scholarly journals Mapping and Estimation of Above-ground Grass Biomass using Sentinel 2A Satellite Data

2021 ◽  
Vol 8 (3) ◽  
pp. 9-15
Author(s):  
Isa Muhammad Zumo ◽  
Mazlan Hashim ◽  
Noor Dyana Hassan
Keyword(s):  
Satellite Data ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Index Number ◽  
Estimation Model ◽  
Livestock Farming ◽  
Grass Biomass ◽  
Allometric Analysis ◽  
Sentinel 2A ◽  
Grazing Lands

Above-Ground Grass Biomass (AGGB) mapping and estimation is one of the important parameters for environmental ecosystem and grazing-lands management, particularly for livestock farming. However, previous models for estimation of AGGB with satellite imagery has some difficulty in choosing a particular satellite and vegetation index that can build a good estimation model at a higher accuracy. This study explores the potentiality of Sentinel 2A data to derive a satellite-based model for AGGB mapping and estimation. The study area was Skudai, Johor in Malaysia Peninsular. Grass parameters of forty grass sample units were measured in the field and their corresponding AGGB was later measured in the laboratory. The samples were used for modelling and assessment. Four indices were tested for their fitness in modelling AGGB from the satellite data. The result from the grass allometric analysis indicates that grass height and volume demonstrate good relationship with the measured AGGB (R² = 0.852 and 0.837 respectively). Vegetation Index Number (VIN) has the best fit for modeling AGGB (R2 = 0.840) compared to other vegetation indices. The derived satellite AGGB estimate was validated with the assessment field and allometry derived AGGB at RMSE = 15.89g and 44.45g, respectively. This study demonstrate that VIN derived from Sentinel 2A MSI satellite data can be used to model AGGB estimation at a good accuracy. Therefore, it will contribute to providing reliable information on AGGB of grazing lands for sustainable livestock farming.

scholarly journals Kajian Transformasi Indeks Vegetasi Citra Satelit Sentinel-2A untuk Estimasi Produksi Daun Kayu Putih Menggunakan Linear Spectral Mixture Analysis

2020 ◽  
Vol 3 (1) ◽  
pp. 63
Author(s):  
Lilik Norvi Purhartanto ◽  
Projo Danoedoro ◽  
Pramaditya Wicaksono
Keyword(s):  
Vegetation Index ◽  
Vegetation Indices ◽  
Spectral Mixture Analysis ◽  
Mixture Analysis ◽  
Estimation Model ◽  
Leaf Production ◽  
Melaleuca Cajuputi ◽  
Linear Spectral Mixture Analysis ◽  
Sentinel 2A ◽  
Spectral Mixture

A forest plantation area of Melaleuca cajuputi at BDH Karangmojo, BKPH Yogyakarta are 2,325.20 ha. One of the efforts to keep its sustainability is to plan the target and realization of cajuputi leaf production considerwith forest condition. Advances in remote sensing technology can be an alternative in estimating the cajuputi leaf production on large areas with an efficient time and high accuracy and able to analyze the quality of cajuputi. This study aims to examine Sentinel-2A capabilities through a relationship model of some vegetation indices integrated with vegetative factors on the production to obtain estimates of leaf production, map and test the estimation model accuracy. The method used is to classify objects in pixels with Linear Spectral Mixture Analysis and build relationship between age, number of plants and vegetation index with cajuputi leaf production. The results showed that the unmixing method has 99,66% accuracy in classifying pixels into the fraction of cajuputi. MERIS Terrestrial Chlorophyll Index of unmixing cajuputi fraction simultaneously with age and number of plants has the highest correlation with value of r = 0,668 to the production and modeled in mapping the estimated cajuputi leaf production at the research location with Standard Error of Estimate is 0,183.

scholarly journals Mapping of Sub-Optimal Land Based on NDVI Sentinel 2a Value: Preliminary Study

2020 ◽  
Vol 17 (3) ◽  
pp. 194-204
Author(s):  
Indarto Indarto ◽  
Rufiani Nadzirah ◽  
Hadrian Reksa Belagama
Keyword(s):  
Vegetation Index ◽  
Agricultural Land ◽  
Vegetation Indices ◽  
Vegetation Density ◽  
Residential Areas ◽  
Primary Input ◽  
Normalised Difference Vegetation Index ◽  
Sentinel 2A ◽  
Marginal Agricultural Land ◽  
Potential Use

