Extract Method of Urban Greenbelt Based on TM Image

2012 ◽  
Vol 610-613 ◽  
pp. 3562-3565
Author(s):  
Xi Chen ◽  
Yong Wang
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Principal Component ◽  
Neutral Network ◽  
Actual Situation ◽  
Image Preprocessing ◽  
Guangzhou City ◽  
Tm Image ◽  
Network Method ◽  
Tasseled Cap Transformation

Based on remote sensing image, the spectral information of urban Greenbelt in Guangzhou City was extracted from TM image by ENVI4.7. After finishing image preprocessing, then used 4 methods (such as principal component analysis, tasseled cap transformation, the normalized difference vegetation index(NDVI) method, SOFM artifical neutral network method) extract greenbelt information of Guangzhou City, and compared the images produced by four methods, according to the actual situation of the study area, we find that SOFM neutral network has the best classification effect.

scholarly journals Modeling the Radial Stem Growth of the Pine (Pinus sylvestris L.) Forests Using the Satellite-Derived NDVI and LST (MODIS/AQUA) Data

Atmosphere ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 12
Author(s):  
Yulia Ivanova ◽  
Anton Kovalev ◽  
Vlad Soukhovolsky
Keyword(s):  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Principal Component ◽  
Stem Growth ◽  
Forest Growth ◽  
Parabolic Approximation ◽  
New Approach ◽  
The Relationship ◽  
Tree Increment

The paper considers a new approach to modeling the relationship between the increase in woody phytomass in the pine forest and satellite-derived Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) (MODIS/AQUA) data. The developed model combines the phenological and forest growth processes. For the analysis, NDVI and LST (MODIS) satellite data were used together with the measurements of tree-ring widths (TRW). NDVI data contain features of each growing season. The models include parameters of parabolic approximation of NDVI and LST time series transformed using principal component analysis. The study shows that the current rate of TRW is determined by the total values of principal components of the satellite indices over the season and the rate of tree increment in the preceding year.

Comparative Study of Change Detection and Urban Expansion Using Multi-Date Multi-Source Data: A Case Study of Greater Khartoum

2012 ◽  
Vol 23 (2) ◽  
pp. 139-172
Author(s):  
Abdullah Salman Alsalman Abdullah Salman Alsalman
Keyword(s):  
Image Processing ◽  
Change Detection ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Urban Expansion ◽  
Remote Sensing Data ◽  
Principal Component ◽  
Test Area ◽  
Image Differencing ◽  
Erdas Imagine

Noting that Khartoum represents the most rapidly expanding city in the Sudan and taking into account that change detection operations are seldom , the present study has been initiated to attempt to produce work that synthesizes land use/land cover (LULC) to investigate change detection using GIS, remote sensing data and digital image processing techniques; estimate, evaluate and map changes that took place in the city from 1975 to 2003. The experiment used the techniques of visual inspection, write-function-memoryinsertion, image differencing, image transformation i.e. normalized difference vegetation index (NDVI), tasseled cap, principal component analysis (PCA), post-classification comparison and GIS. The results of all these various techniques were used by the authors to study change detection of the geographic locale of the test area. Image processing and GIS techniques were performed using Intergraph Image analyst 8.4 and GeoMedia professional version 6, ERDAS Imagine 8.7, and ArcGIS 9.2. Results obtained were discussed and analyzed in a comparative manner and a conclusion regarding the best method for change detection of the test area was derived.

scholarly journals Evaluation of vineyard growth under four irrigation regimes using vegetation and soil on-the-go sensors

SOIL ◽  
2015 ◽  
Vol 1 (1) ◽  
pp. 459-473 ◽  
Author(s):  
J. M. Terrón ◽  
J. Blanco ◽  
F. J. Moral ◽  
L. A. Mancha ◽  
D. Uriarte ◽  
...  
Keyword(s):  
Precision Agriculture ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Soil Depth ◽  
Principal Component ◽  
Temporal Analysis ◽  
Vegetation Growth ◽  
Growth Variability ◽  
Growing Seasons ◽  
Irrigation Treatments

