Pika disturbance intensity observation system via multidimensional stereoscopic surveying for monitoring alpine meadow

Zhao, Jianyun; Jiang, Chuanli; Liu, Wenhui; Ding, Yuanyuan; Li, Guorong

doi:10.1117/1.jrs.16.044524

Cited by 3 publications

(3 citation statements)

References 42 publications

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“…Pikas are a key component of alpine meadow ecosystems ( Wei et al., 2019 ). The presence of pikas can cause extensive disturbance to plants and soils, potentially altering plant-soil C and N and affecting C and N cycling in grassland ecosystems ( Zhao et al., 2022 ).…”

Section: Discussionmentioning

confidence: 99%

Consequences of plateau pika disturbance on plant-soil carbon and nitrogen in alpine meadows

Xu,

Wang,

Wang

et al. 2024

Front. Plant Sci.

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The presence of burrowing mammals can have extensive effects on plants and soils, creating bare soil patches in alpine meadows and potentially altering plant-soil carbon (C) and nitrogen (N). This study focuses on the plateau pika (Ochotona curzoniae) to examine the responses of plant-soil C and N to a small burrowing mammal from quadrat scale to plot scale. The density of active burrow entrances in disturbed plots was used as an indicator of the disturbance intensity of plateau pikas. The study found that the below-ground biomass (BGB) and its C and N, as well as soil C and N concentrations were significantly lower in bare soil areas than in vegetated areas and undisturbed plots. This shows that the quadrat scale limited the estimation of the C and N sequestration potential. Therefore, further research on the plot scale found that the disturbance by plateau pika significantly reduced plant biomass and BGB carbon stock. However, plateau pika did not affect soil C and N stocks or ecosystem C and N stocks. These findings suggest the bare soil patches formed by plateau pika caused plant and soil heterogeneity but had a trade-off effect on plant-soil C and N stocks at the plot scale. Nevertheless, moderate disturbance intensity increased the C and N sequestration potential in grassland ecosystems. These results provide a possible way to estimate how disturbance by small burrowing mammals affects C and N cycling in grassland ecosystems while accurately assessing the effects of small burrowing mammal densities on C and N in grassland ecosystems.

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Section: Discussionmentioning

confidence: 99%

Consequences of plateau pika disturbance on plant-soil carbon and nitrogen in alpine meadows

Xu,

Wang,

Wang

et al. 2024

Front. Plant Sci.

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“…The composition and alteration of soil parameters (e.g., nutrients, heavy metals, PH, and others) are decisive for soil physicochemical properties, and directly or indirectly influence the growth of vegetation and microorganisms, and the stability and health of ecosystem functions [ 4 , 5 ]. Soil nutrient content (e.g., total nitrogen (TN), total phosphorus pentoxide (TP 2 O 5 ), soil organic matter (SOM), and others) is a direct expression of soil fertility, which is essential for all biological processes and is an important indicator of soil degradation [ 6 , 7 , 8 , 9 ]. Soil nutrient composition and content determine vegetation growth, composition, and distribution, and soil nutrients and other chemical components affect the structure of soil microbial communities, enzyme activity, and the cycling of elements such as carbon (C), nitrogen (N) and phosphorus (P) in terrestrial ecosystems [ 7 , 10 , 11 ].…”

Section: Introductionmentioning

confidence: 99%

Vis–NIR Spectroscopy Combined with GAN Data Augmentation for Predicting Soil Nutrients in Degraded Alpine Meadows on the Qinghai–Tibet Plateau

