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Book ; Online: Remote Sensing of Watershed

Wang, Jingzhe / Hu, Zhongwen / Wu, Yangyi / Zhang, Jie

2023

Keywords	Technology: general issues ; History of engineering & technology ; Environmental science, engineering & technology ; multi-source remote sensing ; aquaculture mapping ; texture feature ; Google Earth Engine ; Pearl River Basin ; land surface temperature ; VIC model ; wavelet analysis ; multiscale analysis ; LUCC ; MODIS NDVI ; vegetation cover ; trend analysis ; Sen + Mann-Kendall ; topography ; GWR ; Qinghai-Tibet Plateau ; maize yield ; evapotranspiration ; plastic film mulching ; G-AquaCrop model ; Heihe River basin ; paddy rice ; SEBAL ; interannual variation ; spatial distribution ; Ganfu Plain irrigation system ; vegetation browning ; warming hiatus ; vapor pressure deficit ; spring ; autumn ; Xinjiang ; Qijia culture (QJC) ; spatial diffusion patterns ; cultural hearth ; circle structure ; dryland ; SIF ; EVI ; TROPOMI ; extreme drought ; phenology ; NATT ; China ; drought ; SPEI ; VPD ; vegetation ; GPP ; water level recognition ; hydrological monitoring ; deep learning ; computer vision ; alpine wetland ; water retention service ; models ; Qinghai-Tibetan Plateau ; carbon dioxide emissions ; global climate change ; human footprint index ; human pressures ; macro control ; Ebinur Lake ; spatio-temporal fusion model ; Sentinel-2 ; images reconstruction ; suspended particulate matter (SPM) ; n/a
Language	English
Size	1 electronic resource (288 pages)
Publisher	MDPI - Multidisciplinary Digital Publishing Institute
Publishing place	Basel
Document type	Book ; Online
Note	English
HBZ-ID	HT030382106
ISBN	9783036581316 ; 3036581316
Database	ZB MED Catalogue: Medicine, Health, Nutrition, Environment, Agriculture

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Article ; Online: Genetic Diversity and Fine-Scale Genetic Structure of Spodoptera litura Fabricius (Lepidoptera: Noctuidae) in Southern China Based on Microsatellite Markers

Hu, Zhongwen / Yang, Fangyuan / Zhang, Deping / Zhang, Shimeng / Yu, Xiaofei / Yang, Maofa

Animals. 2023 Feb. 05, v. 13, no. 4

2023

Abstract: Population genetic structure is strongly affected by dispersal events, especially for migratory species. The investigation of population structure is therefore conducive to increasing our understanding of species dispersal. Spodoptera litura (Fabricius) ( ...

Abstract	Population genetic structure is strongly affected by dispersal events, especially for migratory species. The investigation of population structure is therefore conducive to increasing our understanding of species dispersal. Spodoptera litura (Fabricius) (Lepidoptera: Noctuidae) is an important tobacco pest in China causing serious damage to multiple crops. In this study, we explore its dispersal dynamics by clarifying the fine-scale population genetics using 545 S. litura samples collected from tobacco plantations at 24 locations (mainly in Baise, Hechi, and Hezhou, Southern China). We analyzed the genetic diversity, genetic structure, and gene flow of these populations using seven microsatellite loci. Our results revealed high genetic diversity and low population genetic structure among S. litura. The genetic distance was uncorrelated with geographical distance, indicating the complete randomness of dispersal among the local populations. Our results suggest that the movement scope of contemporary S. litura might be much higher than the local-level spatial scale, which will provide a theoretical basis for pest management.
Keywords	Spodoptera litura ; gene flow ; genetic distance ; genetic structure ; genetic variation ; microsatellite repeats ; migratory species ; pest management ; pests ; population structure ; species dispersal ; tobacco ; China
Language	English
Dates of publication	2023-0205
Publishing place	Multidisciplinary Digital Publishing Institute
Document type	Article ; Online
ZDB-ID	2606558-7
ISSN	2076-2615
ISSN	2076-2615
DOI	10.3390/ani13040560
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: Temporal upscaling of MODIS instantaneous FAPAR improves forest gross primary productivity (GPP) simulation