Abstract. Precision agriculture is a useful tool to assess plant growth and development in vineyards. The present study focused on spatial and temporal analysis of vegetation growth variability, in four irrigation treatments with four replicates. The research was carried out in a vineyard located in the southwest of Spain during the 2012 and 2013 growing seasons. Two multispectral sensors mounted on an all-terrain vehicle (ATV) were used in the different growing seasons/stages in order to calculate the vineyard normalized difference vegetation index (NDVI). Soil apparent electrical conductivity (ECa) was also measured up to 0.8 m soil depth using an on-the-go geophysical sensor. All measured data were analysed by means of principal component analysis (PCA). The spatial and temporal NDVI and ECa variations showed relevant differences between irrigation treatments and climatological conditions.

scholarly journals Using Synthetic Remote Sensing Indicators to Monitor the Land Degradation in a Salinized Area

2021 ◽  
Vol 13 (15) ◽  
pp. 2851
Author(s):  
Tao Yu ◽  
Guli Jiapaer ◽  
Anming Bao ◽  
Guoxiong Zheng ◽  
Liangliang Jiang ◽  
...  
Keyword(s):  
Land Degradation ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Salt Content ◽  
Principal Component ◽  
Water Withdrawal ◽  
Moisture Index ◽  
High Positive Correlation ◽  
Amu Darya

Land degradation poses a critical threat to the stability and security of ecosystems, especially in salinized areas. Monitoring the land degradation of salinized areas facilitates land management and ecological restoration. In this research, we integrated the salinization index (SI), albedo, normalized difference vegetation index (NDVI) and land surface soil moisture index (LSM) through the principal component analysis (PCA) method to establish a salinized land degradation index (SDI). Based on the SDI, the land degradation of a typical salinized area in the Central Asia Amu Darya delta (ADD) was analysed for the period 1990–2019. The results showed that the proposed SDI had a high positive correlation (R2 = 0.89, p < 0.001) with the soil salt content based on field sampling, indicating that the SDI can reveal the land degradation characteristics of the ADD. The SDI indicated that the extreme and strong land degradation areas increased from 1990 to 2019, mainly in the downstream and peripheral regions of the ADD. From 1990 to 2000, land degradation improvement over a larger area than developed, conversely, from 2000 to 2019, and especially, from 2000 to 2010, the proportion of land degradation developed was 32%, which was mainly concentrated in the downstream region of the ADD. The spatial autocorrelation analysis indicated that the SDI values of Moran’s I in 1990, 2000, 2010 and 2019 were 0.82, 0.78, 0.82 and 0.77, respectively, suggesting that the SDI was notably clustered in space rather than randomly distributed. The expansion of unused land due to land use change, water withdrawal from the Amu Darya River and the discharge of salt downstream all contributed to land degradation in the ADD. This study provides several valuable insights into the land degradation monitoring and management of this salinized delta and similar settings worldwide.

scholarly journals Classification of Hass avocado (persea americana mill) in terms of its ripening via hyperspectral images

TecnoLógicas ◽  
2019 ◽  
Vol 22 (45) ◽  
pp. 109-128 ◽  
Author(s):  
Jhon Pinto ◽  
Hoover Rueda-Chacón ◽  
Henry Arguello
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Agricultural Sector ◽  
Low Cost ◽  
Color Space ◽  
Principal Component ◽  
Persea Americana ◽  
Hyperspectral Images ◽  
Non Invasive ◽  
Hass Avocado

