/null
+++ b/assets/indicator.html-sgvtXqSr.js
@@ -0,0 +1 @@
+import{_ as e,c as a,e as d,o as i}from"./app-Ze_EQsYO.js";const r={};function n(o,t){return i(),a("div",null,t[0]||(t[0]=[d('
Aligning SJOS to thematic areas and indicator domains
Based on a review of the Safe Operating Spaces (SOS) and Just Operating Spaces (JOS) concepts, a comparison and alignment with EU policy targets, and expert consultations, a set of 12 thematic areas and 34 indicator domains was developed. The SOS components closely align with concepts in the literature on planetary boundaries, with the notable exceptions of ocean acidification and ozone layer depletion. For JOS, significant overlap was found between the literature and EU policies, particularly in the areas of food and nutrition security and health.
Economy-related topics were divided into two thematic areas. The "General Economy" area addresses the relative position of agriculture compared to other sectors in terms of employment, value-added, wages, and input prices. Topics specific to the agricultural sector, such as farm incomes, economic viability, future prospects, and farm structure, are encompassed within the broader thematic area of "Farm resilience." The resulting thematic areas and indicator domains are detailed below.
Safe Operating Spaces Thematic Areas and Indicator Domains
Thematic areaIndicator Domain
BiodiversityGenetic/Functional diversity (outcomes)
Land management (drivers)
Land useLand cover
Soil health
Water useWater use
Nutrient flowsNitrogen cycle
Phosphorus cycle
Chemical pollution (novel entities)Pesticide use
Antimicrobials
Aerosol loadingAir pollution (Particulates)
ClimateGHG emissions
Just Operating Spaces Thematic Areas and Indicator Domains
Thematic areaIndicator Domain
Nutrition securityFood availability
... affordability
... stability
Food waste
Diet quality/food utilization
HealthLife expectancy
Obesity
Animal welfare
Access to clean water
EconomySectoral employment
Sectoral productivity
Sectoral wages
Market organisation
Trade
Energy access
Farm resilienceFarm income
Farm viability
Farm structure
Social equityIncome distribution (sectoral, spatial)
Education
Gender equality
Depending on the progress of partners within BrightSpace working on different thematic areas or indicators, information like data, code or article and reports will be listed.
',8)]))}const c=e(r,[["render",n],["__file","indicator.html.vue"]]),l=JSON.parse('{"path":"/guide/indicator.html","title":"Aligning SJOS to thematic areas and indicator domains","lang":"en-US","frontmatter":{},"headers":[{"level":2,"title":"Safe Operating Spaces Thematic Areas and Indicator Domains","slug":"safe-operating-spaces-thematic-areas-and-indicator-domains","link":"#safe-operating-spaces-thematic-areas-and-indicator-domains","children":[]},{"level":2,"title":"Just Operating Spaces Thematic Areas and Indicator Domains","slug":"just-operating-spaces-thematic-areas-and-indicator-domains","link":"#just-operating-spaces-thematic-areas-and-indicator-domains","children":[]}],"git":{"updatedTime":1733401084000,"contributors":[{"name":"neuenfeldt","email":"sebastian.neuenfeldt@thuenen.de","commits":3},{"name":"sebastianneu","email":"sebastian.neuenfeldt@thuenen.de","commits":2},{"name":"xxThu","email":"83697529+xxThu@users.noreply.github.com","commits":1},{"name":"xxThu","email":"xinxin.yang@thuenen.de","commits":1}]},"filePathRelative":"guide/indicator.md"}');export{c as comp,l as data};
diff --git a/assets/jos.html-B6gVCEa9.js b/assets/jos.html-Cu4QbK_q.js
similarity index 98%
rename from assets/jos.html-B6gVCEa9.js
rename to assets/jos.html-Cu4QbK_q.js
index 007503c..249bf4a 100644
--- a/assets/jos.html-B6gVCEa9.js
+++ b/assets/jos.html-Cu4QbK_q.js
@@ -1 +1 @@
-import{_ as a,c as i,e as t,o as n}from"./app-DgqI7hgw.js";const r={};function o(s,e){return n(),i("div",null,e[0]||(e[0]=[t('
Just Operating Space (JOS) thematic
Farm resilience
Farm income
Approach: FADN spatial - Downscaling
Description
FADN subsidies are part of farm income. As these data is only accessible at farm level, a down scaling approach has been developed and applied.
Data is protected (more than 15 obs. per observation unit).
Main data sources to produce the data
  • Individual FADN data
  • Farm Structure Spatial Units (FSU) established in the VERIFY project (link_FSU)
  • CAPDIS for information on land use and yields (Britz, W., Witzke, P. (2014). CAPRI model documentation 2014) 
    • Information on land use in the FSU is calculated calculated through the Land Area Prediction model (LAPM), based on the methods of Lamboni et al. (2016: Lamboni, M. R. Koeble, A. Leip (2016).
    • Multi-scale land-use disaggregation modelling: concept and application to EU countries. Environmental Modelling & Software 82, 183-217. https://doi.org/10.1016/j.envsoft.2016.04.028), and assessing its accuracy (Leip et al., 2017: Leip, A., Koeble, R., Rotllan-Puig, X., 2017. Land Area Prediction model (LAPM) Accuracy Assessment. European Commission, Ispra (VA), Italy. https://doi.org/10.2760/624482).
    • They include available data of crop areas from the Farm Structure Survey 2010 at a 10 km x 10 km grid, developing a methodology to gap-fill missing data which was filtered out by confidentiality rules (Bujnowska et al., 2019: Bujnowska, A., Gomez-Prieto, O., Kloek, W., Koeble, R., Leip, D., Martinez-Solano, D., Ramos, H. (2019). Disseminating European data at farm level while protecting confidentiality: scientific use files and homogeneous spatial units, in: International Conference on Agricultural Statistics, New Delhi.).
Reports
Article
Article is prepared for submission.
Temporal and spatial coverage
Spatial coverage: EU-wide for all countries contributing to the EU-FADN.
Temporal coverage: depending on progress. Actually 2001-2020 each second year.
Resolution
Farm Structure Spatial Units (FSU). High resolution of grids of different size.