Zhang, Yinghui / Hu, Zhongwen / Wang, Jingzhe / Gao, Xing / Yang, Cheng / Yang, Fengshuo / Wu, Guofeng

International Journal of Applied Earth Observation and Geoinformation. 2023 July, v. 121 p.103360-

2023

Abstract: Gross primary productivity (GPP) is a measure of carbon uptake by terrestrial ecosystems for carbon neutrality and serves as a key indicator for the Sustainable Development Goals. The instantaneous Fraction of Absorbed Photosynthetically Active Radiation ...

Abstract	Gross primary productivity (GPP) is a measure of carbon uptake by terrestrial ecosystems for carbon neutrality and serves as a key indicator for the Sustainable Development Goals. The instantaneous Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) is applied in GPP estimation by light use efficiency (LUE) models. However, an obvious time scale mismatch exists between instantaneous FAPAR and daily accumulated GPP. In the study, we explored the potential of temporal upscaling instantaneous FAPAR in improving GPP simulation. The absorbed photosynthetically active radiation (APAR) derived from instantaneous and upscaled FAPARs were first compared. GPPs were estimated from instantaneous and upscaled FAPAR by MOD17 LUE model using default and optimized parameters. The optimized GPP was finally compared with three GPP products: GLASS, MOD17A2H and MYD17A2H GPP. The APARs and GPPs were evaluated with the Eddy Covariance (EC) GPP at 78 forest sites from the Fluxnet community. Results showed that when compared with EC GPP, the upscaled FAPAR-derived APAR held a higher R² (0.26-0.73) than that from instantaneous FAPARs. The upscaled FAPAR-based GPPs by MOD17 LUE model with default or optimized parameters had a larger R² and smaller RMSE reducing uncertainties by 6.82-7.8% (maximum value: 27.9-49%). The optimized upscaled FAPAR-derived GPP was far superior to the GPP products with larger R² (0.69-0.85) and smaller RMSE (12.8-14.6 g C/m²/8d). It is concluded that temporal upscaling of MODIS instantaneous FAPAR can well improve the estimation of forest GPP and the parameters of MOD17 LUE model should be further optimized. Our study is an effort toward reducing the uncertainties from FAPAR in the GPP estimation for better assessing the achievement of carbon neutrality and Sustainable Development Goals.
Keywords	carbon ; eddy covariance ; forests ; gross primary productivity ; models ; photosynthetically active radiation ; radiation use efficiency ; spatial data ; sustainable development ; Temporal upscale ; FAPAR ; GPP ; Forest ; Fluxnet
Language	English
Dates of publication	2023-07
Publishing place	Elsevier B.V.
Document type	Article ; Online
Note	Use and reproduction
ISSN	1569-8432
DOI	10.1016/j.jag.2023.103360
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: Pruning for image segmentation: Improving computational efficiency for large-scale remote sensing applications

Lv, Xianwei / Persello, Claudio / Zhao, Wufan / Huang, Xiao / Hu, Zhongwen / Ming, Dongping / Stein, Alfred

International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS) ISPRS Journal of Photogrammetry and Remote Sensing. 2023 Aug., v. 202 p.13-29

2023

Abstract: Image segmentation is a fundamental step in object-based image analysis and other workflows. However, high-efficiency remains a challenge, especially for the analysis of large-scale Earth observation images. In recent years, considerable effort has been ... ...

