scholarly journals MONITORING VEGETATION COVER CHANGE USING VEGETATION INDICES IN TANGBO RIVER, BARANGAY TANGBO, SAMBOAN

2019 ◽  
Vol XLII-4/W19 ◽  
pp. 493-498
Author(s):  
J. S. Yang ◽  
F. P. Campomanes ◽  
C. L. Patiño ◽  
M. J. L. Flores
Keyword(s):  
Vegetation Index ◽  
Vegetation Indices ◽  
Reference Points ◽  
Landsat 8 ◽  
Tasseled Cap ◽  
Index Changes ◽  
Using Data ◽  
Tourism Arrivals ◽  
Inverse Pattern

Abstract. Tangbo River is an important resource in Cebu’s southern town of Samboan for being the site of Aguinid Falls, a known tourist destination. Monitoring the changes in the river’s riparian vegetation is important since it has impacts on its ecological role of helping maintain biodiversity and river water quality. This study aims to detect vegetation index changes along the Tangbo River corridor using three vegetation indices: NDVI, EVI, NDMI, and Tasseled Cap indices, specifically for the years 1998, 2004, 2009, 2016, and 2019. It also aims to monitor the changes in NDVI and EVI values alongside tourism arrivals in Aguinid in 2018.Cloudless Landsat 5 (1998, 2004, 2009, and 2016) and Landsat 8 (2019) imagery were selected. Thirty reference points were plotted along the river with a 30-m distance between each point. Vegetation Indices (VI) and Tasseled Cap values were generated using data from these points and were compared for each selected year. NDVI and EVI values from the same reference points used in Landsat were generated from selected cloudless months of 2018 Planetscope imagery. Inbound tourist records were acquired from the tourism office of Samboan and the tourism arrivals for the year 2018 was then graphed with the Planetscope VI values for better visualization.Landsat imagery showed that there was a general upward trend in the vegetation indices from 1998 to 2019. Tasseled Cap Greenness and Wetness showed an increase in values from 1998–2019 while Tasseled Cap Brightness showed the opposite. Results from Planetscope data for the year 2018 showed that there was an inverse pattern between NDVI and tourism arrivals. Tourism arrivals peaked during the months of April and May based on annual records, while VI values dropped. On the other hand, both VI values peaked towards the last quarter of the year while tourist numbers dropped. This suggests that the pattern of VI values and tourism arrivals seemed to be influenced by seasonal changes rather than with each other. Findings from the study shows that further data collection is required to be able to establish a relationship between tourism and vegetation index values.

Assessment of the State of the Territories of Closed Waste Disposal Facilities by Vegetation Indices

2021 ◽  
Vol 25 (9) ◽  
pp. 30-37
Author(s):  
N.N. Sliusar ◽  
A.P. Belousova ◽  
G.M. Batrakova ◽  
R.D. Garifzyanov ◽  
M. Huber-Humer ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Waste Disposal ◽  
Vegetation Cover ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Cost Effective ◽  
Google Earth ◽  
The State ◽  
Landsat 8 ◽  
The Earth

The possibilities of using remote sensing of the Earth data to assess the formation of phytocenoses at reclaimed dumps and landfills are presented. The objects of study are landfills and dumps in the Perm Territory, which differed from each other in the types and timing of reclamation work. The state of the vegetation cover on the reclaimed and self-overgrowing objects was compared with the reference plots with naturally formed herbage of zonal meadow vegetation. The process of reclamation of the territory of closed landfills was assessed by the presence and homogeneity of the vegetation layer and by the values of the vegetation index NDVI. To identify the dynamics of changes in the vegetation cover, we used multi-temporal satellite images from the open resources of Google Earth and images in the visible and infrared ranges of the Landsat-5/TM and Landsat-8/OLI satellites. It is shown that the data of remote sensing of the Earth, in particular the analysis of vegetation indices, can be used to assess the dynamics of overgrowing of territories of reclaimed waste disposal facilities, as well as an additional and cost-effective method for monitoring the restoration of previously disturbed territories.

