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  • 1
    In: Journal of Environmental Management, Elsevier BV, Vol. 248 ( 2019-10), p. 109265-
    Type of Medium: Online Resource
    ISSN: 0301-4797
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2019
    detail.hit.zdb_id: 1469206-5
    SSG: 12
    SSG: 14
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  • 2
    Online Resource
    Online Resource
    Institute of Electrical and Electronics Engineers (IEEE) ; 2020
    In:  IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Vol. 13 ( 2020), p. 113-128
    In: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Institute of Electrical and Electronics Engineers (IEEE), Vol. 13 ( 2020), p. 113-128
    Type of Medium: Online Resource
    ISSN: 1939-1404 , 2151-1535
    Language: Unknown
    Publisher: Institute of Electrical and Electronics Engineers (IEEE)
    Publication Date: 2020
    detail.hit.zdb_id: 2457423-5
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  • 3
    In: Remote Sensing, MDPI AG, Vol. 11, No. 1 ( 2018-12-29), p. 56-
    Abstract: The highly accurate multiresolution leaf area index (LAI) is an important parameter for carbon cycle simulation for bamboo forests at different scales. However, current LAI products have discontinuous resolution with 1 km mostly, that makes it difficult to accurately quantify the spatiotemporal evolution of carbon cycle at different resolutions. Thus, this study used MODIS LAI product (MOD15A2) and MODIS reflectance data (MOD09Q1) of Moso bamboo forest (MBF) from 2015, and it adopted a hierarchical Bayesian network (HBN) algorithm coupled with a dynamic LAI model and the PROSAIL model to obtain high-precision LAI data at multiresolution (i.e., 1000, 500, and 250 m). The results showed the LAIs assimilated using the HBN at the three resolutions corresponded with the actual growth trend of the MBF and correlated significantly with the observed LAI with a determination coefficient (R2) value of 〉 0.80. The highest-precision assimilated LAI was obtained at 1000-m resolution with R2 values of 0.91. The LAI assimilated using the HBN algorithm achieved better accuracy than the MODIS LAI with increases in the R2 value of 2.7 times and decreases in the root mean square error of 87.8%. Therefore, the HBN algorithm applied in this study can effectively obtain highly accurate multiresolution LAI time series data for bamboo forest.
    Type of Medium: Online Resource
    ISSN: 2072-4292
    Language: English
    Publisher: MDPI AG
    Publication Date: 2018
    detail.hit.zdb_id: 2513863-7
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  • 4
    In: Forests, MDPI AG, Vol. 10, No. 8 ( 2019-08-20), p. 708-
    Abstract: Subtropical forests have great potential as carbon sinks; however, the relationship between net ecosystem productivity (NEP) and climate change is still unclear. This study took Zhejiang Province, a subtropical region, as an example. Based on remote sensing classification data of forest resources, the integrated terrestrial ecosystem carbon cycle (InTEC) model was used to simulate the spatiotemporal dynamics of the forest NEP in Zhejiang Province during 1985–2015 and analyze its response to meteorological factors such as temperature, precipitation, relative humidity, and radiation. Three patterns emerged: (1) The optimized InTEC model can better simulate the forest NEP in Zhejiang Province, and the correlation coefficient between the simulated NEP and observed NEP was up to 0.75. (2) From 1985 to 2015, the increase in the total NEP was rapid, with an average annual growth rate of 1.52 Tg·C·yr−1. During 1985–1988, the forests in Zhejiang Province were carbon sources. After 1988, the forests turned into carbon sinks and this continued to increase. During 2000–2015, more than 97% of the forests in Zhejiang Province were carbon sinks. The total NEP reached 32.02 Tg·C·yr−1, and the annual mean NEP increased to 441.91 gC·m−2·yr−1. The carbon sequestration capacity of forests in the east and southwest of Zhejiang Province is higher than that in the northeast of Zhejiang Province. (3) From 2000 to 2015, there was an extremely significant correlation between forest NEP and precipitation, with a correlation coefficient of 0.85. Simultaneously, the forest NEP showed a negative correlation with temperature and radiation, with a correlation coefficient of −0.56 for both, and the forest NEP was slightly negatively correlated with relative humidity. The relative contribution rates of temperature, precipitation, relative humidity, and radiation data to NEP showed that the contribution of precipitation to NEP is the largest, reaching 61%, followed by temperature and radiation at 18% and 17%, respectively. The relative contribution rate of relative humidity is the smallest at only 4%. During the period of 1985–1999, due to significant man-made disturbances, the NEP had a weak correlation with temperature, precipitation, relative humidity, and radiation. The results of this study are important for addressing climate change and illustrating the response mechanism between subtropical forest NEP and climate change.