Abstract	Image segmentation is a fundamental step in object-based image analysis and other workflows. However, high-efficiency remains a challenge, especially for the analysis of large-scale Earth observation images. In recent years, considerable effort has been paid to designing merging criteria, automatic scale selection, and object-specific optimisation. These segmentation methods usually rely on the region-adjacency graph (RAG) model and the nearest neighbour graph (NNG) model, which provide acceptable merging performance. Low efficiency occurs due to many redundant edge weight updates in the RAG model. In this study, we propose a generic dynamic pruning framework to improve the efficiency of existing region-merging-based segmentation algorithms, opening the door for large-scale applications in remote sensing. The proposed pruning framework includes intra-object and inter-object pruning modules for the RAG model. Inter-object pruning divides the RAG model into multiple sub-RAG models to reduce the redundant edge weight updates between adjacent objects. Intra-object pruning iteratively divides the sub-RAG into smaller RAGs. In our experimental analysis, we employ the proposed pruning framework with six region-merging segmentation methods and validate the effectiveness on four 10–20M pixel images and a 100M pixel data set. The pruning framework improves the performance of various segmentation algorithms by reducing computational complexity while maintaining segmentation accuracy. We observed a significant improvement in efficiency, with various achieving super-linear speed-up while maintaining the stability of segmentation accuracy. In single-core mode, the computation time of tested algorithms is enhanced by two to ten times on the four test images. In the multicore mode, speed-up increased up to 40 times with eight CPU cores. The computational cost was reduced by 36.15% to 95.77% in the number of weight updates, which is independent of hardware characteristics. On the large-scale image, two modes achieved speed-ups of 36.07 and 102.74, respectively.
Keywords	data collection ; image analysis ; models ; photogrammetry ; Image segmentation ; Region-merging ; Region adjacency graph ; Nearest neighbour graph ; Pruning
Language	English
Dates of publication	2023-08
Size	p. 13-29.
Publishing place	Elsevier B.V.
Document type	Article ; Online
ZDB-ID	1007774-1
ISSN	0924-2716
ISSN	0924-2716
DOI	10.1016/j.isprsjprs.2023.05.024
Database	NAL-Catalogue (AGRICOLA)

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Article: An applicable and automatic method for earth surface water mapping based on multispectral images

Luo, Xin / Tong, Xiaohua / Hu, Zhongwen

International journal of applied earth observation and geoinformation. 2021 Dec. 01, v. 103

2021

Abstract: Earth’s surface water plays an important role in the global water cycle, environmental processes, and human society, and it is necessary to dynamically capture the distribution and extent of surface water on Earth. However, due to the high complexity of ... ...

Abstract	Earth’s surface water plays an important role in the global water cycle, environmental processes, and human society, and it is necessary to dynamically capture the distribution and extent of surface water on Earth. However, due to the high complexity of the surface environment of Earth, the current surface water mapping methods are limited in applicability and precision. In this study, to explore an automatic and applicable model for surface water mapping, particularly for the regions with highly heterogenous backgrounds, we adopted state-of-the-art deep learning techniques and structured a new model, namely, WatNet, for surface water mapping. Specifically, we combined a state-of-the-art image classification model and a semantic segmentation model into an improved deep learning model. For the fine-scale identification of small water bodies, the combined model was further improved with surface water mapping-tailored design. To learn the surface water features of worldwide regions, a surface water knowledge base that consists of worldwide satellite images was built in this study. The newly structured WatNet model was tested on three highly heterogeneous regions, and as demonstrated by the results, 1) the trained WatNet model achieved the highest accuracies, which were above 95%, for all the selected test regions; 2) the new structured WatNet model yields significant improvements through state-of-the-art model combinations and the surface water-tailored design; and 3) unlike conventional methods, which usually require parameterization in accordance with the specific surface environment, trained WatNet can be directly applied for highly accurate surface water mapping, and, thus, no human labor is required.
Keywords	humans ; hydrologic cycle ; image analysis ; models ; satellites ; spatial data ; surface water
Language	English
Dates of publication	2021-1201
Publishing place	Elsevier B.V.
Document type	Article
ISSN	1569-8432
DOI	10.1016/j.jag.2021.102472
Database	NAL-Catalogue (AGRICOLA)

