Vegetation Response to the 2012–2014 California Drought from GPS and Optical Measurements

Small, E. E.; Roesler, Carolyn; Larson, Kristine M.

doi:10.3390/rs10040630

Cited by 17 publications

(22 citation statements)

References 45 publications

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“…This is mainly due to special climate of California, Mediterranean climate, which is characterized as dry, hot in summer and mild and wet in winter. Therefore, in summer the dry and hot climate will inhibit the vegetation growth, while in winter the suitable mild climate can bring a growing season to the vegetation [40]. The consistency of NMRI spatial distribution with NDVI and GPP further proves the accuracy of the point-surface fusion results.…”

Section: Point-surface Fusion Results Of Nmrimentioning

confidence: 79%

“…The dry climate of the western plateau is an inland climate, and the annual temperature difference of the plateau area is large. The Mediterranean climate is characterized as mild and wet in winter and warm and dry in summer [40].…”

Section: Study Areamentioning

confidence: 99%

“…Furthermore, the NMRI was validated at four sites in Montana, and the results showed that the NMRI is correlated strongly with VWC and NDVI [33]. Recently, the NMRI was also used to evaluate the vegetation response to a recent drought in California, U.S., and was compared with the optical-based remote sensing NDVI [40].…”

Section: The Normalized Microwave Reflection Index (Nmri)mentioning

confidence: 99%

“…However, it has the disadvantages of slow convergence and easily convergence to local minima. Another neural network, the GRNN, which is a special form of a radial basis function neural network, was proposed by Specht [40]. The GRNN improves the local approximation ability and learning speed, because the hidden nodes of the GRNN are often connected by a Gaussian function, which is locally distributed and attenuated to the center of the radial symmetry [48].…”

Section: Generalized Regression Neural Network (Grnn)mentioning

confidence: 99%

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Monitoring the Variation of Vegetation Water Content with Machine Learning Methods: Point–Surface Fusion of MODIS Products and GNSS-IR Observations

Yuan

Yue

et al. 2019

Remote Sensing

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Vegetation water content (VWC) is recognized as an important parameter in vegetation growth studies, natural disasters such as forest fires, and drought prediction. Recently, the Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) has emerged as an important technique for monitoring vegetation information. The normalized microwave reflection index (NMRI) was developed to reflect the change of VWC based on this fact. However, NMRI uses local site-based data, and the sparse distribution hinders the application of NMRI. In this study, we obtained a 500 m spatially continuous NMRI product by integrating GNSS-IR site data with other VWC-related products using the point–surface fusion technique. The auxiliary data in the fusion process include the normalized difference vegetation index (NDVI), gross primary productivity (GPP), and precipitation. Meanwhile, the fusion performance of three machine learning methods, i.e., the back-propagation neural network (BPNN), generalized regression neural network (GRNN), and random forest (RF) are compared and analyzed. The machine learning methods achieve satisfactory results, with cross-validation R values of 0.71–0.83 and RMSEs of 0.025–0.037. The results show a clear improvement over the traditional multiple linear regression method, which achieves R (RMSE) values of only about 0.4 (0.045). It indicates that the machine learning methods can better learn the complex nonlinear relationship between NMRI and the input VWC-related index. Among the machine learning methods, the RF model obtained the best results. Long time-series NMRI images with a 500 m spatial resolution in the western part of the continental U.S. were then obtained. The results show that the spatial distribution of the NMRI product is consistent with a drought situation from 2012 to 2014 in the U.S., which verifies the feasibility of analyzing and predicting drought times and distribution ranges by using the 500 m fusion product.