Visualization
FADN SPATIAL
Farm viability
Nutrition security
Health
Economy
Social equity
',25)]))}const c=a(r,[["render",o],["__file","jos.html.vue"]]),h=JSON.parse('{"path":"/guide/jos.html","title":"Just Operating Space (JOS) thematic","lang":"en-US","frontmatter":{},"headers":[{"level":2,"title":"Farm resilience","slug":"farm-resilience","link":"#farm-resilience","children":[{"level":3,"title":"Farm income","slug":"farm-income","link":"#farm-income","children":[]},{"level":3,"title":"Farm viability","slug":"farm-viability","link":"#farm-viability","children":[]}]},{"level":2,"title":"Nutrition security","slug":"nutrition-security","link":"#nutrition-security","children":[]},{"level":2,"title":"Health","slug":"health","link":"#health","children":[]},{"level":2,"title":"Economy","slug":"economy","link":"#economy","children":[]},{"level":2,"title":"Social equity","slug":"social-equity","link":"#social-equity","children":[]}],"git":{"updatedTime":1732550192000,"contributors":[{"name":"neuenfeldt","email":"sebastian.neuenfeldt@thuenen.de","commits":11},{"name":"xxThu","email":"xinxin.yang@thuenen.de","commits":3}]},"filePathRelative":"guide/jos.md"}');export{c as comp,h as data};
+import{_ as a,c as i,e as t,o as n}from"./app-Ze_EQsYO.js";const r={};function o(s,e){return n(),i("div",null,e[0]||(e[0]=[t('
Just Operating Space (JOS) thematic
Farm resilience
Farm income
Approach: FADN spatial - Downscaling
Description
FADN subsidies are part of farm income. As these data is only accessible at farm level, a down scaling approach has been developed and applied.
Data is protected (more than 15 obs. per observation unit).
Main data sources to produce the data
  • Individual FADN data
  • Farm Structure Spatial Units (FSU) established in the VERIFY project (link_FSU)
  • CAPDIS for information on land use and yields (Britz, W., Witzke, P. (2014). CAPRI model documentation 2014) 
    • Information on land use in the FSU is calculated calculated through the Land Area Prediction model (LAPM), based on the methods of Lamboni et al. (2016: Lamboni, M. R. Koeble, A. Leip (2016).
    • Multi-scale land-use disaggregation modelling: concept and application to EU countries. Environmental Modelling & Software 82, 183-217. https://doi.org/10.1016/j.envsoft.2016.04.028), and assessing its accuracy (Leip et al., 2017: Leip, A., Koeble, R., Rotllan-Puig, X., 2017. Land Area Prediction model (LAPM) Accuracy Assessment. European Commission, Ispra (VA), Italy. https://doi.org/10.2760/624482).
    • They include available data of crop areas from the Farm Structure Survey 2010 at a 10 km x 10 km grid, developing a methodology to gap-fill missing data which was filtered out by confidentiality rules (Bujnowska et al., 2019: Bujnowska, A., Gomez-Prieto, O., Kloek, W., Koeble, R., Leip, D., Martinez-Solano, D., Ramos, H. (2019). Disseminating European data at farm level while protecting confidentiality: scientific use files and homogeneous spatial units, in: International Conference on Agricultural Statistics, New Delhi.).
Reports
Article
Article is prepared for submission.
Temporal and spatial coverage
Spatial coverage: EU-wide for all countries contributing to the EU-FADN.
Temporal coverage: depending on progress. Actually 2001-2020 each second year.
Resolution
Farm Structure Spatial Units (FSU). High resolution of grids of different size.
Visualization
FADN SPATIAL
Farm viability
Nutrition security
Health
Economy
Social equity
',25)]))}const c=a(r,[["render",o],["__file","jos.html.vue"]]),h=JSON.parse('{"path":"/guide/jos.html","title":"Just Operating Space (JOS) thematic","lang":"en-US","frontmatter":{},"headers":[{"level":2,"title":"Farm resilience","slug":"farm-resilience","link":"#farm-resilience","children":[{"level":3,"title":"Farm income","slug":"farm-income","link":"#farm-income","children":[]},{"level":3,"title":"Farm viability","slug":"farm-viability","link":"#farm-viability","children":[]}]},{"level":2,"title":"Nutrition security","slug":"nutrition-security","link":"#nutrition-security","children":[]},{"level":2,"title":"Health","slug":"health","link":"#health","children":[]},{"level":2,"title":"Economy","slug":"economy","link":"#economy","children":[]},{"level":2,"title":"Social equity","slug":"social-equity","link":"#social-equity","children":[]}],"git":{"updatedTime":1732550192000,"contributors":[{"name":"neuenfeldt","email":"sebastian.neuenfeldt@thuenen.de","commits":11},{"name":"xxThu","email":"xinxin.yang@thuenen.de","commits":3}]},"filePathRelative":"guide/jos.md"}');export{c as comp,h as data};
diff --git a/assets/overview.html-BJzRPt0g.js b/assets/overview.html-CbsIujF_.js
similarity index 98%
rename from assets/overview.html-BJzRPt0g.js
rename to assets/overview.html-CbsIujF_.js
index ad2f3ad..c8d3d7c 100644
--- a/assets/overview.html-BJzRPt0g.js
+++ b/assets/overview.html-CbsIujF_.js
@@ -1 +1 @@
-import{_ as a,c as t,e as i,o as r}from"./app-DgqI7hgw.js";const n={};function s(o,e){return r(),t("div",null,e[0]||(e[0]=[i('
Overview
Project
BrightSpace is a European research project aiming to design effective and sustainable strategies to navigate within a Safe and Just Operating Space (link webpage).
Safe and Just Operating Spaces within BrightSpace
An overview of dimensions and indicators of the Safe and Just Operating Spaces (SJOS) can be found in a report (Deliverable D1.1 (D01) An operational concept for dimensions and indicators of the SJOS link D1.1). This report introduces an SJOS framework designed specifically for EU agriculture, integrating environmental, economic, and social dimensions to track progress toward political objectives. The framework's development involved a design phase comprising a comprehensive literature review, alignment with EU policy goals, and consultations with experts. The proposed SJOS encompasses 12 thematic areas and 34 indicator domains, although the specific indicators are yet to be finalized.
Literature Review of impacts of SJOS
If you like to have an overview of mpacts of agricultural production decisions on the safe and just operating space, a systematic literature review has been carried out (article). This study systematically reviews the literature to identify connections between agricultural production decisions and the SJOS framework. It focuses on three categories of production decisions: on-farm technology usage, farm structural change, and field structural change. The study examines the mechanisms through which these categories impact SJOS and evaluates the scope of existing evidence.