scholarly journals Can We Detect the Brownness or Greenness of the Congo Rainforest Using Satellite-Derived Surface Albedo? A Study on the Role of Aerosol Uncertainties

2019 ◽  
Vol 11 (5) ◽  
pp. 1410 ◽  
Author(s):  
Suman Moparthy ◽  
Dominique Carrer ◽  
Xavier Ceamanos
Keyword(s):  
Remote Sensing ◽  
Surface Albedo ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Satellite Observations ◽  
Atmospheric Effects ◽  
Forest Regions ◽  
Aerosol Emissions

The ability of spatial remote sensing in the visible domain to properly detect the slow transitions in the Earth’s vegetation is often a subject of debate. The reason behind this is that the satellite products often used to calculate vegetation indices such as surface albedo or reflectance, are not always correctly decontaminated from atmospheric effects. In view of the observed decline in vegetation over the Congo during the last decade, this study investigates how effectively satellite-derived variables can contribute to the answering of this question. In this study, we use two satellite-derived surface albedo products, three satellite-derived aerosol optical depth (AOD) products, two model-derived AOD products, and synthetic observations from radiative transfer simulations. The study discusses the important discrepancies (of up to 70%) found between these satellite surface albedo products in the visible domain over this region. We conclude therefore that the analysis of trends in vegetation properties based on satellite observations in the visible domain such as NDVI (normalized difference vegetation index), calculated from reflectance or albedo variables, is still quite questionable over tropical forest regions such as the Congo. Moreover, this study demonstrates that there is a significant increase (of up to 14%) in total aerosols within the last decade over the Congo. We note that if these changes in aerosol loads are not correctly taken into account in the retrieval of surface albedo, a greenness change of the surface properties (decrease of visible albedo) of around 8% could be artificially detected. Finally, the study also shows that neglecting strong aerosol emissions due to volcano eruptions could lead to an artificial increase of greenness over the Congo of more than 25% in the year of the eruptions and up to 16% during the 2–3 years that follow.

scholarly journals Assessing wheat spatial variation based on proximal and remote spectral vegetation indices and soil properties

2017 ◽  
pp. 21-30 ◽  
Author(s):  
Lorenzo Barbanti ◽  
Josep Adroher ◽  
Júnior Melo Damian ◽  
Nicola Di Virgilio ◽  
Gloria Falsone ◽  
...  
Keyword(s):  
Soil Properties ◽  
Spatial Variation ◽  
Vegetation Index ◽  
Soil Analysis ◽  
Vegetation Indices ◽  
Yield Potential ◽  
Soil Parameters ◽  
Landsat 8 ◽  
High Performing ◽  
Spectral Vegetation Indices

Assessing the spatial variation of soil and crop properties is the basis for site specific management of crop practices in precision agriculture applications. To this aim, proximal and remote spectral vegetation indices are increasingly replacing soil analysis. In this study the spatial variation of soil properties, proximal and remote spectral vegetation indices were compared in a winter wheat (Triticum aestivum L.) crop grown in a 4.15 ha field in northern Italy. Soil analysis (particle size distribution, pH, carbonates, C, total N, available P, exchangeable cations and electrical conductivity) was geo-referentially carried out; the proximal indices chlorophyll content by N-Tester and normalised difference vegetation index through GreenSeeker were determined in three dates during stem elongation; the remote indices PurePixelTM chlorophyll index and PurePixelTM vegetation index were determined through the Landsat 8 satellite in three dates during the same wheat stage. Dry biomass yield (DBY), grain yield (GY) and yield components were determined at harvest. Soil, proximal and remote data were submitted to principal component analysis (PCA), and the retained PCs were clustered to delineate areas at low, intermediate and high yield potential, based on soil parameters (CLUsp), proximal (CLUpi), and remote vegetation indices (CLUri). DBY and GY were significantly correlated with several soil parameters and vegetation indices. Spatial distribution of soil and crop data consistently depicted a low performing area (GY<3 Mg ha–1) and a high performing one (GY>8 Mg ha–1). CLUsp determined a lower GY difference between low and high performing area (+60%), compared to CLUpi and CLUri (almost +100%). In CLUsp and CLUpi the low and high performing area were of similar size (25 and 29% for the two respective areas in CLUsp; 25 and 33% in CLUpi), whereas in CLUri they were quite different (16 and 46%). Lastly, yield potential levels determined by vegetation indices (CLUpi and CLUri) exhibited a better degree of agreement with DBY and GY levels, than soil parameters (CLUsp). In exchange for this, the above referred soil parameters are quite consistent in time, allowing soil data to be used for more years. On concluding, PCA followed by clustering resulted in a robust delineation of field areas at different yield potential. This is the premise for developing research driven strategies of practical use.

