Remote Sensing Monitoring of Land Restoration Interventions in Semi-Arid Environments with a before-after Control-Impact Statistical Design. Meroni, M., Schucknecht, A., Fasbender, D., Rembold, F., Fava, F., Mauclaire, M., Goffner, D., Di Lucchio, L. M., & Leonardi, U. 59:42–52. ![Remote Sensing Monitoring of Land Restoration Interventions in Semi-Arid Environments with a before-after Control-Impact Statistical Design [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex [Highlights] [::] A rapid, standardised and objective assessment of the biophysical impact of restoration interventions is proposed. [::] The intervention impact is evaluated by a before-after control-impact sampling design. [::] The method provides a statistical test of the no-change hypothesis and the estimation of the relative magnitude of the change. [::] The method is applicable to NDVI and other remote sensing-derived variables. [Abstract] Restoration interventions to combat land degradation are carried out in arid and semi-arid areas to improve vegetation cover and land productivity. Evaluating the success of an intervention over time is challenging due to various constraints (e.g. difficult-to-access areas, lack of long-term records) and the lack of standardised and affordable methodologies. We propose a semi-automatic methodology that uses remote sensing data to provide a rapid, standardised and objective assessment of the biophysical impact, in terms of vegetation cover, of restoration interventions. The Normalised Difference Vegetation Index (NDVI) is used as a proxy for vegetation cover. Recognising that changes in vegetation cover are naturally due to environmental factors such as seasonality and inter-annual climate variability, conclusions about the success of the intervention cannot be drawn by focussing on the intervention area only. We therefore use a comparative method that analyses the temporal variations (before and after the intervention) of the NDVI of the intervention area with respect to multiple control sites that are automatically and randomly selected from a set of candidates that are similar to the intervention area. Similarity is defined in terms of class composition as derived from an ISODATA classification of the imagery before the intervention. The method provides an estimate of the magnitude and significance of the difference in greenness change between the intervention area and control areas. As a case study, the methodology is applied to 15 restoration interventions carried out in Senegal. The impact of the interventions is analysed using 250-m MODIS and 30-m Landsat data. Results show that a significant improvement in vegetation cover was detectable only in one third of the analysed interventions, which is consistent with independent qualitative assessments based on field observations and visual analysis of high resolution imagery. Rural development agencies may potentially use the proposed method for a first screening of restoration interventions.
@article{meroniRemoteSensingMonitoring2017,
title = {Remote Sensing Monitoring of Land Restoration Interventions in Semi-Arid Environments with a before-after Control-Impact Statistical Design},
author = {Meroni, Michele and Schucknecht, Anne and Fasbender, Dominique and Rembold, Felix and Fava, Francesco and Mauclaire, Margaux and Goffner, Deborah and Di Lucchio, Luisa M. and Leonardi, Ugo},
date = {2017-07},
journaltitle = {International Journal of Applied Earth Observation and Geoinformation},
volume = {59},
pages = {42--52},
issn = {0303-2434},
doi = {10.1016/j.jag.2017.02.016},
url = {https://doi.org/10.1016/j.jag.2017.02.016},
abstract = {[Highlights]
[::] A rapid, standardised and objective assessment of the biophysical impact of restoration interventions is proposed. [::] The intervention impact is evaluated by a before-after control-impact sampling design. [::] The method provides a statistical test of the no-change hypothesis and the estimation of the relative magnitude of the change. [::] The method is applicable to NDVI and other remote sensing-derived variables.