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Article: Genetic Diversity and Fine-Scale Genetic Structure of

Hu, Zhongwen / Yang, Fangyuan / Zhang, Deping / Zhang, Shimeng / Yu, Xiaofei / Yang, Maofa

Animals : an open access journal from MDPI

2023 Volume 13, Issue 4

Abstract	Population genetic structure is strongly affected by dispersal events, especially for migratory species. The investigation of population structure is therefore conducive to increasing our understanding of species dispersal.
Language	English
Publishing date	2023-02-05
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2606558-7
ISSN	2076-2615
ISSN	2076-2615
DOI	10.3390/ani13040560
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Stepwise Fusion of Hyperspectral, Multispectral and Panchromatic Images with Spectral Grouping Strategy: A Comparative Study Using GF5 and GF1 Images

Huang, Leping / Hu, Zhongwen / Luo, Xin / Zhang, Qian / Wang, Jingzhe / Wu, Guofeng

Remote Sensing. 2022 Feb. 20, v. 14, no. 4

2022

Abstract: Since hyperspectral satellite images (HSIs) usually hold low spatial resolution, improving the spatial resolution of hyperspectral imaging (HSI) is an effective solution to explore its potential for remote sensing applications, such as land cover mapping ...

Abstract	Since hyperspectral satellite images (HSIs) usually hold low spatial resolution, improving the spatial resolution of hyperspectral imaging (HSI) is an effective solution to explore its potential for remote sensing applications, such as land cover mapping over urban and coastal areas. The fusion of HSIs with high spatial resolution multispectral images (MSIs) and panchromatic (PAN) images could be a solution. To address the challenging work of fusing HSIs, MSIs and PAN images, a novel easy-to-implement stepwise fusion approach was proposed in this study. The fusion of HSIs and MSIs was decomposed into a set of simple image fusion tasks through spectral grouping strategy. HSI, MSI and PAN images were fused step by step using existing image fusion algorithms. According to different fusion order, two strategies ((HSI+MSI)+PAN and HSI+(MSI+PAN)) were proposed. Using simulated and real Gaofen-5 (GF-5) HSI, MSI and PAN images from the Gaofen-1 (GF-1) PMS sensor as experimental data, we compared the proposed stepwise fusion strategies with the traditional fusion strategy (HSI+PAN), and compared the performances of six fusion algorithms under three fusion strategies. We comprehensively evaluated the fused results through three aspects: spectral fidelity, spatial fidelity and computation efficiency evaluation. The results showed that (1) the spectral fidelity of the fused images obtained by stepwise fusion strategies was better than that of the traditional strategy; (2) the proposed stepwise strategies performed better or comparable spatial fidelity than traditional strategy; (3) the stepwise strategy did not significantly increase the time complexity compared to the traditional strategy; and (4) we also provide suggestions for selecting image fusion algorithms using the proposed strategy. The study provided us with a reference for the selection of fusion strategies and algorithms in different application scenarios, and also provided an easy-to-implement solution and useful references for fusing HSI, MSI and PAN images.
Keywords	comparative study ; land cover ; satellites
Language	English
Dates of publication	2022-0220
Publishing place	Multidisciplinary Digital Publishing Institute
Document type	Article
ZDB-ID	2513863-7
ISSN	2072-4292
ISSN	2072-4292
DOI	10.3390/rs14041021
Database	NAL-Catalogue (AGRICOLA)

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Article: Improving estimation of urban land cover fractions with rigorous spatial endmember modeling

Cao, Sen / Feng, Jilu / Hu, Zhongwen / Li, Qingquan / Wu, Guofeng

ISPRS journal of photogrammetry and remote sensing. 2022 July, v. 189

2022

Abstract: The spatial information has been widely utilized in Spectral Mixture Analysis (SMA) studies over the years. However, the spatial autocorrelation, as a prerequisite of these studies, was seldomly examined. In the complex urban systems, it remains largely ... ...

