Update index.md

themes/change/ip/global/ip-vga-encr/index.md

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-title: "Vegetation dieback (pathogens)"
+title: "Vegetation gain (amount) encroachment"
 
-# NOT INCLUDED: header: "Vegetation dieback (pathogens)"
-headerTop: "vegetation dieback (pathogens)"
-title: "Vegetation dieback (pathogens)"
-subtitle: "Vegetation dieback is often caused by a range of pathogens, with Phytophthora ramosum affecting many tree and shrub species."
+# NOT INCLUDED: header: "Vegetation gain (amount) encroachment"
+headerTop: "vegetation gain (amount) encroachment"
+title: "Vegetation gain (amount) encroachment"
+subtitle: "Expansion of whole plant communities (native or exotic) into an area as a consequence of an increase in woody plant density or extent so that the natural equilibrium of woody plant layer (trees and shrubs) and herbaceous (grass and forb) layer densities is shifted towards woody species. "
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-include ip-vga-encr.liquid
+include publications.liquid
 header="EVIDENCE FOR IMPACTS <br>  In the case of this impact, the evidence requirements are:.
 
+<br><ul> Canopy cover </ul> 
+<br><ul> Canopy height  </ul>
+<br><ul> Woody above ground biomass.</ul>
+<br><ul> Green (photosynthetic) fraction </ul>
+
 <br><br>PRESSURE DATASETS
 <br><ul> The Pressure datasets needed are accessible via Living Earth:</ul>
 
