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Article: Spatiotemporal Evolution of Fractional Vegetation Cover and Its Response to Climate Change Based on MODIS Data in the Subtropical Region of China

Liu, Hua / Li, Xuejian / Mao, Fangjie / Zhang, Meng / Zhu, Di’en / He, Shaobai / Huang, Zihao / Du, Huaqiang

Remote Sensing. 2021 Feb. 28, v. 13, no. 5

2021

Abstract: The subtropical vegetation plays an important role in maintaining the structure and function of global ecosystems, and its contribution to the global carbon balance are receiving increasing attention. The fractional vegetation cover (FVC) as an important ...

Abstract	The subtropical vegetation plays an important role in maintaining the structure and function of global ecosystems, and its contribution to the global carbon balance are receiving increasing attention. The fractional vegetation cover (FVC) as an important indicator for monitoring environment change, is widely used to analyze the spatiotemporal pattern of regional and even global vegetation. China is an important distribution area of subtropical vegetation. Therefore, we first used the dimidiate pixel model to extract the subtropical FVC of China during 2001–2018 based on MODIS land surface reflectance data, and then used the linear regression analysis and the variation coefficient to explore its spatiotemporal variations characteristics. Finally, the partial correlation analysis and the partial derivative model were used to analyze the influences and contributions of climate factors on FVC, respectively. The results showed that (1) the subtropical FVC had obvious spatiotemporal heterogeneity; the FVC high-coverage and medium-coverage zones were concentratedly and their combined area accounted for more than 70% of the total study area. (2) The interannual variation in the average subtropical FVC from 2001 to 2018 showed a significant growth trend. (3) In 76.28% of the study area, the regional FVC showed an increasing trend, and the remaining regional FVC showed a decreasing trend. However, the overall fluctuations in the FVC (increasing or decreasing) in the region were relatively stable. (4) The influences of climate factors to the FVC exhibited obvious spatial differences. More than half of all pixels exhibited the influence of the average annual minimum temperature and the annual precipitation had positive on FVC, while the average annual maximum temperature had negative on FVC. (5) The contributions of climate changes to FVC had obvious heterogeneity, and the average annual minimum temperature was the main contribution factor affecting the dynamic variations of FVC.
Keywords	atmospheric precipitation ; carbon ; climate change ; fractional vegetation cover ; models ; reflectance ; regression analysis ; subtropics ; temperature ; China
Language	English
Dates of publication	2021-0228
Publishing place	Multidisciplinary Digital Publishing Institute
Document type	Article
Note	NAL-AP-2-clean
ZDB-ID	2513863-7
ISSN	2072-4292
ISSN	2072-4292
DOI	10.3390/rs13050913
Database	NAL-Catalogue (AGRICOLA)

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Article: Spatiotemporal dynamics in assimilated-LAI phenology and its impact on subtropical bamboo forest productivity

Li, Xuejian / Du, Huaqiang / Zhou, Guomo / Mao, Fangjie / Zheng, Junlong / Liu, Hua / Huang, Zihao / He, Shaobai

International journal of applied earth observation and geoinformation. 2021 Apr., v. 96

2021

Abstract: Phenology has a significant effect on forest growth and directly affects the forest ecosystem carbon cycle. Bamboo forests possess strong carbon sequestration capacities. However, it is not clear whether variations in phenology increase or decrease ... ...

Abstract	Phenology has a significant effect on forest growth and directly affects the forest ecosystem carbon cycle. Bamboo forests possess strong carbon sequestration capacities. However, it is not clear whether variations in phenology increase or decrease carbon uptake and storage in subtropical bamboo forests. We first extracted the length of the growing season (LOS) by coupling a data assimilation algorithm and MODIS leaf area index (LAI) data, and then the LOS was used to drive the integrated terrestrial ecosystem carbon-budget (InTEC) model to simulate gross primary productivity (GPP) and net ecosystem productivity (NEP) in Zhejiang Province from 2001 to 2017. Our results showed that the LOS estimation using the assimilated LAI time series was more reliable than that of the MODIS LAI and enhanced vegetation index (EVI). The annual average LOS increased on average by 0.76 day yr⁻¹ from 2001 to 2017. The GPP and NEP simulations based on the LAI assimilation-based phenology indicated that bamboo forest ecosystems possess strong carbon sequestration capacities and act as carbon sinks, with mean annual GPP and NEP values of 434.74 ± 257.93 g C m⁻² yr⁻¹ and 141.42 ± 82.54 g C m⁻² yr⁻¹, respectively, during 2001–2017. An increase of one day in the regional annual LOS increases the annual average GPP and NEP by 1.34 g C m⁻² yr⁻¹ and 0.75 g C m⁻² yr⁻¹, respectively. Moreover, the interannual variation of NEP was significantly correlated with precipitation and temperature, whereas GPP was not. Our results demonstrated that phenology extraction based on LAI data assimilation should play an important role in the simulation of bamboo forest productivity with ecological process models. The variation in phenology induced by climate change can strengthen the bamboo forest carbon sink, which is of great significance for subtropical forests coping with climate change in the future.
Keywords	algorithms ; bamboos ; carbon ; carbon sequestration ; carbon sinks ; climate change ; forest ecosystems ; forest growth ; gross primary productivity ; leaf area index ; net ecosystem production ; phenology ; simulation models ; spatial data ; temperature ; time series analysis ; China
Language	English
Dates of publication	2021-04
Publishing place	Elsevier B.V.
Document type	Article
ISSN	1569-8432
DOI	10.1016/j.jag.2020.102267
Database	NAL-Catalogue (AGRICOLA)

