scholarly journals Aboveground Biomass Mapping of Crops Supported by Improved CASA Model and Sentinel-2 Multispectral Imagery

2021 ◽  
Vol 13 (14) ◽  
pp. 2755
Author(s):  
Peng Fang ◽  
Nana Yan ◽  
Panpan Wei ◽  
Yifan Zhao ◽  
Xiwang Zhang
Keyword(s):  
Aboveground Biomass ◽  
Production Management ◽  
Vegetation Index ◽  
Net Primary Productivity ◽  
Vegetation Indices ◽  
Biophysical Parameters ◽  
Edge Information ◽  
Casa Model ◽  
Red Edge ◽  
Biomass Mapping

The net primary productivity (NPP) and aboveground biomass mapping of crops based on remote sensing technology are not only conducive to understanding the growth and development of crops but can also be used to monitor timely agricultural information, thereby providing effective decision making for agricultural production management. To solve the saturation problem of the NDVI in the aboveground biomass mapping of crops, the original CASA model was improved using narrow-band red-edge information, which is sensitive to vegetation chlorophyll variation, and the fraction of photosynthetically active radiation (FPAR), NPP, and aboveground biomass of winter wheat and maize were mapped in the main growing seasons. Moreover, in this study, we deeply analyzed the seasonal change trends of crops’ biophysical parameters in terms of the NDVI, FPAR, actual light use efficiency (LUE), and their influence on aboveground biomass. Finally, to analyze the uncertainty of the aboveground biomass mapping of crops, we further discussed the inversion differences of FPAR with different vegetation indices. The results demonstrated that the inversion accuracies of the FPAR of the red-edge normalized vegetation index (NDVIred-edge) and red-edge simple ratio vegetation index (SRred-edge) were higher than those of the original CASA model. Compared with the reference data, the accuracy of aboveground biomass estimated by the improved CASA model was 0.73 and 0.70, respectively, which was 0.21 and 0.13 higher than that of the original CASA model. In addition, the analysis of the FPAR inversions of different vegetation indices showed that the inversion accuracies of the red-edge vegetation indices NDVIred-edge and SRred-edge were higher than those of the other vegetation indices, which confirmed that the vegetation indices involving red-edge information can more effectively retrieve FPAR and aboveground biomass of crops.

scholarly journals NEW VEGETATION INDICES FOR FULL AND COMPACT POLARIMETRIC SAR DATA: IN PREPARATION FOR THE RADARSAT CONSTELLATION MISSION (RCM)

2020 ◽  
Vol IV-3/W2-2020 ◽  
pp. 41-46
Author(s):  
D. Ratha ◽  
D. Mandal ◽  
S. Dey ◽  
A. Bhattacharya ◽  
A. Frery ◽  
...  
Keyword(s):  
Time Series ◽  
Vegetation Index ◽  
Time Series Data ◽  
Geodesic Distance ◽  
Vegetation Indices ◽  
Series Data ◽  
Biophysical Parameters ◽  
Area Index ◽  
Plant Area Index ◽  
Sar Data

Abstract. In this paper, we present two radar vegetation indices for full-pol and compact-pol SAR data, respectively. Both are derived using the notion of a geodesic distance between observation and well-known scattering models available in the literature. While the full-pol version depends on a generalized volume scattering model, the compact-pol version uses the ideal depolariser to model the randomness in the vegetation. We have utilized the RADARSAT Constellation Mission (RCM) time-series data from the SAMPVEX16-MB campaign in the Manitoba region of Canada for comparing and assessing the indices in terms of the change in the biophysical parameters as well. The compact-pol data for comparison is simulated from the full-pol RCM time series. Both the indices show better performance at correlating with biophysical parameters such as Plant Area Index (PAI) and Volumetric Water Content (VWC) for wheat and soybean crops, in comparison to the state-of-art Radar Vegetation Index (RVI) of Kim and van Zyl. These indices are timely for the upcoming release of the data from the RCM, which will provide data in both full and compact-pol modes, aimed at better crop monitoring from space.

scholarly journals Assessment of pine aboveground biomass within Northern Steppe of Ukraine using Sentinel-2 data

2020 ◽  
Vol 66 (No. 8) ◽  
pp. 339-348
Author(s):  
Viktoriia Lovynska ◽  
Yuriy Buchavyi ◽  
Petro Lakyda ◽  
Svitlana Sytnyk ◽  
Yuriy Gritzan ◽  
...  
Keyword(s):  
Aboveground Biomass ◽  
Vegetation Index ◽  
Spectral Characteristics ◽  
Free Access ◽  
Biophysical Parameters ◽  
Normalised Difference Vegetation Index ◽  
Pine Stands ◽  
Predicted Values ◽  
Vegetative Indices ◽  
Sentinel 2