Normalised Difference Vegetation Index (NDVI) is one of the vegetation indices used to analyse vegetation density. This study presents the potential use of NDVI to map dry-marginal-agricultural land (Dry-MAL). The study conducted in the eastern part of Situbondo, which includes three districts, namely, Arjasa, Asembagus and Jangkar. Sentinel-2A (recorded in 2018) and 450 Control points (GCPs) are used as the primary input. The region is an area with distinctive climate characteristics, where the dry season is longer than the rainy season. Analysis using "SNAP plug-ins" and "QGIS". Research procedures include (1) data inventory, (2) data pre-processing, (3) data processing and (4) accuracy testing. The NDVI classification can distinguish six (6) classes of land-use, i.e., water bodies, residential areas, dry MAL, non-irrigated rural area, irrigated paddy fields, forest-plantations. The NDVI classification produces Overall and Kappa accuracy values =  66,9% and 61,6%. Although the overall and kappa accuracy is below the standard, however, the result will benefit for further research of index vegetation or soil more applied for the identification of Dry-MAL

scholarly journals Uji Akurasi Beberapa Indeks Vegetasi dalam Mengestimasi Kerapatan Hutan Mangrove dengan Citra Sentinel-2A di Taman Nasional Bali Barat

2020 ◽  
Vol 3 (2) ◽  
pp. 59
Author(s):  
Maulana Ilham Fahmy Alam ◽  
I Wayan Nuarsa ◽  
Ni Luh Putu Ria Puspitha
Keyword(s):  
Vegetation Index ◽  
Statistical Tests ◽  
Vegetation Indices ◽  
Field Measurements ◽  
Coefficient Of Determination ◽  
Test Model ◽  
Significance Level ◽  
Sentinel 2A ◽  
Index Value ◽  
Density Results

Vegetation Indices is one of the remote sensing parameters that can be used to estimate the mangrove forest density. The purpose of this study is to determine the vegetation index with the best accuracy to estimate the condition of mangrove density, as well as determine the spatial distribution of mangrove density in the TNBB area. This study uses Sentinel-2A satellite imagery data and five different vegetation indices, namely NDVI, NNIR, EVI, mRE-SR, and vegetation index developed in this study. The method of determining samples in the field uses stratified random and proportional sampling. Data collection of canopy density used hemispherical photography method, which is taking vertical photos with a 180o angle of view using a camera with a Fish Eye or Wide lens. Data analysis used in this study is regression analysis, coefficient of determination test, model validation test, and paired t test. From statistical tests conducted on several vegetation indices, the mRE-SR vegetation index value shows the best results on all the accuracy parameters tested.  The R2 value was generated by the mRE-SR vegetation index from the relationship between mangrove density results from field measurements with the vegetation index value and the estimated density results shows that the highest values, namely 0.909 and 0.935. These results show that the mRE-SR vegetation index is the best vegetation index in explaining the variation of mangrove density in the field. The mRE-SR vegetation index also has the lowest deviation of the estimated value, with the resulting SE values in the two linear relationships of 1,592 and 0,999. In addition, the mRE-SR vegetation index has a P (T <= t) two-tail value greater than the significance level (0.05), the results means that two values of the tested variables are not significant different. The calculation results show that the total area of mangroves in TNBB is 409.21 ha. From the percentage of density obtained, the mangrove density class was only distributed in the medium and solid density classes.

scholarly journals ACCURACY ANALYSIS OF SENTINEL 2A AND LANDSAT 8 OLI+ SATELLITE DATASETS OVER KANO STATE (NIGERIA) USING VEGETATION SPECTRAL INDICES

2020 ◽  
Vol XLIV-3/W1-2020 ◽  
pp. 65-72
Author(s):  
O. A. Isioye ◽  
E. A. Akomolafe ◽  
U. H. Ikwueze
Keyword(s):  
Vegetation Index ◽  
Vegetation Indices ◽  
Stress Index ◽  
Landsat 8 ◽  
Agricultural Practice ◽  
Landsat 8 Oli ◽  
Area Index ◽  
Spatial Consistency ◽  
The Mean ◽  
Sentinel 2A

