Ratio-based vegetation indices for biomass estimation depending on grassland characteristics

Karakoç, Ahmet; Karabulut, Murat

doi:10.3906/bot-1902-50

Cited by 6 publications

(7 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Among various satellite data, most researchers preferred to directly select some vegetation indices highly correlated with vegetation growth and status. Therefore, the NDVI has always been popular in studies [9,38,[64][65][66]. In addition, EVI, SAVI, and LSWI were utilized in some studies [18,26,39].…”

Section: Parameter Estimation 21 Agbmentioning

confidence: 99%

“…In addition, some other factors have also been studied. KarakoÇ et al [66] investigated the spectral properties of grasslands at different altitudes and with different biomass densities in the Mediterranean region. The performance of linear and exponential models based on various forms of the NDVI and simple ratio (SR) were compared, which indicated that the spectral saturation effect due to high biomass densities severely degraded the estimation accuracy, while the selection of indices depending on the spectral characteristics of grasslands was more crucial than the choice of regression models.…”

Section: Parameter Estimation 21 Agbmentioning

confidence: 99%

See 1 more Smart Citation

Review of Remote Sensing Applications in Grassland Monitoring

Wang

Zhang

et al. 2022

Remote Sensing

View full text Add to dashboard Cite

The application of remote sensing technology in grassland monitoring and management has been ongoing for decades. Compared with traditional ground measurements, remote sensing technology has the overall advantage of convenience, efficiency, and cost effectiveness, especially over large areas. This paper provides a comprehensive review of the latest remote sensing estimation methods for some critical grassland parameters, including above-ground biomass, primary productivity, fractional vegetation cover, and leaf area index. Then, the applications of remote sensing monitoring are also reviewed from the perspective of their use of these parameters and other remote sensing data. In detail, grassland degradation and grassland use monitoring are evaluated. In addition, disaster monitoring and carbon cycle monitoring are also included. Overall, most studies have used empirical models and statistical regression models, while the number of machine learning approaches has an increasing trend. In addition, some specialized methods, such as the light use efficiency approaches for primary productivity and the mixed pixel decomposition methods for vegetation coverage, have been widely used and improved. However, all the above methods have certain limitations. For future work, it is recommended that most applications should adopt the advanced estimation methods rather than simple statistical regression models. In particular, the potential of deep learning in processing high-dimensional data and fitting non-linear relationships should be further explored. Meanwhile, it is also important to explore the potential of some new vegetation indices based on the spectral characteristics of the specific grassland under study. Finally, the fusion of multi-source images should also be considered to address the deficiencies in information and resolution of remote sensing images acquired by a single sensor or satellite.

show abstract

Section: Parameter Estimation 21 Agbmentioning

confidence: 99%

Section: Parameter Estimation 21 Agbmentioning

confidence: 99%

Review of Remote Sensing Applications in Grassland Monitoring

Wang

Zhang

et al. 2022

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…On the other hand, remote sensing methods are a powerful alternative for acquiring data at a wide variety of spatial and temporal scales (Bellini et al, 2023;Yuan et al, 2023). The surface reflectance can be measured directly in the field using spectroradiometers (Karakoç & Karabulut, 2019) or estimated through satellite imagery (Lei et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

“…Since the variations are centered on specific bands, researchers are increasingly interested in hyperspectral data tools due to their capacity to collect data in hundreds of bands. Hence, hyperspectral data are widely used in the investigation of the biophysical and biochemical properties of vegetation (Danson & Bowyer, 2004;Colombo et al, 2008;Karakoç & Karabulut, 2019). Here, airborne and satellite-born sensors provide data for landscape-level analysis, while portable/handheld spectroradiometers are preferable for leaf or canopy-level surveys.…”

Section: Introductionmentioning

confidence: 99%

Evaluating canopy water content in grasslands by using in situ hyperespectral data

Karakoç,

Karabulut

2023

Mediterr. Bot.

View full text Add to dashboard Cite

The water content of vegetation is considered a key parameter for ecological analysis as well as for agricultural and forestry applications. Remote sensing methods offer significant advantages over traditional field methods for determining the water content of vegetation at the leaf, canopy, and landscape levels. In this study, the potential of hyperspectral vegetation indices in predicting canopy water content in grasslands was evaluated. For this purpose, data were collected from 3 several grasslands located at ~500 m, ~1200 m and ~1400 m altitudes. 71 samples were collected from each study area, and a total of 213 samples were analyzed. In this context, 59 ratio-based hyperspectral vegetation indices were tested. The relationship between canopy water content and hyperspectral vegetation indices was evaluated with linear, exponential, logarithmic and power regression models. The results showed that the NW-3 (920,970) index significantly represents the canopy water content variable. It was determined that the exponential regression model created with this index was able to explain the variations in canopy water content up to 85%. On the other hand, it has been detected that the high level of water content in the vegetation creates a significant saturation problem. Another finding of this study is that the predictive power reaches higher levels in low canopy water content characteristics. The results of this study show that in situ hyperspectral data has a very high potential in determining vegetation water content in grasslands.

show abstract

“…Mapping AGB from space is a difficult task that requires robust prediction models [56]. In the past, many studies have been carried out using both linear and non-linear modeling approaches [57][58][59]. In many cases, it was found that when the prediction models become too complex, they operate with statistically insignificant predictor variables [60][61][62].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Forest Aboveground Biomass Using Multitemporal Multispectral Remote Sensing Data

2021

View full text Add to dashboard Cite

Forest aboveground biomass (AGB) is a prime forest parameter that requires global level estimates to study the global carbon cycle. Light detection and ranging (LiDAR) is the state-of-the-art technology for AGB prediction but it is expensive, and its coverage is restricted to small areas. On the contrary, spaceborne Earth observation data are effective and economical information sources to estimate and monitor AGB at a large scale. In this paper, we present a study on the use of different spaceborne multispectral remote sensing data for the prediction of forest AGB. The objective is to evaluate the effects of temporal, spectral, and spatial capacities of multispectral satellite data for AGB prediction. The study was performed on multispectral data acquired by Sentinel-2, RapidEye, and Dove satellites which are characterized by different spatial resolutions, temporal availability, and number of spectral bands. A systematic process of least absolute shrinkage and selection operator (lasso) variable selection generalized linear modeling, leave-one-out cross-validation, and analysis was accomplished on each satellite dataset for AGB prediction. Results point out that the multitemporal data based AGB models were more effective in prediction than the single-time models. In addition, red-edge and short wave infrared (SWIR) channel dependent variables showed significant improvement in the modeling results and contributed to more than 50% of the selected variables. Results also suggest that high spatial resolution plays a smaller role than spectral and temporal information in the prediction of AGB. The overall analysis emphasizes a good potential of spaceborne multispectral data for developing sophisticated methods for AGB prediction especially with specific spectral channels and temporal information.

show abstract

Ratio-based vegetation indices for biomass estimation depending on grassland characteristics

Cited by 6 publications

References 31 publications

Review of Remote Sensing Applications in Grassland Monitoring

Review of Remote Sensing Applications in Grassland Monitoring

Evaluating canopy water content in grasslands by using in situ hyperespectral data

Prediction of Forest Aboveground Biomass Using Multitemporal Multispectral Remote Sensing Data

Contact Info

Product

Resources

About