We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
Estimating Forest Aboveground Carbon Storage in Hang-Jia-Hu Using Landsat TM/OLI Data and Random Forest Model
Summary
Researchers used Landsat satellite imagery and machine learning to estimate forest carbon storage in a region of China over two decades. The study demonstrates remote sensing as a practical tool for tracking carbon stocks and the effects of land-use change.
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.
Sign in to start a discussion.
More Papers Like This
Continuous Monitoring of Forests in Wetland Ecosystems with Remote Sensing and Probability Sampling
This paper is not about microplastics; it develops a remote-sensing statistical method for monitoring above-ground biomass in wetland forest areas to improve carbon accounting.
Soil Organic Carbon Estimation via Remote Sensing and Machine Learning Techniques: Global Topic Modeling and Research Trend Exploration
Researchers used advanced topic modeling and bibliometric analysis to map global research trends in estimating soil organic carbon using remote sensing and machine learning. They identified key research clusters including satellite imagery analysis, deep learning methods, and regional carbon mapping efforts. The study provides a roadmap for future research priorities in monitoring soil carbon stocks, which is critical for understanding climate change.
Spatio-Temporal Analysis of Oil Spill Impact and Recovery Pattern of Coastal Vegetation and Wetland Using Multispectral Satellite Landsat 8-OLI Imagery and Machine Learning Models
Researchers used Landsat 8 satellite imagery and machine learning to assess the spatial extent and recovery trajectory of oil spill damage to coastal vegetation and wetlands in Nigeria, demonstrating that remote sensing combined with AI models can track long-term ecosystem recovery.
Analysis of Potential Supply of Ecosystem Services in Forest Remnants through Neural Networks
Researchers applied an artificial neural network to geospatial indicators to assess the potential supply of regulating ecosystem services from forest remnants in Campinas, Brazil. The study analyzed landscape configuration factors and evaluated how both the supply of and societal demand for ecosystem services influence the actual benefits provided by fragmented forest patches.
Extraction the Spatial Distribution of Mangroves in the Same Month Based on Images Reconstructed with the FSDAF Model
Researchers applied the FSDAF spatiotemporal fusion model to reconstruct cloud-free satellite images for the same target month, enabling accurate extraction of mangrove spatial distributions in coastal wetlands despite the persistent cloud cover that limits image availability in mangrove-growing regions. The approach demonstrated improved accuracy in mapping mangrove extent compared to methods relying on mosaicked images spanning several months.