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Article: Spatiotemporal LUCC Simulation under Different RCP Scenarios Based on the BPNN_CA_Markov Model: A Case Study of Bamboo Forest in Anji County

Huang, Zihao / Du, Huaqiang / Li, Xuejian / Zhang, Meng / Mao, Fangjie / Zhu, Di’en / He, Shaobai / Liu, Hua

ISPRS international journal of geo-information. 2020 Dec. 02, v. 9, no. 12

2020

Abstract: Simulating spatiotemporal land use and land cover change (LUCC) data precisely under future climate scenarios is an important basis for revealing the carbon cycle response of forest ecosystems to LUCC. In this paper, a coupling model consisting of a back ...

Abstract	Simulating spatiotemporal land use and land cover change (LUCC) data precisely under future climate scenarios is an important basis for revealing the carbon cycle response of forest ecosystems to LUCC. In this paper, a coupling model consisting of a back propagation neural network (BPNN), Markov chain, and cellular automata (CA) was designed to simulate the LUCC in Anji County, Zhejiang Province, under four climate scenarios (Representative Concentration Pathway (RCP) 2.6, 4.5, 6.0, 8.5) from 2024 to 2049 and to analyze the temporal and spatial distribution of bamboo forests in Anji County. Our results provide four outcomes. (1) The transition probability matrices indicate that the area of bamboo forests shows an expansion trend, and the largest contribution to the expansion of bamboo forests is the cultivated land. The Markov chain composed of the average transition probability matrix could perform excellently, with only small errors when simulating the areas of different land-use types. (2) Based on the optimized BPNN, which had a strong generalization ability, a high prediction accuracy, and area under the curve (AUC) values above 0.9, we could obtain highly reliable land suitability probabilities. After introducing more driving factors related to bamboo forests, the prediction of bamboo forest changes will be more accurate. (3) The BPNN_CA_Markov coupling model could achieve high-precision simulation of LUCC at different times, with an overall accuracy greater than 70%, and the consistency of the LUCC simulation from one time to another also had good performance, with a figure of merit (FOM) of approximately 40%. (4) Under the future four RCP scenarios, bamboo forest evolution had similar spatial characteristics; that is, bamboo forests were projected to expand in the northeast, south, and southwest mountainous areas of Anji County, while bamboo forests were projected to decline mainly around the junction of the central and mountainous areas of Anji County. Comparing the simulation results of different scenarios demonstrates that 74% of the spatiotemporal evolution of bamboo forests will be influenced by the interactions and competition among different land-use types and other driving factors, and 26% will come from different climate scenarios, among which the RCP8.5 scenario will have the greatest impact on the bamboo forest area and spatiotemporal evolution, while the RCP2.6 scenario will have the smallest impact. In short, this study proposes effective methods and ideas for LUCC simulation in the context of climate change and provides accurate data support for analyzing the impact of LUCC on the carbon cycle of bamboo forests.
Keywords	Markov chain ; accuracy ; area ; bamboos ; carbon cycle ; case studies ; climate ; climate change ; decline ; evolution ; exhibitions ; forest ecosystems ; forests ; land ; land suitability ; land use ; land use and land cover maps ; models ; mountains ; paper ; prediction ; spatial data ; spatial distribution ; China
Language	English
Dates of publication	2020-1202
Publishing place	Multidisciplinary Digital Publishing Institute
Document type	Article
Note	NAL-light
ZDB-ID	2655790-3
ISSN	2220-9964
ISSN	2220-9964
DOI	10.3390/ijgi9120718
Database	NAL-Catalogue (AGRICOLA)

