Seasonal spectral variation of Zostera noltii and its influence on pigment-based Vegetation Indices

2013 ◽  
Vol 446 ◽  
pp. 86-94 ◽  
Author(s):  
A. Bargain ◽  
M. Robin ◽  
V. Méléder ◽  
P. Rosa ◽  
E. Le Menn ◽  
...  
Keyword(s):  
Vegetation Indices ◽  
Spectral Variation ◽  
Zostera Noltii

scholarly journals Spectral radiance characteristics and vegetative indices of crops -A ground based remote sensing technique

MAUSAM ◽  
2022 ◽  
Vol 46 (1) ◽  
pp. 75-80
Author(s):  
R. C. DUBEY ◽  
S. D. GAIKWAD ◽  
V. S. NAWATHE ◽  
R. G. DEKHANE ◽  
S. N. BIDYANTA
Keyword(s):  
Vegetation Indices ◽  
Spectral Radiance ◽  
Growth Stages ◽  
Spectral Variation ◽  
Phenological Stages ◽  
Remote Sensing Technique ◽  
Agricultural College ◽  
Tasseled Cap Transformation ◽  
Vegetative Indices ◽  
Simple Difference

The spectral radiance characteristics and vegetation indices like simple difference, ratio vegetation, normalised vegetation perpendicular vegetation transformed vegetation and tasseled cap transformation of mung been sunflower and groundnut crops at different growth stages have been studied. The experiment was conducted in post rainy season during 1990-91 in the farm of Agricultural College. Pune using hand held multi-spectral radiometer. The significance of spectral variation of radiance and vegetative indices with respect to the phenological stages are discussed.   

scholarly journals RSPD: A Novel Remote Sensing Index of Plant Biodiversity Combining Spectral Variation Hypothesis and Productivity Hypothesis

2021 ◽  
Vol 13 (15) ◽  
pp. 3007
Author(s):  
Hao Sun ◽  
Jiaqi Hu ◽  
Jiaxiang Wang ◽  
Jingheng Zhou ◽  
Ling Lv ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Large Scale ◽  
Vegetation Indices ◽  
Terrestrial Ecosystem ◽  
Nutrient Utilization ◽  
Diversity Indices ◽  
Google Earth ◽  
Vegetation Classification ◽  
Spectral Variation ◽  
Sentinel 2

Plant diversity (PD) plays an important role in maintaining the healthy function of an ecosystem through affecting the productivity, stability, and nutrient utilization of a terrestrial ecosystem. Remote sensing is a vital way to monitor the status and changes of PD. Most of the existing methods rely on a field botany survey to construct a statistical relationship between PD and remote sensing observations. However, a field botany survey is too costly to be applied widely. In this study, we constructed a new remote sensing index of PD (RSPD), combining the spectral variation hypothesis and productivity hypothesis. Concretely, the RSPD integrated the multi-band spectral reflectance and several spectral greenness, moisture, and red-edge vegetation indices with the principles of Shannon information entropy and Euclidean distance. The RSPD was evaluated by comparing the classical coefficient of variation (CV) method and the Shannon and Simpson diversity indices based on vegetation classification results. Two cases were selected, where Case I was in Beijing and Case II was located in part of Huai’an, China. Sentinel-2 data in three years of 2016, 2018, and 2020 and higher-resolution Pléiades-1 data in 2018 were also utilized. The results demonstrate that: (1) the RSPD is basically consistent with the CV in spatiotemporal variation; (2) the RSPD outperforms the CV as compared with Shannon and Simpson diversity indices that are based on vegetation classification results with Sentinel-2 and Pléiades-1 data; (3) the RSPD outperforms the CV as compared with visual interpretations with Google Earth image. The suggested index can reflect the richness and evenness of plant species, which is inherent in its calculation formula. Moreover, it has a great potential for large-scale regional and long-term series monitoring.

HEALTHINESS OF OIL PALM PLANTATION TOWARDS SUSTAINABILITY OF ENVIRONMENT

2020 ◽  
Vol 7 (1) ◽  
pp. 21
Author(s):  
Faradina Marzukhi ◽  
Nur Nadhirah Rusyda Rosnan ◽  
Md Azlin Md Said
Keyword(s):  
Oil Palm ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Satellite Image ◽  
Vegetation Indices ◽  
Soil Nutrient ◽  
Oil Palm Plantation ◽  
Sustainable Environment ◽  
Moisture Analysis ◽  
The Relationship

The aim of this study is to analyse the relationship between vegetation indices of Normalized Difference Vegetation Index (NDVI) and soil nutrient of oil palm plantation at Felcra Nasaruddin Bota in Perak for future sustainable environment. The satellite image was used and processed in the research. By Using NDVI, the vegetation index was obtained which varies from -1 to +1. Then, the soil sample and soil moisture analysis were carried in order to identify the nutrient values of Nitrogen (N), Phosphorus (P) and Potassium (K). A total of seven soil samples were acquired within the oil palm plantation area. A regression model was then made between physical condition of the oil palms and soil nutrients for determining the strength of the relationship. It is hoped that the risk map of oil palm healthiness can be produced for various applications which are related to agricultural plantation.