    Type of Medium: Online Resource
    ISSN: 1999-4907
    Language: English
    Publisher: MDPI AG
    Publication Date: 2019
    detail.hit.zdb_id: 2527081-3
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  • 5
    In: Land Degradation & Development, Wiley, Vol. 31, No. 8 ( 2020-05-15), p. 939-958
    Abstract: Properly mapping the sustainability of bamboo forest production plays an important role in providing basic strategies for decision makers to ensure sustainable use of bamboo resources. Understanding the response pattern of drought, poor management, elevation, and barren soil to bamboo forest ecosystem productivity is critical to formulating appropriate improvement strategies of sustainable management of bamboo forest productivity for addressing growing challenges of bamboo forest land degradation. The objectives of this study were to quantify differences in productivity, meteorological, topographical, soil, and bamboo distribution and structure factors under different sustainable management levels of bamboo forest ecosystem productivity in order to support management decision making in a spatiotemporally explicit context. We constructed an innovative three‐layer index system for the sustainable management of bamboo forest productivity by integrating productivity, meteorological, soil, topographic, bamboo distribution, and structure factors to promote sustainable management and spatiotemporal decision making, particularly in bamboo forest areas with low productivity. The partial least squares (PLS) path model was used to analyze the spatiotemporal effects of different factors on bamboo forest productivity and to create sustainable management maps that could be used for spatially informed decision making regarding bamboo forest production. The results showed the spatial and temporal variations in gross primary productivity (GPP), net primary productivity (NPP), and net ecosystem exchange (NEE) in bamboo forests. The sustainable management index was also mapped each year throughout the study area. We divided the index value range into five management‐friendly classes, which were shown to be directly related to GPP, NPP, NEE, Slope, Aspect, soil texture, hydrolytic nitrogen, and Abundance. We found that the areas with relatively high sustainable management levels (I and II) occupied only 18.94% of the bamboo forest area and exhibited a highly clustered distribution. Most of the other areas (78.67%) had relatively low levels of sustainable management (III and IV), and their distribution was rather scattered. The remaining 2.39% of the bamboo forest area that had the lowest sustainable management level (V) was small in area, fragmented, and not conducive to intensive management. The results of the present study can serve as a useful reference for bamboo forest management, which is of great importance for bamboo‐based ecosystems and economies.
    Type of Medium: Online Resource
    ISSN: 1085-3278 , 1099-145X
    URL: Issue
    Language: English
    Publisher: Wiley
    Publication Date: 2020
    detail.hit.zdb_id: 2021787-0
    detail.hit.zdb_id: 1319202-4
    SSG: 14
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  • 6
    Online Resource
    Online Resource
    American Association for the Advancement of Science (AAAS) ; 2023
    In:  Journal of Remote Sensing Vol. 3 ( 2023-01)
    In: Journal of Remote Sensing, American Association for the Advancement of Science (AAAS), Vol. 3 ( 2023-01)
    Abstract: Estimating potential height of forests is one of key tasks in forest restoration planning. Since regional maximum height statistics is difficult to account for local heterogeneity, biotic and abiotic mechanism-based methods are required. Different from the mainstream models that possesses either hydraulic constraint or mechanical constraint, we used a more lightweight model based on balance of water availability and consumption, named the Allometric Scaling and Resource Limitations model. Several enhancements were added, making up the third version of the model, and we deployed it using Google Earth Engine (GEE). A map of potential tree height at 90-m resolution is created for beech–maple–birch forests in northeastern United States. Within the oldest forests among the study area, the model reproduces the tree height level of ~25 m with root mean square deviation (RMSD) of 3.71 m from a high-resolution product of canopy height estimates. Under a threshold of 20% deviation, 82.9% of pixels agree with the existing tree heights. Outside of the oldest forests, RMSD raises to 5.01 m, and agreement drops to 75.3%. Over the entire study area, 6.6% total pixels of interest have a predicted height below the current level. A total of 16.7% pixels have larger predictions relative to existing forest heights, with a half of them classified as mistakes of overestimation. Errors may come from uncertainty in climate reanalysis data and inadequate shading effects modeling. Our work confirms the applicability of this lightweight model for this static prediction task and explores the deployment of ecological mechanism-based models on the GEE platform.
