Evaluation of Ratio-Based Vegetation Indices For Annual Crops’ Biomass Estimation. Lebna Watershed, Capbon, Tunisia

This paper provides a comprehensive literature review on forest aboveground biomass (AGB) estimation and mapping through high-resolution optical satellite imagery (≤5 m spatial resolution). Based on the literature review, 44 peer-reviewed journal articles were published in 15 years (2004–2019). Twenty-one studies were conducted across six continents in Asia, eight in North America and Africa, five in South America, and four in Europe. This review article gives a glance at the published methodologies for AGB prediction modeling and validation. The literature review suggested that, along with the integration of other sensors, QuickBird, WorldView-2, and IKONOS satellite images were most widely used for AGB estimations, with higher estimation accuracies. All studies were grouped into six satellite-derived independent variables, including tree crown, image textures, tree shadow fraction, canopy height, vegetation indices, and multiple variables. Using these satellite-derived independent variables, most of the studies used linear regression (41%), while 30% used linear (multiple regression and 18% used non-linear (machine learning) regression, while very few (11%) studies used non-linear (multiple and exponential) regression for estimating AGB. In the context of global forest AGB estimations and monitoring, the advantages, strengths, and limitations were discussed to achieve better accuracy and transparency towards the performance-based payment mechanism of the REDD+ program. Apart from technical limitations, we realized that very few studies talked about real-time monitoring of AGB or quantifying AGB change, a dimension that needs exploration.

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Biomass estimation of cultivated red algae Pyropia using unmanned aerial platform based multispectral imaging

Plant Methods ◽

10.1186/s13007-021-00711-y ◽

2021 ◽

Vol 17 (1) ◽

Author(s):

Shuai Che ◽

Guoying Du ◽

Ning Wang ◽

Kun He ◽

Zhaolan Mo ◽

...

Keyword(s):

High Throughput ◽

Vegetation Index ◽

Multispectral Imaging ◽

Vegetation Indices ◽

Spectral Imaging ◽

Shielding Effect ◽

Quadratic Model ◽

Biomass Estimation ◽

Marine Aquaculture ◽

Significant Difference

Abstract Background Pyropia is an economically advantageous genus of red macroalgae, which has been cultivated in the coastal areas of East Asia for over 300 years. Realizing estimation of macroalgae biomass in a high-throughput way would great benefit their cultivation management and research on breeding and phenomics. However, the conventional method is labour-intensive, time-consuming, manually destructive, and prone to human error. Nowadays, high-throughput phenotyping using unmanned aerial vehicle (UAV)-based spectral imaging is widely used for terrestrial crops, grassland, and forest, but no such application in marine aquaculture has been reported. Results In this study, multispectral images of cultivated Pyropia yezoensis were taken using a UAV system in the north of Haizhou Bay in the midwestern coast of Yellow Sea. The exposure period of P. yezoensis was utilized to prevent the significant shielding effect of seawater on the reflectance spectrum. The vegetation indices of normalized difference vegetation index (NDVI), ratio vegetation index (RVI), difference vegetation index (DVI) and normalized difference of red edge (NDRE) were derived and indicated no significant difference between the time that P. yezoensis was completely exposed to the air and 1 h later. The regression models of the vegetation indices and P. yezoensis biomass per unit area were established and validated. The quadratic model of DVI (Biomass = − 5.550DVI2 + 105.410DVI + 7.530) showed more accuracy than the other index or indices combination, with the highest coefficient of determination (R2), root mean square error (RMSE), and relative estimated accuracy (Ac) values of 0.925, 8.06, and 74.93%, respectively. The regression model was further validated by consistently predicting the biomass with a high R2 value of 0.918, RMSE of 8.80, and Ac of 82.25%. Conclusions This study suggests that the biomass of Pyropia can be effectively estimated using UAV-based spectral imaging with high accuracy and consistency. It also implied that multispectral aerial imaging is potential to assist digital management and phenomics research on cultivated macroalgae in a high-throughput way.

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Applicability of different vegetation indices for pasture biomass estimation in the North-Central region of Mongolia

Geocarto International ◽

10.1080/10106049.2021.1974956 ◽

2021 ◽

pp. 1-16

Author(s):

Batbileg Bayaraa ◽

Akira Hirano ◽

Myagmartseren Purevtseren ◽

Battsengel Vandansambuu ◽

Byambasuren Damdin ◽

...