The present study offers the results of the spectral characteristics, calculated vegetative indices and biophysical parameters of pine stands of the Northern Steppe of Ukraine region obtained using Sentinel-2 data. For the development of regression models with the prediction of the biomass of pine forests using the obtained spectral characteristics, we used the results of the assessment of the aboveground biomass by the method of field surveys. The results revealed the highest correlation relations between the parameters of the general and trunk biomass with the normalised difference vegetation index (NDVI) and transformed vegetation index (TVI) vegetative indices and the fraction of absorbed photosynthetic active radiation (FARAP) and fraction of vegetation cover (FCOVER) biophysical parameters. To generate the models of determining the forest aboveground biomass (AGB), we used both the single- and two-factor models, the most optimum of which were those containing the NDVI predictor separately and in combination with the FCOVER predictor. The predicted values of the total AGB for the mentioned models equalled 32.5 to 236.3 and 39.9 to 253.4 t·ha<sup>–1</sup>. We performed mapping of the AGB of pine stands of the Northern Steppe using multi-spectral Sentinel-2 images, particularly the spectral characteristics of their derivatives (vegetative indices, biophysical parameters). This study demonstrated promising results for conducting an AGB-mapping of pine woods in the studied region using free-access resources.

scholarly journals A Transformed Triangular Vegetation Index for Estimating Winter Wheat Leaf Area Index

2019 ◽  
Vol 12 (1) ◽  
pp. 16 ◽  
Author(s):  
Naichen Xing ◽  
Wenjiang Huang ◽  
Qiaoyun Xie ◽  
Yue Shi ◽  
Huichun Ye ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Saturation Effect ◽  
Estimation Accuracy ◽  
Growth Monitoring ◽  
Area Index ◽  
Multispectral Data ◽  
Red Edge

Leaf area index (LAI) is a key parameter in plant growth monitoring. For several decades, vegetation indices-based empirical method has been widely-accepted in LAI retrieval. A growing number of spectral indices have been proposed to tailor LAI estimations, however, saturation effect has long been an obstacle. In this paper, we classify the selected 14 vegetation indices into five groups according to their characteristics. In this study, we proposed a new index for LAI retrieval-transformed triangular vegetation index (TTVI), which replaces NIR and red bands of triangular vegetation index (TVI) into NIR and red-edge bands. All fifteen indices were calculated and analyzed with both hyperspectral and multispectral data. Best-fit models and k-fold cross-validation were conducted. The results showed that TTVI performed the best predictive power of LAI for both hyperspectral and multispectral data, and mitigated the saturation effect. The R2 and RMSE values were 0.60, 1.12; 0.59, 1.15, respectively. Besides, TTVI showed high estimation accuracy for sparse (LAI < 4) and dense canopies (LAI > 4). Our study provided the value of the Red-edge bands of the Sentinel-2 satellite sensors in crop LAI retrieval, and demonstrated that the new index TTVI is applicable to inverse LAI for both low-to-moderate and moderate-to-high vegetation cover.

scholarly journals Crop Type Classification Using Vegetation Indices of RapidEye Imagery

2014 ◽  
Vol XL-7 ◽  
pp. 195-198 ◽  
Author(s):  
M. Ustuner ◽  
F. B. Sanli ◽  
S. Abdikan ◽  
M. T. Esetlili ◽  
Y. Kurucu
Keyword(s):  
Remote Sensing ◽  
Classification Accuracy ◽  
Vegetation Index ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Support Vector ◽  
Red Edge ◽  
Crop Type ◽  
Type Classification

Cutting-edge remote sensing technology has a significant role for managing the natural resources as well as the any other applications about the earth observation. Crop monitoring is the one of these applications since remote sensing provides us accurate, up-to-date and cost-effective information about the crop types at the different temporal and spatial resolution. In this study, the potential use of three different vegetation indices of RapidEye imagery on crop type classification as well as the effect of each indices on classification accuracy were investigated. The Normalized Difference Vegetation Index (NDVI), the Green Normalized Difference Vegetation Index (GNDVI), and the Normalized Difference Red Edge Index (NDRE) are the three vegetation indices used in this study since all of these incorporated the near-infrared (NIR) band. RapidEye imagery is highly demanded and preferred for agricultural and forestry applications since it has red-edge and NIR bands. The study area is located in Aegean region of Turkey. Radial Basis Function (RBF) kernel was used here for the Support Vector Machines (SVMs) classification. Original bands of RapidEye imagery were excluded and classification was performed with only three vegetation indices. The contribution of each indices on image classification accuracy was also tested with single band classification. Highest classification accuracy of 87, 46 % was obtained using three vegetation indices. This obtained classification accuracy is higher than the classification accuracy of any dual-combination of these vegetation indices. Results demonstrate that NDRE has the highest contribution on classification accuracy compared to the other vegetation indices and the RapidEye imagery can get satisfactory results of classification accuracy without original bands.

