Estimating Forest Aboveground Carbon Storage in Hang-Jia-Hu Using Landsat TM/OLI Data and Random Forest Model

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.

[1]  B. He,et al.  Carbon sequestration from China’s afforestation projects , 2015, Environmental Earth Sciences.

[2]  Ning Han,et al.  Exploring the synergistic use of multi-scale image object metrics for land-use/land-cover mapping using an object-based approach , 2015 .

[3]  Davide Giudici,et al.  Generation and Calibration of High-Resolution DEM From Single-Baseline Spaceborne Interferometry: The “Split-Swath” Approach , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[4]  Giorgos Mountrakis,et al.  A meta-analysis of remote sensing research on supervised pixel-based land-cover image classification processes: General guidelines for practitioners and future research , 2016 .

[5]  Feng Zhang,et al.  Using negative soil adjustment factor in soil-adjusted vegetation index (SAVI) for aboveground living biomass estimation in arid grasslands. , 2018 .

[6]  T. Dixon,et al.  High-resolution DEM generation from spaceborne and terrestrial remote sensing data for improved volcano hazard assessment — A case study at Nevado del Ruiz, Colombia , 2019, Remote Sensing of Environment.

[7]  Xiaojun Xu,et al.  Assimilating spatiotemporal MODIS LAI data with a particle filter algorithm for improving carbon cycle simulations for bamboo forest ecosystems. , 2019, The Science of the total environment.

[8]  Pingheng Li,et al.  Spatial and temporal patterns of carbon storage from 1992 to 2002 in forest ecosystems in Guangdong, Southern China , 2012, Plant and Soil.

[9]  Weiliang Fan,et al.  Estimating bamboo forest aboveground biomass using EnKF-assimilated MODIS LAI spatiotemporal data and machine learning algorithms , 2018, Agricultural and Forest Meteorology.

[10]  Hong Sun,et al.  Simulation of Forest Carbon Fluxes Using Model Incorporation and Data Assimilation , 2016, Remote. Sens..

[11]  Iryna Dronova,et al.  Spectral vegetation indices of wetland greenness: Responses to vegetation structure, composition, and spatial distribution , 2019 .

[12]  Paul C. Boutros,et al.  The parameter sensitivity of random forests , 2016, BMC Bioinformatics.

[13]  T. Yue,et al.  Estimation of China’s forest stand biomass carbon sequestration based on the continuous biomass expansion factor model and seven forest inventories from 1977 to 2013 , 2019, Forest Ecology and Management.

[14]  Xiaobo Qin,et al.  Biomass estimation of alpine grasslands under different grazing intensities using spectral vegetation indices , 2012 .

[15]  Dimitrios Gounaridis,et al.  Urban land cover thematic disaggregation, employing datasets from multiple sources and RandomForests modeling , 2016, Int. J. Appl. Earth Obs. Geoinformation.

[16]  Hongqi Li,et al.  The linear random forest algorithm and its advantages in machine learning assisted logging regression modeling , 2019, Journal of Petroleum Science and Engineering.

[17]  E. Pattey,et al.  Assessment of vegetation indices for regional crop green LAI estimation from Landsat images over multiple growing seasons , 2012 .

[18]  Rik Leemans,et al.  Simulating the carbon flux between the terrestrial environment and the atmosphere , 1994 .

[19]  Ning Han,et al.  Spatiotemporal Estimation of Bamboo Forest Aboveground Carbon Storage Based on Landsat Data in Zhejiang, China , 2018, Remote. Sens..

[20]  M. Bindi,et al.  Combination of ground and remote sensing data to assess carbon stock changes in the main urban park of Florence , 2019, Urban Forestry & Urban Greening.

[21]  Xuejian Li,et al.  Estimating and Analyzing the Spatiotemporal Pattern of Aboveground Carbon in Bamboo Forest by Combining Remote Sensing Data and Improved BIOME-BGC Model , 2018, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[22]  Shilong Piao,et al.  Forest biomass carbon stocks in China over the past 2 decades: Estimation based on integrated inventory and satellite data , 2005 .

[23]  Tran Thai Binh,et al.  The Ha Tien Plain – wetland monitoring using remote-sensing techniques , 2014, Remote Sensing the Mekong.

[24]  Lijuan Liu,et al.  Comparative Analysis of Modeling Algorithms for Forest Aboveground Biomass Estimation in a Subtropical Region , 2018, Remote. Sens..

[25]  Lijuan Liu,et al.  Forest aboveground biomass estimation in Zhejiang Province using the integration of Landsat TM and ALOS PALSAR data , 2016, Int. J. Appl. Earth Obs. Geoinformation.

[26]  Leonardo Vanneschi,et al.  Burned area estimations derived from Landsat ETM+ and OLI data: Comparing Genetic Programming with Maximum Likelihood and Classification and Regression Trees , 2018, ISPRS Journal of Photogrammetry and Remote Sensing.

[27]  B. Kong,et al.  Quantitative Estimation of Biomass of Alpine Grasslands Using Hyperspectral Remote Sensing☆ , 2019, Rangeland Ecology and Management.

[28]  E. Mitchard.  The tropical forest carbon cycle and climate change , 2018, Nature.

[29]  Giles M. Foody,et al.  Good practices for estimating area and assessing accuracy of land change , 2014 .

[30]  Jorge M. Palmeirim,et al.  Mapping Mediterranean scrub with satellite imagery: biomass estimation and spectral behaviour , 2004 .

[31]  M. Batistella,et al.  Exploring TM Image Texture and its Relationships with Biomass Estimation in Rondônia, Brazilian Amazon. , 2005 .

