RELATIONSHIP OF SPECTRAL REFLECTANCE AND NDVI TO SOME SOIL PROPERTIES OF BRICKS FACTORIES SOILS IN NAHRAWAN AREA,BAGHDAD IRAQ

A study was conducted to investigate the effect of the brick industry on the environmental system of these project soils of the brick factories in Alnahrawan district. Remote sensing techniques was used to study the relationship between the spectral reflectivity and the vegetative index on the one hand and some surface soil characters of the project and to determine the variation in vegetation cover for the same area and for two different periods.Ten sites were selected to study spectral reflectivity under similar geomorphological conditions near the brickworks project in the Anahrawan district with an area of 10,000 hectares. Soil samples were taken from the surface and at a depth of 0-30 cm. Some chemical and physical characters of research soil were analyzed in the soil department laboratories, college of Agriculture, Baghdad University.Several satellite images taken from the satellite Land sat (ETM) 2013 and another from same satellite in 1990 T.M to determining the change between the two periods. After obtaining remote sensing data (reflectivity and vegetation index).the correlation analysis was carried out between these data. It was observed that the soil salinity values were decreased due to the drainage that the area was confined between the Tigris River and the Diyala tributary which leads to good natural drainage.The attached tables indicate that thedigital numbers of the soil sampling sites in 2013 are highly significant correlated, While some of the characters did not show the use of this region industrially. After calculating the difference between the two images to determine the change. A 100% change was observed and the vegetation cover was sharply reduced between the two images. as well as the extension of the land of empty land, although these lands are still suitable for agriculture.

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MONITORING DESERTIFICATION IN SOME REGIONS OF IRAQ USING GIS TECHNIQUES

IRAQI JOURNAL OF AGRICULTURAL SCIENCES ◽

10.36103/ijas.v52i3.1351 ◽

2021 ◽

Vol 52 (3) ◽

pp. 620-625

Author(s):

Y. K. Al-Timimi

Keyword(s):

Remote Sensing ◽

Environmental Economic ◽

Vegetation Cover ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Marked Decrease ◽

Social Systems ◽

Sand Dunes ◽

Rate Of Increase ◽

Remote Sensing Techniques

Desertification is one of the phenomena that threatening the environmental, economic, and social systems. This study aims to evaluate and monitor desertification in the central parts of Iraq between the Tigris and Euphrates rivers through the use of remote sensing techniques and geographic information systems. The Normalized difference vegetation index NDVI and the crust index CI were used, which were applied to two of the Landsat ETM + and OLI satellite imagery during the years 1990 and 2019. The research results showed that the total area of the vegetation cover was 2620 km2 in 1990, while there was a marked decrease in the area Vegetation cover 764 km2 in 2019, accounting for 34.8% (medium desertification) and 10.2% (high desertification), respectively. Also, the results showed that sand dunes occupied an area of 767 km2 in 1990, while the area of sand dunes increased to 1723 km2 in 2019, with a rate of 10.2%) medium desertification (and 22.9% (severe desertification), respectively. It was noted that the overall rate of decrease in vegetation cover was 21.33 km2year-1 while the overall rate of increase in ground erosion in the area is 10.99 km2year-1.

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Effect of Locust Invasion and Mitigation Using Remote Sensing Techniques: A Case Study of North Sindh Pakistan

Photogrammetric Engineering & Remote Sensing ◽

10.14358/pers.21-00025r2 ◽

2022 ◽

Vol 88 (1) ◽

pp. 47-53

Author(s):

Muhammad Nasar Ahmad ◽

Zhenfeng Shao ◽

Orhan Altan

Keyword(s):

Remote Sensing ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Remote Sensing Data ◽

Google Earth ◽

Landsat 8 ◽

Abrupt Decrease ◽

Computing Platform ◽

Remote Sensing Techniques ◽

Processing Techniques

This study comprises the identification of the locust outbreak that happened in February 2020. It is not possible to conduct ground-based surveys to monitor such huge disasters in a timely and adequate manner. Therefore, we used a combination of automatic and manual remote sensing data processing techniques to find out the aftereffects of locust attack effectively. We processed MODIS -normalized difference vegetation index (NDVI ) manually on ENVI and Landsat 8 NDVI using the Google Earth Engine (GEE ) cloud computing platform. We found from the results that, (a) NDVI computation on GEE is more effective, prompt, and reliable compared with the results of manual NDVI computations; (b) there is a high effect of locust disasters in the northern part of Sindh, Thul, Ghari Khairo, Garhi Yaseen, Jacobabad, and Ubauro, which are more vulnerable; and (c) NDVI value suddenly decreased to 0.68 from 0.92 in 2020 using Landsat NDVI and from 0.81 to 0.65 using MODIS satellite imagery. Results clearly indicate an abrupt decrease in vegetation in 2020 due to a locust disaster. That is a big threat to crop yield and food production because it provides a major portion of food chain and gross domestic product for Sindh, Pakistan.

