Estimates of subtropical forest biomass based on airborne LiDAR and Landsat 8 OLI data

2015 ◽  
Vol 39 (4) ◽  
pp. 309-321 ◽  
Author(s):  
XU Ting ◽  
◽  
CAO Lin ◽  
SHEN Xin ◽  
SHE Guang-Hui
Keyword(s):  
Forest Biomass ◽  
Subtropical Forest ◽  
Airborne Lidar ◽  
Landsat 8 ◽  
Landsat 8 Oli

scholarly journals Canopy cover of mangrove estimation based on airborne LIDAR & Landsat 8 OLI

2019 ◽  
Vol 335 ◽  
pp. 012029 ◽  
Author(s):  
L B Prasetyo ◽  
W I Nursal ◽  
Y Setiawan ◽  
Y Rudianto ◽  
K Wikantika ◽  
...  
Keyword(s):  
Canopy Cover ◽  
Airborne Lidar ◽  
Landsat 8 ◽  
Landsat 8 Oli

scholarly journals SUBTROPICAL FOREST BIOMASS ESTIMATION USING AIRBORNE LiDAR AND HYPERSPECTRAL DATA

2016 ◽  
Vol XLI-B8 ◽  
pp. 747-749
Author(s):  
Yong Pang ◽  
Zengyuan Li
Keyword(s):  
Forest Ecosystem ◽  
Forest Biomass ◽  
Subtropical Forest ◽  
Hyperspectral Data ◽  
Airborne Lidar ◽  
Observation System ◽  
Biomass Estimation ◽  
Lidar Data ◽  
Forest Types ◽  
Waveform Lidar

Forests have complex vertical structure and spatial mosaic pattern. Subtropical forest ecosystem consists of vast vegetation species and these species are always in a dynamic succession stages. It is very challenging to characterize the complexity of subtropical forest ecosystem. In this paper, CAF’s (The Chinese Academy of Forestry) LiCHy (LiDAR, CCD and Hyperspectral) Airborne Observation System was used to collect waveform Lidar and hyperspectral data in Puer forest region, Yunnan province in the Southwest of China. The study site contains typical subtropical species of coniferous forest, evergreen broadleaf forest, and some other mixed forests. The hypersectral images were orthorectified and corrected into surface reflectance with support of Lidar DTM product. The fusion of Lidar and hyperspectral can classify dominate forest types. The lidar metrics improved the classification accuracy. Then forest biomass estimation was carried out for each dominate forest types using waveform Lidar data, which get improved than single Lidar data source.

Canopy cover estimation of agroforestry based on airborne LiDAR and Landsat 8 OLI

2019 ◽  
Author(s):  
Yoga Rudianto ◽  
Lilik Budi Prasetyo ◽  
Yudi Setiawan ◽  
Sahid Hudjimartsu
Keyword(s):  
Canopy Cover ◽  
Airborne Lidar ◽  
Landsat 8 ◽  
Landsat 8 Oli

Machine learning based estimation of aboveground biomass in subalpine forests using Landsat 8 OLI and Sentinel-2B images in Jiuzhaigou National Nature Reserve, Eastern Tibet Plateau

2021 ◽  
Author(s):  
Ke Luo ◽  
Xiaolu Tang ◽  
Liang Liu ◽  
Xinrui Luo ◽  
Jingji Li
Keyword(s):  
Machine Learning ◽  
Nature Reserve ◽  
Forest Biomass ◽  
Tibet Plateau ◽  
Landsat 8 ◽  
Field Observations ◽  
Landsat 8 Oli ◽  
The North ◽  
Eastern Tibet ◽  
Model Efficiency