Keyword(s):

Central Region ◽

Vegetation Indices ◽

Biomass Estimation ◽

North Central Region ◽

North Central ◽

The North

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Biomass estimation of alpine grasslands under different grazing intensities using spectral vegetation indices

Canadian Journal of Remote Sensing ◽

10.5589/m11-050 ◽

2011 ◽

Vol 37 (4) ◽

pp. 413-421 ◽

Cited By ~ 16

Author(s):

Minjie Duan ◽

Qingzhu Gao ◽

Yunfan Wan ◽

Yue Li ◽

Yaqi Guo ◽

...

Keyword(s):

Vegetation Indices ◽

Biomass Estimation ◽

Alpine Grasslands ◽

Spectral Vegetation Indices ◽

Grazing Intensities

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BIOMASS AND CARBON STOCK ESTIMATION USING IN-SITU OBSERVATIONS AND GIS IN GILGIT BALTISTAN, PAKISTAN

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xliv-m-3-2021-49-2021 ◽

2021 ◽

Vol XLIV-M-3-2021 ◽

pp. 49-55

Author(s):

E. Fatima ◽

S. S. Ali

Keyword(s):

Carbon Stock ◽

Anthropogenic Activities ◽

Satellite Image ◽

Vegetation Indices ◽

Accurate Method ◽

Carbon Accounting ◽

Biomass Estimation ◽

Landsat 8 ◽

Inventory Data ◽

Gilgit Baltistan

Abstract. Carbon dioxide (CO2) emission and other greenhouse gases are rising day by day due to anthropogenic activities which lead to global warming and cause natural disasters. Thus REDD+ comes up with an initiative to reduce emissions from deforestation through Carbon accounting, in which the under developing countries Measure, Report, and Verify (MRV) the sum of Above Ground Biomass (AGB)/carbon stored in a particular forest. Nonetheless, the major challenge for REDD+ is to find an accurate method for biomass estimation. The purpose of this study was to model and map the AGB and carbon stock of Gilgit-Baltistan, Pakistan. For this purpose, we linked Landsat 8 and forest inventory data to assess the potential of Vegetation Indices (Vis) derived AGB estimation. Inventory data consisted of the tree measurements from 480 plots that data was collected in the year (June–Oct) 2016 in a 72,971 km2 (28,174 sq mi) study area, in Gilgit-Baltistan. Out of these plots, 287 was used in Calibration and 191 is used for Validation. This paper provides a regression equation between the reflection values from the Landsat-8 satellite image and sample areas where terrestrial aboveground biomass (AGB) was calculated by direct measurement method. As a result of the calculations made, a positive linear correlation between AGB and NDVI was relatively high compared to other vegetation indices i.e 0.59 in the year 2016 or for the year 2013.

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ESTIMATING ABOVEGROUND BIOMASS OF BAMBOO AND MIXED BAMBOO FOREST IN THUA THIEN-HUE PROVINCE, VIET NAM USING PALSAR-2 AND LANDSAT OLI DATA

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-4-w20-11-2019 ◽

2019 ◽

Vol XLII-4/W20 ◽

pp. 11-19

Author(s):

T. T. Cat Tuong ◽

H. Tani ◽

X. F. Wang ◽

V.-M. Pham

Keyword(s):

Linear Regression ◽

Regression Model ◽

Linear Regression Model ◽

Vegetation Indices ◽

Biomass Estimation ◽

Landsat 8 ◽

Landsat Data ◽

Bamboo Forest ◽

Landsat 8 Oli ◽

L Band

Abstract. In this study, above-ground biomass (AGB) performance was evaluated by PALSAR-2 L-band and Landsat data for bamboo and mixed bamboo forest. The linear regression model was chosen and validated for forest biomass estimation in A Luoi district, Thua Thien Hue province, Vietnam. A Landsat 8 OLI image and a dual-polarized ALOS/PALSAR-2 L-band (HH, HV polarizations) were used. In addition, 11 diferrent vegetation indices were extracted to test the performance of Landsat data in estimating forest AGB Total of 54 plots were collected in the bamboo and mixed bamboo forest in 2016. The linear regression is used to evaluate the sensitivity of biomass to the obtained parameters, including radar polarization, optical properties, and some vegetation indices which are extracted from Landsat data. The best-fit linear regression is selected by using the Bayesian Model Average for biomass estimation. Leave-one-out cross-validation (LOOCV) was employed to test the robustness of the model through the coefficient of determination (R squared – R2) and Root Mean Squared Error (RMSE). The results show that Landsat 8 OLI data has a slightly better potential for biomass estimation than PALSAR-2 in the bamboo and mixed bamboo forest. Besides, the combination of PALSAR-2 and Landsat 8 OLI data also has a no significant improvement (R2 of 0.60) over the performance of models using only SAR (R2 of 0.49) and only Landsat data (R2 of 0.58–0.59). The univariate model was selected to estimate AGB in the bamboo and mixed bamboo forest. The model showed good accuracy with an R2 of 0.59 and an RMSE of 29.66 tons ha−1. The comparison between two approaches using the entire dataset and LOOCV demonstrates no significant difference in R (0.59 and 0.56) and RMSE (29.66 and 30.06 tons ha−1). This study performs the utilization of remote sensing data for biomass estimation in bamboo and mixed bamboo forest, which is a lack of up-to-date information in forest inventory. This study highlights the utilization of the linear regression model for estimating AGB of the bamboo forest with a limited number of field survey samples. However, future research should include a comparison with non-linear and non-parametric models.