[32]  Hong Wang,et al.  [Parameter sensitivity of simulating net primary productivity of Larix olgensis forest based on BIOME-BGC model]. , 2016, Ying yong sheng tai xue bao = The journal of applied ecology.

[33]  Hamami Latifa,et al.  Spatio Temporal Analysis of Vegetation by Vegetation Indices from Multi-dates Satellite Images: Application to a Semi Arid Area in ALGERIA , 2013 .

[34]  David B. Lobell,et al.  Crop type mapping without field-level labels: Random forest transfer and unsupervised clustering techniques , 2019, Remote Sensing of Environment.

[35]  A. Goetz,et al.  Atmospheric correction algorithms for hyperspectral remote sensing data of land and ocean , 2009 .

[36]  Qingpeng Yang,et al.  Spatiotemporal patterns of carbon storage in forest ecosystems in Hunan Province, China , 2019, Forest Ecology and Management.

[37]  Kai Li,et al.  Co-mention network of R packages: Scientific impact and clustering structure , 2018, J. Informetrics.

[38]  R.M. Haralick,et al.  Statistical and structural approaches to texture , 1979, Proceedings of the IEEE.

[39]  A. Huete,et al.  A Modified Soil Adjusted Vegetation Index , 1994 .

[40]  J. Macgregor,et al.  Image texture analysis: methods and comparisons , 2004 .

[41]  Longzhu Guo,et al.  Remote sensing for vegetation monitoring in carbon capture storage regions: A review , 2019, Applied Energy.

[42]  Wei Gong,et al.  Analyzing the performance of PROSPECT model inversion based on different spectral information for leaf biochemical properties retrieval , 2018 .

[43]  R. Fournier,et al.  A comparison of four methods to map biomass from Landsat-TM and inventory data in western Newfoundland , 2006 .

[44]  F. Tubiello,et al.  New estimates of CO2 forest emissions and removals: 1990-2015 , 2015 .

[45]  Kerry A. Naish,et al.  A practical introduction to Random Forest for genetic association studies in ecology and evolution , 2018, Molecular ecology resources.

[46]  Aleksander Madry,et al.  Towards Deep Learning Models Resistant to Adversarial Attacks , 2017, ICLR.

[47]  Fangjie Mao,et al.  [Spatiotemporal dynamic simulation on aboveground carbon storage of bamboo forest and its influence factors in Zhejiang Province, China.] , 2019, Ying yong sheng tai xue bao = The journal of applied ecology.

[48]  Mohammadmehdi Saberioon,et al.  Soil organic carbon and texture retrieving and mapping using proximal, airborne and Sentinel-2 spectral imaging , 2018, Remote Sensing of Environment.

[49]  Boqiang Lin,et al.  Valued forest carbon sinks: How much emissions abatement costs could be reduced in China , 2019, Journal of Cleaner Production.

[50]  Qiang Yang,et al.  Federated Machine Learning , 2019, ACM Trans. Intell. Syst. Technol..

[51]  D. Eamus,et al.  Storage of organic carbon in the soils of Mexican temperate forests , 2019, Forest Ecology and Management.

[52]  Antanas Verikas,et al.  Mining data with random forests: A survey and results of new tests , 2011, Pattern Recognit..

[53]  Jingyun Fang,et al.  Forest biomass carbon sinks in East Asia, with special reference to the relative contributions of forest expansion and forest growth , 2014, Global change biology.

[54]  E. Lahive,et al.  Microplastics in freshwater and terrestrial environments: Evaluating the current understanding to identify the knowledge gaps and future research priorities. , 2017, The Science of the total environment.

[55]  D. Lu,et al.  Examining impacts of the Belo Monte hydroelectric dam construction on land-cover changes using multitemporal Landsat imagery , 2018, Applied Geography.

[56]  R. B. Jackson,et al.  A Large and Persistent Carbon Sink in the World’s Forests , 2011, Science.

[57]  Yongchao Zhao,et al.  CPBAC: A quick atmospheric correction method using the topographic information , 2016 .

[58]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[59]  Sarah J. Graves,et al.  A tree-based approach to biomass estimation from remote sensing data in a tropical agricultural landscape , 2018, Remote Sensing of Environment.

[60]  He Chun-yang.  Land Use/cover Change Detection with Multi-source Data , 2007 .

[61]  O. Hagner,et al.  A method for calibrated maximum likelihood classification of forest types , 2007 .

[62]  David B. Lindenmayer,et al.  Re-evaluation of forest biomass carbon stocks and lessons from the world's most carbon-dense forests , 2009, Proceedings of the National Academy of Sciences.

[63]  K. Paustian,et al.  Measuring and understanding carbon storage in afforested soils by physical fractionation , 2002 .

[64]  Venkata Ravibabu Mandla,et al.  Evaluation of atmospheric corrections on hyperspectral data with special reference to mineral mapping , 2017 .

[65]  D. Bargiel,et al.  A new method for crop classification combining time series of radar images and crop phenology information. , 2017 .

[66]  Jonathan Li,et al.  Quantifying the Carbon Storage in Urban Trees Using Multispectral ALS Data , 2018, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[67]  J. Townshend,et al.  Global land cover classi(cid:142) cation at 1 km spatial resolution using a classi(cid:142) cation tree approach , 2004 .

[68]  Robert F. Chen,et al.  An assessment of Landsat-8 atmospheric correction schemes and remote sensing reflectance products in coral reefs and coastal turbid waters , 2018, Remote Sensing of Environment.

[69]  H. Du,et al.  Spatiotemporal evolution and impacts of climate change on bamboo distribution in China. , 2019, Journal of environmental management.

[70]  Klara Dolos,et al.  Comparing Generalized Linear Models and random forest to model vascular plant species richness using LiDAR data in a natural forest in central Chile , 2016 .