',8)]))}const p=a(n,[["render",s],["__file","overview.html.vue"]]),l=JSON.parse('{"path":"/guide/overview.html","title":"Overview","lang":"en-US","frontmatter":{},"headers":[{"level":2,"title":"Project","slug":"project","link":"#project","children":[]},{"level":2,"title":"Safe and Just Operating Spaces within BrightSpace","slug":"safe-and-just-operating-spaces-within-brightspace","link":"#safe-and-just-operating-spaces-within-brightspace","children":[]},{"level":2,"title":"Literature Review of impacts of SJOS","slug":"literature-review-of-impacts-of-sjos","link":"#literature-review-of-impacts-of-sjos","children":[]},{"level":2,"title":"","slug":"","link":"#","children":[]}],"git":{"updatedTime":1731666019000,"contributors":[{"name":"sebastianneu","email":"sebastian.neuenfeldt@thuenen.de","commits":2},{"name":"xxThu","email":"xinxin.yang@thuenen.de","commits":1}]},"filePathRelative":"guide/overview.md"}');export{p as comp,l as data};
+import{_ as a,c as t,e as i,o as r}from"./app-Ze_EQsYO.js";const n={};function s(o,e){return r(),t("div",null,e[0]||(e[0]=[i('
Overview
Project
BrightSpace is a European research project aiming to design effective and sustainable strategies to navigate within a Safe and Just Operating Space (link webpage).
Safe and Just Operating Spaces within BrightSpace
An overview of dimensions and indicators of the Safe and Just Operating Spaces (SJOS) can be found in a report (Deliverable D1.1 (D01) An operational concept for dimensions and indicators of the SJOS link D1.1). This report introduces an SJOS framework designed specifically for EU agriculture, integrating environmental, economic, and social dimensions to track progress toward political objectives. The framework's development involved a design phase comprising a comprehensive literature review, alignment with EU policy goals, and consultations with experts. The proposed SJOS encompasses 12 thematic areas and 34 indicator domains, although the specific indicators are yet to be finalized.
Literature Review of impacts of SJOS
If you like to have an overview of mpacts of agricultural production decisions on the safe and just operating space, a systematic literature review has been carried out (article). This study systematically reviews the literature to identify connections between agricultural production decisions and the SJOS framework. It focuses on three categories of production decisions: on-farm technology usage, farm structural change, and field structural change. The study examines the mechanisms through which these categories impact SJOS and evaluates the scope of existing evidence.
',8)]))}const p=a(n,[["render",s],["__file","overview.html.vue"]]),l=JSON.parse('{"path":"/guide/overview.html","title":"Overview","lang":"en-US","frontmatter":{},"headers":[{"level":2,"title":"Project","slug":"project","link":"#project","children":[]},{"level":2,"title":"Safe and Just Operating Spaces within BrightSpace","slug":"safe-and-just-operating-spaces-within-brightspace","link":"#safe-and-just-operating-spaces-within-brightspace","children":[]},{"level":2,"title":"Literature Review of impacts of SJOS","slug":"literature-review-of-impacts-of-sjos","link":"#literature-review-of-impacts-of-sjos","children":[]},{"level":2,"title":"","slug":"","link":"#","children":[]}],"git":{"updatedTime":1731666019000,"contributors":[{"name":"sebastianneu","email":"sebastian.neuenfeldt@thuenen.de","commits":2},{"name":"xxThu","email":"xinxin.yang@thuenen.de","commits":1}]},"filePathRelative":"guide/overview.md"}');export{p as comp,l as data};
diff --git a/assets/safe.html-BYzGfLNV.js b/assets/safe.html-rIQH0UmW.js
similarity index 99%
rename from assets/safe.html-BYzGfLNV.js
rename to assets/safe.html-rIQH0UmW.js
index 1d45f34..95e7c8c 100644
--- a/assets/safe.html-BYzGfLNV.js
+++ b/assets/safe.html-rIQH0UmW.js
@@ -1 +1 @@
-import{_ as a,c as t,e as n,o as i}from"./app-DgqI7hgw.js";const r={};function o(s,e){return i(),t("div",null,e[0]||(e[0]=[n('
Safe Operating Space (SOS) thematic
Biodiversity
Approach: Grass land intensity - mowing event detection
Description
Spatially explicit knowledge on grassland extent and management is critical to understand and monitor the impact of grassland use intensity on ecosystem services and biodiversity. While regional studies allow detailed insights into land use and ecosystem service interactions, information on a national scale can aid biodiversity assessments. However, for most European countries this information is not yet widely available. We used an analysis-ready-data cube that contains dense time series of co-registered Sentinel-2 and Landsat 8 data, covering the extent of Germany. We propose an algorithm that detects mowing events in the time series based on residuals from an assumed undisturbed phenology, as an indicator of grassland use intensity.
Main data sources to produce the data
Among others
  • Sentinel-1, Sentinel-2, Landsat 8 and additional environmental data
Article
Schwieder, M., Wesemeyer, M., Frantz, D., Pfoch, K., Erasmi, S., Pickert, J., Nendel, C., & Hostert, P. (2022). Mapping grassland mowing events across Germany based on combined Sentinel-2 and Landsat 8 time series. Remote Sensing of Environment, 269, 112795, https://doi.org/10.1016/j.rse.2021.112795
Temporal and spatial coverage
Spatial coverage: Germany.
Temporal coverage: 2017-2021.
Resolution
Field level parcel data of different size for grass land.
Data
Schwieder, M., Lobert, F., Tetteh, G. O., & Erasmi, S. (2024). Grassland mowing events across Germany detected from combined Sentinel-2 and Landsat time series for the years 2017 - 2021 [Data set]. Zenodo. https://doi.org/10.5281/zenodo.10609590
Visualization
Mowing events map
Land use
Approach: crop type detection
Description
Based on the multi-year dataset major crop sequences of cereals and leaf crops are mapped. Most crop sequences were dominated by winter cereals followed by summer cereals. Monocultures of summer cereals were mainly revealed in the Northwest of Germany. It is showcased that high spatial and thematic detail in combination with annual mapping will stimulate research on crop cycles and studies to assess the impact of environmental policies on management decisions. The results demonstrate the capabilities of integrated optical time series and SAR data in combination with variables describing local and seasonal environmental conditions for annual large-area crop type mapping.