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MANGROVE ABOVE GROUND BIOMASS ESTIMATION USING COMBINATION OF LANDSAT 8 AND ALOS PALSAR DATA

International Journal of Remote Sensing and Earth Sciences (IJReSES) ◽

10.30536/j.ijreses.2015.v12.a2687 ◽

2017 ◽

Vol 12 (2) ◽

pp. 85 ◽

Cited By ~ 2

Author(s):

Gathot Winarso ◽

Yenni Vetrita ◽

Anang D. Purwanto ◽

Nanin Anggraini ◽

Soni Darmawan ◽

...

Keyword(s):

Carbon Stock ◽

Vegetation Indices ◽

Emission Trading ◽

Remote Sensing Data ◽

Tree Height ◽

Biomass Estimation ◽

Landsat 8 ◽

Alos Palsar ◽

Sar Data ◽

Estimation Models

Mangrove ecosystem is important coastal ecosystem, both ecologically and economically. Mangrove provides rich-carbon stock, most carbon-rich forest among ecosystems of tropical forest. It is very important for the country to have a large mangrove area in the context of global community of climate change policy related to emission trading in the Kyoto Protocol. Estimation of mangrove carbon-stock using remote sensing data plays an important role in emission trading in the future. Estimation models of above ground mangrove biomass are still limited and based on common forest biomass estimation models that already have been developed. Vegetation indices are commonly used in the biomass estimation models, but they have low correlation results according to several studies. Synthetic Aperture Radar (SAR) data with capability in detecting volume scattering has potential applications for biomass estimation with better correlation. This paper describes a new model which was developed using a combination of optical and SAR data. Biomass is volume dimension related to canopy and height of the trees. Vegetation indices could provide two dimensional information on biomass by recording the vegetation canopy density and could be well estimated using optical remote sensing data. One more dimension to be 3 dimensional feature is height of three which could be provided from SAR data. Vegetation Indices used in this research was NDVI extracted from Landsat 8 data and height of tree estimated from ALOS PALSAR data. Calculation of field biomass data was done using non-decstructive allometric based on biomass estimation at 2 different locations that are Segara Anakan Cilacap and Alas Purwo Banyuwangi, Indonesia. Correlation between vegetation indices and field biomass with ALOS PALSAR-based biomass estimation was low. However, multiplication of NDVI and tree height with field biomass correlation resulted R2 0.815 at Alas Purwo and R2 0.081 at Segara Anakan. Low correlation at Segara anakan was due to failed estimation of tree height. It seems that ALOS PALSAR height was not accurate for determination of areas dominated by relative short trees as we found at Segara Anakan Cilacap, but the result was quite good for areas dominated by high trees. To improve the accuracy of tree height estimation, this method still needs validation using more data.

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Graph-Based Data Fusion Applied to: Change Detection and Biomass Estimation in Rice Crops

Remote Sensing ◽

10.3390/rs12172683 ◽

2020 ◽

Vol 12 (17) ◽

pp. 2683

Author(s):

David Alejandro Jimenez-Sierra ◽

Hernán Darío Benítez-Restrepo ◽

Hernán Darío Vargas-Cardona ◽

Jocelyn Chanussot

Keyword(s):

Data Fusion ◽

Change Detection ◽

Mean Squared Error ◽

Vegetation Indices ◽

Remote Sensing Data ◽

Biomass Estimation ◽

Fusion Algorithm ◽

Simple Task ◽

Recent Approach ◽

Complementary Nature

The complementary nature of different modalities and multiple bands used in remote sensing data is helpful for tasks such as change detection and the prediction of agricultural variables. Nonetheless, correctly processing a multi-modal dataset is not a simple task, owing to the presence of different data resolutions and formats. In the past few years, graph-based methods have proven to be a useful tool in capturing inherent data similarity, in spite of different data formats, and preserving relevant topological and geometric information. In this paper, we propose a graph-based data fusion algorithm for remotely sensed images applied to (i) data-driven semi-unsupervised change detection and (ii) biomass estimation in rice crops. In order to detect the change, we evaluated the performance of four competing algorithms on fourteen datasets. To estimate biomass in rice crops, we compared our proposal in terms of root mean squared error (RMSE) concerning a recent approach based on vegetation indices as features. The results confirm that the proposed graph-based data fusion algorithm outperforms state-of-the-art methods for change detection and biomass estimation in rice crops.