    Type of Medium: Online Resource
    ISSN: 2694-1589
    Language: English
    Publisher: American Association for the Advancement of Science (AAAS)
    Publication Date: 2023
    detail.hit.zdb_id: 3060865-X
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  • 7
    In: Forests, MDPI AG, Vol. 10, No. 11 ( 2019-11-09), p. 1004-
    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.
    Type of Medium: Online Resource
    ISSN: 1999-4907
    Language: English
    Publisher: MDPI AG
    Publication Date: 2019
    detail.hit.zdb_id: 2527081-3
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  • 8
    In: Science of The Total Environment, Elsevier BV, Vol. 694 ( 2019-12), p. 133803-
    Type of Medium: Online Resource
    ISSN: 0048-9697
    RVK:
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2019
    detail.hit.zdb_id: 1498726-0
    detail.hit.zdb_id: 121506-1
    SSG: 12
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  • 9
    In: ISPRS International Journal of Geo-Information, MDPI AG, Vol. 9, No. 2 ( 2020-01-21), p. 64-
    Abstract: Analysis of urban land use dynamics is essential for assessing ecosystem functionalities and climate change impacts. The focus of this study is on monitoring the characteristics of urban expansion in Hang-Jia-Hu and evaluating its influences on forests by applying 30-m multispectral Landsat data and a machine learning algorithm. Firstly, remote sensed images were preprocessed with radiation calibration, atmospheric correction and topographic correction. Then, the C5.0 decision tree was used to establish classification trees and then applied to make land use maps. Finally, spatiotemporal changes were analyzed through dynamic degree and land use transfer matrix. In addition, average land use transfer probability matrix (ATPM) was utilized for the prediction of land use area in the next 20 years. The results show that: (1) C5.0 decision tree performed with precise accuracy in land use classification, with an average total accuracy and kappa coefficient of more than 90.04% and 0.87. (2) During the last 20 years, land use in Hang-Jia-Hu has changed extensively. Urban area expanded from 5.84% in 1995 to 21.32% in 2015, which has brought about enormous impacts on cultivated land, with 198,854 hectares becoming urban, followed by forests with 19,823 hectares. (3) Land use area prediction based on the ATPM revealed that urbanization will continue to expand at the expense of cultivated land, but the impact on the forests will be greater than the past two decades. Rationality of urban land structure distribution is important for economic and social development. Therefore, remotely sensed technology combined with machine learning algorithms is of great significance to the dynamic detection of resources in the process of urbanization.
    Type of Medium: Online Resource
    ISSN: 2220-9964
    Language: English
    Publisher: MDPI AG
    Publication Date: 2020
    detail.hit.zdb_id: 2655790-3
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  • 10
    Online Resource
    Online Resource
    Shanghai Institute of Optics and Fine Mechanics ; 2020
    In:  Laser & Optoelectronics Progress Vol. 57, No. 10 ( 2020), p. 101001-
    In: Laser & Optoelectronics Progress, Shanghai Institute of Optics and Fine Mechanics, Vol. 57, No. 10 ( 2020), p. 101001-
    Type of Medium: Online Resource
    ISSN: 1006-4125
    Uniform Title: 基于FCN的无人机可见光影像树种分类
    URL: Issue
    Language: English , Chinese
    Publisher: Shanghai Institute of Optics and Fine Mechanics
    Publication Date: 2020
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