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Article: Phenology estimation of subtropical bamboo forests based on assimilated MODIS LAI time series data

Li, Xuejian / Du, Huaqiang / Zhou, Guomo / Mao, Fangjie / Zhang, Meng / Han, Ning / Fan, Weiliang / Liu, Hua / Huang, ZiHao / He, Shaobai / Mei, Tingting

International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS) ISPRS journal of photogrammetry and remote sensing. 2021 Mar., v. 173

2021

Abstract: Phenology plays an important role in revealing the spatiotemporal evolution of forest ecosystem carbon cycles. The accuracy of vegetation phenology estimates based on remote sensing has improved in temperate zones. However, subtropical vegetation is ... ...

Abstract	Phenology plays an important role in revealing the spatiotemporal evolution of forest ecosystem carbon cycles. The accuracy of vegetation phenology estimates based on remote sensing has improved in temperate zones. However, subtropical vegetation is complex, and the corresponding phenology estimates using remote sensing face great challenges. Bamboo forests are subtropical unique forest types and exhibit on– and off-years, fast growth, high productivity and carbon sequestration capability. In this study, we propose a new method to improve phenology estimates of bamboo forests by coupling the particle filter (PF) assimilation algorithm and a logistic model. The phenological metrics are estimated using high-precision leaf area index (LAI) assimilation products and a logistic model from 2001 to 2018, and the results are compared to those extracted from Moderate-Resolution Imaging Spectroradiometer (MODIS) LAI and the enhanced vegetation index (EVI) calculated based on the MODIS reflectance data. The results reveal that the R² values between the start of the growing season (SOS) and end of the growing season (EOS) estimated by the assimilated LAI and ground-observed values are the highest (>0.50) and the root mean square errors (RMSEs) are the smallest (<6.35 days). A negative correlation occurs between the EVI-simulated and ground-observed SOS and EOS values, which indicates that EVI products cannot be adopted to estimate the phenology of bamboo forests. Compared to the MODIS LAI, the R² values of the predicted SOS and EOS by the assimilated LAI data are improved by 3.67 times and 12.50%, respectively, and the RMSEs are reduced by 58.91% and 41.13%, respectively. Therefore, the new method solves the problem whereby the phenology of subtropical bamboo forests cannot be accurately extracted from MODIS LAI and EVI products. The temporal and spatial patterns of the SOS and EOS of bamboo forests are estimated with the new method from 2001 to 2018, and the SOS exhibits obvious spatial heterogeneity during on– and off-years, and the SOS during the on-years occurs slightly earlier than that during the off-years. A total of 70.13% of all pixels exhibit a SOS advance trend, while more than half of the areas (58.42%) present an EOS delay trend. The results indicate that coupling the data assimilation algorithm and phenology method greatly improves the estimation precision and reduces the estimation errors of the SOS and EOS of bamboo forests.
Keywords	algorithms ; bamboos ; carbon ; carbon sequestration ; forest ecosystems ; leaf area index ; logit analysis ; phenology ; photogrammetry ; reflectance ; spatial variation ; time series analysis
Language	English
Dates of publication	2021-03
Size	p. 262-277.
Publishing place	Elsevier B.V.
Document type	Article
Note	NAL-AP-2-clean
ZDB-ID	1007774-1
ISSN	0924-2716
ISSN	0924-2716
DOI	10.1016/j.isprsjprs.2021.01.018
Database	NAL-Catalogue (AGRICOLA)

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

Dong, Luofan / Du, Huaqiang / Han, Ning / Li, Xuejian / Zhu, Di’en / Mao, Fangjie / Zhang, Meng / Zheng, Junlong / Liu, Hua / Huang, Zihao / He, Shaobai

Remote Sensing. 2020 Mar. 16, v. 12, no. 6

2020

Abstract: 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 ... ...

