Evaluating success of various restorative interventions through drone- and field-collected data, using six putative framework species in Argentinian Patagonia

Restoration Ecology

Volumn 28, Issue S1, 2020, Pages A44-A53

  Pérez, Daniel R ^a   Pilustrelli, Claudio ^a   Farinaccio, Fernando M ^a   Sabino, Guillermo ^a   Aronson, James ^b  

^a UNIVERSIDAD NACIONAL DEL COMAHUE   (Argentina)

^b MISSOURI BOTANICAL GARDEN   (United States)

Author keywords

drone imagery; drylands framework species; multispectral images; outplanting; scarification

Indexed keywords

ARID REGION; BIOME; EXPERIMENTAL STUDY; ORTHOPHOTO; PLANTATION; RESTORATION ECOLOGY; SEEDLING; SURVIVAL; UNMANNED VEHICLE;

ARGENTINA; PATAGONIA;

EID: 85073794144     PISSN: 10612971     EISSN: 1526100X     Source Type: Journal    
DOI: 10.1111/rec.13025     Document Type: Article

References (50)

1. Abdullah MM, Gholoum M, Abbas H (2018) Satellite vs. UAV's remote sensing of arid ecosystems: a review with in an ecological perspective. Environmental Analysis & Ecology Studies 2:1–5
2. Abella SR, Craig DC, Suazo AA (2012) Outplanting but not seeding establishes native desert perennials. Native Plants 12:81–89
3. Autoridad Interjurisdiccional de Cuencas (AIC) (2017) Informes hidrometeorológicos. http://www.aic.gov.ar/aic/publicaciones.aspx (accessed 30 April 2019)
4. Almeida DRA, Broadbent EN, Zambrano AMA, Wilkinson BE, Ferreira ME, Chazdon R, et al. (2019) Monitoring the structure of forest restoration plantations with a drone-lidar system. International Journal of Applied Earth Observation and Geoinformation 79:192–198
5. Álvarez JA, Villagra PE (2009) Prosopis flexuosa DC. (Fabaceae, Mimosoideae). Kurtziana 35:49–63
6. Anderson K, Gaston KJ (2013) Lightweight unmanned aerial vehicles will revolutionize spatial ecology. Frontiers in Ecology and the Environment 11:138–146
7. Baena S, Boyd DS, Moata J (2017a) UAVs in pursuit of plant conservation—real world experiences. Ecological Informatics 47:2–9, https://doi.org/10.1016/j.ecoinf.2017.11.001
8. Baena S, Moat J, Whaley O, Boyd DS (2017b) Identifying species from the air: UAV's and the very high resolution challenge for plant conservation. PLoS One 12:e0188714. https://doi.org/10.1371/journal.pone.0188714
9. Bainbridge DA (2007) A guide for desert and dryland restoration: new hope for arid lands. Island Press, Washington D.C.
10. Bertiller MB, Bisigato AJ (2004) Temporal and micro-spatial patterning of seedling establishment. Consequences for patch dynamics in the southern Monte, Argentina. Plant Ecology 174:235–246
11. Blignaut JN, Aronson J, de Wit M (2014) The economics of restoration: looking back and leaping forward. Annals of the New York Academy of Sciences 1322:35–47
12. Booth TD, Cox SE (2008) Image-based monitoring to measure ecological change in rangeland. Frontiers of Ecology and the Environment 6:185–190
13. Booth TD, Cox SE (2011) Art to science: tools for greater objectivity in resource monitoring. Rangelands 33:27–34
14. Booth TD, Cox SE, Meikle TW, Fitzgerald C (2006) The accuracy of ground-cover measurements. Rangeland Ecology & Management 59:179–188
15. Brancalion PHS, van Melis J (2017) On the need for innovation in ecological restoration. Annals of the Missouri Botanical Garden 102:227–236
16. Busso CA, Bonvissuto GL (2009) Structure of vegetation patches in northwestern Patagonia, Argentina. Biodiversity and Conservation 18:3017–3041
17. Busso CA, Fernández OA (2017) Arid and semi-arid rangelands of Argentina. Pages 261–291. In: Gaur MK, Squires VR (eds) Climate variability impacts on land use and livelihoods in drylands. Springer International Publishing, New York
18. Busso CA, Pérez DR (2018) Opportunities, limitations and gaps in the ecological restoration of drylands in Argentina. Annals of Arid Zone 57:191–200
19. Buters TM, Bateman PW, Robinson T, Belton D, Dixon KW, Cross AT (2019) Methodological ambiguity and inconsistency constrain unmanned aerial vehicles as a silver bullet for monitoring ecological restoration. Remote Sensing 11:1180. https://doi.org/10.3390/rs11101180
20. Cagney J, Cox SE, Booth DTTD (2011) Comparison of point intercept and image analysis for monitoring rangeland transects. Rangeland Ecology & Management 64:309–315
21. Campanella MV, Rostagno CM, Videla LS, Bisigato AJ (2018) Land degradation affects shrub growth responses to precipitation in a semiarid rangeland of northeastern Patagonia (Argentina). Austral Ecology 43:280–287
22. Copeland SM, Munson SM, Pilliod DS, Welty JL, Bradford JB, Butterfield BJ (2018) Long-term trends in restoration and associated land treatments in the southwestern United States. Restoration Ecology 26:311–322
23. Cruzan MB, Weinstein BG, Grasty MR, Kohrn BA, Hendrickson EC, Arredondo TM, Thompson PG (2016) Small unmanned aerial vehicles (micro-UAV's, drones) in plant ecology. Applications in Plant Sciences 4:1–11. https://doi.org/10.3732/apps.1600041
24. Cunliffe AM, Brazier RE, Anderson K (2016) Ultra-fine grain landscape-scale quantification of dryland vegetation structure with drone-acquired structure-from-motion photogrammetry. Remote Sensing of Environment 183:129–143