Main data sources to produce the data
Among others
  • Sentinel-2 and Landsat 8 data
  • Sentinel-1 data
  • Topographic variables elevation, hillslope, and aspect from a digital elevation model (DEM) with a spatial resolution of 10 m provided by the German Federal Agency for Cartography and Geodesy
  • High-resolution (1 x 1 km) climatological data on seasonal mean air temperature and precipitation from German Weather Service
  • Reference data: Integrated Administration and Control System (IACS)
Article
Blickensdoerfer, L., Schwieder, M., Pflugmacher, D., Nendel, C., Erasmi, S., Hostert, P. (2022). Mapping of crop types and crop sequences with combined time series of Sentinel-1, Sentinel-2 and Landsat 8 data for Germany. Remote Sens Environ 269:112831, DOI:10.1016/j.rse.2021.112831
Temporal and spatial coverage
Spatial coverage: Germany.
Temporal coverage: 2017-2021.
Resolution
Field level parcel data of different size.
Data
https://doi.org/10.5281/zenodo.10617622
Visualization
Agricultural land use
Approach: parcel segmentation
Description
Effective monitoring of agricultural lands requires accurate spatial information about the locations and boundaries of agricultural fields. Through satellite imagery, such information can be mapped on a large scale at a high temporal frequency. Various methods exist in the literature for segmenting agricultural fields from satellite images. Edge-based, region-based, or hybrid segmentation methods are traditional methods that have widely been used for segmenting agricultural fields. Lately, the use of deep neural networks (DNNs) for various tasks in remote sensing has been gaining traction. Therefore, to identify the optimal method for segmenting agricultural fields from satellite images, we evaluated three state-of-the-art DNNs, namely Mask R-CNN, U-Net, and FracTAL ResUNet against the multi-resolution segmentation (MRS) algorithm, which is a region-based and a more traditional segmentation method. DNNs, particularly FracTAL ResUNet, can be effectively used for large-scale segmentation of agricultural fields from satellite images.
This work is especially important for crop type detection.
Main data sources to produce the data
Among others
  • Sentinel-2
  • ATKIS
  • Reference data: agricultural parcels of the Geospatial Aid Application (GSAA)
Article
Tetteh GO, Schwieder M, Erasmi S, Conrad C, Gocht A (2023). Comparison of an optimised multiresolution segmentation approach with deep neural networks for delineating agricultural fields from Sentinel-2 images. J Photogramm Remote Sensing Geoinf Sci 91(4):295-312, DOI:10.1007/s41064-023-00247-x.
Spatial coverage
Spatial coverage: Lower Saxony - Federal state of Germany.
Resolution
Field level parcel data of different size.
Water use
Nutrient flows
Chemical pollution (novel entities)
Aerosol loading
Climate
Comments regarding the current status
  • Database extracted from De Laurentiis et al (2023). Data comprises estimated food loss and waste along the chain by product disaggregation level for each of the EU Member States for the period 2003-2020. Estimated data are available in a public domain area: Joint Research Centre Data Catalogue. Drivers according to the literature and extracted from open access sources, as an example: Population is available at: Population on 1 January by age and sex. Real GDP per capita is available at: Real GDP per capita. Unemployment rate is available at: Total unemployment rate. The databases have been merged, and preliminary statistical analysis has been conducted, along with the initial stages of econometric analysis yielding some preliminary results.
  • Cap context and result indicators were downloaded and compared as an important input for the definition of the Safe and Just operating space. Download links: Result Indicators, Context Indicators
  • The FADN data received in April 2024 are by default split in organic (fully converted) and conventional to permit a statistical analysis of productivity and intensity differences between these two farm specializations and to generate input data for the FarmDyn model.
  • Improved application of lancet diet shifts in raw product models; Application for EFSA consumer data ongoing. Food intake quantities stem from the German National Nutrition Survey II (NVSII) from 2006 with 13,926 randomly selected participants, representative of the German-speaking population, between the ages of 14 and 80 questioned in 24h-recalls about their intake of food and beverages (Brombach et al., 2006; Krems et al., 2006). The participants were selected randomly in around 500 municipalities and are representative of the German-speaking population. The 24h-recalls are available at EFSA’s comprehensive food consumption database (EFSA, 2023).
  • This dataset comprises spatial biophysical data regarding aspect, elevation and slope derived from Global and European Digital Elevation Model (COP-DEM) and topsoil properties derived from Topsoil physical properties for Europe to the NUTS regions across Europe. The dataset consists of two files, each corresponding to a different level of NUTS coding (NUTS 2/3).
',57)]))}const d=a(r,[["render",o],["__file","safe.html.vue"]]),p=JSON.parse('{"path":"/guide/safe.html","title":"Safe Operating Space (SOS) thematic","lang":"en-US","frontmatter":{},"headers":[{"level":2,"title":"Biodiversity","slug":"biodiversity","link":"#biodiversity","children":[{"level":3,"title":"Approach: Grass land intensity - mowing event detection","slug":"approach-grass-land-intensity-mowing-event-detection","link":"#approach-grass-land-intensity-mowing-event-detection","children":[]}]},{"level":2,"title":"Land use","slug":"land-use","link":"#land-use","children":[{"level":3,"title":"Approach: crop type detection","slug":"approach-crop-type-detection","link":"#approach-crop-type-detection","children":[]},{"level":3,"title":"Approach: parcel segmentation","slug":"approach-parcel-segmentation","link":"#approach-parcel-segmentation","children":[]}]},{"level":2,"title":"Water use","slug":"water-use","link":"#water-use","children":[]},{"level":2,"title":"Nutrient flows","slug":"nutrient-flows","link":"#nutrient-flows","children":[]},{"level":2,"title":"Chemical pollution (novel entities)","slug":"chemical-pollution-novel-entities","link":"#chemical-pollution-novel-entities","children":[]},{"level":2,"title":"Aerosol loading","slug":"aerosol-loading","link":"#aerosol-loading","children":[]},{"level":2,"title":"Climate","slug":"climate","link":"#climate","children":[{"level":3,"title":"Comments regarding the current status","slug":"comments-regarding-the-current-status","link":"#comments-regarding-the-current-status","children":[]}]}],"git":{"updatedTime":1732550192000,"contributors":[{"name":"neuenfeldt","email":"sebastian.neuenfeldt@thuenen.de","commits":7},{"name":"xxThu","email":"xinxin.yang@thuenen.de","commits":1}]},"filePathRelative":"guide/safe.md"}');export{d as comp,p as data};
+import{_ as a,c as t,e as n,o as i}from"./app-Ze_EQsYO.js";const r={};function o(s,e){return i(),t("div",null,e[0]||(e[0]=[n('
Safe Operating Space (SOS) thematic
Biodiversity
Approach: Grass land intensity - mowing event detection
Description
Spatially explicit knowledge on grassland extent and management is critical to understand and monitor the impact of grassland use intensity on ecosystem services and biodiversity. While regional studies allow detailed insights into land use and ecosystem service interactions, information on a national scale can aid biodiversity assessments. However, for most European countries this information is not yet widely available. We used an analysis-ready-data cube that contains dense time series of co-registered Sentinel-2 and Landsat 8 data, covering the extent of Germany. We propose an algorithm that detects mowing events in the time series based on residuals from an assumed undisturbed phenology, as an indicator of grassland use intensity.