 Datasets include precipitation.</ul>
 
 <br><br>CASE STUDY
-<br><ul> For Newport, the case study shows a proge</ul>
-
-Owers, C. J., Lucas, R. M., Clewley, D., Planque, C., Punalekar, S., Tissott, B., Chua, S. M. T., Bunting, P., Mueller, N., & Metternicht, G. (2021). Living Earth: Implementing national standardised land cover classification systems for Earth Observation in support of sustainable development. Big Earth Data, 5(3), 368-390. https://doi.org/10.1080/20964471.2021.1948179.
-
-<br><br>(See also: https://docs.dea.ga.gov.au/guides/about/publications/)
-
-<br><br>SWITZERLAND
-
-<br><ul> Annoni, A., Nativi, S., Çöltekin, A., Desha, C., Eremchenko, E., Gevaert, C.M., Giuliani, G., Chen, M., Perez-Mora, L., Strobl, J. and Tumampos, S., 2023. Digital earth: yesterday, today, and tomorrow. International Journal of Digital Earth, 16(1), pp.1022-1072. https://doi.org/10.1080/17538947.2023.2187467. </ul> 
-
-<br><ul> Claire Obuchowicz, Charlotte Poussin & Gregory Giuliani (2023) Change in observed long-term greening across Switzerland – evidence from a three decades NDVI time-series and its relationship with climate and land cover factors, Big Earth Data, https://doi.org/10.1080/20964471.2023.2268322.  </ul>
-
-<br><ul> Külling N., Adde A., Fopp F., Schweiger A.K., Broennimann O., Rey P.-L., Giuliani G., Goicolea T., Petitpierre B., Zimmermann N.E., Pellissier L., Altermatt F., Lehmann A., Guisan A., SWECO25 (2025).  A cross-thematic raster database for ecological research in Switzerland, Accepted by Nature Scientific Data.</ul>
-
-<br><ul> Markos A., Sims N., Giuliani G. (2022) Beyond the SDG 15.3.1 Good Practice Guidance 1.0 using the Google Earth Engine platform: developing a self-adjusting algorithm to detect significant changes in Water Use Efficiency and Net Primary Production, Big Earth Data https://doi.org/10.1080/20964471.2022.2076375. </ul>
-
-<br><ul> Moomen A., Lacroix P., Benvenuti A., Planque M., Piller T., Davis K., Miranda M., Ibrahim E., Giuliani G. (2022) Assessing the Applications of Earth Observation Data for Monitoring Artisanal and Small-Scale Gold Mining (ASGM) in Developing Countries, Remote Sensing 14(13):2971 https://doi.org/10.3390/rs14132971.</ul>  
-
-<br><ul> Sudmanns M., Augustin H., Killough B., Giuliani G., Tiede D., Leith A., Yuan F. (2022) Think global, cube local: An Earth Observation Data Cube's contribution to the Digital Earth vision, Big Earth Data https://doi.org/10.1080/20964471.2022.2099236 </ul>
-
-<br><ul> Thomas I.N., Giuliani G. (2023) Exploring Switzerland’s Land Cover Change dynamics using a national statistical survey, Land 12, 1386 https://doi.org/10.3390/land12071386. </ul> 
-
-<br><ul> Poussin C., Timoner P., Chatenoux B., Giuliani G., Peduzzi P. (2023) Improved Landsat-based snow cover mapping accuracy using a spatiotemporal NDSI and generalised linear mixed model, Science of Remote Sensing 7:100078 https://doi.org/10.1016/j.srs.2023.100078. </ul>
-
-<br><ul> Giuliani G., Rodila D., Külling N., Maggini R., Lehmann A. (2022) Downscaling Switzerland Land Use/Land Cover data using nearest neighbors and an expert system, Land 11(5):615 https://doi.org/10.3390/land11050615.</ul> 
-
-<br><ul> Lehmann A., Mazzetti P., Santoro M., Masó J., Serral I., Spengler D., Niamir A., Lacroix P., Ambrosone M., McCallum I., Kussul N., Patias P., Rodila D., Ray N., Giuliani G. (2022) Essential Variables from Earth Observation for environmental multi-scale indicators and policies. Environmental Science and Policy 131:105-117 https://doi.org/10.1016/j.envsci.2021.12.024. </ul>
-
-<br><ul> Chatenoux B., Richard J.-P. Small D., Roeoesli C., Wingate V., Poussin C., Rodila D., Peduzzi P., Steinmeier C., Ginzler C., Psomas A., Schaepman M., Giuliani G. (2021) The Swiss Data Cube: Analysis Ready Data archive using Earth Observations of Switzerland, Nature Scientific Data. 8:295 https://doi.org/10.1038/s41597-021-01076-6. </ul>
-
-<br><ul> Poussin C., Massot A., Ginzler C., Weber D., Chatenoux B., Lacroix P., Piller T., Nguyen L., Giuliani G. (2021) Drying conditions in Switzerland - Indication from a 35-year Landsat trend analysis of vegetation water content estimates to support SDGs, Big Earth Data 5(4):445-475 https://doi.org/10.1080/20964471.2021.1974681. </ul>
-
-<br><ul> Giuliani G., Mazzetti P., Santoro M., Nativi S., Van Bemmelen J., Colangeli G., Lehmann A. (2020) Knowledge generation using satellite Earth Observations to support Sustainable Development Goals (SDG): a use case on Land Degradation, International Journal of Applied Earth Observation and Geoinformation 88:102068 https://doi.org/10.1016/j.jag.2020.102068. </ul>  
-
-<br><ul> Giuliani G., Chatenoux B., Benvenuti A., Lacroix P., Santoro M., Mazzetti P. (2020) Monitoring Land Degradation at national level using satellite Earth Observation time-series data to support SDG15 - Exploring the potential of Data Cube, Big Earth Data https://doi.org/10.1080/20964471.2020.1711633. </ul> 
-
-<br><ul> Giuliani G., Chatenoux B., Piller T., Moser F., Lacroix P., Data Cube on Demand (DCoD): Generating Earth Observation Data Cube anywhere (2020) International Journal of Applied Earth Observation and Geoinformation 87:102035 https://doi.org/10.1016/j.jag.2019.102035.</ul> 
-
-<br><ul> Giuliani G., Chatenoux B., De Bono A., Rodila D., Richard J.-P., Allenbach K., Dao H., Peduzzi P. (2017) Building an Earth Observations Data Cube: lessons learned from the Swiss Data Cube (SDC) on generating Analysis Ready Data (ARD). Big Earth Data 1(1):1-18 https://doi.org/10.1080/20964471.2017.1398903. </ul>
+<br><ul> The case study is the Amazon Basin where pastures used actively for extended periods but then abandonmed are encroached upon from refugia of plant communities (e.g., Vismia species) </ul>
 
 <br>See also https://www.swissdatacube.org/index.php/publications/))
 
-<br><br>SOUTHEAST ASIA (MALAYSIA, PAPUA NEW GUINEA).
+<br><br>RESULTS).
 
-<br><ul> Lucas, R.; Otero, V.; Van De Kerchove, R.; Lagomasino, D.; Satyanarayana, B.; Fatoyinbo, T.; Dahdouh-Guebas, F. Monitoring Matang’s Mangroves in Peninsular Malaysia through Earth Observations: A Globally Relevant Approach. Land Degrad. Dev. 2021, 32, 354–373. </ul> 
+<br><ul> The following shows the net change in cover from herbaceous to woody in the lifform layer with a corresponding increase in above ground biomnass that is considered significnat (based on the Mann-Kendall and Senn slope) </ul> 
 
-<br><ul> Lucas, R., Van De Kerchove, R., Otero, V., Lagomasino, D., Fatoyinbo, L., Omar, H., Satyanarayana, B. and Dahdouh-Guebas, F. (2020).  Structural characterisation of mangrove forests achieved through combining multiple sources of remote sensing data. Remote Sensing of Environment. 237, 111543.</ul>
 "
 -%}