Analysis of Remote Sensing based Vegetation Indices (VIs) for Unmanned Aerial System (UAS): A Review

2020 ◽  
Vol 3 (2) ◽  
pp. 58-73
Author(s):  
Vijay Bhagat ◽  
Ajaykumar Kada ◽  
Suresh Kumar
Keyword(s):  
Remote Sensing ◽  
Spatial Data ◽  
Satellite Remote Sensing ◽  
Vegetation Indices ◽  
Unmanned Aerial System ◽  
Sustainable Land Management ◽  
Efficient Tool ◽  
Site Specific ◽  
The Earth ◽  
Interesting Area

Unmanned Aerial System (UAS) is an efficient tool to bridge the gap between high expensive satellite remote sensing, manned aerial surveys, and labors time consuming conventional fieldwork techniques of data collection. UAS can provide spatial data at very fine (up to a few mm) and desirable temporal resolution. Several studies have used vegetation indices (VIs) calculated from UAS based on optical- and MSS-datasets to model the parameters of biophysical units of the Earth surface. They have used different techniques of estimations, predictions and classifications. However, these results vary according to used datasets and techniques and appear very site-specific. These existing approaches aren’t optimal and applicable for all cases and need to be tested according to sensor category and different geophysical environmental conditions for global applications. UAS remote sensing is a challenging and interesting area of research for sustainable land management.

Worldview-2 and Landsat 8 Satellite Data for Seaweed Mapping along Karachi Coast

2016 ◽  
Vol 5 (2) ◽  
Author(s):  
Muhammad Danish Siddiqui ◽  
Arjumand Z Zaidi
Keyword(s):  
Satellite Data ◽  
Spatial Scales ◽  
Economic Value ◽  
Vegetation Indices ◽  
Cost Effective ◽  
Geographical Information ◽  
Landsat 8 ◽  
Coverage Area ◽  
Breeding Sites ◽  
Marine Plant

<span>Seaweed is a marine plant or algae which has economic value in many parts of the world. The purpose of <span>this study is to evaluate different satellite sensors such as high-resolution WorldView-2 (WV2) satellite <span>data and Landsat 8 30-meter resolution satellite data for mapping seaweed resources along the coastal<br /><span>waters of Karachi. The continuous monitoring and mapping of this precious marine plant and their <span>breeding sites may not be very efficient and cost effective using traditional survey techniques. Remote <span>Sensing (RS) and Geographical Information System (GIS) can provide economical and more efficient <span>solutions for mapping and monitoring coastal resources quantitatively as well as qualitatively at both <span>temporal and spatial scales. Normalized Difference Vegetation Indices (NDVI) along with the image <span>enhancement techniques were used to delineate seaweed patches in the study area. The coverage area of <span>seaweed estimated with WV-2 and Landsat 8 are presented as GIS maps. A more precise area estimation <span>wasachieved with WV-2 data that shows 15.5Ha (0.155 Km<span>2<span>)of seaweed cover along Karachi coast that is <span>more representative of the field observed data. A much larger area wasestimated with Landsat 8 image <span>(71.28Ha or 0.7128 Km<span>2<span>) that was mainly due to the mixing of seaweed pixels with water pixels. The <span>WV-2 data, due to its better spatial resolution than Landsat 8, have proven to be more useful than Landsat<br /><span>8 in mapping seaweed patches</span></span></span></span></span></span></span></span></span></span></span></span></span></span><br /><br class="Apple-interchange-newline" /></span></span></span></span></span>

scholarly journals Early detection of pine wilt disease in Pinus tabuliformis in North China using a field portable spectrometer and UAV-based hyperspectral imagery

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Run Yu ◽  
Lili Ren ◽  
Youqing Luo
Keyword(s):  
Early Stage ◽  
Vegetation Indices ◽  
Pine Wilt Disease ◽  
Wilt Disease ◽  
Pinus Tabulaeformis ◽  
Pine Tree ◽  
Preventative Measures ◽  
Pine Wilt ◽  
Pine Trees ◽  
Airborne Vehicle

Abstract Background Pine wilt disease (PWD) is a major ecological concern in China that has caused severe damage to millions of Chinese pines (Pinus tabulaeformis). To control the spread of PWD, it is necessary to develop an effective approach to detect its presence in the early stage of infection. One potential solution is the use of Unmanned Airborne Vehicle (UAV) based hyperspectral images (HIs). UAV-based HIs have high spatial and spectral resolution and can gather data rapidly, potentially enabling the effective monitoring of large forests. Despite this, few studies examine the feasibility of HI data use in assessing the stage and severity of PWD infection in Chinese pine. Method To fill this gap, we used a Random Forest (RF) algorithm to estimate the stage of PWD infection of trees sampled using UAV-based HI data and ground-based data (data directly collected from trees in the field). We compared relative accuracy of each of these data collection methods. We built our RF model using vegetation indices (VIs), red edge parameters (REPs), moisture indices (MIs), and their combination. Results We report several key results. For ground data, the model that combined all parameters (OA: 80.17%, Kappa: 0.73) performed better than VIs (OA: 75.21%, Kappa: 0.66), REPs (OA: 79.34%, Kappa: 0.67), and MIs (OA: 74.38%, Kappa: 0.65) in predicting the PWD stage of individual pine tree infection. REPs had the highest accuracy (OA: 80.33%, Kappa: 0.58) in distinguishing trees at the early stage of PWD from healthy trees. UAV-based HI data yielded similar results: the model combined VIs, REPs and MIs (OA: 74.38%, Kappa: 0.66) exhibited the highest accuracy in estimating the PWD stage of sampled trees, and REPs performed best in distinguishing healthy trees from trees at early stage of PWD (OA: 71.67%, Kappa: 0.40). Conclusion Overall, our results confirm the validity of using HI data to identify pine trees infected with PWD in its early stage, although its accuracy must be improved before widespread use is practical. We also show UAV-based data PWD classifications are less accurate but comparable to those of ground-based data. We believe that these results can be used to improve preventative measures in the control of PWD.

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