Main data sources to produce the data
Among others
  • Sentinel-1, Sentinel-2, Landsat 8 and additional environmental data
Article
Schwieder, M., Wesemeyer, M., Frantz, D., Pfoch, K., Erasmi, S., Pickert, J., Nendel, C., & Hostert, P. (2022). Mapping grassland mowing events across Germany based on combined Sentinel-2 and Landsat 8 time series. Remote Sensing of Environment, 269, 112795, https://doi.org/10.1016/j.rse.2021.112795
Temporal and spatial coverage
Spatial coverage: Germany.
Temporal coverage: 2017-2021.
Resolution
Field level parcel data of different size for grass land.
Data
Schwieder, M., Lobert, F., Tetteh, G. O., & Erasmi, S. (2024). Grassland mowing events across Germany detected from combined Sentinel-2 and Landsat time series for the years 2017 - 2021 [Data set]. Zenodo. https://doi.org/10.5281/zenodo.10609590
Visualization
Mowing events map
Land use
Approach: crop type detection
Description
Based on the multi-year dataset major crop sequences of cereals and leaf crops are mapped. Most crop sequences were dominated by winter cereals followed by summer cereals. Monocultures of summer cereals were mainly revealed in the Northwest of Germany. It is showcased that high spatial and thematic detail in combination with annual mapping will stimulate research on crop cycles and studies to assess the impact of environmental policies on management decisions. The results demonstrate the capabilities of integrated optical time series and SAR data in combination with variables describing local and seasonal environmental conditions for annual large-area crop type mapping.
Main data sources to produce the data
Among others
  • Sentinel-2 and Landsat 8 data
  • Sentinel-1 data
  • Topographic variables elevation, hillslope, and aspect from a digital elevation model (DEM) with a spatial resolution of 10 m provided by the German Federal Agency for Cartography and Geodesy
  • High-resolution (1 x 1 km) climatological data on seasonal mean air temperature and precipitation from German Weather Service
  • Reference data: Integrated Administration and Control System (IACS)
Article
Blickensdoerfer, L., Schwieder, M., Pflugmacher, D., Nendel, C., Erasmi, S., Hostert, P. (2022). Mapping of crop types and crop sequences with combined time series of Sentinel-1, Sentinel-2 and Landsat 8 data for Germany. Remote Sens Environ 269:112831, DOI:10.1016/j.rse.2021.112831
Temporal and spatial coverage
Spatial coverage: Germany.
Temporal coverage: 2017-2021.
Resolution
Field level parcel data of different size.
Data
https://doi.org/10.5281/zenodo.10617622
Visualization
Agricultural land use
Approach: parcel segmentation
Description
Effective monitoring of agricultural lands requires accurate spatial information about the locations and boundaries of agricultural fields. Through satellite imagery, such information can be mapped on a large scale at a high temporal frequency. Various methods exist in the literature for segmenting agricultural fields from satellite images. Edge-based, region-based, or hybrid segmentation methods are traditional methods that have widely been used for segmenting agricultural fields. Lately, the use of deep neural networks (DNNs) for various tasks in remote sensing has been gaining traction. Therefore, to identify the optimal method for segmenting agricultural fields from satellite images, we evaluated three state-of-the-art DNNs, namely Mask R-CNN, U-Net, and FracTAL ResUNet against the multi-resolution segmentation (MRS) algorithm, which is a region-based and a more traditional segmentation method. DNNs, particularly FracTAL ResUNet, can be effectively used for large-scale segmentation of agricultural fields from satellite images.
This work is especially important for crop type detection.
Main data sources to produce the data
Among others
  • Sentinel-2
  • ATKIS
  • Reference data: agricultural parcels of the Geospatial Aid Application (GSAA)
Article
Tetteh GO, Schwieder M, Erasmi S, Conrad C, Gocht A (2023). Comparison of an optimised multiresolution segmentation approach with deep neural networks for delineating agricultural fields from Sentinel-2 images. J Photogramm Remote Sensing Geoinf Sci 91(4):295-312, DOI:10.1007/s41064-023-00247-x.
Spatial coverage
Spatial coverage: Lower Saxony - Federal state of Germany.
Resolution
Field level parcel data of different size.
Water use
Nutrient flows
Chemical pollution (novel entities)
Aerosol loading
Climate
Comments regarding the current status
  • Database extracted from De Laurentiis et al (2023). Data comprises estimated food loss and waste along the chain by product disaggregation level for each of the EU Member States for the period 2003-2020. Estimated data are available in a public domain area: Joint Research Centre Data Catalogue. Drivers according to the literature and extracted from open access sources, as an example: Population is available at: Population on 1 January by age and sex. Real GDP per capita is available at: Real GDP per capita. Unemployment rate is available at: Total unemployment rate. The databases have been merged, and preliminary statistical analysis has been conducted, along with the initial stages of econometric analysis yielding some preliminary results.
  • Cap context and result indicators were downloaded and compared as an important input for the definition of the Safe and Just operating space. Download links: Result Indicators, Context Indicators
  • The FADN data received in April 2024 are by default split in organic (fully converted) and conventional to permit a statistical analysis of productivity and intensity differences between these two farm specializations and to generate input data for the FarmDyn model.
  • Improved application of lancet diet shifts in raw product models; Application for EFSA consumer data ongoing. Food intake quantities stem from the German National Nutrition Survey II (NVSII) from 2006 with 13,926 randomly selected participants, representative of the German-speaking population, between the ages of 14 and 80 questioned in 24h-recalls about their intake of food and beverages (Brombach et al., 2006; Krems et al., 2006). The participants were selected randomly in around 500 municipalities and are representative of the German-speaking population. The 24h-recalls are available at EFSA’s comprehensive food consumption database (EFSA, 2023).