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ON THE INFORMATION CONTENT OF SPECTRAL CHANNELS AND NDVI OF THE LANDSAT 5 TM LANDSAT 7 ETM+ REMOTE SENSING SATELLITE FOR ASSESSING THE STAGES OF FOREST OVERGROWTH OF ABANDONED AGRICULTURAL LANDS

ÈKOBIOTEH ◽

10.31163/2618-964x-2021-4-3-178-185 ◽

2021 ◽

Vol 4 (3) ◽

pp. 178-185

Author(s):

I.R. Tuktamyshev ◽

◽

P.S. Shirokikh ◽

R.Y. Mullagulov ◽

◽

...

Keyword(s):

Remote Sensing ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Arable Land ◽

Vegetation Indices ◽

Remote Sensing Data ◽

The Urals ◽

Landsat 7 ◽

Landsat 5 Tm ◽

The Difference

Abandoned arable land is a widespread phenomenon in land use. Methods based on the use of remote sensing data are most suitable for studying and monitoring farmlands overgrown with forest. Multispectral satellite images and vegetation indices can reflect the difference at certain stages of the successional development of fallow vegetation. The aim of the work is to evaluate the informative value of individual channels of medium-resolution images of Landsat satellites and the normalized difference vegetation index (NDVI) for identifying vegetation areas at various stages of reforestation succession on abandoned arable land in the zone of distribution of broad-leaved forests in the Urals. As the source material we used 30 georeferenced relevés of different overgrowth stages made in 2012, and 9 cloudless Landsat 5 TM and Landsat 7 ETM+ images for the period from April to October 2011. Using the data, NDVI and values of three spectral bands (Red, NIR, Thermal) were calculated for the relevé points. The most informative when dividing the stages of reforestation on abandoned fields in the zone of distribution of broad-leaved forests in the Urals were the NDVI vegetation index and the surface temperature estimated by the thermal channel. In addition, the red band can be useful for identifying the initial stage of succession.

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Estimation of soil heat flux in a neotropical Wetland region using remote sensing techniques

Revista Brasileira de Meteorologia ◽

10.1590/0102-778620120568 ◽

2014 ◽

Vol 29 (4) ◽

pp. 469-482 ◽

Cited By ~ 6

Author(s):

Victor Hugo de Morais Danelichen ◽

Marcelo Sacardi Biudes ◽

Maísa Caldas Souza ◽

Nadja Gomes Machado ◽

Bernardo Barbosa da Silva ◽

...

Keyword(s):

Remote Sensing ◽

Heat Flux ◽

Vegetation Index ◽

Correlation Coefficients ◽

Remote Sensing Data ◽

Soil Heat Flux ◽

Biophysical Parameters ◽

Area Index ◽

Satellite Sensors ◽

Remote Sensing Techniques

The direct estimation of the soil heat flux (G) by remote sensing data is not possible. For this, several models have been proposed empirically from the relation of G measures and biophysical parameters of various types of coverage or not vegetated in different places on earth. Thus, the objective of this study was to evaluate the relation between G/Rn ratio and biophysical variables obtained by satellite sensors and evaluate the parameterization of different models to estimate G spatially in three sites with different soil cover types. The net radiation (Rn) and G were measured directly in two pastures at Miranda Farm and Experimental Farm and and Monodominant Forest of Cambará. Rn, G, and G/Rn ratio and MODIS products, such as albedo (α), surface temperature (LST), vegetation index (NDVI) and leaf area index (LAI) varied seasonally at all sites and inter-sites. The sites were different from each other by presenting different relation between measures of Rn, G and G/Rn ratio and biophysical parameters. Among the original models, the model proposed by Bastiaanssen (1995) showed the best performance with r = 0.76, d = 0.95, MAE = 5.70 W m-2 and RMSE = 33.68 W m-2. As the reparameterized models, correlation coefficients had no significant change, but the coefficient Willmott (d) increased and the MAE and RMSE had a small decrease.

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Degradação Ambiental na Microrregião do Carirí Paraibano (Environmental Degradation in the Micro Region of Cariri Paraibano)

Revista Brasileira de Geografia Física ◽

10.26848/rbgf.v6i4.233080 ◽

2013 ◽

Vol 6 (4) ◽

pp. 823

Author(s):

Glauciene Justino Ferreira da Silva ◽

Nadjacleia Vilar Almeida ◽

Lidiane Cristina Félix Gomes ◽

Otávia Karla Apolinário dos Santos

Keyword(s):

Remote Sensing ◽

Environmental Degradation ◽

Vegetation Cover ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Spectral Response ◽

