Trend-cycles of vegetation dynamics as a tool for land degradation assessment and monitoring
The use of time series of Normalized Difference Vegetation Index (NDVI), obtained from satellite sensors has become frequent in studies for land degradation assessment and monitoring. Linear trends of NDVI are usually considered as indicators of vegetation dynamics and widely used as proxies for lan...
| Main Authors: | , , , , , , |
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| Format: | Artículo |
| Language: | Inglés |
| Published: |
Elsevier
2019
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| Subjects: | |
| Online Access: | http://hdl.handle.net/20.500.12123/6024 https://www.sciencedirect.com/science/article/pii/S1470160X19305308 https://doi.org/10.1016/j.ecolind.2019.105545 |
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| author | Easdale, Marcos Horacio Farina, Clara Maria Hara, Sofía Perez Leon, Natalia Jesica Umaña, Fernando Tittonell, Pablo Adrian Bruzzone, Octavio Augusto |
| author_browse | Bruzzone, Octavio Augusto Easdale, Marcos Horacio Farina, Clara Maria Hara, Sofía Perez Leon, Natalia Jesica Tittonell, Pablo Adrian Umaña, Fernando |
| author_facet | Easdale, Marcos Horacio Farina, Clara Maria Hara, Sofía Perez Leon, Natalia Jesica Umaña, Fernando Tittonell, Pablo Adrian Bruzzone, Octavio Augusto |
| author_sort | Easdale, Marcos Horacio |
| collection | INTA Digital |
| description | The use of time series of Normalized Difference Vegetation Index (NDVI), obtained from satellite sensors has become frequent in studies for land degradation assessment and monitoring. Linear trends of NDVI are usually considered as indicators of vegetation dynamics and widely used as proxies for land degradation. Yet, long-term trends of NDVI often exhibit unidirectional (monotonic) but also cyclic (non-monotonic) dynamics, including mid-term oscillations, both of which are poorly captured by linear trends. Trend-cycle is a time series analysis that represents a smoothed version of a seasonally adjusted time series, which provides information on long-term movements while including changes in direction underlying the series. We assessed NDVI trend-cycles in Patagonia (Argentina) as proxies for land dynamics, integrating trend and medium-term cycles (> 4 years). We used MODIS images between years 2000 and mid-2018; trend-cycles were analysed using the Basis Pursuit method. We observed that trend-cycles explained a significant portion of total temporal variability (reaching almost 20%), from which most patterns were explained by non-monotonic behaviour. We identified five major patterns in vegetation dynamics: decreasing (0.1% of area), increasing (0.6%), recovery (48.8%), relapsing (36.8%) and no trend-cycle (13.8%). Contrary to what is generally seen in the literature, monotonic patterns and particularly decreasing trend-cycles were marginally recorded in the last 18 years of NDVI records in Patagonia.Instead, the greater proportion of the area was classified as initial or advanced recovery and initial relapsing patterns, which refer to phases of a cyclic behaviour. We call for the need to revisit the conceptualization of land degradation assessment by means of remote sensing, and to critically review the ability of linear trends to reflect vegetation dynamics. Finally, we discuss the potential use of trend-cycle as a tool to monitor land dynamics and progress towards land degradation neutrality. |
| format | Artículo |
| id | INTA6024 |
| institution | Instituto Nacional de Tecnología Agropecuaria (INTA -Argentina) |
| language | Inglés |
| publishDate | 2019 |
| publishDateRange | 2019 |
| publishDateSort | 2019 |
| publisher | Elsevier |
| publisherStr | Elsevier |
| record_format | dspace |
| spelling | INTA60242019-10-01T11:27:46Z Trend-cycles of vegetation dynamics as a tool for land degradation assessment and monitoring Easdale, Marcos Horacio Farina, Clara Maria Hara, Sofía Perez Leon, Natalia Jesica Umaña, Fernando Tittonell, Pablo Adrian Bruzzone, Octavio Augusto Desertificación Degradación Desertification Degradation Monitoring Vigilancia NDVI Sistemas de Monitoreo MARAS Región Patagónica The use of time series of Normalized Difference Vegetation Index (NDVI), obtained from satellite sensors has become frequent in studies for land degradation assessment and monitoring. Linear trends of NDVI are usually considered as indicators of vegetation dynamics and widely used as proxies for land degradation. Yet, long-term trends of NDVI often exhibit unidirectional (monotonic) but also cyclic (non-monotonic) dynamics, including mid-term oscillations, both of which are poorly captured by linear trends. Trend-cycle is a time series analysis that represents a smoothed