  • This dataset comprises spatial biophysical data regarding aspect, elevation and slope derived from Global and European Digital Elevation Model (COP-DEM) and topsoil properties derived from Topsoil physical properties for Europe to the NUTS regions across Europe. The dataset consists of two files, each corresponding to a different level of NUTS coding (NUTS 2/3).
',57)]))}const d=a(r,[["render",o],["__file","safe.html.vue"]]),p=JSON.parse('{"path":"/guide/safe.html","title":"Safe Operating Space (SOS) thematic","lang":"en-US","frontmatter":{},"headers":[{"level":2,"title":"Biodiversity","slug":"biodiversity","link":"#biodiversity","children":[{"level":3,"title":"Approach: Grass land intensity - mowing event detection","slug":"approach-grass-land-intensity-mowing-event-detection","link":"#approach-grass-land-intensity-mowing-event-detection","children":[]}]},{"level":2,"title":"Land use","slug":"land-use","link":"#land-use","children":[{"level":3,"title":"Approach: crop type detection","slug":"approach-crop-type-detection","link":"#approach-crop-type-detection","children":[]},{"level":3,"title":"Approach: parcel segmentation","slug":"approach-parcel-segmentation","link":"#approach-parcel-segmentation","children":[]}]},{"level":2,"title":"Water use","slug":"water-use","link":"#water-use","children":[]},{"level":2,"title":"Nutrient flows","slug":"nutrient-flows","link":"#nutrient-flows","children":[]},{"level":2,"title":"Chemical pollution (novel entities)","slug":"chemical-pollution-novel-entities","link":"#chemical-pollution-novel-entities","children":[]},{"level":2,"title":"Aerosol loading","slug":"aerosol-loading","link":"#aerosol-loading","children":[]},{"level":2,"title":"Climate","slug":"climate","link":"#climate","children":[{"level":3,"title":"Comments regarding the current status","slug":"comments-regarding-the-current-status","link":"#comments-regarding-the-current-status","children":[]}]}],"git":{"updatedTime":1732550192000,"contributors":[{"name":"neuenfeldt","email":"sebastian.neuenfeldt@thuenen.de","commits":7},{"name":"xxThu","email":"xinxin.yang@thuenen.de","commits":1}]},"filePathRelative":"guide/safe.md"}');export{d as comp,p as data};
diff --git a/assets/setupDevtools-7MC2TMWH-BdSvw6wQ.js b/assets/setupDevtools-7MC2TMWH-CG_XTxCu.js
similarity index 99%
rename from assets/setupDevtools-7MC2TMWH-BdSvw6wQ.js
rename to assets/setupDevtools-7MC2TMWH-CG_XTxCu.js
index 06855e3..39f11d4 100644
--- a/assets/setupDevtools-7MC2TMWH-BdSvw6wQ.js
+++ b/assets/setupDevtools-7MC2TMWH-CG_XTxCu.js
@@ -1 +1 @@
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diff --git a/baz/index.html b/baz/index.html
index c4d8bbc..ca8e728 100644
--- a/baz/index.html
+++ b/baz/index.html
@@ -33,11 +33,11 @@
     
     BrightSpace
     
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+    
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diff --git a/examples/examples.html b/examples/examples.html
index 3e12cda..8744219 100644
--- a/examples/examples.html
+++ b/examples/examples.html
@@ -33,8 +33,8 @@
     
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Markdown Extension Examples
This page demonstrates some of the built-in markdown extensions provided by VitePress.
Syntax Highlighting
VitePress provides Syntax Highlighting powered by Shiki, with additional features like line-highlighting:
Input
```js
@@ -73,6 +73,6 @@
 This is a details block.
 :::
 
Output
Info
This is an info box.
Tips
This is a tip.
Warning
This is a warning.
Caution
This is a dangerous warning.
Details
This is a details block.
Emoji
🎉 💯
More
Check out the documentation for the full list of markdown extensions.

-    
+    
   
 
diff --git a/get-started.html b/get-started.html
index 1052e43..94fae16 100644
--- a/get-started.html
+++ b/get-started.html
@@ -33,11 +33,11 @@
     
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Overview
Indicator
Data
  • aaa
  • jjjj
  • EU and FAO-WHO Codex Alimentarius dataset on pesticides downloaded and screened for further use in econometric models. The UNCTAD-TRAINS NTMs database has been matched with bilateral trade data (UN ComTrade) at HS6 digits (720 products), for a panel between 2010-2020. RASFF database has been downloaded and we are in the process of matching with HS6 nomenclature.
Tips
Theme components are under the same browser API access restrictions.

-    
+    
   
 
diff --git a/guide/indicator.html b/guide/indicator.html
index ce4d9e0..87bcd36 100644
--- a/guide/indicator.html
+++ b/guide/indicator.html
@@ -33,11 +33,11 @@
     
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Aligning SJOS to thematic areas and indicator domains
Based on a review of the Safe Operating Spaces (SOS) and Just Operating Spaces (JOS) concepts, a comparison and alignment with EU policy targets, and expert consultations, a set of 12 thematic areas and 34 indicator domains was developed. The SOS components closely align with concepts in the literature on planetary boundaries, with the notable exceptions of ocean acidification and ozone layer depletion. For JOS, significant overlap was found between the literature and EU policies, particularly in the areas of food and nutrition security and health.
Economy-related topics were divided into two thematic areas. The "General Economy" area addresses the relative position of agriculture compared to other sectors in terms of employment, value-added, wages, and input prices. Topics specific to the agricultural sector, such as farm incomes, economic viability, future prospects, and farm structure, are encompassed within the broader thematic area of "Farm resilience." The resulting thematic areas and indicator domains are detailed below.
Safe Operating Spaces Thematic Areas and Indicator Domains
Thematic areaIndicator Domain
BiodiversityGenetic/Functional diversity (outcomes)
Land management (drivers)
Land useLand cover
Soil health
Water useWater use
Nutrient flowsNitrogen cycle
Phosphorus cycle
Chemical pollution (novel entities)Pesticide use
Antimicrobials
Aerosol loadingAir pollution (Particulates)
ClimateGHG emissions
Just Operating Spaces Thematic Areas and Indicator Domains
Thematic areaIndicator Domain
Nutrition securityFood availability
... affordability
... stability
Food waste
Diet quality/food utilization
HealthLife expectancy
Obesity
Animal welfare
Access to clean water
EconomySectoral employment
Sectoral productivity
Sectoral wages
Market organisation
Trade
Energy access
Farm resilienceFarm income
Farm viability
Farm structure
Social equityIncome distribution (sectoral, spatial)
Education
Gender equality
Depending on the progress of partners within BrightSpace working on different thematic areas or indicators, information like data, code or article and reports will be listed.