25. Dixon RM (1988) Land imprinting for vegetative restoration. Restoration and Management Notes 6:24–25
26. Elliott S (2016) The potential for automating assisted natural regeneration of tropical forest ecosystems. Biotropica 48:825–833
27. Elliott S, Blakesley D, Hardwick K (2013) Restoring tropical forests: a practical guide. Royal Botanic Gardens, Kew, United Kingdom
28. González FM, Pérez DR (2017) Contributions of ecological facilitation for restoring environments with high conservation value in the Argentine Patagonia. Phyton, International Journal of Experimental Botany 86:332–339
29. Goosem S, Tucker NIJ (2013) Repairing the rainforest. 2nd edition. Wet Tropics Management Authority and Biotropica Australia Pty. Ltd., Cairns, Australia
30. Husson E, Ecke F, Reese H (2016) Comparison of manual mapping and automated object-based image analysis of non-submerged aquatic vegetation from very-high resolution UAS images. Remote Sensing 8:1–18. https://doi:10.3390/rs8090724
31. James JJ, Sheley RL, Erickson T, Rollins KS, Taylor MH, Dixon KW (2013) A systems approach to restoring degraded drylands. Journal of Applied Ecology 50:730–739
32. Laliberte AS, Goforth MA, Steele CM, Rango A (2011) Multispectral remote sensing from unmanned aircraft: image processing workflows and applications for rangeland environments. Remote Sensing 3:2529–2551
33. Maselli F, Conese C, Petkiv L, Resti R (1992) Inclusion of prior probabilities derived from a nonparametric process into the maximum likelihood classifier. Photogrammetric Engineering & Remote Sensing 58:201–207
34. Matteucci S, Colma A (1982) Metodología para el estudio de la vegetación. Secretaría General de la Organización de los Estados Americanos, Washington D.C.
35. Olofsson P, Herold M, Stehman SV, Woodcock CE, Wulder MA (2014) Good practices for estimating area and assessing accuracy of land change. Remote Sensing of Environment 148:42–57
36. Pérez DR, Rovere AE, Farinaccio FM (2010) Rehabilitación en el desierto: ensayos con plantas nativas en Aguada Pichana, Neuquén, Patagonia. Vázquez Mazzini Editores, Buenos Aires. Argentina
37. Pérez DR, Farinaccio FM, Aronson J (2019a) Towards a dryland framework species approach. Research in progress in the Monte Austral of Argentina. Journal of Arid Environments 161:1–10
38. Pérez DR, González FM, Ceballos C, Oneto ME, Aronson J (2019b) Direct seeding and outplantings in drylands of Argentinean Patagonia: estimated costs, and prospects for large-scale restoration and rehabilitation. Restoration Ecology. https://doi.org/10.1111/rec.12961
39. Pérez DR, González FM, Rodríguez Araujo ME, Paredes D, Meinardi E (2019c) Restoration of society-nature relationship based on education: a model and progress in Patagonian drylands. Ecological Restoration 37:182–191
40. Piñeiro G, Perelman S, Guerschman JP, Paruelo JM (2008) How to evaluate models: observed vs. predicted or predicted vs. observed? Ecological Modelling 216:316–322
41. Piñeiro J, Maestre FT, Bartolomé L, Valdecantos A (2013) Ecotechnology as a tool for restoring degraded drylands: a meta-analysis of field experiments. Ecological Engineering 61:133–144
42. Rango A, Laliberte AS, Herrick JE, Winters C, Havstad KM, Steele C, Browning D (2009) Unmanned aerial vehicle based remote sensing for rangeland assessment, monitoring and management. Journal of Applied Remote Sensing 3:033542. http://dx.doi.org/10.1117/1.3216822
43. Rueden CT, Schindelin J, Hiner MC, DeZonia BE, Walter AE, Arena ET, Eliceiri KW (2017) ImageJ2: imageJ for the next generation of scientific image data. BMC Bioinformatics 18:1–29
44. Rundel P, Villagra PE, Dillon MO, Roig Juñent SA, Debandi G (2007) Arid and semi-arid ecosystems. Pages 158–164. In: Veblen TT, Young K, Orme A (eds) The physical geography of South America. Oxford University Press, Oxford, United Kingdom
45. Schindelin J, Arganda-Carreras I, Frise E, Kaynig V, Longair M, Pietzsch T, et al. (2012) Fiji: an open-source platform for biological-image analysis. Nature Methods 9:676–682
46. Tateishi R, Tsend-Ayush J, Aboel Ghar M (2007) Sampling method for validation of large area land cover mapping. Journal of the Remote Sensing Society of Japan 27:195–204
47. UNCCD (United Nations Convention to Combat Desertification) (2011) A visual synthesis. http://www.unccd.int/Lists/SiteDocumentLibrary/Publications/Desertification-EN.pdf (accessed 10 June 2019)
48. Valbuena R, Hernando A, Manzanera JA, Görgens EB, García-Abril DRA, Coomes DA (2017) Enhancing of accuracy assessment for forest above-ground biomass estimates obtained from remote sensing via hypothesis testing and over fitting evaluation. Ecological Modelling 366:15–26
49. Yirdaw E, Tigabu M, Monge AA (2017) Rehabilitation of degraded dryland ecosystems—review. Silva Fennica 51:1–31
50. Zahawi RA, Dandois JA, Holl KD, Nadwodny D, Reid JL, Ellis EC (2015) Using lightweight unmanned aerial vehicles to monitor tropical forest recovery. Biological Conservation 186:287–229