Reusing Remote Sensing-Based Validation Data: Comparing Direct and Indirect Approaches for Afforestation Monitoring
Afforestation is one of the most effective processes for removing carbon dioxide from the atmosphere and combating global warming. Landsat data and machine learning approaches can be used to map afforestation (i) indirectly, by constructing two maps of the same area over different periods and then p...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-03-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/15/6/1638 |
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| author | Saverio Francini Alice Cavalli Giovanni D’Amico Ronald E. McRoberts Mauro Maesano Michele Munafò Giuseppe Scarascia Mugnozza Gherardo Chirici |
| author_facet | Saverio Francini Alice Cavalli Giovanni D’Amico Ronald E. McRoberts Mauro Maesano Michele Munafò Giuseppe Scarascia Mugnozza Gherardo Chirici |
| author_sort | Saverio Francini |
| collection | DOAJ |
| description | Afforestation is one of the most effective processes for removing carbon dioxide from the atmosphere and combating global warming. Landsat data and machine learning approaches can be used to map afforestation (i) indirectly, by constructing two maps of the same area over different periods and then predicting changes, or (ii) directly, by constructing a single map and analyzing observations of change in both the response and remotely sensed variables. Of crucial importance, no comprehensive comparisons of direct and indirect approaches for afforestation monitoring are known to have been conducted to date. Afforestation maps estimated through the analysis of remotely sensed data may serve as intermediate products for guiding the selection of samples and the production of statistics. In this and similar studies, a huge effort is dedicated to collecting validation data. In turn, those validation datasets have varying sampling intensities in different areas, which complicates their use for assessing the accuracies of new maps. As a result, the work done to collect data is often not sufficiently exploited, with some validation datasets being used just once. In this study, we addressed two main aims. First, we implemented a methodology to reuse validation data acquired via stratified sampling with strata constructed from remote sensing maps. Second, we used this method for acquiring data for comparing map accuracy estimates and the precision of estimates for direct and indirect approaches for country-wide mapping of afforestation that occurred in Italy between 1985 and 2019. To facilitate these comparisons, we used Landsat imagery, random forest classification, and Google Earth Engine. The herein-presented method produced different accuracy estimates with 95% confidence interval and for different map classes. Afforestation accuracies ranged between 53 ± 5.9% for the indirect map class inside the buffer—defined as a stratum within 120 m of the forest/non-forest mask boundaries—and 26 ± 3.4% for the direct map outside the buffer. The accuracy in non-afforestation map classes was much greater, ranging from 87 ± 1.9% for the indirect map inside the buffer to 99 ± 1.3% for the direct map outside the buffer. Additionally, overall accuracies (with 95% CI) were estimated with large precision for both direct and indirect maps (87 ± 1.3% and 89 ± 1.6%, respectively), confirming (i) the effectiveness of the method we introduced for reusing samples and (ii) the relevance of remotely sensed data and machine learning for monitoring afforestation. |
| first_indexed | 2024-03-11T05:57:30Z |
| format | Article |
| id | doaj.art-a4baf96bf44142c5a6c4f18e37984a8c |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-11T05:57:30Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-a4baf96bf44142c5a6c4f18e37984a8c2023-11-17T13:39:53ZengMDPI AGRemote Sensing2072-42922023-03-01156163810.3390/rs15061638Reusing Remote Sensing-Based Validation Data: Comparing Direct and Indirect Approaches for Afforestation MonitoringSaverio Francini0Alice Cavalli1Giovanni D’Amico2Ronald E. McRoberts3Mauro Maesano4Michele Munafò5Giuseppe Scarascia Mugnozza6Gherardo Chirici7Department of Agricultural, Food and Forestry Systems, University of Florence, 50145 Firenze, ItalyItalian Institute for Environmental Protection and Research (ISPRA), Via Vitaliano Brancati 48, 00144 Rome, ItalyDepartment of Agricultural, Food and Forestry Systems, University of Florence, 50145 Firenze, ItalyDepartment of Forest Resources, University of Minnesota, Saint Paul, MN 55108, USADepartment of Innovation