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Aligning SJOS to thematic areas and indicator domains
Based on a review of the Safe Operating Spaces (SOS) and Just Operating Spaces (JOS) concepts, a comparison and alignment with EU policy targets, and expert consultations, a set of 12 thematic areas and 34 indicator domains was developed. The SOS components closely align with concepts in the literature on planetary boundaries, with the notable exceptions of ocean acidification and ozone layer depletion. For JOS, significant overlap was found between the literature and EU policies, particularly in the areas of food and nutrition security and health.
Economy-related topics were divided into two thematic areas. The "General Economy" area addresses the relative position of agriculture compared to other sectors in terms of employment, value-added, wages, and input prices. Topics specific to the agricultural sector, such as farm incomes, economic viability, future prospects, and farm structure, are encompassed within the broader thematic area of "Farm resilience." The resulting thematic areas and indicator domains are detailed below.
Safe Operating Spaces Thematic Areas and Indicator Domains
Thematic areaIndicator Domain
BiodiversityGenetic/Functional diversity (outcomes)
Land management (drivers)
Land useLand cover
Soil health
Water useWater use
Nutrient flowsNitrogen cycle
Phosphorus cycle
Chemical pollution (novel entities)Pesticide use
Antimicrobials
Aerosol loadingAir pollution (Particulates)
ClimateGHG emissions
Just Operating Spaces Thematic Areas and Indicator Domains
Thematic areaIndicator Domain
Nutrition securityFood availability
... affordability
... stability
Food waste
Diet quality/food utilization
HealthLife expectancy
Obesity
Animal welfare
Access to clean water
EconomySectoral employment
Sectoral productivity
Sectoral wages
Market organisation
Trade
Energy access
Farm resilienceFarm income
Farm viability
Farm structure
Social equityIncome distribution (sectoral, spatial)
Education
Gender equality
Depending on the progress of partners within BrightSpace working on different thematic areas or indicators, information like data, code or article and reports will be listed.

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Just Operating Space (JOS) thematic
Farm resilience
Farm income
Approach: FADN spatial - Downscaling
Description
FADN subsidies are part of farm income. As these data is only accessible at farm level, a down scaling approach has been developed and applied.
Data is protected (more than 15 obs. per observation unit).
Main data sources to produce the data
  • Individual FADN data
  • Farm Structure Spatial Units (FSU) established in the VERIFY project (link_FSU)
  • CAPDIS for information on land use and yields (Britz, W., Witzke, P. (2014). CAPRI model documentation 2014) 
    • Information on land use in the FSU is calculated calculated through the Land Area Prediction model (LAPM), based on the methods of Lamboni et al. (2016: Lamboni, M. R. Koeble, A. Leip (2016).
    • Multi-scale land-use disaggregation modelling: concept and application to EU countries. Environmental Modelling & Software 82, 183-217. https://doi.org/10.1016/j.envsoft.2016.04.028), and assessing its accuracy (Leip et al., 2017: Leip, A., Koeble, R., Rotllan-Puig, X., 2017. Land Area Prediction model (LAPM) Accuracy Assessment. European Commission, Ispra (VA), Italy. https://doi.org/10.2760/624482).
    • They include available data of crop areas from the Farm Structure Survey 2010 at a 10 km x 10 km grid, developing a methodology to gap-fill missing data which was filtered out by confidentiality rules (Bujnowska et al., 2019: Bujnowska, A., Gomez-Prieto, O., Kloek, W., Koeble, R., Leip, D., Martinez-Solano, D., Ramos, H. (2019). Disseminating European data at farm level while protecting confidentiality: scientific use files and homogeneous spatial units, in: International Conference on Agricultural Statistics, New Delhi.).
Reports
Article
Article is prepared for submission.
Temporal and spatial coverage
Spatial coverage: EU-wide for all countries contributing to the EU-FADN.
Temporal coverage: depending on progress. Actually 2001-2020 each second year.
Resolution
Farm Structure Spatial Units (FSU). High resolution of grids of different size.
Visualization
FADN SPATIAL
Farm viability
Nutrition security
Health
Economy
Social equity

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Overview
Project
BrightSpace is a European research project aiming to design effective and sustainable strategies to navigate within a Safe and Just Operating Space (link webpage).
Safe and Just Operating Spaces within BrightSpace
An overview of dimensions and indicators of the Safe and Just Operating Spaces (SJOS) can be found in a report (Deliverable D1.1 (D01) An operational concept for dimensions and indicators of the SJOS link D1.1). This report introduces an SJOS framework designed specifically for EU agriculture, integrating environmental, economic, and social dimensions to track progress toward political objectives. The framework's development involved a design phase comprising a comprehensive literature review, alignment with EU policy goals, and consultations with experts. The proposed SJOS encompasses 12 thematic areas and 34 indicator domains, although the specific indicators are yet to be finalized.
Literature Review of impacts of SJOS
If you like to have an overview of mpacts of agricultural production decisions on the safe and just operating space, a systematic literature review has been carried out (article). This study systematically reviews the literature to identify connections between agricultural production decisions and the SJOS framework. It focuses on three categories of production decisions: on-farm technology usage, farm structural change, and field structural change. The study examines the mechanisms through which these categories impact SJOS and evaluates the scope of existing evidence.

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Safe Operating Space (SOS) thematic
Biodiversity
Approach: Grass land intensity - mowing event detection
Description
Spatially explicit knowledge on grassland extent and management is critical to understand and monitor the impact of grassland use intensity on ecosystem services and biodiversity. While regional studies allow detailed insights into land use and ecosystem service interactions, information on a national scale can aid biodiversity assessments. However, for most European countries this information is not yet widely available. We used an analysis-ready-data cube that contains dense time series of co-registered Sentinel-2 and Landsat 8 data, covering the extent of Germany. We propose an algorithm that detects mowing events in the time series based on residuals from an assumed undisturbed phenology, as an indicator of grassland use intensity.