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Unmanned Aircraft System (UAS) Technology and Applications in Agriculture

Agronomy ◽

10.3390/agronomy9100618 ◽

2019 ◽

Vol 9 (10) ◽

pp. 618 ◽

Cited By ~ 18

Author(s):

Samuel C. Hassler ◽

Fulya Baysal-Gurel

Keyword(s):

Precision Agriculture ◽

Weed Management ◽

Plant Stress ◽

Vegetation Indices ◽

Point Clouds ◽

Unmanned Aircraft ◽

Ease Of Use ◽

Biomass Estimation ◽

Trade Offs ◽

Legal Constraints

Numerous sensors have been developed over time for precision agriculture; though, only recently have these sensors been incorporated into the new realm of unmanned aircraft systems (UAS). This UAS technology has allowed for a more integrated and optimized approach to various farming tasks such as field mapping, plant stress detection, biomass estimation, weed management, inventory counting, and chemical spraying, among others. These systems can be highly specialized depending on the particular goals of the researcher or farmer, yet many aspects of UAS are similar. All systems require an underlying platform—or unmanned aerial vehicle (UAV)—and one or more peripherals and sensing equipment such as imaging devices (RGB, multispectral, hyperspectral, near infra-red, RGB depth), gripping tools, or spraying equipment. Along with these wide-ranging peripherals and sensing equipment comes a great deal of data processing. Common tools to aid in this processing include vegetation indices, point clouds, machine learning models, and statistical methods. With any emerging technology, there are also a few considerations that need to be analyzed like legal constraints, economic trade-offs, and ease of use. This review then concludes with a discussion on the pros and cons of this technology, along with a brief outlook into future areas of research regarding UAS technology in agriculture.

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Mapping Productivity and Essential Biophysical Parameters of Cultivated Tropical Grasslands from Sentinel-2 Imagery

Agronomy ◽

10.3390/agronomy10050711 ◽

2020 ◽

Vol 10 (5) ◽

pp. 711

Author(s):

Amparo Cisneros ◽

Peterson Fiorio ◽

Patricia Menezes ◽

Nieves Pasqualotto ◽

Shari Van Wittenberghe ◽

...

Keyword(s):

Vegetation Indices ◽

Agricultural Science ◽

Grass Species ◽

Biomass Estimation ◽

Panicum Maximum ◽

Biophysical Parameters ◽

Area Index ◽

Tropical Grasslands ◽

Urochloa Brizantha ◽

Sentinel 2

Nitrogen (N) is the main nutrient element that maintains productivity in forages; it is inextricably linked to dry matter increase and plant support capacity. In recent years, high spectral and spatial resolution remote sensors, e.g., the European Space Agency (ESA)’s Sentinel satellite missions, have become freely available for agricultural science, and have proven to be powerful monitoring tools. The use of vegetation indices has been essential for crop monitoring and biomass estimation models. The objective of this work is to test and demonstrate the applicability of different vegetation indices to estimate the biomass productivity, the foliar nitrogen content (FNC), the plant height and the leaf area index (LAI) of several tropical grasslands species submitted to different nitrogen (N) rates in an experimental area of São Paulo, Brazil. Field reflectance data of Panicum maximum and Urochloa brizantha species’ cultivars were taken and convoluted to the Sentinel-2 satellite bands. Subsequently, different vegetation indices (Normalized Difference Vegetation Index (NDI), Three Band Index (TBI), Difference light Height (DLH), Three Band Dall’Olmo (DO), and Normalized Area Over reflectance Curve (NAOC)) were tested for the experimental grassland areas, and composed of Urochloa decumbens and Urochloa brizantha grass species, which were sampled and destructively analyzed. Our results show the use of different relevant Sentinel-2 bands in the visible (VIS)–near infrared (NIR) regions for the estimation of the different biophysical parameters. The FNC obtained the best correlation for the TBI index combining blue, green and red bands with a determination coefficient (R2) of 0.38 and Root Mean Square Error (RMSE) of 3.4 g kg−1. The estimation of grassland productivity based on red-edge and NIR bands showed a R2 = 0.54 and a RMSE = 1800 kg ha−1. For the LAI, the best index was the NAOC (R2 = 0.57 and RMSE = 1.4 m2 m−2). High values of FNC, productivity and LAI based on different sets of Sentinel-2 bands were consistently obtained for areas under N fertilization.

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