<p>Although forests cover about one third of global land surface, forests act as important biophysical, biogeochemical, hydrological, economic and cultural roles in the Earth systems. Forests contribute up to 75% of terrestrial gross primary production and store more carbon in forest biomass and soil compared to the atmosphere. Forest aboveground biomass (AGB) plays a crucial role in regional and global ecological balance. However, due to the difficulties in measuring forest biomass in the field at regional scales, a quantitative estimation with high accuracy of forest AGB by linking remote sensing is still a challenge, particularly in mountainous region. Thus, we combined the Landsat 8 OLI and Sentinel-2B data to estimate subalpine forest AGB using linear regression (LR), and two machine learning approaches - random forest (RF) and extreme gradient boosting (XGBoost), with the linkage of field observations in Jiuzhaigou National Nature Reserve, Eastern Tibet Plateau. A 10-fold cross validation (CV) method was used to evaluate the model accuracy, and then the proximity between the predicted value and the actual value was compared. The model efficiency (pseudo R<sup>2</sup>) and root mean square error (RMSE) were used as the accuracy evaluation criteria. Based on 54 field observations, results showed that mean forest AGB was 180.6 Mg ha<sup>-1</sup>with a strong spatial variability from 61.7 to 475.1 Mg ha<sup>-1</sup>. AGB varied significantly among forest types that AGB in coniferous forests was significantly higher than coniferous mixed forests and broad-leaved forests. Landsat 8 OLI and Sentinel-2B imagery were successfully applied to estimate AGB separately or combined. Integrating the Landsat 8 OLI and Sentinel-2B imagery significantly improved model efficiency for different modelling approaches. For the regression algorithms, machine learning method outperformed the linear regression. Among LR, RF and XGBoost approaches, XGBoost performed best with a model efficiency (R<sup>2</sup>) of 0.71 and root mean square error values of 46 Mg ha<sup>-1</sup> and subsequently used for spatial modelling. Modelled results indicated a strong spatial variability in AGB, with a total 6.6×10<sup>6</sup> Mg across the study area. AGB distribution in the study area had obvious spatial characteristics, which was closely related to the elevation. It was mainly concentrated in the north and central areas, while in the southern region the AGB was relatively low, which was contrary to the trend of the elevation variation in the study area where the terrain was high in the south and low in the north. Our study highlighted a potential way to improve the estimate accuracy of forest AGB in mountainous region by integrating the Landsat 8 OLI and Sentinel-2B data using machine learning algorithms.</p>

scholarly journals Synergistic use of Landsat 8 OLI image and airborne LiDAR data for above-ground biomass estimation in tropical lowland rainforests

2017 ◽  
Vol 406 ◽  
pp. 163-171 ◽  
Author(s):  
Mui-How Phua ◽  
Shazrul Azwan Johari ◽  
Ong Cieh Wong ◽  
Keiko Ioki ◽  
Maznah Mahali ◽  
...  
Keyword(s):  
Airborne Lidar ◽  
Biomass Estimation ◽  
Lidar Data ◽  
Landsat 8 ◽  
Above Ground Biomass ◽  
Landsat 8 Oli ◽  
Tropical Lowland ◽  
Ground Biomass ◽  
Airborne Lidar Data

scholarly journals Estimation of Forest Biomass in Beijing (China) Using Multisource Remote Sensing and Forest Inventory Data

Forests ◽  
2020 ◽  
Vol 11 (2) ◽  
pp. 163 ◽  
Author(s):  
Yan Zhu ◽  
Zhongke Feng ◽  
Jing Lu ◽  
Jincheng Liu
Keyword(s):  
Remote Sensing ◽  
Forest Inventory ◽  
Forest Biomass ◽  
Mixed Forest ◽  
Accurate Estimation ◽  
Landsat 8 ◽  
Forest Types ◽  
Landsat 8 Oli ◽  
Inventory Data ◽  
Forest Inventory Data

Forest biomass reflects the material cycle of forest ecosystems and is an important index to measure changes in forest structure and function. The accurate estimation of forest biomass is the research basis for measuring carbon storage in forest systems, and it is important to better understand the carbon cycle and improve the efficiency of forest policy and management activities. In this study, to achieve an accurate estimation of meso-scale (regional) forest biomass, we used Ninth Beijing Forest Inventory data (FID), Landsat 8 OLI Image data and ALOS-2 PALSAR-2 data to establish different forest types (coniferous forest, mixed forest, and broadleaf forest) of biomass models in Beijing. We assessed the potential of forest inventory, optical (Landsat 8 OLI) and radar (ALOS-2 PALSAR-2) data in estimating and mapping forest biomass. From these data, a wide range of parameters related to forest structure were obtained. Random forest (RF) models were established using these parameters and compared with traditional multiple linear regression (MLR) models. Forest biomass in Beijing was then estimated. The results showed the following: (1) forest inventory data combined with multisource remote sensing data can better fit forest biomass than forest inventory data alone. Among the three forest types, mixed forest has the best fitting model. Forest inventory variables and multisource remote sensing variables can match each other in time and space, capturing almost all spatial variability. (2) The 2016 forest biomass density in Beijing was estimated to be 52.26 Mg ha−1 and ranged from 19.1381–195.66 Mg ha−1. The areas with high biomass were mainly distributed in the north and southwest of Beijing, while the areas with low biomass were mainly distributed in the southeast and central areas of Beijing. (3) The estimates from the RF model are better than those from the MLR model, showing a high R 2 and a low root mean square error (RMSE). The R 2 values of the MLR models of three forest types were greater than 0.5, and RMSEs were less than 15.5 Mg ha−1, The R 2 values of the RF models were higher than 0.6, and the RMSEs were lower than 13.5 Mg ha−1. We conclude that the methods in this paper can help improve the accurate estimation of regional biomass and provide a basis for the planning of relevant forestry decision-making departments.