Abstract. This study explores the capabilities of Sentinel-2 over Landsat-8 Operational Land Imager (OLI) imageries for vegetation monitoring in the vegetated region of Minjibir LGA in Kano State. Accurate vegetation mapping is essential for monitoring crop and sustainable agricultural practice. Vegetation indices, comprising the Normalized Difference Vegetation Index (NDVI), Green Chlorophyll Index (GCI), Leaf Area Index (LAI) and Moisture Stress Index (MSI) were determined for each year. The findings showed an increase in Sentinel 2A value of the vegetation indices with respect to Landsat 8 throughout the time of the study (2015–2019). The best average performance over the supervised classification was obtained using Sentinel-2A bands, which are dependent on the training sample and resolution. While the spectral consistency of the data was inferred by cross-calibration analysis using regression analysis. The spatial consistency was assessed by descriptive statistical analysis of examined variables. Regarding the spatial consistency, the mean and standard deviation values of all variables were steady for all seasons excluding for the mean value of the LAI and MSI. Based on this finding, it is recommended that Sentinel-2A data could be used as a complementary data source with Landsat 8 OLI in vegetation assessment.

scholarly journals Coconut Tree Stress Detection as an Indicator of Red Palm Weevil (RPW) Attack Using Sentinel Data

2020 ◽  
Vol 7 (3) ◽  
pp. 1-9
Author(s):  
Faradina Marzukhi ◽  
Md Azlin Md Said ◽  
Amirul Audi Ahmad
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Stress Detection ◽  
Red Palm Weevil ◽  
Promising Alternative ◽  
Coconut Tree ◽  
Palm Weevil ◽  
Tree Stress ◽  
Sentinel 2A

The red palm weevil (RPW) is one of the worst destructive pests of palms in the world. This study focuses for the first time on the coconut tree stress detection and discrimination among different stages of red palm weevil (RPW) stress-attacks using vegetation indices (VI) and the percentage of accuracy assessed. Different spectral indices were assessed using Sentinel 2A data of year 2018. Based on field identification, four classes of coconut tree were considered and evaluated using visual maps of VI: severe, moderate, early and healthy coconut trees. Results showed that the vegetation indices Normalized Differenced Vegetation Index (NDVI), Renormalized Difference Vegetation Index (RDVI), SQRT (IR/R), Difference Vegetation Index (DVI) and Green Vegetation Index (GVI) are sensitive to coconut trees caused by RPW attacks. They discriminated among the considered classes with more than 50% accurate from census data of field observation compared with remote sensing data of Sentinel 2A image.  Nevertheless, they express the healthiness of tree stress between 0.308 – 0.673 range with 55% to 91% accurate. According to these results, it was concluded that remote sensing technique using Sentinel 2A data is a promising alternative for RPW detection based on VI.

scholarly journals Damage assessment of chilli thrips using high resolution multispectral satellite data

2021 ◽  
Vol 21 (4) ◽  
pp. 480-487
Author(s):  
Mathyam Prabhakar ◽  
Merugu Thirupathi, ◽  
G. Srasvan Kumar ◽  
U. Sai Sravan ◽  
M. Kalpana ◽  
...  
Keyword(s):  
Satellite Imagery ◽  
Satellite Data ◽  
Land Surface ◽  
Vegetation Indices ◽  
Superior Performance ◽  
Water Index ◽  
Sensing Technology ◽  
Significant Difference ◽  
Normalized Difference Water Index ◽  
Sentinel 2A

