An Effective Method for Generating Spatiotemporally Continuous 30 m Vegetation Products

Li, Xiuxia; Liang, Shunlin; Jin, Huaan

doi:10.3390/rs13040719

Cited by 5 publications

(4 citation statements)

References 53 publications

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“…The theory of the spatiotemporal fusion model based on reconstruction is relatively simple and has strong operability; however, the calculation of weight function relies too much on the pixel information of input images. The fusion results are poor when the time gap of a high-resolution image is long or the quality of a low-resolution image is poor [21,44]. In future research, we should consider using machine learning algorithms to investigate the non-linear relationship between images with different resolutions, and to build a model according to this, obtaining high spatial and temporal resolution images with high precision.…”

Section: Discussionmentioning

confidence: 99%

Adaptability Evaluation of the Spatiotemporal Fusion Model in the Summer Maize Planting Area of the Southeast Loess Plateau

He,

Yang,

et al. 2023

Agronomy

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Precise regional crop yield estimates based on the high-spatiotemporal-resolution remote sensing data are essential for directing agronomic practices and policies to increase food security. This study used the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM), the flexible spatiotemporal data fusion (FSADF), and the spatial and temporal non-local filter based fusion model (STNLFFM) to calculate the normalized differential vegetation index (NDVI) of the summer maize planting area in the Southeast Loess Plateau based on the Sentinel-2 and MODIS data. The spatiotemporal resolution was 10 m and 1 d, respectively. Then, we evaluated the adaptability of the ESTARFM, FSADF, and STNLFFM fusion models in the field from the perspectives of spatial and textural characteristics of the data, summer maize NDVI growing curves, and yield estimation accuracy through qualitative visual discrimination and quantitative statistical analysis. The results showed that the fusion of ESTARFM–NDVI, FSDAF–NDVI, and STNLFFM–NDVI could precisely represent the variation tendency and local mutation information of NDVI during the growth period of summer maize, compared with MODIS–NDVI. The correlation between STNLFFM–NDVI and Sentinel-2–NDVI was favorable, with large correlation coefficients and a small root mean square error (RMSE). In the NDVI growing curve simulation of summer maize, STNLFFM introduced overall weights based on non-local mean filtering, which could significantly improve the poor fusion results at seedling and maturity stages caused by the long gap period of the high-resolution data in ESTARFM. Moreover, the accuracy of yield estimation was as follows (from high to low): STNLFFM (R = 0.742, mean absolute percentage error (MAPE) = 6.22%), ESTARFM (R = 0.703, MAPE = 6.80%), and FSDAF (R = 0.644, MAPE = 10.52%). The FADSF fusion model was affected by the spatial heterogeneity in the semi-humid areas, and the yield simulation accuracy was low. In the semi-arid areas, the FADSF fusion model had the advantages of less input data and a faster response.

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Section: Discussionmentioning

confidence: 99%

Adaptability Evaluation of the Spatiotemporal Fusion Model in the Summer Maize Planting Area of the Southeast Loess Plateau

He,

Yang,

et al. 2023

Agronomy

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“…The NDVI values of pure bare soil and pure vegetation will directly affect the accuracy of the model (Li, 2003). Many studies have defined the NDVI v value as the DN at a cumulative contribution of 95%, and the NDVI s as the DN at 5%, based on the experience from previous Landsat data studies (Li et al, 2022).…”

Section: Methodsmentioning

confidence: 99%

Remote Sensing Estimation Methods for Determining FVC in Northwest Desert Arid Low Disturbance Areas Based on GF-2 Imagery