Jiang

Zhao

Ding

et al. 2023

Sensors

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Soil nutrients play vital roles in vegetation growth and are a key indicator of land degradation. Accurate, rapid, and non-destructive measurement of the soil nutrient content is important for ecological conservation, degradation monitoring, and precision farming. Currently, visible and near-infrared (Vis–NIR) spectroscopy allows for rapid and non-destructive monitoring of soil nutrients. However, the performance of Vis–NIR inversion models is extremely dependent on the number of samples. Limited samples may lead to low prediction accuracy of the models. Therefore, modeling and prediction based on a small sample size remain a challenge. This study proposes a method for the simultaneous augmentation of soil spectral and nutrient data (total nitrogen (TN), soil organic matter (SOM), total potassium oxide (TK2O), and total phosphorus pentoxide (TP2O5)) using a generative adversarial network (GAN). The sample augmentation range and the level of accuracy improvement were also analyzed. First, 42 soil samples were collected from the pika disturbance area on the QTP. The collected soils were measured in the laboratory for Vis–NIR and TN, SOM, TK2O, and TP2O5 data. A GAN was then used to augment the soil spectral and nutrient data simultaneously. Finally, the effect of adding different numbers of generative samples to the training set on the predictive performance of a convolutional neural network (CNN) was analyzed and compared with another data augmentation method (extended multiplicative signal augmentation, EMSA). The results showed that a GAN can generate data very similar to real data and with better diversity. A total of 15, 30, 60, 120, and 240 generative samples (GAN and EMSA) were randomly selected from 300 generative samples to be included in the real data to train the CNN model. The model performance first improved and then deteriorated, and the GAN was more effective than EMSA. Further shortening the interval for adding GAN data revealed that the optimal ranges were 30–40, 50–60, 30–35, and 25–35 for TK2O, TN, TP2O5, and SOM, respectively, and the validation set accuracy was maximized in these ranges. Therefore, the above method can compensate to some extent for insufficient samples in the hyperspectral prediction of soil nutrients, and can quickly and accurately estimate the content of soil TK2O, TN, TP2O5, and SOM.

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“…Diverse soil properties can directly indicate the soil's fertility and quality, thereby serving as indicators for monitoring degradation. Therefore, the accurate, rapid, and nondestructive acquisition of soil properties is essential for precision agricultural and pastoral production, ecological monitoring, and management [13].…”

Section: Introductionmentioning

confidence: 99%

Integration of Vis–NIR Spectroscopy and Machine Learning Techniques to Predict Eight Soil Parameters in Alpine Regions

Jiang,

Zhao,

2023

Agronomy

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Visible and near-infrared spectroscopy (Vis–NIR, 350–1100 nm) has great potential for predicting soil properties. However, current research on the hyperspectral prediction of soil parameters in agricultural areas of alpine regions and the types of parameters included is limited, and optimal spectral treatments and predictive models applicable to different parameters have not been sufficiently investigated. Therefore, we evaluated the accuracy of predicting total nitrogen (TN), phosphorus pentoxide (TP2O5), total potassium oxide (TK2O), alkali-hydrolyzable nitrogen (AHN), effective phosphorus (AP), effective potassium (AK), soil organic matter (SOM), and pH in the Qinghai–Tibet Plateau using the Vis–NIR technique in combination with spectral transformations, correlation analysis, feature selection, and machine learning. The results show that spectral transformations improve the correlation between spectra and parameters but are dependent on the parameter type and the method used. Continuum removal (CR), logarithmic first-order differential (FDL), and inverse first-order differential (FDR) had the most significant effects. The feature bands were extracted using the SPA and modeled using partial least squares (PLSR), random forest (RF), support vector machine (SVM), extreme gradient boosting (XGBoost), and backpropagation neural networks (BPNNs). The accuracy was evaluated based on R2, RMSE, RPD, and RPIQ. We found that the PLSR model only enables the prediction of SOM and pH with lower accuracy than the remaining models. XGBoost can predict all of the parameters but only for AHN; the prediction performance is better than other methods (R2 = 0.776, RMSE = 0.043 g/kg, and RPIQ = 2.88). The RF, SVM, and BPNN models cannot predict AK, AP, and AHN, respectively. In addition, TP2O5, AP, and pH are best suited for modeling using RF (RPIQ = 2.776, 3.011, and 3.198); TN, AK, and SOM are best suited for modeling using BPNN (RPIQ = 2.851, 2.394, and 3.085); and AHN and TK2O are best suited for XGBoost and SVM, respectively (RPIQ = 2.880 and 3.217). Therefore, this study can provide technical and data support for the accurate and efficient acquisition of soil parameters in alpine agriculture.

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Pika disturbance intensity observation system via multidimensional stereoscopic surveying for monitoring alpine meadow

Cited by 3 publications

References 42 publications

Consequences of plateau pika disturbance on plant-soil carbon and nitrogen in alpine meadows

Consequences of plateau pika disturbance on plant-soil carbon and nitrogen in alpine meadows

Vis–NIR Spectroscopy Combined with GAN Data Augmentation for Predicting Soil Nutrients in Degraded Alpine Meadows on the Qinghai–Tibet Plateau

Integration of Vis–NIR Spectroscopy and Machine Learning Techniques to Predict Eight Soil Parameters in Alpine Regions

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