The use of non-invasive and low-cost methodologies allows the monitoring of fruit ripening and quality control, without affecting the product under study. In particular, the Hass avocado is of high importance for the agricultural sector in Colombia because the country is strongly promoting its export, which has generated an expansion in the number of acres cultivated with this fruit. Therefore, this paper aims to study and analyze the ripening state of Hass avocados through non-invasive hyperspectral images, using principal component analysis (PCA) along with spectral vegetation indices, such as the normalized difference vegetation index (NDVI), ratio vegetation index (RVI), photochemical reflectance index (PRI), colorimetry analysis in the CIE L*a*b* color space, and color index triangular greenness index (TGI). In particular, this work conducts a quantitative analysis of the ripening process of a population of 7 Hass avocados over 10 days. The avocados under study were classified into three categories: unripe, close-to-ripe, and ripe. The obtained results show that it is possible to characterize the ripening state of avocados through hyperspectral images using a non-invasive acquisition system. Further, it is possible to know the post-harvest ripening state of the avocado at any given day.

scholarly journals Analisis Komparasi Metode Pemetaan Ekosistem Mangrove Menggunakan Penginderaan Jauh dan Sistem Informasi Geografis

2020 ◽  
Vol 17 (2) ◽  
pp. 49-55
Author(s):  
Isma Yulia Rahma
Keyword(s):  
Forest Canopy ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Principal Component ◽  
Mangrove Ecosystem ◽  
Forest Canopy Density ◽  
Distribution Mapping ◽  
Information System Technology ◽  
The Right

The use of tools and methods in mapping mangrove ecosystem continues to change.Nowday's trend in mapping is to use remote sensing and digital geographic Information system technology. There are several commonly used methods for mapping the mangrove ecosystem, but we should be aware that choosing the right method of analysis will greatly support the quality of research. The research method is literature review from various books and accredited scientific journals. Subsequently conducted analysis of application methods of mapping mangrove ecosystem of various case studies and research needs. Based on research, there are five methods and analysis used i.e.manual interpretation with Mirror stereoscope, NDVI (Normalized Difference Vegetation Index) as the most common analysis for mangrove distribution mapping. Multivariat PCA (Principal Component Analysis), FCD (Forest Canopy Density) model, and copmpare methods to mapping the extensive changes of mangrove ecosystems. Therefore, this article can be an input for the prospective mangrove ecosystem researchers in determining the preciese method of analysis.  

scholarly journals MAPPING POTENTIAL HABITATS FOR ARTHROPOD VECTORS OF TRYPANOSOMIASIS INFECTION IN NORTHERN NIGERIA: AN INTRODUCTORY SYNTHESIS

2018 ◽  
Vol XLII-4/W9 ◽  
pp. 147-152 ◽  
Author(s):  
H. A. Umar ◽  
M. F. Abdul Khanan ◽  
D. A. Umar ◽  
M. S. Shiru ◽  
M. Isma&amp;apos;il ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Principal Component ◽  
Tsetse Fly ◽  
Sub Saharan Africa ◽  
Northern Nigeria ◽  
Changing Climate ◽  
Arthropod Vectors ◽  
Potential Habitats ◽  
Animal Trypanosomiasis

<p><strong>Abstract.</strong> This paper presents an introductory synthesis for mapping potential habitats of arthropod vectors responsible for animal trypanosomiasis diseases in Northern Nigeria, where there is high production of livestock. Animal trypanosomiasis is considered an arthropod-borne viral disease which is endemic in 36 countries of sub-Saharan Africa and particularly in northern Nigeria. The disease which is transmitted by the vector tsetse fly remains a threat to both humans and livestock in many rural communities of Nigeria. The outbreak of the disease is known to occur as a result of the changing climate which relates to changes in sea surface temperatures otherwise known as “El Niño Southern Oscillations” (ENSO). Trypanosomiasis is mainly experienced whenever there are changes in global precipitation as a result of the changing climate. Monthly Satellite data of Normalized Difference Vegetation Index (NDVI) at 2.5&amp;deg; spatial resolution was sourced from NASA-MODIS/CMD and subjected to principal component analysis using standardized principal components of GIS with a digital elevation model (DEM) supplemented in the analysis. Results revealed pockets of probable habitats of arthropod vectors to be around forest islands characterized by dry woodland and savanna, and in other cases around gallery forests and few lowland and riverine areas. This study demonstrates that geospatial technology is a cost effective tool in mapping of the arthropod vector habitats for Northern Nigeria.</p>

scholarly journals Soil chemical analysis of gangetic delta plain by combined use of multispectral imagery and XRF spectroscopy