[Abstract]
Restoration interventions to combat land degradation are carried out in arid and semi-arid areas to improve vegetation cover and land productivity. Evaluating the success of an intervention over time is challenging due to various constraints (e.g. difficult-to-access areas, lack of long-term records) and the lack of standardised and affordable methodologies. We propose a semi-automatic methodology that uses remote sensing data to provide a rapid, standardised and objective assessment of the biophysical impact, in terms of vegetation cover, of restoration interventions. The Normalised Difference Vegetation Index (NDVI) is used as a proxy for vegetation cover. Recognising that changes in vegetation cover are naturally due to environmental factors such as seasonality and inter-annual climate variability, conclusions about the success of the intervention cannot be drawn by focussing on the intervention area only. We therefore use a comparative method that analyses the temporal variations (before and after the intervention) of the NDVI of the intervention area with respect to multiple control sites that are automatically and randomly selected from a set of candidates that are similar to the intervention area. Similarity is defined in terms of class composition as derived from an ISODATA classification of the imagery before the intervention. The method provides an estimate of the magnitude and significance of the difference in greenness change between the intervention area and control areas. As a case study, the methodology is applied to 15 restoration interventions carried out in Senegal. The impact of the interventions is analysed using 250-m MODIS and 30-m Landsat data. Results show that a significant improvement in vegetation cover was detectable only in one third of the analysed interventions, which is consistent with independent qualitative assessments based on field observations and visual analysis of high resolution imagery. Rural development agencies may potentially use the proposed method for a first screening of restoration interventions.},
keywords = {*imported-from-citeulike-INRMM,~INRMM-MiD:c-14364107,~to-add-doi-URL,arid-climate,degradation,land-cover,modis,ndvi,remote-sensing,restoration,senegal,statistics,vegetation}
}
Downloads: 0
{"_id":"98c8Y7TowZ6NDqHQ3","bibbaseid":"meroni-schucknecht-fasbender-rembold-fava-mauclaire-goffner-dilucchio-etal-remotesensingmonitoringoflandrestorationinterventionsinsemiaridenvironmentswithabeforeaftercontrolimpactstatisticaldesign","authorIDs":[],"author_short":["Meroni, M.","Schucknecht, A.","Fasbender, D.","Rembold, F.","Fava, F.","Mauclaire, M.","Goffner, D.","Di Lucchio, L. M.","Leonardi, U."],"bibdata":{"bibtype":"article","type":"article","title":"Remote Sensing Monitoring of Land Restoration Interventions in Semi-Arid Environments with a before-after Control-Impact Statistical Design","author":[{"propositions":[],"lastnames":["Meroni"],"firstnames":["Michele"],"suffixes":[]},{"propositions":[],"lastnames":["Schucknecht"],"firstnames":["Anne"],"suffixes":[]},{"propositions":[],"lastnames":["Fasbender"],"firstnames":["Dominique"],"suffixes":[]},{"propositions":[],"lastnames":["Rembold"],"firstnames":["Felix"],"suffixes":[]},{"propositions":[],"lastnames":["Fava"],"firstnames":["Francesco"],"suffixes":[]},{"propositions":[],"lastnames":["Mauclaire"],"firstnames":["Margaux"],"suffixes":[]},{"propositions":[],"lastnames":["Goffner"],"firstnames":["Deborah"],"suffixes":[]},{"propositions":[],"lastnames":["Di","Lucchio"],"firstnames":["Luisa","M."],"suffixes":[]},{"propositions":[],"lastnames":["Leonardi"],"firstnames":["Ugo"],"suffixes":[]}],"date":"2017-07","journaltitle":"International Journal of Applied Earth Observation and Geoinformation","volume":"59","pages":"42–52","issn":"0303-2434","doi":"10.1016/j.jag.2017.02.016","url":"https://doi.org/10.1016/j.jag.2017.02.016","abstract":"[Highlights] [::] A rapid, standardised and objective assessment of the biophysical impact of restoration interventions is proposed. [::] The intervention impact is evaluated by a before-after control-impact sampling design. [::] The method provides a statistical test of the no-change hypothesis and the estimation of the relative magnitude of the change. [::] The method is applicable to NDVI and other remote sensing-derived variables. [Abstract] Restoration interventions to combat land degradation are carried out in arid and semi-arid areas to improve vegetation cover and land productivity. Evaluating the success of an intervention over time is challenging due to various constraints (e.g. difficult-to-access areas, lack of long-term records) and the lack of standardised and affordable methodologies. We propose a semi-automatic methodology that uses remote sensing data to provide a rapid, standardised and objective assessment of the biophysical impact, in terms of vegetation cover, of restoration interventions. The Normalised Difference Vegetation Index (NDVI) is used as a proxy for vegetation cover. Recognising that changes in vegetation cover are naturally due to environmental factors such as seasonality and inter-annual climate variability, conclusions about the success of the intervention cannot be drawn by focussing on the intervention area only. We therefore use a comparative method that analyses the temporal variations (before and after the intervention) of the NDVI of the intervention area with respect to multiple control sites that are automatically and randomly selected from a set of candidates that are similar to the intervention area. Similarity is defined in terms of class composition as derived from an ISODATA classification of the imagery before the intervention. The method provides an estimate of the magnitude and significance of the difference