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Section: Point-surface Fusion Results Of Nmrimentioning

confidence: 79%

Section: Study Areamentioning

confidence: 99%

Section: The Normalized Microwave Reflection Index (Nmri)mentioning

confidence: 99%

Section: Generalized Regression Neural Network (Grnn)mentioning

confidence: 99%

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Monitoring the Variation of Vegetation Water Content with Machine Learning Methods: Point–Surface Fusion of MODIS Products and GNSS-IR Observations

Yuan

Yue

et al. 2019

Remote Sensing

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“…Attractant treatments appeared to mitigate the negative effects of drought on bird visitation, even producing an increase in bird abundance across a time period that saw declining bird abundance in control treatments. Perhaps the drought‐induced death of vegetation (Small, Roesler, & Larson, ) increased predation risk at ponds, and attractant treatments mitigated the perceived or actual threat of predation by providing protected perching habitat. The drought might have led to actual reductions in the population density of birds (i.e., reflected in the decline in abundance observed at control ponds), or contributed to birds moving to alternative, more optimal habitat, including the ponds with installed attractants.…”

Section: Discussionmentioning

confidence: 99%

An effective method for ecosystem‐scale manipulation of bird abundance and species richness

Wood

Summerside

Johnson

2019

Ecology and Evolution

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Manipulation experiments are a cornerstone of ecological research, but can be logistically challenging to execute—particularly when they are intended to isolate the ecological role of large, vagile species, like birds. Despite indirect evidence that birds are influential in many ecosystems, large‐scale, multi‐year bird manipulation experiments are rare. When these studies are conducted, they are typically realized with caged or netted exclosures, an approach that can be expensive, risky for wildlife, and difficult to maintain. In cases where caged exclosures are not appropriate, alternate approaches are needed to allow rigorous empirical studies on the ecological role of birds. Here, we present and validate a method for experimentally increasing the abundance and richness of birds at the scale of entire aquatic ecosystems. Unlike bird exclusion, this approach is experimentally tractable, appealing to land managers, and possible to deploy over large spatial scales. We tested the efficacy of our approach for increasing bird abundance and species richness at 16 central California ponds. Based on bird visitation data obtained by summer camera trapping, our approach significantly increased bird species richness and abundance at manipulated ponds compared to control ponds. Attractant treatments mitigated the negative effects of a major drought on bird species richness and generated a near doubling of bird abundance in the presence of attractants. Treatments had no effect on most mammal species, with the exception of ground squirrels, which increased in abundance in the presence of attractants. These results suggest that attractants are effective in increasing bird abundance and richness. We encourage researchers to consider this approach for experimentally isolating the ecological role of birds in aquatic and open terrestrial ecosystems, especially in cases where cost or logistical constraints preclude the use of caged or netted exclosures.

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Drought index downscaling using AI-based ensemble technique and satellite data

Behfar,

Sharghi,

Nourani

et al. 2024

Theor Appl Climatol

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This study introduces and validates an artificial intelligence (AI)–based downscaling method for Standardized Precipitation Indices (SPI) in the northwest of Iran, utilizing PERSSIAN-CDR data and MODIS-derived drought-dependent variables. The correlation between SPI and two drought-dependent variables at a spatial resolution of 0.25° from 2000 to 2015 served as the basis for predicting SPI values at a finer spatial resolution of 0.05° for the period spanning 2016 to 2021. Shallow AI models (Support Vector Regression, Adaptive Neural Fuzzy Inference System, Feedforward Neural Network) and the Long Short-Term Memory (LSTM) deep learning method are employed for downscaling, followed by an ensemble post-processing technique for shallow AI models. Validation against rain gauge data indicates that all methods improve SPI simulation compared to PERSIANN-CDR products. The ensemble technique excels by 20% and 25% in the training and test phases, respectively, achieving the mean Determination Coefficient (DC) score of 0.67 in the validation phase. Results suggest that the deep learning LSTM method is less suitable for limited observed data compared to ensemble techniques. Additionally, the proposed methodology successfully detects approximately 80% of drought conditions. Notably, SPI-6 outperforms other temporal scales. This study advances the understanding of AI-driven downscaling for SPI, emphasizing the efficacy of ensemble approaches and providing valuable insights for regions with limited observational data.

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Vegetation Response to the 2012–2014 California Drought from GPS and Optical Measurements

Cited by 17 publications

References 45 publications

Monitoring the Variation of Vegetation Water Content with Machine Learning Methods: Point–Surface Fusion of MODIS Products and GNSS-IR Observations

Monitoring the Variation of Vegetation Water Content with Machine Learning Methods: Point–Surface Fusion of MODIS Products and GNSS-IR Observations

An effective method for ecosystem‐scale manipulation of bird abundance and species richness

Drought index downscaling using AI-based ensemble technique and satellite data

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