Mapping Moso bamboo forest and its on-year and off-year distribution in a subtropical region using time-series Sentinel-2 and Landsat 8 data

2019 ◽  
Vol 231 ◽  
pp. 111265 ◽  
Author(s):  
Longwei Li ◽  
Nan Li ◽  
Dengsheng Lu ◽  
Yuyun Chen
Keyword(s):  
Time Series ◽  
Moso Bamboo ◽  
Landsat 8 ◽  
Bamboo Forest ◽  
Subtropical Region ◽  
Moso Bamboo Forest ◽  
Sentinel 2

scholarly journals Multiscale leaf area index assimilation for Moso bamboo forest based on Sentinel-2 and MODIS data

2021 ◽  
Vol 104 ◽  
pp. 102519
Author(s):  
Jiayi Ji ◽  
Xuejian Li ◽  
Huaqiang Du ◽  
Fangjie Mao ◽  
Weiliang Fan ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Moso Bamboo ◽  
Bamboo Forest ◽  
Area Index ◽  
Modis Data ◽  
Moso Bamboo Forest ◽  
Sentinel 2

scholarly journals Comparison of Multi-Temporal PlanetScope Data with Landsat 8 and Sentinel-2 Data for Estimating Airborne LiDAR Derived Canopy Height in Temperate Forests

2020 ◽  
Vol 12 (11) ◽  
pp. 1876 ◽  
Author(s):  
Katsuto Shimizu ◽  
Tetsuji Ota ◽  
Nobuya Mizoue ◽  
Hideki Saito
Keyword(s):  
Time Series ◽  
Prediction Accuracy ◽  
Time Series Data ◽  
Airborne Lidar ◽  
Canopy Height ◽  
Forest Monitoring ◽  
Series Data ◽  
Landsat 8 ◽  
Multi Temporal ◽  
Sentinel 2

Developing accurate methods for estimating forest structures is essential for efficient forest management. The high spatial and temporal resolution data acquired by CubeSat satellites have desirable characteristics for mapping large-scale forest structural attributes. However, most studies have used a median composite or single image for analyses. The multi-temporal use of CubeSat data may improve prediction accuracy. This study evaluates the capabilities of PlanetScope CubeSat data to estimate canopy height derived from airborne Light Detection and Ranging (LiDAR) by comparing estimates using Sentinel-2 and Landsat 8 data. Random forest (RF) models using a single composite, multi-seasonal composites, and time-series data were investigated at different spatial resolutions of 3, 10, 20, and 30 m. The highest prediction accuracy was obtained by the PlanetScope multi-seasonal composites at 3 m (relative root mean squared error: 51.3%) and Sentinel-2 multi-seasonal composites at the other spatial resolutions (40.5%, 35.2%, and 34.2% for 10, 20, and 30 m, respectively). The results show that RF models using multi-seasonal composites are 1.4% more accurate than those using harmonic metrics from time-series data in the median. PlanetScope is recommended for canopy height mapping at finer spatial resolutions. However, the unique characteristics of PlanetScope data in a spatial and temporal context should be further investigated for operational forest monitoring.

Rates of soil respiration components in response to inorganic and organic fertilizers in an intensively-managed Moso bamboo forest

Geoderma ◽  
2021 ◽  
Vol 403 ◽  
pp. 115212
Author(s):  
Kaiping Huang ◽  
Yongfu Li ◽  
Junguo Hu ◽  
Caixian Tang ◽  
Shaobo Zhang ◽  
...  
Keyword(s):  
Soil Respiration ◽  
Moso Bamboo ◽  
Organic Fertilizers ◽  
Bamboo Forest ◽  
Intensively Managed ◽  
Moso Bamboo Forest ◽  
Inorganic And Organic

Stand-scale transpiration estimates in a Moso bamboo forest: (I) Applicability of sap flux measurements

2010 ◽  
Vol 260 (8) ◽  
pp. 1287-1294 ◽  
Author(s):  
Tomonori Kume ◽  
Yuka Onozawa ◽  
Hikaru Komatsu ◽  
Kenji Tsuruta ◽  
Yoshinori Shinohara ◽  
...  
Keyword(s):  
Moso Bamboo ◽  
Bamboo Forest ◽  
Sap Flux ◽  
Flux Measurements ◽  
Moso Bamboo Forest

Object-based classification using SPOT-5 imagery for Moso bamboo forest mapping

2014 ◽  
Vol 35 (3) ◽  
pp. 1126-1142 ◽  
Author(s):  
Ning Han ◽  
Huaqiang Du ◽  
Guomo Zhou ◽  
Xiaoyan Sun ◽  
Hongli Ge ◽  
...  
Keyword(s):  
Moso Bamboo ◽  
Bamboo Forest ◽  
Forest Mapping ◽  
Object Based ◽  
Moso Bamboo Forest ◽  
Spot 5

scholarly journals Nitrogen addition decreases methane uptake caused by methanotroph and methanogen imbalances in a Moso bamboo forest