in greenness change between the intervention area and control areas. As a case study, the methodology is applied to 15 restoration interventions carried out in Senegal. The impact of the interventions is analysed using 250-m MODIS and 30-m Landsat data. Results show that a significant improvement in vegetation cover was detectable only in one third of the analysed interventions, which is consistent with independent qualitative assessments based on field observations and visual analysis of high resolution imagery. Rural development agencies may potentially use the proposed method for a first screening of restoration interventions.","keywords":"*imported-from-citeulike-INRMM,~INRMM-MiD:c-14364107,~to-add-doi-URL,arid-climate,degradation,land-cover,modis,ndvi,remote-sensing,restoration,senegal,statistics,vegetation","bibtex":"@article{meroniRemoteSensingMonitoring2017,\n title = {Remote Sensing Monitoring of Land Restoration Interventions in Semi-Arid Environments with a before-after Control-Impact Statistical Design},\n author = {Meroni, Michele and Schucknecht, Anne and Fasbender, Dominique and Rembold, Felix and Fava, Francesco and Mauclaire, Margaux and Goffner, Deborah and Di Lucchio, Luisa M. and Leonardi, Ugo},\n date = {2017-07},\n journaltitle = {International Journal of Applied Earth Observation and Geoinformation},\n volume = {59},\n pages = {42--52},\n issn = {0303-2434},\n doi = {10.1016/j.jag.2017.02.016},\n url = {https://doi.org/10.1016/j.jag.2017.02.016},\n abstract = {[Highlights]\n\n[::] A rapid, standardised and objective assessment of the biophysical impact of restoration interventions is proposed. [::] The intervention impact is evaluated by a before-after control-impact sampling design. [::] The method provides a statistical test of the no-change hypothesis and the estimation of the relative magnitude of the change. [::] The method is applicable to NDVI and other remote sensing-derived variables.\n\n[Abstract]\n\nRestoration interventions to combat land degradation are carried out in arid and semi-arid areas to improve vegetation cover and land productivity. Evaluating the success of an intervention over time is challenging due to various constraints (e.g. difficult-to-access areas, lack of long-term records) and the lack of standardised and affordable methodologies. We propose a semi-automatic methodology that uses remote sensing data to provide a rapid, standardised and objective assessment of the biophysical impact, in terms of vegetation cover, of restoration interventions. The Normalised Difference Vegetation Index (NDVI) is used as a proxy for vegetation cover. Recognising that changes in vegetation cover are naturally due to environmental factors such as seasonality and inter-annual climate variability, conclusions about the success of the intervention cannot be drawn by focussing on the intervention area only. We therefore use a comparative method that analyses the temporal variations (before and after the intervention) of the NDVI of the intervention area with respect to multiple control sites that are automatically and randomly selected from a set of candidates that are similar to the intervention area. Similarity is defined in terms of class composition as derived from an ISODATA classification of the imagery before the intervention. The method provides an estimate of the magnitude and significance of the difference in greenness change between the intervention area and control areas. As a case study, the methodology is applied to 15 restoration interventions carried out in Senegal. The impact of the interventions is analysed using 250-m MODIS and 30-m Landsat data. Results show that a significant improvement in vegetation cover was detectable only in one third of the analysed interventions, which is consistent with independent qualitative assessments based on field observations and visual analysis of high resolution imagery. Rural development agencies may potentially use the proposed method for a first screening of restoration interventions.},\n keywords = {*imported-from-citeulike-INRMM,~INRMM-MiD:c-14364107,~to-add-doi-URL,arid-climate,degradation,land-cover,modis,ndvi,remote-sensing,restoration,senegal,statistics,vegetation}\n}\n\n","author_short":["Meroni, M.","Schucknecht, A.","Fasbender, D.","Rembold, F.","Fava, F.","Mauclaire, M.","Goffner, D.","Di Lucchio, L. M.","Leonardi, U."],"key":"meroniRemoteSensingMonitoring2017","id":"meroniRemoteSensingMonitoring2017","bibbaseid":"meroni-schucknecht-fasbender-rembold-fava-mauclaire-goffner-dilucchio-etal-remotesensingmonitoringoflandrestorationinterventionsinsemiaridenvironmentswithabeforeaftercontrolimpactstatisticaldesign","role":"author","urls":{"Paper":"https://doi.org/10.1016/j.jag.2017.02.016"},"keyword":["*imported-from-citeulike-INRMM","~INRMM-MiD:c-14364107","~to-add-doi-URL","arid-climate","degradation","land-cover","modis","ndvi","remote-sensing","restoration","senegal","statistics","vegetation"],"downloads":0},"bibtype":"article","biburl":"https://tmpfiles.org/dl/58794/INRMM.bib","creationDate":"2020-07-02T22:41:14.973Z","downloads":0,"keywords":["*imported-from-citeulike-inrmm","~inrmm-mid:c-14364107","~to-add-doi-url","arid-climate","degradation","land-cover","modis","ndvi","remote-sensing","restoration","senegal","statistics","vegetation"],"search_terms":["remote","sensing","monitoring","land","restoration","interventions","semi","arid","environments","before","control","impact","statistical","design","meroni","schucknecht","fasbender","rembold","fava","mauclaire","goffner","di lucchio","leonardi"],"title":"Remote Sensing Monitoring of Land Restoration Interventions in Semi-Arid Environments with a before-after Control-Impact Statistical Design","year":null,"dataSources":["DXuKbcZTirdigFKPF"]}