Abstract	The spatial information has been widely utilized in Spectral Mixture Analysis (SMA) studies over the years. However, the spatial autocorrelation, as a prerequisite of these studies, was seldomly examined. In the complex urban systems, it remains largely unknown whether and how land cover spectra at different locations are spatially autocorrelated and to which extent spatial autocorrelation can explain the great spectral variability. This study proposed an SMA method with Spatial Endmember Modeling (SEMSMA) to improve the fraction estimation of major urban land cover types. SEMSMA used the Incremental Spatial Autocorrelation (ISA) analysis to quantify spatial autocorrelation patterns in broadband reflectance. With the patterns, SEMSMA predicted endmember signatures that incorporated both spatial (location-oriented) and non-spatial spectral variability via the Restricted Maximum Likelihood-Empirical Best Linear Unbiased Predictor (REML-EBLUP) and Monte Carlo sampling. The spectral variabilities were then addressed in pixel unmixing by an iterative mixture analysis method. Results of ISA demonstrated diverse spatial autocorrelation patterns in reflectance of urban land covers. These patterns can be related to the pathways that spatial processes acted on land cover properties. The predicted endmember signatures explained most of the spectral variability for all land cover types. Subpixel fraction estimates showed that SEMSMA outperformed multiple endmember SMA (MESMA) and spatially adaptive SMA (SASMA), based on the root mean square error (0.04–0.15), the mean absolute error (0.01–0.09), and the Systematic Error (SE) (−80.00–0.01). The low SE of SEMSMA revealed unbiased estimation of land cover fractions. Results of this study underscored the necessity of spatial autocorrelation examination and the advantage of rigorous spatial endmember modeling when incorporating spatial information into SMA.
Keywords	autocorrelation ; land cover ; photogrammetry ; reflectance ; spatial data
Language	English
Dates of publication	2022-07
Size	p. 36-49.
Publishing place	Elsevier B.V.
Document type	Article
ZDB-ID	1007774-1
ISSN	0924-2716
ISSN	0924-2716
DOI	10.1016/j.isprsjprs.2022.04.019
Database	NAL-Catalogue (AGRICOLA)

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Article: Mapping Aquaculture Areas with Multi-Source Spectral and Texture Features: A Case Study in the Pearl River Basin (Guangdong), China

Xu, Yue / Hu, Zhongwen / Zhang, Yinghui / Wang, Jingzhe / Yin, Yumeng / Wu, Guofeng

Remote Sensing. 2021 Oct. 27, v. 13, no. 21

2021

Abstract: Aquaculture has grown rapidly in the field of food industry in recent years; however, it brought many environmental problems, such as water pollution and reclamations of lakes and coastal wetland areas. Thus, the evaluation and management of aquaculture ... ...

Abstract	Aquaculture has grown rapidly in the field of food industry in recent years; however, it brought many environmental problems, such as water pollution and reclamations of lakes and coastal wetland areas. Thus, the evaluation and management of aquaculture industry are needed, in which accurate aquaculture mapping is an essential prerequisite. Due to the difference between inland and marine aquaculture areas and the difficulty in processing large amounts of remote sensing images, the accurate mapping of different aquaculture types is still challenging. In this study, a novel approach based on multi-source spectral and texture features was proposed to map simultaneously inland and marine aquaculture areas. Time series optical Sentinel-2 images were first employed to derive spectral indices for obtaining texture features. The backscattering and texture features derived from the synthetic aperture radar (SAR) images of Sentinel-1A were then used to distinguish aquaculture areas from other geographical entities. Finally, a supervised Random Forest classifier was applied for large scale aquaculture area mapping. To address the low efficiency in processing large amounts of remote sensing images, the proposed approach was implemented on the Google Earth Engine (GEE) platform. A case study in the Pearl River Basin (Guangdong Province) of China showed that the proposed approach obtained aquaculture map with an overall accuracy of 89.5%, and the implementation of proposed approach on GEE platform greatly improved the efficiency for large scale aquaculture area mapping. The derived aquaculture map may support decision-making services for the sustainable development of aquaculture areas and ecological protection in the study area, and the proposed approach holds great potential for mapping aquacultures on both national and global scales.
Keywords	Internet ; aquaculture industry ; case studies ; decision making ; food industry ; mariculture ; rivers ; sustainable development ; synthetic aperture radar ; texture ; time series analysis ; water pollution ; watersheds ; wetlands ; China
Language	English
Dates of publication	2021-1027
Publishing place	Multidisciplinary Digital Publishing Institute
Document type	Article
ZDB-ID	2513863-7
ISSN	2072-4292
ISSN	2072-4292
DOI	10.3390/rs13214320
Database	NAL-Catalogue (AGRICOLA)