Remote sensing technology offers an effective, rapid and reliable tool for assessing pest severity in vegetation. Ground based hyperspectral radiometry studies revealed significant difference in the reflectance spectra between healthy and thrip damaged vegetation. Space borne multispectral reflectance from Sentinel 2A satellite data of chilli thrip infested canopy has significant differences in red region (Band 4 – 664.6 nm), NIR region (Bands 5, 6, 7, 8 & 8A having central wavelengths at 704.1, 740.5, 782.8 & 832.8 nm, respectively) and SWIR region (Bands 11 & 12 having central wavelengths at 1613.7 and 2202.4 nm). In this study, an attempt was made to discriminate healthy and pest affected chilli crop in the multispectral satellite imagery using several multispectral vegetation indices. Of these, land surface water index, LSWI (p=0.018) and normalized difference water index, NDWI (p=0.001) were found significant. These indices were used to classify chilli fields in the satellite imagery into severe, moderate and healthy classes. Superior performance of LSWI over NDWI with overall accuracy of 93.80 and Kappa Coefficient of 0.89 was observed. Moran's Index was used to study the spatial distribution of chilli thrips and observed strong clustering (I= 0.9073, p=0.0001).

scholarly journals Sentinel-2 Data in an Evaluation of the Impact of the Disturbances on Forest Vegetation

2020 ◽  
Vol 12 (12) ◽  
pp. 1914 ◽  
Author(s):  
Josef Lastovicka ◽  
Pavel Svec ◽  
Daniel Paluba ◽  
Natalia Kobliuk ◽  
Jan Svoboda ◽  
...  
Keyword(s):  
Bark Beetle ◽  
Satellite Data ◽  
National Park ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Forest Vegetation ◽  
Forest Recovery ◽  
Vegetation Changes ◽  
Tasseled Cap ◽  
Sentinel 2

In this article, we investigated the detection of forest vegetation changes during the period of 2017 to 2019 in the Low Tatras National Park (Slovakia) and the Sumava National Park (Czechia) using Sentinel-2 data. The evaluation was based on a time-series analysis using selected vegetation indices. The case studies represented five different areas according to the type of the forest vegetation degradation (one with bark beetle calamity, two areas with forest recovery mode after a bark beetle calamity, and two areas without significant disturbances). The values of the trajectories of the vegetation indices (normalized difference vegetation index (NDVI) and normalized difference moisture index (NDMI)) and the orthogonal indices (tasseled cap greenness (TCG) and tasseled cap wetness (TCW)) were analyzed and validated by in situ data and aerial photographs. The results confirm the abilities of the NDVI, the NDMI and the TCW to distinguish disturbed and undisturbed areas. The NDMI vegetation index was particularly useful for the detection of the disturbed forest and forest recovery after bark beetle outbreaks and provided relevant information regarding the health of the forest (the individual stages of the disturbances and recovery mode). On the contrary, the TCG index demonstrated only limited abilities. The TCG could distinguish healthy forest and the gray-attack disturbance phase; however, it was difficult to use this index for detecting different recovery phases and to distinguish recovery phases from healthy forest. The areas affected by the disturbances had lower values of NDVI and NDMI indices (NDVI quartile range Q2–Q3: 0.63–0.71; NDMI Q2–Q3: 0.10–0.19) and the TCW index had negative values (Q2–Q3: −0.06–−0.05)). The analysis was performed with a cloud-based tool—Sentinel Hub. Cloud-based technologies have brought a new dimension in the processing and analysis of satellite data and allowed satellite data to be brought to end-users in the forestry sector. The Copernicus program and its data from Sentinel missions have evoked new opportunities in the application of satellite data. The usage of Sentinel-2 data in the research of long-term forest vegetation changes has a high relevance and perspective due to the free availability, distribution, and well-designed spectral, temporal, and spatial resolution of the Sentinel-2 data for monitoring forest ecosystems.

scholarly journals Digital mapping of solonchak complexes using Sentinel-2A data

2020 ◽  
Vol 222 ◽  
pp. 01010
Author(s):  
Azamat Suleymanov ◽  
Ilyusya Gabbasova ◽  
Mikhail Komissarov
Keyword(s):  
Satellite Data ◽  
Regression Models ◽  
Vegetation Index ◽  
Arable Land ◽  
Quadratic Function ◽  
Steppe Zone ◽  
Linear Quadratic ◽  
Republic Of Bashkortostan ◽  
Soil Conductivity ◽  
Sentinel 2A