version of a seasonally adjusted time series, which provides information on long-term movements while including changes in direction underlying the series. We assessed NDVI trend-cycles in Patagonia (Argentina) as proxies for land dynamics, integrating trend and medium-term cycles (> 4 years). We used MODIS images between years 2000 and mid-2018; trend-cycles were analysed using the Basis Pursuit method. We observed that trend-cycles explained a significant portion of total temporal variability (reaching almost 20%), from which most patterns were explained by non-monotonic behaviour. We identified five major patterns in vegetation dynamics: decreasing (0.1% of area), increasing (0.6%), recovery (48.8%), relapsing (36.8%) and no trend-cycle (13.8%). Contrary to what is generally seen in the literature, monotonic patterns and particularly decreasing trend-cycles were marginally recorded in the last 18 years of NDVI records in Patagonia.Instead, the greater proportion of the area was classified as initial or advanced recovery and initial relapsing patterns, which refer to phases of a cyclic behaviour. We call for the need to revisit the conceptualization of land degradation assessment by means of remote sensing, and to critically review the ability of linear trends to reflect vegetation dynamics. Finally, we discuss the potential use of trend-cycle as a tool to monitor land dynamics and progress towards land degradation neutrality. Fil: Easdale, Marcos Horacio. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Bariloche. Consejo Nacional de Investigaciones Cientificas y Técnicas. Instituto de Investigaciones Forestales y Agropecuarias Bariloche; Argentina Fil: Fariña, Clara María. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Bariloche. Consejo Nacional de Investigaciones Cientificas y Técnicas. Instituto de Investigaciones Forestales y Agropecuarias Bariloche; Argentina Fil: Hara, Sofía. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Bariloche. Consejo Nacional de Investigaciones Cientificas y Técnicas. Instituto de Investigaciones Forestales y Agropecuarias Bariloche; Argentina Fil: Pérez León, Natalia. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Bariloche. Consejo Nacional de Investigaciones Cientificas y Técnicas. Instituto de Investigaciones Forestales y Agropecuarias Bariloche; Argentina Fil: Umaña, Fernando. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Bariloche; Argentina Fil: Tittonell, Pablo Adrian. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Bariloche. Consejo Nacional de Investigaciones Cientificas y Técnicas. Instituto de Investigaciones Forestales y Agropecuarias Bariloche; Argentina Fil: Bruzzone, Octavio Augusto.Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Bariloche. Consejo Nacional de Investigaciones Cientificas y Técnicas. Instituto de Investigaciones Forestales y Agropecuarias Bariloche; Argentina 2019-10-01T11:15:46Z 2019-10-01T11:15:46Z 2019-07 info:ar-repo/semantics/artículo info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion http://hdl.handle.net/20.500.12123/6024 https://www.sciencedirect.com/science/article/pii/S1470160X19305308 1470-160X https://doi.org/10.1016/j.ecolind.2019.105545 eng info:eu-repo/semantics/restrictedAccess application/pdf Elsevier Ecological Indicators 107 : 105545 (December 2019) |
| spellingShingle | Desertificación Degradación Desertification Degradation Monitoring Vigilancia NDVI Sistemas de Monitoreo MARAS Región Patagónica Easdale, Marcos Horacio Farina, Clara Maria Hara, Sofía Perez Leon, Natalia Jesica Umaña, Fernando Tittonell, Pablo Adrian Bruzzone, Octavio Augusto Trend-cycles of vegetation dynamics as a tool for land degradation assessment and monitoring |
| title | Trend-cycles of vegetation dynamics as a tool for land degradation assessment and monitoring |
| title_full | Trend-cycles of vegetation dynamics as a tool for land degradation assessment and monitoring |
| title_fullStr | Trend-cycles of vegetation dynamics as a tool for land degradation assessment and monitoring |
| title_full_unstemmed | Trend-cycles of vegetation dynamics as a tool for land degradation assessment and monitoring |
| title_short | Trend-cycles of vegetation dynamics as a tool for land degradation assessment and monitoring |
| title_sort | trend cycles of vegetation dynamics as a tool for land degradation assessment and monitoring |
| topic | Desertificación Degradación Desertification Degradation Monitoring Vigilancia NDVI Sistemas de Monitoreo MARAS Región Patagónica |
| url | http://hdl.handle.net/20.500.12123/6024 https://www.sciencedirect.com/science/article/pii/S1470160X19305308 https://doi.org/10.1016/j.ecolind.2019.105545 |
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