Spatiotemporal evolution and impacts of climate change on bamboo distribution in China

Li, Xuejian; Mao, Fangjie; Du, Huaqiang; Zhou, Guomo; Xing, Luqi; Liu, Tengyan; Han, Ning; Liu, Yuli; Zhu, Di’en; Zheng, Junlong; Dong, Long; Zhang, Meng

doi:10.1016/j.jenvman.2019.109265

Cited by 38 publications

(25 citation statements)

References 22 publications

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“…Medium and low spatial resolution spectral images, such as Landsat TM (Thematic mapper), Landsat OLI (Operational Land Imager) imageryand the Moderate Resolution Imaging Spectroradiometer (MODIS) products, have been investigated for decades since spectrums indirectly relate to plant growth status [2,7,15,16]. Scholars used these data to reveal climate change and environmental shifts at impressive scales [17,18].…”

Section: Introductionmentioning

confidence: 99%

Application of Convolutional Neural Network on Lei Bamboo Above-Ground-Biomass (AGB) Estimation Using Worldview-2

Dong

Han

et al. 2020

Remote Sensing

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Above-ground biomass (AGB) directly relates to the productivity of forests. Precisely, AGB mapping for regional forests based on very high resolution (VHR) imagery is widely needed for evaluation of productivity. However, the diversity of variables and algorithms and the difficulties inherent in high resolution optical imagery make it complex. In this paper, we explored the potentials of the state-of-art algorithm convolutional neural networks (CNNs), which are widely used for its high-level representation, but rarely applied for AGB estimation. Four experiments were carried out to compare the performance of CNNs and other state-of-art Machine Learning (ML) algorithms: (1) performance of CNN using bands, (2) performance of Random Forest (RF), support vector regression (SVR), artificial neural network (ANN) on bands, and vegetation indices (VIs). (3) Performance of RF, SVR, and ANN on gray-level co-occurrence matrices (GLCM), and exploratory spatial data analysis (ESDA), and (4) performance of RF, SVR, and ANN based on all combined data and ESDA+VIs. CNNs reached satisfactory results (with R2 = 0.943) even with limited input variables (i.e., only bands). In comparison, RF and SVR with elaborately designed data obtained slightly better accuracy than CNN. For examples, RF based on GLCM textures reached an R2 of 0.979 and RF based on all combined data reached a close R2 of 0.974. However, the results of ANN were much worse (with the best R2 of 0.885).

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Section: Introductionmentioning

confidence: 99%

Application of Convolutional Neural Network on Lei Bamboo Above-Ground-Biomass (AGB) Estimation Using Worldview-2

Dong

Han

et al. 2020

Remote Sensing

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“…Moso bamboo (Phyllostachys edulis), the most widely distributed giant running bamboo species in China (more than 4 million ha in 2005; Song et al, 2011), is high water-demand plant (Li et al, 2019;Shi et al, 2020) and inversely may impact local hydrologic cycling and regional water balance (Komatsu et al, 2010;Gu et al, 2019) and thus has been increasingly studied in recent years.…”

Section: Introductionmentioning

confidence: 99%

Spring Leafing Phenology Favors Younger Culms of Moso Bamboo: Aspects From Water Use Relations

et al. 2020

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As the most widely distributed giant running bamboo species in China, Moso bamboo (Phyllostachys edulis) can accomplish both development of newly sprouted culms and leaf renewal of odd-year-old culms within a few months in spring. The two phenological events in spring may together change water distribution among culms in different age categories within a stand, which may differ from our conventional understanding of the negative age effect on bamboo water use. Therefore, to explore the effect of spring shooting and leaf phenology on age-specific water use of Moso bamboo and potential water redistribution, we monitored water use of four culm age categories (newly sprouted, 1-, 2-, and 3-year-old; namely A0, A1, A2, A3) in spring from March to June 2018. For newly sprouting culms, the spring phenological period was classified into five stages (incubation, culm-elongation, branch-development, leafing, established). Over these phenological stages, age-specific accumulated sap flux density showed different patterns. The oldest culms, A3, were not influenced by leaf renewal and kept nearly constant and less water use than the other aged culms. However, A2, which did not renew their leaves, had the most water use at the two initial stages (incubation, culm-elongation) but consumed less water than A0 and A1 after the fourth stage (leafing). At the end of June, water use of the four age categories sorted in order of A0 > A1 > A2 > A3, which confirms the conventional thought and observations, i.e., a negative age effect. The results indicate that new leaf flushing may benefit younger culms (A1 and A0) more than older culms (A2 and A3), i.e., increasing their transpiration response to radiation and share of the stand transpiration. With the underground connected rhizome system, the bamboo stand as an integration seems to balance its water use among culms of different ages to support the water use of freshly sprouted culms during their developing period.

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“…The methods of estimating in forest AGC generally include field survey, model simulation, and remote sensing inversion [21][22][23][24]. The traditional field survey method has high precision [17], but it is often limited by manpower and material resources, yielding short durations of observation.…”

Section: Introductionmentioning

confidence: 99%

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

Zhang

Zhou

et al. 2019

Forests

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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.

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Spatiotemporal evolution and impacts of climate change on bamboo distribution in China

Cited by 38 publications

References 22 publications

Application of Convolutional Neural Network on Lei Bamboo Above-Ground-Biomass (AGB) Estimation Using Worldview-2

Application of Convolutional Neural Network on Lei Bamboo Above-Ground-Biomass (AGB) Estimation Using Worldview-2

Spring Leafing Phenology Favors Younger Culms of Moso Bamboo: Aspects From Water Use Relations

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

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