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Quan Li ◽  
Changhui Peng ◽  
Junbo Zhang ◽  
Yongfu Li ◽  
Xinzhang Song
Keyword(s):  
Community Structure ◽  
Forest Soils ◽  
Nitrogen Addition ◽  
N Deposition ◽  
Moso Bamboo ◽  
Bamboo Forest ◽  
N Addition ◽  
Atmospheric Methane ◽  
Key Factor ◽  
Moso Bamboo Forest

AbstractForest soils play an important role in controlling global warming by reducing atmospheric methane (CH4) concentrations. However, little attention has been paid to how nitrogen (N) deposition may alter microorganism communities that are related to the CH4 cycle or CH4 oxidation in subtropical forest soils. We investigated the effects of N addition (0, 30, 60, or 90 kg N ha−1 yr−1) on soil CH4 flux and methanotroph and methanogen abundance, diversity, and community structure in a Moso bamboo (Phyllostachys edulis) forest in subtropical China. N addition significantly increased methanogen abundance but reduced both methanotroph and methanogen diversity. Methanotroph and methanogen community structures under the N deposition treatments were significantly different from those of the control. In N deposition treatments, the relative abundance of Methanoculleus was significantly lower than that in the control. Soil pH was the key factor regulating the changes in methanotroph and methanogen diversity and community structure. The CH4 emission rate increased with N addition and was negatively correlated with both methanotroph and methanogen diversity but positively correlated with methanogen abundance. Overall, our results suggested that N deposition can suppress CH4 uptake by altering methanotroph and methanogen abundance, diversity, and community structure in subtropical Moso bamboo forest soils.

scholarly journals Fusing Geostationary Satellite Observations with Harmonized Landsat-8 and Sentinel-2 Time Series for Monitoring Field-Scale Land Surface Phenology

2021 ◽  
Vol 13 (21) ◽  
pp. 4465
Author(s):  
Yu Shen ◽  
Xiaoyang Zhang ◽  
Weile Wang ◽  
Ramakrishna Nemani ◽  
Yongchang Ye ◽  
...  
Keyword(s):  
Time Series ◽  
Land Surface ◽  
Cloud Cover ◽  
Landsat 8 ◽  
Field Scale ◽  
Spatiotemporal Resolution ◽  
Essential Information ◽  
Land Surface Phenology ◽  
The Earth ◽  
Sentinel 2

Accurate and timely land surface phenology (LSP) provides essential information for investigating the responses of terrestrial ecosystems to climate changes and quantifying carbon and surface energy cycles on the Earth. LSP has been widely investigated using daily Visible Infrared Imaging Radiometer Suite (VIIRS) or Moderate Resolution Imaging Spectroradiometer (MODIS) observations, but the resultant phenometrics are frequently influenced by surface heterogeneity and persistent cloud contamination in the time series observations. Recently, LSP has been derived from Landsat-8 and Sentinel-2 time series providing detailed spatial pattern, but the results are of high uncertainties because of poor temporal resolution. With the availability of data from Advanced Baseline Imager (ABI) onboard a new generation of geostationary satellites that observe the earth every 10–15 min, daily cloud-free time series could be obtained with high opportunities. Therefore, this study investigates the generation of synthetic high spatiotemporal resolution time series by fusing the harmonized Landsat-8 and Sentinel-2 (HLS) time series with the temporal shape of ABI data for monitoring field-scale (30 m) LSP. The algorithm is verified by detecting the timings of greenup and senescence onsets around north Wisconsin/Michigan states, United States, where cloud cover is frequent during spring rainy season. The LSP detections from HLS-ABI are compared with those from HLS or ABI alone and are further evaluated using PhenoCam observations. The result indicates that (1) ABI could provide ~3 times more high-quality observations than HLS around spring greenup onset; (2) the greenup and senescence onsets derived from ABI and HLS-ABI are spatially consistent and statistically comparable with a median difference less than 1 and 10-days, respectively; (3) greenup and senescence onsets derived from HLS data show sharp boundaries around the orbit-overlapped areas and shifts of ~13 days delay and ~15 days ahead, respectively, relative to HLS-ABI detections; and (4) HLS-ABI greenup and senescence onsets align closely to PhenoCam observations with an absolute average difference of less than 2 days and 5 days, respectively, which are much better than phenology detections from ABI or HLS alone. The result suggests that the proposed approach could be implemented the monitor of 30 m LSP over regions with persistent cloud cover.

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