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Article: Deep Learning Based Thin Cloud Removal Fusing Vegetation Red Edge and Short Wave Infrared Spectral Information for Sentinel-2A Imagery

Li, Jun / Wu, Zhaocong / Hu, Zhongwen / Li, Zilong / Wang, Yisong / Molinier, Matthieu

Remote Sensing. 2021 Jan. 05, v. 13, no. 1

2021

Abstract: Thin clouds seriously affect the availability of optical remote sensing images, especially in visible bands. Short-wave infrared (SWIR) bands are less influenced by thin clouds, but usually have lower spatial resolution than visible (Vis) bands in high ... ...

Abstract	Thin clouds seriously affect the availability of optical remote sensing images, especially in visible bands. Short-wave infrared (SWIR) bands are less influenced by thin clouds, but usually have lower spatial resolution than visible (Vis) bands in high spatial resolution remote sensing images (e.g., in Sentinel-2A/B, CBERS04, ZY-1 02D and HJ-1B satellites). Most cloud removal methods do not take advantage of the spectral information available in SWIR bands, which are less affected by clouds, to restore the background information tainted by thin clouds in Vis bands. In this paper, we propose CR-MSS, a novel deep learning-based thin cloud removal method that takes the SWIR and vegetation red edge (VRE) bands as inputs in addition to visible/near infrared (Vis/NIR) bands, in order to improve cloud removal in Sentinel-2 visible bands. Contrary to some traditional and deep learning-based cloud removal methods, which use manually designed rescaling algorithm to handle bands at different resolutions, CR-MSS uses convolutional layers to automatically process bands at different resolution. CR-MSS has two input/output branches that are designed to process Vis/NIR and VRE/SWIR, respectively. Firstly, Vis/NIR cloudy bands are down-sampled by a convolutional layer to low spatial resolution features, which are then concatenated with the corresponding features extracted from VRE/SWIR bands. Secondly, the concatenated features are put into a fusion tunnel to down-sample and fuse the spectral information from Vis/NIR and VRE/SWIR bands. Third, a decomposition tunnel is designed to up-sample and decompose the fused features. Finally, a transpose convolutional layer is used to up-sample the feature maps to the resolution of input Vis/NIR bands. CR-MSS was trained on 28 real Sentinel-2A image pairs over the globe, and tested separately on eight real cloud image pairs and eight simulated cloud image pairs. The average SSIM values (Structural Similarity Index Measurement) for CR-MSS results on Vis/NIR bands over all testing images were 0.69, 0.71, 0.77, and 0.81, respectively, which was on average 1.74% higher than the best baseline method. The visual results on real Sentinel-2 images demonstrate that CR-MSS can produce more realistic cloud and cloud shadow removal results than baseline methods.
Keywords	algorithms ; artificial intelligence ; branches ; degradation ; information ; measurement ; paper ; remote sensing ; satellites ; testing ; vegetation
Language	English
Dates of publication	2021-0105
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/rs13010157
Database	NAL-Catalogue (AGRICOLA)

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