2016 ◽  
Vol 4 (2) ◽  
pp. 92
Author(s):  
Neha Singh ◽  
Harshita Asthana ◽  
Chandrasekhar Azad Vishwakarma ◽  
Ratan Sen ◽  
Saumitra Mukherjee
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Landsat Imagery ◽  
Agricultural Productivity ◽  
Principal Component ◽  
Vegetation Pattern ◽  
X Ray Diffraction ◽  
Band Ratio ◽  
Bengal Delta ◽  
Delta Plain

24 Parganas districts of West Bengal are very well known for their agricultural productivity. These districts are the part of the mature delta plain of the Bengal delta which is formed by the deposition of weathered sediments through Himalayan Rivers. The agricultural productivity of an area depends mainly upon the fertility of soil which in turn depends on the presence of essential nutrients in it. Thus, the present study was carried out to assess the types of minerals present in the soil which provide the elements that act as the nutrients to the plant. Band ratio technique using the Landsat imagery and X-Ray Diffraction was carried out for the study of mineral composition. XRF was done for the elemental composition of the soil samples and Principal Component Analysis was carried out to assess the sources of these nutrients in the soil. Normalized Difference Vegetation Index was also calculated using Landsat imagery to study the vegetation pattern in the area. The study suggests that the area is mainly comprised of clay and ferrous minerals and contains nearly all the elements that act as macro-and micro-nutrients. However, the study also shows the accumulation of some of the heavy metals which may be due to the excessive use of fertilizers.

scholarly journals Classification of Soybean Pubescence from Multispectral Aerial Imagery

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Robert W. Bruce ◽  
Istvan Rajcan ◽  
John Sulik
Keyword(s):  
Plant Breeding ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Accurate Determination ◽  
Principal Component ◽  
Training Data ◽  
Aerial Imagery ◽  
Aerial Images ◽  
Support Vector

The accurate determination of soybean pubescence is essential for plant breeding programs and cultivar registration. Currently, soybean pubescence is classified visually, which is a labor-intensive and time-consuming activity. Additionally, the three classes of phenotypes (tawny, light tawny, and gray) may be difficult to visually distinguish, especially the light tawny class where misclassification with tawny frequently occurs. The objectives of this study were to solve both the throughput and accuracy issues in the plant breeding workflow, develop a set of indices for distinguishing pubescence classes, and test a machine learning (ML) classification approach. A principal component analysis (PCA) on hyperspectral soybean plot data identified clusters related to pubescence classes, while a Jeffries-Matusita distance analysis indicated that all bands were important for pubescence class separability. Aerial images from 2018, 2019, and 2020 were analyzed in this study. A 60-plot test (2019) of genotypes with known pubescence was used as reference data, while whole-field images from 2018, 2019, and 2020 were used to examine the broad applicability of the classification methodology. Two indices, a red/blue ratio and blue normalized difference vegetation index (blue NDVI), were effective at differentiating tawny and gray pubescence types in high-resolution imagery. A ML approach using a support vector machine (SVM) radial basis function (RBF) classifier was able to differentiate the gray and tawny types (83.1% accuracy and kappa=0.740 on a pixel basis) on images where reference training data was present. The tested indices and ML model did not generalize across years to imagery that did not contain the reference training panel, indicating limitations of using aerial imagery for pubescence classification in some environmental conditions. High-throughput classification of gray and tawny pubescence types is possible using aerial imagery, but light tawny soybeans remain difficult to classify and may require training data from each field season.

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