Main data sources to produce the data
Among others
  • Sentinel-1, Sentinel-2, Landsat 8 and additional environmental data
Article
Schwieder, M., Wesemeyer, M., Frantz, D., Pfoch, K., Erasmi, S., Pickert, J., Nendel, C., & Hostert, P. (2022). Mapping grassland mowing events across Germany based on combined Sentinel-2 and Landsat 8 time series. Remote Sensing of Environment, 269, 112795, https://doi.org/10.1016/j.rse.2021.112795
Temporal and spatial coverage
Spatial coverage: Germany.
Temporal coverage: 2017-2021.
Resolution
Field level parcel data of different size for grass land.
Data
Schwieder, M., Lobert, F., Tetteh, G. O., & Erasmi, S. (2024). Grassland mowing events across Germany detected from combined Sentinel-2 and Landsat time series for the years 2017 - 2021 [Data set]. Zenodo. https://doi.org/10.5281/zenodo.10609590
Visualization
Mowing events map
Land use
Approach: crop type detection
Description
Based on the multi-year dataset major crop sequences of cereals and leaf crops are mapped. Most crop sequences were dominated by winter cereals followed by summer cereals. Monocultures of summer cereals were mainly revealed in the Northwest of Germany. It is showcased that high spatial and thematic detail in combination with annual mapping will stimulate research on crop cycles and studies to assess the impact of environmental policies on management decisions. The results demonstrate the capabilities of integrated optical time series and SAR data in combination with variables describing local and seasonal environmental conditions for annual large-area crop type mapping.
Main data sources to produce the data
Among others
  • Sentinel-2 and Landsat 8 data
  • Sentinel-1 data
  • Topographic variables elevation, hillslope, and aspect from a digital elevation model (DEM) with a spatial resolution of 10 m provided by the German Federal Agency for Cartography and Geodesy
  • High-resolution (1 x 1 km) climatological data on seasonal mean air temperature and precipitation from German Weather Service
  • Reference data: Integrated Administration and Control System (IACS)
Article
Blickensdoerfer, L., Schwieder, M., Pflugmacher, D., Nendel, C., Erasmi, S., Hostert, P. (2022). Mapping of crop types and crop sequences with combined time series of Sentinel-1, Sentinel-2 and Landsat 8 data for Germany. Remote Sens Environ 269:112831, DOI:10.1016/j.rse.2021.112831
Temporal and spatial coverage
Spatial coverage: Germany.
Temporal coverage: 2017-2021.
Resolution
Field level parcel data of different size.
Data
https://doi.org/10.5281/zenodo.10617622
Visualization
Agricultural land use
Approach: parcel segmentation
Description
Effective monitoring of agricultural lands requires accurate spatial information about the locations and boundaries of agricultural fields. Through satellite imagery, such information can be mapped on a large scale at a high temporal frequency. Various methods exist in the literature for segmenting agricultural fields from satellite images. Edge-based, region-based, or hybrid segmentation methods are traditional methods that have widely been used for segmenting agricultural fields. Lately, the use of deep neural networks (DNNs) for various tasks in remote sensing has been gaining traction. Therefore, to identify the optimal method for segmenting agricultural fields from satellite images, we evaluated three state-of-the-art DNNs, namely Mask R-CNN, U-Net, and FracTAL ResUNet against the multi-resolution segmentation (MRS) algorithm, which is a region-based and a more traditional segmentation method. DNNs, particularly FracTAL ResUNet, can be effectively used for large-scale segmentation of agricultural fields from satellite images.
This work is especially important for crop type detection.
Main data sources to produce the data
Among others
  • Sentinel-2
  • ATKIS
  • Reference data: agricultural parcels of the Geospatial Aid Application (GSAA)
Article
Tetteh GO, Schwieder M, Erasmi S, Conrad C, Gocht A (2023). Comparison of an optimised multiresolution segmentation approach with deep neural networks for delineating agricultural fields from Sentinel-2 images. J Photogramm Remote Sensing Geoinf Sci 91(4):295-312, DOI:10.1007/s41064-023-00247-x.
Spatial coverage
Spatial coverage: Lower Saxony - Federal state of Germany.
Resolution
Field level parcel data of different size.
Water use
Nutrient flows
Chemical pollution (novel entities)
Aerosol loading
Climate
Comments regarding the current status
  • Database extracted from De Laurentiis et al (2023). Data comprises estimated food loss and waste along the chain by product disaggregation level for each of the EU Member States for the period 2003-2020. Estimated data are available in a public domain area: Joint Research Centre Data Catalogue. Drivers according to the literature and extracted from open access sources, as an example: Population is available at: Population on 1 January by age and sex. Real GDP per capita is available at: Real GDP per capita. Unemployment rate is available at: Total unemployment rate. The databases have been merged, and preliminary statistical analysis has been conducted, along with the initial stages of econometric analysis yielding some preliminary results.
  • Cap context and result indicators were downloaded and compared as an important input for the definition of the Safe and Just operating space. Download links: Result Indicators, Context Indicators
  • The FADN data received in April 2024 are by default split in organic (fully converted) and conventional to permit a statistical analysis of productivity and intensity differences between these two farm specializations and to generate input data for the FarmDyn model.
  • Improved application of lancet diet shifts in raw product models; Application for EFSA consumer data ongoing. Food intake quantities stem from the German National Nutrition Survey II (NVSII) from 2006 with 13,926 randomly selected participants, representative of the German-speaking population, between the ages of 14 and 80 questioned in 24h-recalls about their intake of food and beverages (Brombach et al., 2006; Krems et al., 2006). The participants were selected randomly in around 500 municipalities and are representative of the German-speaking population. The 24h-recalls are available at EFSA’s comprehensive food consumption database (EFSA, 2023).
  • This dataset comprises spatial biophysical data regarding aspect, elevation and slope derived from Global and European Digital Elevation Model (COP-DEM) and topsoil properties derived from Topsoil physical properties for Europe to the NUTS regions across Europe. The dataset consists of two files, each corresponding to a different level of NUTS coding (NUTS 2/3).

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Database and Monitoring System
Designing a Roadmap for Effective and Sustainable Strategies for Assessing and Addressing the Challenges of EU Agriculture to Navigate within a Safe and Just Operating Space.
Funded by the european union. Views and opinions expressed are those of the authors only and do not necessarily reflect those of the European Union or the European Commission. Neither the European Union nor the granting authority can be held responsible for them.
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© 2024 BrightSpace Grant agreement ID 101060075

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