Land salinization is an up-to-date issue being broadly studied all over the world. In Russia, salinization processes are predominantly observed in the southern regions, where the main areas of arable land are situated. This research is devoted to mapping of saline lands with the help of satellite data. The study was performed on a 100-hectare plot in the Trans-Ural steppe zone (Republic of Bashkortostan, Russia). A correlation was determined between the level of soil salinity and the main spectral indices associated with Sentinel-2A satellite data. Regression models used 5 salinity indices, vegetation index NDVI, and values of soil conductivity. Linear, quadratic, and logarithmic functions were used. By calculation, the salinity index 5 (G×R)/B demonstrated the best correlation values with the salinity level of (R=0.88, R2=0.77) while using the quadratic function. The vegetation index NDVI revealed no correlation, owing to the poor development or dried-up condition of vegetation. On the basis of the developed regression models, salinity maps are drawn, in which the areas of solonchak complexes are defined.

Examining the Integration of Landsat Operational Land Imager with Sentinel-1 and Vegetation Indices in Mapping Southern Yellow Pines (Loblolly, Shortleaf, and Virginia Pines)

2022 ◽  
Vol 88 (1) ◽  
pp. 29-38
Author(s):  
Clement E. Akumu ◽  
Eze O. Amadi
Keyword(s):  
Satellite Data ◽  
Forest Inventory ◽  
Classification Accuracy ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Canopy Cover ◽  
Optical Data ◽  
Modified Soil

The mapping of southern yellow pines (loblolly, shortleaf, and Virginia pines) is important to supporting forest inventory and the management of forest resources. The overall aim of this study was to examine the integration of Landsat Operational Land Imager (OLI ) optical data with Sentinel-1 microwave C-band satellite data and vegetation indices in mapping the canopy cover of southern yellow pines. Specifically, this study assessed the overall mapping accuracies of the canopy cover classification of southern yellow pines derived using four data-integration scenarios: Landsat OLI alone; Landsat OLI and Sentinel-1; Landsat OLI with vegetation indices derived from satellite data—normalized difference vegetation index, soil-adjusted vegetation index, modified soil-adjusted vegetation index, transformed soil-adjusted vegetation index, and infrared percentage vegetation index; and 4) Landsat OLI with Sentinel-1 and vegetation indices. The results showed that the integration of Landsat OLI reflectance bands with Sentinel-1 backscattering coefficients and vegetation indices yielded the best overall classification accuracy, about 77%, and standalone Landsat OLI the weakest accuracy, approximately 67%. The findings in this study demonstrate that the addition of backscattering coefficients from Sentinel-1 and vegetation indices positively contributed to the mapping of southern yellow pines.

scholarly journals Evaluation of satellite based indices for gross primary production estimates in a sparse savanna in the Sudan

2009 ◽  
Vol 6 (1) ◽  
pp. 129-138 ◽  
Author(s):  
M. Sjöström ◽  
J. Ardö ◽  
L. Eklundh ◽  
B. A. El-Tahir ◽  
H. A. M. El-Khidir ◽  
...  
Keyword(s):  
Water Stress ◽  
Primary Production ◽  
Satellite Data ◽  
Vegetation Index ◽  
Gross Primary Production ◽  
Vegetation Indices ◽  
Arid Ecosystems ◽  
Stress Index ◽  
Accurate Information ◽  
Production Modeling

Abstract. One of the more frequently applied methods for integrating controls on primary production through satellite data is the Light Use Efficiency (LUE) approach. Satellite indices such as the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) and the Shortwave Infrared Water Stress Index (SIWSI) have previously shown promise as predictors of primary production in several different environments. In this study, we evaluate NDVI, EVI and SIWSI derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite sensor against in-situ measurements from central Sudan in order to asses their applicability in LUE-based primary production modeling within a water limited environment. Results show a strong correlation between vegetation indices and gross primary production (GPP), demonstrating the significance of vegetation indices for deriving information on primary production with relatively high accuracy at similar areas. Evaluation of SIWSI however, reveal that the fraction of vegetation apparently is to low for the index to provide accurate information on canopy water content, indicating that the use of SIWSI as a predictor of water stress in satellite data-driven primary production modeling in similar semi-arid ecosystems is limited.

Sign in / Sign up

Export Citation Format

Share Document