scholarly journals Effect of Image Fusion on Vegetation Index Quality—A Comparative Study from Gaofen-1, Gaofen-2, Gaofen-4, Landsat-8 OLI and MODIS Imagery

2020 ◽  
Vol 12 (10) ◽  
pp. 1550 ◽  
Author(s):  
Prakash Ghimire ◽  
Deng Lei ◽  
Nie Juan
Keyword(s):  
Image Fusion ◽  
Vegetation Index ◽  
Signal To Noise Ratio ◽  
Low Cost ◽  
Spectral Response ◽  
Vegetation Indices ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Pass Filter ◽  
Modis Imagery

In recent years, the use of image fusion method has received increasing attention in remote sensing, vegetation cover changes, vegetation indices (VIs) mapping, etc. For making high-resolution and good quality (with low-cost) VI mapping from a fused image, its quality and underlying factors need to be identified properly. For example, same-sensor image fusion generally has a higher spatial resolution ratio (SRR) (1:3 to 1:5) but multi-sensor fusion has a lower SRR (1:8 to 1:10). In addition to SRR, there might be other factors affecting the fused vegetation index (FVI) result which have not been investigated in detail before. In this research, we used a strategy on image fusion and quality assessment to find the effect of image fusion for VI quality using Gaofen-1 (GF1), Gaofen-2 (GF2), Gaofen-4 (GF4), Landsat-8 OLI, and MODIS imagery with their panchromatic (PAN) and multispectral (MS) bands in low SRR (1:6 to 1:15). For this research, we acquired a total of nine images (4 PAN+5 MS) on the same (almost) date (GF1, GF2, GF4 and MODIS images were acquired on 2017/07/13 and the Landsat-8 OLI image was acquired on 2017/07/17). The results show that image fusion has the least impact on Green Normalized Vegetation Index (GNDVI) and Atmospherically Resistant Vegetation Index (ARVI) compared to other VIs. The quality of VI is mostly insensitive with image fusion except for the high-pass filter (HPF) algorithm. The subjective and objective quality evaluation shows that Gram-Schmidt (GS) fusion has the least impact on FVI quality, and with decreasing SRR, the FVI quality is decreasing at a slow rate. FVI quality varies with types image fusion algorithms and SRR along with spectral response function (SRF) and signal-to-noise ratio (SNR). However, the FVI quality seems good even for small SRR (1:6 to 1:15 or lower) as long as they have good SNR and minimum SRF effect. The findings of this study could be cost-effective and highly applicable for high-quality VI mapping even in small SRR (1:15 or even lower).

scholarly journals Land Cover and Forest Type Classification by Values of Vegetation Indices and Forest Structure of Tropical Lowland Forests in Central Vietnam

2020 ◽  
Vol 2020 ◽  
pp. 1-18
Author(s):  
Hung Nguyen Trong ◽  
The Dung Nguyen ◽  
Martin Kappas
Keyword(s):  
Land Cover ◽  
Vegetation Index ◽  
Forest Type ◽  
Vegetation Indices ◽  
Landsat 8 ◽  
Tropical Lowland ◽  
Central Vietnam ◽  
Lowland Forests ◽  
Type Classification ◽  
Sentinel 2