scholarly journals SUBTROPICAL FOREST BIOMASS ESTIMATION USING AIRBORNE LiDAR AND HYPERSPECTRAL DATA

2016 ◽  
Vol XLI-B8 ◽  
pp. 747-749
Author(s):  
Yong Pang ◽  
Zengyuan Li
Keyword(s):  
Forest Ecosystem ◽  
Forest Biomass ◽  
Subtropical Forest ◽  
Hyperspectral Data ◽  
Airborne Lidar ◽  
Observation System ◽  
Biomass Estimation ◽  
Lidar Data ◽  
Forest Types ◽  
Waveform Lidar

Forests have complex vertical structure and spatial mosaic pattern. Subtropical forest ecosystem consists of vast vegetation species and these species are always in a dynamic succession stages. It is very challenging to characterize the complexity of subtropical forest ecosystem. In this paper, CAF’s (The Chinese Academy of Forestry) LiCHy (LiDAR, CCD and Hyperspectral) Airborne Observation System was used to collect waveform Lidar and hyperspectral data in Puer forest region, Yunnan province in the Southwest of China. The study site contains typical subtropical species of coniferous forest, evergreen broadleaf forest, and some other mixed forests. The hypersectral images were orthorectified and corrected into surface reflectance with support of Lidar DTM product. The fusion of Lidar and hyperspectral can classify dominate forest types. The lidar metrics improved the classification accuracy. Then forest biomass estimation was carried out for each dominate forest types using waveform Lidar data, which get improved than single Lidar data source.

Analisis Pengaruh Kerapatan Vegetasi Terhadap Suhu Permukaan dan Keterkaitannya Dengan Fenomena UHI

2020 ◽  
Vol 21 (1) ◽  
pp. 99
Author(s):  
Dewi Miska Indrawati ◽  
Suharyadi Suharyadi ◽  
Prima Widayani
Keyword(s):  
Urban Heat Island ◽  
Landsat 8 ◽  
Heat Island ◽  
Landsat 8 Oli ◽  
Urban Heat ◽  
Split Window Algorithm

Kota Mataram adalahpusat dan ibukota dari provinsi Nusa Tenggara Barat yang tentunya menjadi pusat semua aktivitas masyarakat disekitar daerah tersebut sehingga menyebabkan peningkatan urbanisasi. Semakin meningkatnya peningkatan urbanisasi yan terjadi di perkotaan akan menyebabkan perubahan penutup lahan, dari awalnya daerah bervegetasi berubah menjadi lahan terbangun. Oleh karena itu, akan memicu peningkatan suhu dan menyebabkan adanya fenomena UHI dikota Mataram.Tujuan dari penelitian ini untuk mengetahui hubungan kerapatan vegetasi dengan kondisi suhu permukaan yang ada diwilayah penelitian dan memetakan fenomena UHI di Kota Mataram. Citra Landsat 8 OLI tahun 2018 yang digunakan terlebih dahulu dikoreksi radiometrik dan geometrik. Metode untuk memperoleh data kerapatan vegetasi menggunakan transformasi NDVI, LST menggunakan metode Split Window Algorithm (SWA) dan identifikasi fenomena urban heat island. Hasil penelitian yang diperoleh menunjukkan kerapatan vegetasi mempunyai korelasi dengan nilai LST. Hasil korelasi dari analisis pearson yang didapatkan antara kerapatan vegetasi terhadap suhu permukaan menghasilkan nilai -0,744. Fenomena UHIterjadi di pusat Kota Mataram dapat dilihat dengan adanya nilai UHI yaitu 0-100C. Semakin besar nilai UHI, semakin tinggi perbedaan LSTnya.

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