scholarly journals High-Precision Stand Age Data Facilitate the Estimation of Rubber Plantation Biomass: A Case Study of Hainan Island, China

2020 ◽  
Vol 12 (23) ◽  
pp. 3853
Author(s):  
Bangqian Chen ◽  
Tin Yun ◽  
Jun Ma ◽  
Weili Kou ◽  
Hailiang Li ◽  
...  
Keyword(s):  
Regression Model ◽  
Forest Biomass ◽  
Hainan Island ◽  
Stand Age ◽  
Biomass Estimation ◽  
Tropical Region ◽  
Rubber Plantation ◽  
Large Area ◽  
Biomass Density ◽  
Scatter Plots

Rubber (Hevea brasiliensis Muell.) plantations constitute one of the most important agro-ecosystems in the tropical region of China and Southeast Asia, playing an important role in the carbon budget there. Accurately obtaining their biomass over a large area is challenging because of difficulties in acquiring the Diameter at Breast Height (DBH) through remote sensing and the problem of biomass saturation. The stand age, which is closely related to the forest biomass, was proposed for biomass estimation in this study. A stand age map at an annual scale for Hainan Island, which is the second largest natural rubber production base in China, was generated using all Landsat and Sentinel-2 (LS2) data (1987–2017). Scatter plots and the correlation coefficient method were used to explore the relationship (e.g., biomass saturation) between rubber biomass and different LS2-based variables. Subsequently, a regression model fitted with the stand age (R2 = 0.96) and a Random Forest (RF) model parameterizing with LS2-based variables and/or the stand age were respectively employed to estimate rubber biomass for Hainan Island. The results show that rubber biomass was saturated around 65 Mg/ha with all LS2-based variables. The regression model estimated biomass accurately (R2 = 0.79 and Root Mean Square Error (RMSE) = 14.00 Mg/ha) and eliminated the saturation problem significantly. In addition to LS2-based variables, adding a stand age parameter to the RF models was found to significantly improve the prediction accuracy (R2 = 0.82–0.96 and RMSE = 4.08–10.59 Mg/ha, modeling using samples of different biomass sizes). However, all RF models overestimated the biomass of young plantations and underestimated the biomass of old plantations. A hybrid model integrating the optimal results of RF and regression models reduced estimation bias and generated the best performance (R2 = 0.83 and RMSE = 12.48 Mg/ha). The total rubber biomass of Hainan Island in 2017 was about 5.40 × 107 Mg. The northward and westward expansions after 2000 had great impact on the biomass distribution, leading to a higher biomass density for the inland coastal strip from south to northeast and a lower biomass density in the northern and western regions.

Forest Biomass Estimation Based on Remote Sensing Method for North Daxingan Mountains

2011 ◽  
Vol 339 ◽  
pp. 336-341 ◽  
Author(s):  
Yuan Yuan Zhang ◽  
Feng Ri Li ◽  
Fu Xiang Liu
Keyword(s):  
Remote Sensing ◽  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Forest Biomass ◽  
Multiple Linear Regression Model ◽  
Biomass Estimation ◽  
Individual Tree ◽  
Forest Region

Using the Landsat 5 TM images in 2002 as source data，the paper constructed individual tree biomass models of seven principal species based on the data from field surveying and fixed Plots in Tahe and Amur forest Region in Daxiangan Mountains. The remote sensing biomass model between TM images and data from forest fixed Plots was developed by the methods of multiple linear regression and BP neutral net. The result showed that R in multiple linear regression model was 0.764 and the model passed the F test, D-W test and multi-collinearity test. In the independent sample estimation，The neutral net model with the precision of 91.25% was significantly higher than multiple linear regression model with the precision of 81.02%. Although the“black-box”neutral net model could not give the concrete analytical equation, this kind of model with high precision might be applied to estimate the forest biomass in large level forest biomass.

scholarly journals Identifying Establishment Year and Pre-Conversion Land Cover of Rubber Plantations on Hainan Island, China Using Landsat Data during 1987–2015

2018 ◽  
Vol 10 (8) ◽  
pp. 1240 ◽  
Author(s):  
Bangqian Chen ◽  
Xiangming Xiao ◽  
Zhixiang Wu ◽  
Tin Yun ◽  
Weili Kou ◽  
...  
Keyword(s):  
Land Cover ◽  
Best Management Practices ◽  
Management Practices ◽  
Rubber Tree ◽  
Hainan Island ◽  
Stand Age ◽  
Rubber Plantation ◽  
Land Cover Types ◽  
Rubber Plantations ◽  
Evergreen Forests

