The homogeneous turbid medium assumption inherent to the Beer-Lambert’s law can lead to a reduction in the shading effect between leaves when non-green vegetation canopies are present, resulting in an overestimation of the fraction of absorbed photosynthetically active radiation (FAPAR). This paper proposed a method to improve the FAPAR estimation (FAPARFVC) based on Beer-Lambert’s law by incorporating fractional vegetation coverage (FVC). Initially, the canopy-scale leaf area index (LAI) of the green canopy distribution area within the pixel (sample site) was determined based on the FVC. Subsequently, the canopy-scale FAPAR was calculated within the green canopy distribution area, adhering to the assumption of a homogeneous turbid medium in the Beer-Lambert’s law. Finally, the average FAPAR across the pixel (sample site) was calculated based on the FVC. This paper conducted a case study using measured data from the BigFoot Project and grass savanna in Senegal, West Africa, as well as Moderate Resolution Imaging Spectroradiometer (MODIS) LAI/FPAR products. The results indicated that the FAPARFVC approach demonstrated superior accuracy compared to the FAPAR determined by MODIS LAI, according to the Beer-Lambert’s law (FAPARLAI) and MODIS FPAR products (FAPARMOD). The mean absolute percentage error of FAPARFVC was 48.2%, which is 25.6% and 52.1% lower than that of FAPARLAI and FAPARMOD, respectively. The mean percentage error of FAPARFVC was 16.8%, which was 71.6% and 73.4% lower than that of FAPARLAI and FAPARMOD, respectively. The improvements in accuracy and the decrease in overestimation for FAPARFVC became more pronounced with increasing FVC compared to FAPARLAI. The findings suggested that the FAPARFVC method enhanced the accuracy of FAPAR estimation under the presence of non-green vegetation canopies. The method can be extended to regional scale FAPAR and gross primary production (GPP) estimations, thereby providing more accurate inputs for understanding its tempo-spatial patterns and drivers.
Abstract. In this study, we proposed a method to map urban encroachment onto farmland using satellite image time series (SITS) based on the hierarchical hidden Markov model (HHMM). In this method, the farmland change process is decomposed into three hierarchical levels, i.e., the land cover level, the vegetation phenology level, and the SITS level. Then a three-level HHMM is constructed to model the multi-level semantic structure of farmland change process. Once the HHMM is established, a change from farmland to built-up could be detected by inferring the underlying state sequence that is most likely to generate the input time series. The performance of the method is evaluated on MODIS time series in Beijing. Results on both simulated and real datasets demonstrate that our method improves the change detection accuracy compared with the HMM-based method.
This study proposed a multi-target hierarchical detection (MTHD) method to simultaneously and automatically detect multiple directional land cover changes. MTHD used a hierarchical strategy to detect both abrupt and trend land cover changes successively. First, Grubbs’ test eliminated short-lived changes by considering them outliers. Then, the Brown-Forsythe test and the combination of Tomé’s method and the Chow test were applied to determine abrupt changes. Finally, Sen’s slope estimation coordinated with the Mann-Kendall test detection method was used to detect trend changes. Results demonstrated that both abrupt and trend land cover changes could be detected accurately and automatically. The overall accuracy of abrupt land cover changes was 87.0% and the kappa index was 0.74. Detected trends of land cover change indicated high consistency between NDVI (Normalized Difference Vegetation Index), change trends from LTS (Landsat Thematic Mapper and Enhanced Thematic Mapper Plus time series dataset), and MODIS (Moderate Resolution Imaging Spectroradiometer) time series datasets with the percentage of samples indicating consistency of 100%. For cropland, trends of millet yield per unit and average NDVI of cropland indicated high consistency with a linear regression determination coefficient of 0.94 (p < 0.01). Compared with other multi-target change detection methods, the changes detected by the MTHD could be related closely with specific ecosystem changes, reducing the risk of false changes in the area with frequent and strong interannual fluctuations.