Xinyue,

Xiaoping,

Dongming

et al. 2023

Journal of Resources and Ecology

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Fractional vegetation cover (FVC) is a vital indicator of surface vegetation. Studies of regional vegetation cover are helpful for understanding the status of the regional ecological environment and can provide important references for the formulation of ecological restoration plans and the evaluation of restoration effects. In vegetation cover related research, studies on extraction methods have attracted much attention. Studies have shown that the universality of vegetation cover extraction methods is poor, as well as the existing studies were mostly conducted on agricultural and forest land in wet, semi-humid and semi-arid areas, while few have investigated arid areas with sparse vegetation that is mainly shrubs and grass. To investigate the accuracy and applicability of different methods for estimating vegetation cover in the near-natural zone of the northwest arid desert, this study extracted six vegetation indices (NDVI, SAVI, MSAVI, ARVI, EVI, and MVI), which could effectively exclude soil and meteorological information to obtain pure vegetation information based on GF-2 multispectral-panchromatic fusion images. Two types of models were then established, including the single VI models (DP model) and multi-VI models (R model, RF model and PCA model), three statistics (SSE, R 2 , RMSE) were introduced to validate model accuracy and four-fold cross-validation was used to probe the models for overfitting. After filtering the models through these methods, the selected model was applied to invert the vegetation coverage in the study area. The results show three key aspects of this system. (1) Among the various models, the DP model constructed using the EVI and the RF model are more suitable for FVC extraction in the study area. This conclusion was further verified by the significant correlation between the inversion results of the FVC for the entire study area by applying these two models.(2) The values of pure bare soil and vegetation pixels (VIs and VIv) in the DP model will obviously affect the accuracy of the model. Thus, the empirical values should not be blindly adopted in actual research. (3) The vegetation distributions in the figures of the FVC results are similar to the outline of the mountains in the study area, indicating that the coverage distribution may be greatly affected by topographic factors. It is recommended that this aspect should be introduced in subsequent studies.

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“…The value domain of FVC is [0,1], and when the value is larger, the vegetation coverage is higher. The quantile method is used for the assignment, and 5% and 95% confidence intervals are extracted 70 , which attenuates some of the errors generated by NDVI during the acquisition and transmission process. The formula is as follows:…”

Section: Imbalance Indexmentioning

confidence: 99%

Spatial distribution characteristics and influencing factors of traditional villages based on geodetector: Jiarong Tibetan in Western Sichuan, China

Li,

Fan,

Yuan

et al. 2024

Sci Rep

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Jiarong Tibetan is a regional group with distinctive regional characteristics and possess precious traditional village resources. Studying the spatial distribution and influencing factors of traditional villages is of vital significance for the protection and renewal of villages and the revitalization of cultural heritage. Nevertheless, due to the fact that the Jiarong Tibetan inhabited area has not been clearly defined for a long time, there is a lack of holistic discussion on the distribution features and driving mechanisms of traditional villages in this region. In order to fill this research gap, the paper is the first to break away from the existing county administrative divisions to define the study area. Moreover, the analysis is carried out by using the nearest neighbor index, kernel density, GoeDa and Geodetector, etc. for traditional villages at national-level and provincial-level. The results show that the spatial distribution of the traditional villages of Jiarong Tibetan is characterized by typical aggregation, with the core intensive area in Danba County and the sub-core intensive area in the central and northern parts. The results of factor detection show that the spatial distribution pattern of the traditional villages is the consequence of the synergistic effect of multiple factors, and the interaction effect is significantly enhanced. The economic level and climatic conditions play a controlling role, and population, elevation, intangible cultural heritage and rivers also have notable effects. The findings of study can offer scientific guidance and suggestions for the inheritance and development of traditional villages in Jiarong Tibetan settlement area.

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An Effective Method for Generating Spatiotemporally Continuous 30 m Vegetation Products

Cited by 5 publications

References 53 publications

Adaptability Evaluation of the Spatiotemporal Fusion Model in the Summer Maize Planting Area of the Southeast Loess Plateau

Adaptability Evaluation of the Spatiotemporal Fusion Model in the Summer Maize Planting Area of the Southeast Loess Plateau

Remote Sensing Estimation Methods for Determining FVC in Northwest Desert Arid Low Disturbance Areas Based on GF-2 Imagery

Spatial distribution characteristics and influencing factors of traditional villages based on geodetector: Jiarong Tibetan in Western Sichuan, China

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