scholarly journals Use of Selected Spectral Ratios to Assess the Response of Pineapple to Potassium Nutrition

2021 ◽  
Vol 1 (1) ◽  
pp. 16-22
Author(s):  
Siva K. Balasundram ◽  
Yen Mee Chong
Keyword(s):  
Vegetation Index ◽  
Spectral Response ◽  
Vegetation Indices ◽  
Biochemical Properties ◽  
Canopy Reflectance ◽  
Red Edge ◽  
K Deficiency ◽  
High Precipitation ◽  
Spectral Ranges ◽  
Biophysical Changes

Potassium (K) nutrition in pineapple grown on tropical peat can be problematic due to high precipitation which encourages leaching losses. Non-destructive tools that can assess K deficiency and the accompanying changes in biophysical and biochemical properties within pineapple is a good strategy to employ. In this study, we assessed the biophysical changes in pineapple (var. MD2) in response to different K rates by using a hyperspectral approach. K deficiency was detected at 171 days after planting. Shortage of K also exhibited a shift in red edge towards shorter wavelengths between 500-700 nm. In addition, spectral ranges of 430-680 nm, as well as 680-752 nm were found to be most effective in differentiating spectral response to varying K rates. Three vegetation indices, i.e. Normalized Pigment Chlorophyll Index (NPCI), Plant Senescence Index (PSRI) and Red-edge Vegetation Index (RVSI) were found to best describe K treatment effects on pineapple canopy reflectance. This study could be extended further to include pineapple varieties other than MD2, and also key nutrients, such as N and P, for better fertilizer management in peat-grown pineapple.

scholarly journals Landscape-Scale Aboveground Biomass Estimation in Buffer Zone Community Forests of Central Nepal: Coupling In Situ Measurements with Landsat 8 Satellite Data

2018 ◽  
Vol 10 (11) ◽  
pp. 1848 ◽  
Author(s):  
Santa Pandit ◽  
Satoshi Tsuyuki ◽  
Timothy Dube
Keyword(s):  
Aboveground Biomass ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Buffer Zone ◽  
Landsat 8 ◽  
Community Forest ◽  
Pooled Data ◽  
Spectral Bands ◽  
Simple Ratio

Knowledge of forest productivity status is an important indicator of the amount of biomass accumulated and the role of terrestrial ecosystems in the carbon cycle. However, accurate and up-to-date information on forest biomass and forest succession remain rudimentary within natural forests. This study sought to understand and establish the potential of a new-generation sensor in estimating aboveground biomass (AGB) stored in the natural forest, also known as ‘community forest’ or buffer zone community forest (BZCF), in the Parsa National Park, Nepal. The utility of the 30-m resolution Landsat 8 Operational Land Imager (OLI) and in situ data was tested using two statistical approaches, namely multiple linear regression (MLR) and random forest (RF). The analysis was done based on four computational procedures. These included spectral bands, vegetation indices and pooled dataset (spectral bands + vegetation indices), and model selected important variables. AGB estimation based on pooled data showed that the RF algorithm produced better results when compared to the use of the MLR model. For instance, the RF model estimated AGB with an R2 value of 0.87 and a root mean square error of 20.50 t ha−1, as well as an R2 value of 0.95 and a RMSE of 13.3 t ha−1 when using selected important variables. Comparatively, the MLR using pooled data produced an R2 value of 0.56 and RMSE value of 37.01 t ha−1. The RF model selected Optimized Soil Adjusted Vegetation index (OSAVI), Simple ratio (SR), Modified simple ratio (MSR), and Normalized difference Vegetation index (NDVI) as the most important variables for estimating AGB, whereas MLR selected band 5 and SR. These findings demonstrate the relevance of the relatively new Landsat 8 sensor in the estimation of AGB in community buffer zones.