This paper aims to (i) optimize the application of multiple bands of satellite images for land cover classification by using random forest algorithms and (ii) assess correlations and regression of vegetation indices of a better-performed land cover classification image with vertical and horizontal structures of tropical lowland forests in Central Vietnam. In this study, we used Sentinel-2 and Landsat-8 to classify seven land cover classes of which three forest types were substratified as undisturbed, low disturbed, and disturbed forests where forest inventory of 90 plots, as ground-truth, was randomly sampled to measure forest tree parameters. A total of 3226 training points were sampled on seven land cover types. The performance of Landsat-8 showed out-of-bag error of 31.6%, overall accuracy of 68%, kappa of 67.5%, while Sentinel-2 showed out-of-bag error of 14.3% and overall accuracy of 85.7% and kappa of 83%. Ten vegetation indices of the better-performed image were extracted to find out (i) the correlation and regression of horizontal and vertical structures of trees and (ii) assess the variation values between ground-truthing plots and training sample plots in three forest types. The result of the t test on vegetation indices showed that six out of ten vegetation indices were significant at p<0.05. Seven vegetation indices had a correlation with the horizontal structure, but four vegetation indices, namely, Enhanced Vegetation Index, Perpendicular Vegetation Index, Difference Vegetation Index, and Transformed Normalized Difference Vegetation Index, had better correlations r = 0.66, 0.65, 0.65, 0.63 and regression results were of R2 = 0.44, 0.43, 0.43, and 0.40, respectively. The correlations of tree height were r = 0.46, 0.43, 0.43, and 0.49 and its regressions were of R2 = 0.21, 0.19, 0.18, and 0.24, respectively. The results show the possibility of using random forest algorithm with Sentinel-2 in forest type classification in line with vegetation indices application.

scholarly journals Chlorophyll Changes Using Neural Network and Vegetation Indices in Tropical deciduous forest

2020 ◽  
Author(s):  
Saurabh Kumar Gupta ◽  
Arvind Chandra Pandey
Keyword(s):  
Neural Network ◽  
Vegetation Index ◽  
Deciduous Forest ◽  
Plant Stress ◽  
Vegetation Indices ◽  
Radiative Transfer Model ◽  
Tropical Deciduous Forest ◽  
Transfer Model ◽  
Landsat 8 ◽  
Spatio Temporal

Abstract Background: Ongoing climate and Earth’s atmosphere changes create profound effect on distribution and composition of forest, as well as on the fauna that depends on forest. The Sentinel-2A satellite data eases the mapping of Leaf Chlorophyll Content (LCC) at higher spatial and temporal resolution. In the present study, the temporal dimension of LCC was evaluated as an indicator of plant stress. LCC was retrieved using the inversion of the radiative transfer model based on an artificial neural network. The data used for Spatio-temporal modelling of LCC was Landsat data.Result: From the Sentinel imagery derived vegetation indices, it was found that the narrowband indices having high correlation with LCC were pigment specific simple ratio and normalized difference index (45) (R2 > 0.7; p < 0.001) centred at 665 nm, 705 nm, and 740 nm. Landsat 8 infrared percentage vegetation index had a strong relationship with LCC (R2 =0.8). The Spatio-temporal (1997 to 2017) plant stress were detected using changes in LCC through an equation of correlation. The negative changes and deterioration of LCC were seen in the forest during the year 1997 to 20I7(rate = -1.2 µgcm-2year-1) showing higher rate of forest health decline. Conclusion: The 33% of plant stress increased currently in the protected forest mainly because of anthropogenic influences. These vast decline in the chlorophyll gives rise to various photosynthetic vulnerabilities in forest ecosystem and indirectly affects human including wildlife.

scholarly journals USING LANDSAT 8 IMAGE TIME SERIES FOR CROP MAPPING IN A REGION OF CERRADO, BRAZIL