Knowing the stand age of rubber tree (Hevea brasiliensis) plantations is vitally important for best management practices, estimations of rubber latex yields, and carbon cycle studies (e.g., biomass, carbon pools, and fluxes). However, the stand age (as estimated from the establishment year of rubber plantation) is not available across large regions. In this study, we analyzed Landsat time series images from 1987–2015 and developed algorithms to identify (1) the establishment year of rubber plantations; and (2) the pre-conversion land cover types, such as old rubber plantations, evergreen forests, and cropland. Exposed soil during plantation establishment and linear increases in canopy closure during non-production periods (rubber seedling to mature plantation) were used to identify the establishment year of rubber plantations. Based on the rubber plantation map for 2015 (overall accuracy = 97%), and 1981 Landsat images since 1987, we mapped the establishment year of rubber plantations on Hainan Island (R2 = 0.85/0.99, and RMSE = 2.34/0.54 years at pixel/plantation scale). The results show that: (1) significant conversion of croplands and old rubber plantations to new rubber plantations has occurred substantially in the northwest and northern regions of Hainan Island since 2000, while old rubber plantations were mainly distributed in the southeastern inland strip; (2) the pattern of rubber plantation expansion since 1987 consisted of fragmented plantations from smallholders, and there was no tendency to expand towards a higher altitude and steep slope regions; (3) the largest land source for new rubber plantations since 1988 was old rubber plantations (1.26 × 105 ha), followed by cropland (0.95 × 105 ha), and evergreen forests (0.68 × 105 ha). The resultant algorithms and maps of establishment year and pre-conversion land cover types are likely to be useful in plantation management, and ecological assessments of rubber plantation expansion in China.

scholarly journals Remote Sensing of Tropical Rainforest Biomass Changes in Hainan Island, China from 2003 to 2018

2021 ◽  
Vol 13 (9) ◽  
pp. 1696
Author(s):  
Meizhi Lin ◽  
Qingping Ling ◽  
Huiqing Pei ◽  
Yanni Song ◽  
Zixuan Qiu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Tropical Rainforest ◽  
Hainan Island ◽  
Environmental Information ◽  
Assessment System ◽  
Tropical Rainforests ◽  
Biomass Estimation ◽  
Forest Age ◽  
Biomass Density ◽  
Saturation Phenomenon

The largest area of tropical rainforests in China is on Hainan Island, and it is an important part of the world’s tropical rainforests. The structure of the tropical rainforests in Hainan is complex, the biomass density is high, and conducting ground surveys is difficult, costly, and time-consuming. Remote sensing is a good monitoring method for biomass estimation. However, the saturation phenomenon of such data from different satellite sensors results in low forest biomass estimation accuracy in tropical rainforests with high biomass density. Based on environmental information, the biomass of permanent sample plots, and forest age, this study established a tropical rainforest database for Hainan. Forest age and 14 types of environmental information, combined with an enhanced vegetation index (EVI), were introduced to establish a tropical rainforest biomass estimation model for remote sensing that can overcome the saturation phenomenon present when using remote sensing data. The fitting determination coefficient R2 of the model was 0.694. The remote sensing estimate of relative bias was 2.29%, and the relative root mean square error was 35.41%. The tropical rainforest biomass in Hainan Island is mainly distributed in the central mountainous and southern areas. The tropical rainforests in the northern and coastal areas have been severely damaged by tourism and real estate development. Particularly in low-altitude areas, large areas of tropical rainforest have been replaced by economic forests. Furthermore, the tropical rainforest areas in some cities and counties have decreased, affecting the increase in tropical rainforest biomass. On Hainan Island, there were few tropical rainforests in areas with high rainfall. Therefore, afforestation in these areas could maximize the ecological benefits of tropical rainforests. To further strengthen the protection, there is an urgent need to establish a feasible, reliable, and effective tropical rainforest loss assessment system using quantitative scientific methodologies.

scholarly journals Tropical-Forest Biomass Estimation at X-Band From the Spaceborne TanDEM-X Interferometer

2015 ◽  
Vol 12 (2) ◽  
pp. 239-243 ◽  
Author(s):  
Robert Treuhaft ◽  
Fabio Gonzalves ◽  
Joao Roberto dos Santos ◽  
Michael Keller ◽  
Michael Palace ◽  
...  
Keyword(s):  
Tropical Forest ◽  
Forest Biomass ◽  
Biomass Estimation ◽  
X Band

Forest Biomass Estimation Based on Forest Inventory Data in Middle and Last Scales

2011 ◽  
Vol 183-185 ◽  
pp. 220-224
Author(s):  
Ming Ze Li ◽  
Wen Yi Fan ◽  
Ying Yu
Keyword(s):  
Tree Species ◽  
Forest Inventory ◽  
Forest Biomass ◽  
Biomass Estimation ◽  
Total Biomass ◽  
Inventory Data ◽  
Biomass Equation ◽  
Tree Species Composition ◽  
Forest Inventory Data ◽  
Ecosystem Carbon

The forest biomass (which is referred to the arbor aboveground biomass in this research) is one of the most primary factors to determine the forest ecosystem carbon storages. There are many kinds of estimating methods adapted to various scales. It is a suitable method to estimate forest biomass of the farm or the forestry bureau in middle and last scales. First each subcompartment forest biomass should be estimated, and then the farm or the forestry bureau forest biomass was estimated. In this research, based on maoershan farm region, first the single tree biomass equation of main tree species was established or collected. The biomass of each specie was calculated according to the materials of tally, such as height, diameter and so on in the forest inventory data. Secondly, each specie’s biomass and total biomass in subcompartment were calculated according to the tree species composition in forest management investigation data. Thus the forest biomass spatial distribution was obtained by taking subcompartment as a unit. And last the forest total biomass was estimated.

scholarly journals Forest Biomass Estimation Using SAR and Optical Images

2015 ◽  
Vol 3 (1) ◽  
pp. 15-26
Author(s):  
Mohamad Reza Ramezani ◽  
Mahmood Reza Sahebi ◽  
◽  
Keyword(s):  
Forest Biomass ◽  
Biomass Estimation ◽  
Optical Images
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