in Biology, Agri-Food and Forest Systems (DIBAF), University of Tuscia, Via San Camillo de Lellis SNC, 01100 Viterbo, ItalyItalian Institute for Environmental Protection and Research (ISPRA), Via Vitaliano Brancati 48, 00144 Rome, ItalyDepartment of Innovation in Biology, Agri-Food and Forest Systems (DIBAF), University of Tuscia, Via San Camillo de Lellis SNC, 01100 Viterbo, ItalyDepartment of Agricultural, Food and Forestry Systems, University of Florence, 50145 Firenze, ItalyAfforestation is one of the most effective processes for removing carbon dioxide from the atmosphere and combating global warming. Landsat data and machine learning approaches can be used to map afforestation (i) indirectly, by constructing two maps of the same area over different periods and then predicting changes, or (ii) directly, by constructing a single map and analyzing observations of change in both the response and remotely sensed variables. Of crucial importance, no comprehensive comparisons of direct and indirect approaches for afforestation monitoring are known to have been conducted to date. Afforestation maps estimated through the analysis of remotely sensed data may serve as intermediate products for guiding the selection of samples and the production of statistics. In this and similar studies, a huge effort is dedicated to collecting validation data. In turn, those validation datasets have varying sampling intensities in different areas, which complicates their use for assessing the accuracies of new maps. As a result, the work done to collect data is often not sufficiently exploited, with some validation datasets being used just once. In this study, we addressed two main aims. First, we implemented a methodology to reuse validation data acquired via stratified sampling with strata constructed from remote sensing maps. Second, we used this method for acquiring data for comparing map accuracy estimates and the precision of estimates for direct and indirect approaches for country-wide mapping of afforestation that occurred in Italy between 1985 and 2019. To facilitate these comparisons, we used Landsat imagery, random forest classification, and Google Earth Engine. The herein-presented method produced different accuracy estimates with 95% confidence interval and for different map classes. Afforestation accuracies ranged between 53 ± 5.9% for the indirect map class inside the buffer—defined as a stratum within 120 m of the forest/non-forest mask boundaries—and 26 ± 3.4% for the direct map outside the buffer. The accuracy in non-afforestation map classes was much greater, ranging from 87 ± 1.9% for the indirect map inside the buffer to 99 ± 1.3% for the direct map outside the buffer. Additionally, overall accuracies (with 95% CI) were estimated with large precision for both direct and indirect maps (87 ± 1.3% and 89 ± 1.6%, respectively), confirming (i) the effectiveness of the method we introduced for reusing samples and (ii) the relevance of remotely sensed data and machine learning for monitoring afforestation.https://www.mdpi.com/2072-4292/15/6/1638remote sensinglandsatgoogle earth enginerandom forestsmachine learningcloud computing |
| spellingShingle | Saverio Francini Alice Cavalli Giovanni D’Amico Ronald E. McRoberts Mauro Maesano Michele Munafò Giuseppe Scarascia Mugnozza Gherardo Chirici Reusing Remote Sensing-Based Validation Data: Comparing Direct and Indirect Approaches for Afforestation Monitoring Remote Sensing remote sensing landsat google earth engine random forests machine learning cloud computing |
| title | Reusing Remote Sensing-Based Validation Data: Comparing Direct and Indirect Approaches for Afforestation Monitoring |
| title_full | Reusing Remote Sensing-Based Validation Data: Comparing Direct and Indirect Approaches for Afforestation Monitoring |
| title_fullStr | Reusing Remote Sensing-Based Validation Data: Comparing Direct and Indirect Approaches for Afforestation Monitoring |
| title_full_unstemmed | Reusing Remote Sensing-Based Validation Data: Comparing Direct and Indirect Approaches for Afforestation Monitoring |
| title_short | Reusing Remote Sensing-Based Validation Data: Comparing Direct and Indirect Approaches for Afforestation Monitoring |
| title_sort | reusing remote sensing based validation data comparing direct and indirect approaches for afforestation monitoring |
| topic | remote sensing landsat google earth engine random forests machine learning cloud computing |
| url | https://www.mdpi.com/2072-4292/15/6/1638 |
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