Reconstructing normalized difference vegetation index (NDVI) time series datasets is essential for monitoring long-term changes in terrestrial vegetation. Here, a temporal–spatial iteration (TSI) method was developed to estimate the NDVIs of contaminated pixels, based on reliable data. The NDVIs of contaminated pixels were first computed through linear interpolation of adjacent high-quality pixels in the time series. Then, the NDVIs of remaining contaminated pixels were determined based on the NDVI of a high-quality pixel located in the same ecological zone, showing the most similar NDVI change trajectories. These two steps were repeated iteratively, using the estimated NDVIs as high-quality pixels to predict undetermined NDVIs of contaminated pixels until the NDVIs of all contaminated pixels were estimated. A case study was conducted in Inner Mongolia, China. The accuracies of estimated NDVIs using TSI were higher than the asymmetric Gaussian, Savitzky–Golay, and window-regression methods. Root mean square error (RMSE) and mean absolute percent error (MAPE) decreased by 16.7%–86.6% and 18.3%–33.0%, respectively. The TSI method performed better over a range of environmental conditions, the variation of performance by the compared methods was 1.4–5 times that of the TSI method. The TSI method will be most applicable when large numbers of contaminated pixels exist.
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Mapping the spatial distribution of artificial grassland for ecological restoration is of great significance for evaluating its secondary degradation and negative consequences, such as nonpoint source pollution of water bodies in the Three-River Headwaters (TRH) region. Because of the numerous challenges faced in obtaining ground training samples caused by adverse natural conditions, inclement cloudy weather and spectral similarity between natural and artificial grassland, commonly used classification or temporal-profile extraction methods have proven ineffective in identifying artificial grasslands. To overcome these challenges, we present a novel artificial grassland detection index for mapping their distribution using optical images with a resolution of 10 ∼ 30 m, along with their corresponding quality control data based on the Google Earth Engine cloud computing platform. The index is calculated using the ratio of the normalized difference vegetation index during the sowing and emergence period and the growth peak period of artificial grassland. A case study was conducted in Maqin County in the TRH region covering an area of 1.35 × 104 km2 from 2017 to 2021. Our proposed method demonstrated high accuracy and achieved a favorable balance between commission errors and omission errors. Over the study period, the average overall accuracy and Cohen's kappa were 96.2% and 0.91, respectively; with average precision, recall, and F1-score of artificial grassland being 89.6%, 99.2%, and 94.0%, respectively. The proposed method exhibited excellent robustness for the critical threshold used, with the average overall accuracy, F1-score, precision, and recall of artificial grassland between 2017 and 2021 consistently exceeding 90% for threshold values ranging from 1.5 to 2.0 throughout the study period. These findings suggest that our proposed method is capable of efficiently and accurately obtaining the detailed spatiotemporal distribution of artificial grassland in the TRH region. Moreover, the method also meets the pressing requirement for the rapid acquisition of detailed spatiotemporal distribution of artificial grassland across the Qinghai-Tibet Plateau.
The ecological responses of vegetation to climatic variables along a spatial gradient can be used as guidelines for chronic ecosystem response predictions under global climate change. The chronic responses of aboveground net primary productivity (ANPP) to climatic factors on the Three-river Headwaters (TRH) region were assessed for major vegetation types at the herbage developmental stages using the geographical detectors model, the Normalized Difference Vegetation Index from remote sensing images, and precipitation and air temperature datasets from 2000 to 2019. The results indicated that precipitation was the dominant driver for alpine meadows and steppes, while the impact of temperature on alpine steppes was more evident than that in alpine meadows, and alpine shrubs were more responsive to warming changes. This was possibly because shallower root systems and larger plant leaf area promote the responses of ANPP to precipitation for alpine meadows and steppes, and dormant buds higher from the ground facilitate the impacts of temperature for alpine shrubs. Grasslands also demonstrated differentiated responses to climatic factors at different developmental stages. Precipitation during the previous withering (September–October), pre-growing season (March–April), and vegetative growth (May–June) stages were more influential for ANPP variations. In contrast, ANPP during the reproductive period from July to August exhibited a weaker response to climatic controls. This was possibly attributed to the bud bank replenishment in fall and spring, the largest biomass growth rates in vegetative growth stage, and the non-structural carbohydrates transferred from roots in reproductive stage for perennial herbaceous plants. An elevated temperature, combined with no significant change in precipitation on the TRH may lead to a decrease in ANPP in alpine meadows but alpine shrub expansion, resulting in a reduced supply of palatable forage sources and bringing challenges to grassland management.
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