2016 ◽  
Vol XLI-B8 ◽  
pp. 845-850 ◽  
Author(s):  
H. Bendini ◽  
I. D. Sanches ◽  
T. S. Körting ◽  
L. M. G. Fonseca ◽  
A. J. B. Luiz ◽  
...  
Keyword(s):  
Land Use ◽  
Time Series ◽  
Vegetation Index ◽  
Agricultural Land ◽  
Vegetation Indices ◽  
Agricultural Land Use ◽  
Landsat 8 ◽  
Land Use Classification ◽  
Crop Mapping ◽  
Phenological Metrics

The objective of this research is to classify agricultural land use in a region of the Cerrado (Brazilian Savanna) biome using a time series of Enhanced Vegetation Index (EVI) from Landsat 8 OLI. Phenological metrics extracted from EVI time series, a Random Forest algorithm and data mining techniques are used in the process of classification. The area of study is a region in the Cerrado in a region of the municipality of Casa Branca, São Paulo state, Brazil. The results are encouraging and demonstrate the potential of phenological parameters obtained from time series of OLI vegetation indices for agricultural land use classification.

scholarly journals Comparison between Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and Landsat 8 Operational Land Imager (OLI) Assessment of Vegetation Indices

2017 ◽  
Vol 1 (2) ◽  
pp. 355-366
Author(s):  
E. O. Makinde ◽  
A. D. Obigha
Keyword(s):  
Statistical Analysis ◽  
Land Cover ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Thematic Mapper ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Water Index ◽  
Landsat 7 ◽  
Index 2

The Landsat system has contributed significantly to the understanding of the Earth observation for over forty years. Since May 2013, data from Landsat 8 has been available online for download, with substantial differences from its predecessors, having an extended number of spectral bands and narrower bandwidths. The objectives of this research were majorly to carry out a cross comparison analysis between vegetation indices derived from Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and Landsat 8 Operational Land Imager (OLI) and also performed statistical analysis on the results derived from the vegetation indices. Also, this research carried out a change detection on four land cover classes present within the study area, as well as projected the land cover for year 2030. The methods applied in this research include, carrying out image classification on the Landsat imageries acquired between 1984 – 2016 to ascertain the changes in the land cover types, calculated the mean values of differenced vegetation indices derived from the four land covers between Landsat 7 ETM+ and Landsat 8 OLI. Statistical analysis involving regression and correlation analysis were also carried out on the vegetation indices derived between the two sensors, as well as scatter plot diagrams with linear regression equation and coefficients of determination (R2). The results showed no noticeable differences between Landsat 7 and Landsat 8 sensors, which demonstrates high similarities. This was observed because Global Environmental Monitoring Index (GEMI), Improved Modified Triangular Vegetation Index 2 (MTVI2), Normalized Burn Ratio (NBR), Normalized Difference Vegetation Index (NDVI), Modified Normalized Difference Water Index (MNDWI), Leaf Area Index (LAI) and Land Surface Water Index (LSWI) had smaller standard deviations. However, Renormalized Difference Vegetation Index (RDVI), Anthocyanin Reflectance Index 1 (ARI1) and Anthocyanin Reflectance Index 2 (ARI2) performed relatively poorly because their standard deviations were high. the correlation analysis of the vegetation indices that both sensors had a very high linear correlation coefficient with R2 greater than 0.99. It was concluded from this research that Landsat 7 ETM+ and Landsat 8 OLI can be used as complimentary data.

scholarly journals Comparative Analysis of Landsat-8, Sentinel-2, and GF-1 Data for Retrieving Soil Moisture over Wheat Farmlands

2020 ◽  
Vol 12 (17) ◽  
pp. 2708 ◽  
Author(s):  
Qi Wang ◽  
Jiancheng Li ◽  
Taoyong Jin ◽  
Xin Chang ◽  
Yongchao Zhu ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Optical Sensors ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Cloud Model ◽  
Vegetation Indices ◽  
Optical Data ◽  
Landsat 8 ◽  
Different Sources ◽  
Sentinel 2