Using portable RapidSCAN active canopy sensor for rice nitrogen status diagnosis

2017 ◽  
Vol 8 (2) ◽  
pp. 349-352 ◽  
Author(s):  
J. Lu ◽  
Y. Miao ◽  
W. Shi ◽  
J. Li ◽  
J. Wan ◽  
...  
Keyword(s):  
Northeast China ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Stem Elongation ◽  
Vegetation Indices ◽  
Sanjiang Plain ◽  
N Nutrition ◽  
Red Edge ◽  
Edge Based ◽  
On Farm

The objective of this study was to determine how much improvement red edge-based vegetation indices (VIs) obtained with the RapidSCAN sensor would achieve for estimating rice nitrogen (N) nutrition index (NNI) at stem elongation stage (SE) as compared with commonly used normalized difference vegetation index (NDVI) and ratio vegetation index (RVI) in Northeast China. Sixteen plot experiments and seven on-farm experiments were conducted from 2014 to 2016 in Sanjiang Plain, Northeast China. The results indicated that the performance of red edge-based VIs for estimation of rice NNI was better than NDVI and RVI. N sufficiency index calculated with RapidSCAN VIs (NSI_VIs) (R2=0.43–0.59) were more stable and more strongly related to NNI than the corresponding VIs (R2=0.12–0.38).

scholarly journals Leaf Area of Overstory and Understory in Pine Plantations in the Flatwoods

2010 ◽  
Vol 34 (4) ◽  
pp. 154-160 ◽  
Author(s):  
Alicia Peduzzi ◽  
H. Lee Allen ◽  
Randolph H. Wynne
Keyword(s):  
Leaf Area ◽  
Loblolly Pine ◽  
Vegetation Index ◽  
Landsat Imagery ◽  
Vegetation Indices ◽  
Slash Pine ◽  
Pine Plantations ◽  
Site Factors ◽  
Biophysical Parameters ◽  
Area Index

Abstract Leaf area index (LAI) was measured in summer and winter for the overstory and understory in 7- and 10-year-old loblolly and slash pine plantations on poorly drained, somewhat poorly drained, and moderately well-drained soils. LAI and vegetation indices (simple ratio [SR], normalized difference vegetation index [NDVI], vegetation index, and enhanced vegetation index) were also calculated using Landsat imagery. LAI values observed for the overstory were low in most of the plots (around 2 m2 m−2 in slash pine and around 3 m2 m−2 in loblolly pine), whereas the understory LAI was very high (around 2 m2 m−2), which can be attributed to the lack of canopy closure observed in all plots. No significant differences were found in the understory LAI values across soil drainage classes. Total LAI (overstory LAI plus understory LAI) values were weakly correlated with the vegetation indices. The LAI values estimated using Landsat data were typically half of the values estimated on the ground. Significant correlations were observed between the vegetation indices (SR and NDVI) and stand and site factors, suggesting that the satellite-derived indices were more related to the stand biophysical parameters than to in situ LAI estimates.

scholarly journals TREE AGE AS ADJUSTMENT FACTOR TO NDVI

2018 ◽  
Vol 41 (3) ◽  
Author(s):  
Elias Fernando Berra ◽  
Denise Cybis Fontana ◽  
Tatiana Mora Kuplich
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Dynamic Range ◽  
Vegetation Indices ◽  
Tree Age ◽  
Stand Age ◽  
Adjustment Factor ◽  
Biophysical Parameters ◽  
Wood Volume ◽  
The Relationship

ABSTRACT This study aimed to increase satellite-derived Normalized Difference Vegetation Index (NDVI) sensitivity to biophysical parameters changes with aid of a forest age-based adjustment factor. This factor is defined as a ratio between stand age and age of rotation, which value multiplied by Landsat-5/TM-derived NDVI generated the so-called adjusted index NDVI_a. Soil Adjusted Vegetation Index (SAVI) was also calculated. The relationship between these vegetation indices (VI) with Eucalyptus and Pinus stands’ wood volume was investigated. The adjustment factor caused an increase in NDVI dynamic range values, since older stands tended to be assigned with highest NDVI values, while younger ones tended to be forced to assume lower NDVI values. As a result, direct and significant relationship between NDVI_a and wood volume could be maintained for wider ranges of wood volume. However, it was observed that NDVI_a was only statistically superior to NDVI and SAVI when a detailed age dataset is available. It is conclude that, the stand age has potential to improve NDVI sensitivity to biophysical parameters allowing that quantitative estimates could be made since young to adult stands.

Sign in / Sign up

Export Citation Format

Share Document