Abstract
Dynamic monitoring of carbon storage in forests resources is important for tracking ecosystem functionalities and climate change impacts. In this study, we used multi-year Landsat data combined with a Random Forest (RF) algorithm to estimate the forest aboveground carbon (AGC) in a forest area in China (Hang-Jia-Hu) and analyzed its spatiotemporal changes during the past two decades. Maximum likelihood classification was applied to make land-use maps. Remote sensing variables, such as the spectral band, vegetation indices, and derived texture features, were extracted from 20 Landsat TM and OLI images over five different years (2000, 2004, 2010, 2015, and 2018). These variables were subsequently selected according to their importance and subsequently used in the RF algorithm to build an estimation model of forest AGC. The results showed the following: (1) Verification of classification results showed maximum likelihood can extract land information effectively. Our land cover classification yielded overall accuracies between 86.86% and 89.47%. (2) Additionally, our RF models showed good performance in predicting forest AGC, with R2 from 0.65 to 0.73 in the training and testing phase and a RMSE range between 3.18 and 6.66 Mg/ha. RMSEr in the testing phase ranged from 20.27 to 22.27 with a low model error. (3) The estimation results indicated that forest AGC in the past two decades increased with density at 10.14 Mg/ha, 21.63 Mg/ha, 26.39 Mg/ha, 29.25 Mg/ha, and 44.59 Mg/ha in 2000, 2004, 2010, 2015, and 2018. The total forest AGC storage had a growth rate of 285%. (4) Our study showed that, although forest area decreased in the study area during the time period under study, the total forest AGC increased due to an increment in forest AGC density. However, such an effect is overridden in the vicinity of cities by intense urbanization and the loss of forest covers. Our study demonstrated that the combined use of remote sensing data and machine learning techniques can improve our ability to track the forest changes in support of regional natural resource management practices.
Highlights
Forests comprise a major part of the terrestrial ecosystems, occupying about 30% of the world’s land area, and they are the main contributor to carbon (C) emissions and removal [1,2,3]
Khatami et al [47] classified images used a surveillance classification algorithm based on remote sensing data to classify images, and the results showed that the Random Forest (RF) algorithm is superior to the traditional decision tree algorithm
It illustrates that the overall classification accuracies in different years were above 86.86%, and the highest was 89.47%
Summary
Forests comprise a major part of the terrestrial ecosystems, occupying about 30% of the world’s land area, and they are the main contributor to carbon (C) emissions and removal [1,2,3]. Forests 2019, 10, 1004 more than 80% of forest aboveground carbon (AGC) in terrestrial ecosystems, more than 70% of global soil organic C [4,5,6] and more than double the amount of C in the atmosphere [7]. The traditional field survey method has high precision [17], but it is often limited by manpower and material resources, yielding short durations of observation
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