Soil moisture is an important variable in ecological, hydrological, and meteorological studies. An effective method for improving the accuracy of soil moisture retrieval is the mutual supplementation of multi-source data. The sensor configuration and band settings of different optical sensors lead to differences in band reflectivity in the inter-data, further resulting in the differences between vegetation indices. The combination of synthetic aperture radar (SAR) data with multi-source optical data has been widely used for soil moisture retrieval. However, the influence of vegetation indices derived from different sources of optical data on retrieval accuracy has not been comparatively analyzed thus far. Therefore, the suitability of vegetation parameters derived from different sources of optical data for accurate soil moisture retrieval requires further investigation. In this study, vegetation indices derived from GF-1, Landsat-8, and Sentinel-2 were compared. Based on Sentinel-1 SAR and three optical data, combined with the water cloud model (WCM) and the advanced integral equation model (AIEM), the accuracy of soil moisture retrieval was investigated. The results indicate that, Sentinel-2 data were more sensitive to vegetation characteristics and had a stronger capability for vegetation signal detection. The ranking of normalized difference vegetation index (NDVI) values from the three sensors was as follows: the largest was in Sentinel-2, followed by Landsat-8, and the value of GF-1 was the smallest. The normalized difference water index (NDWI) value of Landsat-8 was larger than that of Sentinel-2. With reference to the relative components in the WCM model, the contribution of vegetation scattering exceeded that of soil scattering within a vegetation index range of approximately 0.55–0.6 in NDVI-based models and all ranges in NDWI1-based models. The threshold value of NDWI2 for calculating vegetation water content (VWC) was approximately an NDVI value of 0.4–0.55. In the soil moisture retrieval, Sentinel-2 data achieved higher accuracy than data from the other sources and thus was more suitable for the study for combination with SAR in soil moisture retrieval. Furthermore, compared with NDVI, higher accuracy of soil moisture could be retrieved by using NDWI1 (R2 = 0.623, RMSE = 4.73%). This study provides a reference for the selection of optical data for combination with SAR in soil moisture retrieval.

Measuring UHI using Landsat 8 OLI and TIRS data with NDVI and NDBI in Municipality of Prishtina

2021 ◽  
Vol 14 (11) ◽  
pp. 25-36
Author(s):  
Florim Isufi ◽  
Albert Berila ◽  
Shpejtim Bulliqi
Keyword(s):  
Regression Analysis ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Landsat 8 ◽  
Strong Positive Correlation ◽  
Strong Negative Correlation ◽  
Urban Heat ◽  
Surface Urban Heat Island

The study is aimed at investigating the phenomenon of the Surface Urban Heat Island (SUHI) over the municipality of Prishtina. The SUHI was investigated based on the relationship between Land Surface Temperature (LST) estimated from Landsat 8 Thermal Infrared Sensor (TIRS) band with Normalized Difference Built-up Index (NDBI) and Normalized Difference Vegetation Index (NDVI) from Landsat 8 Operational Land Imager (OLI) bands using Geographic Information System (GIS). To understand this relationship, a regression analysis was performed. Regression analysis in both cases showed high relationships between LST, NDVI and NDBI. LST relationships with NDVI showed a strong negative correlation having an R2 value of 0.7638 highlighting the extraordinary role of vegetation towards reducing the SUHI effect while LST relationships with NDBI showed a strong positive correlation having an R2 value of 0.8038 highlighting the role that built-up areas have in strengthening the SUHI effect. Built-up areas and bare surfaces are responsible for generating the SUHI effect while vegetation and water bodies minimize this effect by creating freshness. The maps in which the SUHI phenomenon are identified, are extremely important and should be paid great attention by the city leaders themselves. This should be done in order for urban planning policies to go to those areas where such a harmful phenomenon occurs in order for the lives of citizens to be as healthy as possible.

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