Reusing Remote Sensing-Based Validation Data: Comparing Direct and Indirect Approaches for Afforestation Monitoring

• Published in: Remote sensing (Basel, Switzerland) Vol. 15; no. 6; p. 1638
• Main Authors: Francini, Saverio, Cavalli, Alice, D’Amico, Giovanni, McRoberts, Ronald E., Maesano, Mauro, Munafò, Michele, Scarascia Mugnozza, Giuseppe, Chirici, Gherardo
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.03.2023
• Subjects: Accuracy; Afforestation; Artificial intelligence; Buffers; Carbon dioxide; Carbon dioxide removal; Classification; Climate change; Cloud computing; Comparative analysis; confidence interval; Confidence intervals; Data acquisition; Data collection; Datasets; Distribution; Environmental aspects; Estimates; Global warming; google earth engine; Identification and classification; Image classification; Internet; Italy; Landsat; Landsat satellites; Learning algorithms; Machine learning; Methods; Monitoring; random forests; Remote sensing; Reuse; Sampling; Satellite imagery; Statistical analysis; Variables; Italy
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs15061638

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.