Abstract	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).
Keywords	aboveground biomass ; algorithms ; artificial intelligence ; bamboos ; forests ; neural networks ; regression analysis ; remote sensing ; spatial data ; vegetation index
Language	English
Dates of publication	2020-0316
Publishing place	Multidisciplinary Digital Publishing Institute
Document type	Article
ZDB-ID	2513863-7
ISSN	2072-4292
ISSN	2072-4292
DOI	10.3390/rs12060958
Database	NAL-Catalogue (AGRICOLA)

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Article: Intelligent Mapping of Urban Forests from High-Resolution Remotely Sensed Imagery Using Object-Based U-Net-DenseNet-Coupled Network

He, Shaobai / Du, Huaqiang / Zhou, Guomo / Li, Xuejian / Mao, Fangjie / Zhu, Di’en / Xu, Yanxin / Zhang, Meng / Huang, Zihao / Liu, Hua / Luo, Xin

Remote Sensing. 2020 Nov. 30, v. 12, no. 23

2020

Abstract: The application of deep learning techniques, especially deep convolutional neural networks (DCNNs), in the intelligent mapping of very high spatial resolution (VHSR) remote sensing images has drawn much attention in the remote sensing community. However, ...

Abstract	The application of deep learning techniques, especially deep convolutional neural networks (DCNNs), in the intelligent mapping of very high spatial resolution (VHSR) remote sensing images has drawn much attention in the remote sensing community. However, the fragmented distribution of urban land use types and the complex structure of urban forests bring about a variety of challenges for urban land use mapping and the extraction of urban forests. Based on the DCNN algorithm, this study proposes a novel object-based U-net-DenseNet-coupled network (OUDN) method to realize urban land use mapping and the accurate extraction of urban forests. The proposed OUDN has three parts: the first part involves the coupling of the improved U-net and DenseNet architectures; then, the network is trained according to the labeled data sets, and the land use information in the study area is classified; the final part fuses the object boundary information obtained by object-based multiresolution segmentation into the classification layer, and a voting method is applied to optimize the classification results. The results show that (1) the classification results of the OUDN algorithm are better than those of U-net and DenseNet, and the average classification accuracy is 92.9%, an increase in approximately 3%; (2) for the U-net-DenseNet-coupled network (UDN) and OUDN, the urban forest extraction accuracies are higher than those of U-net and DenseNet, and the OUDN effectively alleviates the classification error caused by the fragmentation of urban distribution by combining object-based multiresolution segmentation features, making the overall accuracy (OA) of urban land use classification and the extraction accuracy of urban forests superior to those of the UDN algorithm; (3) based on the Spe-Texture (the spectral features combined with the texture features), the OA of the OUDN in the extraction of urban land use categories can reach 93.8%, thereby the algorithm achieved the accurate discrimination of different land use types, especially urban forests (99.7%). Therefore, this study provides a reference for feature setting for the mapping of urban land use information from VHSR imagery.
Keywords	accuracy ; algorithms ; area ; artificial intelligence ; classification ; data collection ; extraction ; information ; land use ; neural networks ; remote sensing ; texture ; urban forests
Language	English
Dates of publication	2020-1130
Publishing place	Multidisciplinary Digital Publishing Institute
Document type	Article
Note	NAL-light
ZDB-ID	2513863-7
ISSN	2072-4292
ISSN	2072-4292
DOI	10.3390/rs12233928
Database	NAL-Catalogue (AGRICOLA)

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

Zhang, Meng / Du, Huaqiang / Zhou, Guomo / Li, Xuejian / Mao, Fangjie / Dong, Luofan / Zheng, Junlong / Liu, Hua / Huang, Zihao / He, Shaobai

Forests. 2019 Nov. 09, v. 10, no. 11

2019

Abstract: 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 ... ...

Abstract	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.
Keywords	Landsat ; algorithms ; artificial intelligence ; carbon ; carbon sequestration ; cities ; climate change ; ecosystems ; forests ; land cover ; land use ; models ; monitoring ; prediction ; remote sensing ; spatial data ; statistical analysis ; texture ; urbanization ; vegetation index ; China
Language	English
Dates of publication	2019-1109
Publishing place	Multidisciplinary Digital Publishing Institute
Document type	Article
ZDB-ID	2527081-3
ISSN	1999-4907
ISSN	1999-4907
DOI	10.3390/f10111004
Database	NAL-Catalogue (AGRICOLA)

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Article: Spatiotemporal Evolution of Fractional Vegetation Cover and Its Response to Climate Change Based on MODIS Data in the Subtropical Region of China

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Article: Spatiotemporal dynamics in assimilated-LAI phenology and its impact on subtropical bamboo forest productivity

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Article: Spatiotemporal LUCC Simulation under Different RCP Scenarios Based on the BPNN_CA_Markov Model: A Case Study of Bamboo Forest in Anji County

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Article: Phenology estimation of subtropical bamboo forests based on assimilated MODIS LAI time series data

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

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Article: Intelligent Mapping of Urban Forests from High-Resolution Remotely Sensed Imagery Using Object-Based U-Net-DenseNet-Coupled Network

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

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