Semiarid Region ◽

Savanna Vegetation ◽

Remote Sensing Techniques ◽

Cover Class

O desmatamento de grandes áreas de vegetação de caatinga para dar lugar às lavouras e servir de pasto aos rebanhos tem contribuído para a degradação ambiental na região Semiárida. Os poucos remanescentes do Bioma Caatinga no Semiárido nordestino sofrem com a pressão exercida pelo avanço agropecuário e pelo descaso de órgãos ambientais de fiscalização. Na microrregião do Cariri paraibano muitos municípios tem perdido a cobertura vegetal em virtude da necessidade de terras para cultivo. Diante do exposto, fica clara a necessidade de estudos sobre a degradação ambiental, para isso o uso das geotecnologias tem proporcionado o monitoramento das alterações provocadas sem manejo adequado dos recursos naturais. O Sensoriamento Remoto e as imagens de sensores orbitais têm sido amplamente empregados em estudos ambientais, possibilitando a extração de informações. Desta forma, este trabalho objetivou avaliar a dinâmica da ocupação do solo e a cobertura vegetal no município de Pararí-PB entre os anos de 1988 e 2005, por meio de técnicas de Sensoriamento Remoto e análise espacial, além de contribuir com o estudo da degradação ambiental no Semiárido. Os resultados obtidos com os mapas de cobertura do solo evidenciaram que a classe solo exposto ocupou as áreas anteriormente pertencentes à classe vegetação densa e rala, expondo o solo do município aos efeitos das chuvas intensas e irregulares. O Índice de Vegetação da Diferença Normalizada (NDVI) melhor representou o estado da cobertura vegetal existente nos anos estudados, e a resposta espectral do solo e vegetação foram influenciados pela precipitação na época em que as imagens foram obtidas. A B S T R A C T The deforestation of large areas of savanna vegetation to make way for crops and serve as pasture to flocks has contributed to environmental degradation in the semiarid region. The few remaining Caatinga Biome in the northeastern Caatinga semiarid suffer from the pressure exerted by agricultural advances and the neglect of environmental enforcement agencies. In micro Cariri many municipalities have lost vegetation cover due to the need for land for cultivation. Given the above, it is clear the need for studies on environmental degradation. In that purpose, the use of geotechnology has provided monitoring changes caused without proper management of natural resources. The Remote Sensing and images from satellite sensors have been widely used in environmental studies, enabling the extraction of information. Thus, this study aimed to evaluate the dynamics of land use and vegetation cover in the municipality of Pararí-PB between the years 1988 and 2005, using remote sensing techniques and spatial analysis, and contributing to the study of environmental degradation in the semiarid. The results obtained with the maps of land cover class showed exposed soil areas previously occupied belonging to the class sparse and dense vegetation, exposing the municipality to the effects of heavy rains. The normalized difference vegetation index (NDVI) best represented the state of vegetation existing in the years studied, and the spectral response of soil and vegetation were influenced by precipitation at the time the pictures were taken. Key-words: Semiarid, vegetation cover, Remote Sensing.

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Assessment of possibility for using remote sensing data and Markov chains for prediction of vegetation cover development

Proceedings of the National Academy of Sciences of Belarus Agrarian Series ◽

10.29235/1817-7204-2020-58-2-176-184 ◽

2020 ◽

Vol 58 (2) ◽

pp. 176-184

Author(s):

T. N. Myslyva ◽

V. I. Bushueva ◽

V. A. Volyntseva

Keyword(s):

Remote Sensing ◽

Markov Chains ◽

Vegetation Cover ◽

Vegetation Index ◽

Global Climate ◽

Remote Sensing Data ◽

Agricultural Crops ◽

Earth Remote Sensing ◽

Sensing Data ◽

The Earth

In conditions of global climate change, it is important to develop reliable models allowing to reliably predict plant development based on combination of the Earth remote sensing data and statistical modeling. Modeling by means of Markov chains is an efficient and at the same time simple way to predict random events, which include prediction of performance of phytomass of agricultural crops. The Earth remote sensing data obtained from the Sentinel-2 satellite with spatial resolution of 10 m were used to calculate the value of vegetation index NDVI and obtain different time rasters (2017-2019) with different degrees of vegetation cover development. To construct the matrix of probability of transition from one state to another for different levels of vegetation cover development, functionality of geoinformation systems (GIS) were used allowing to classify raster images, transform them into vector layers, and establish intersection areas. The probability matrix was later used to predict vegetation cover development using the Markov model as a predictor. The developed prediction model was tested for feasibility of the χ2 test. The results obtained showed that both the modeled values and the actual area of vegetation distribution with different degrees of development, determined from the available raster image of 2019, correlated well with each other. The research results can be useful both in developing forecasting methods and in directly predicting the crop yield of primarily dense-cover agricultural crops, as well as for estimating performance of pastures and creating efficient pasture rotations.

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Post Classification Comparison Change Detection of GuangZhou Metropolis, China

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.467-469.19 ◽

2011 ◽

Vol 467-469 ◽

pp. 19-22 ◽

Cited By ~ 3

Author(s):

Xiao Feng Yang ◽

Xing Ping Wen

Keyword(s):

Remote Sensing ◽

Change Detection ◽

Southern China ◽

Remote Sensing Data ◽

Comparison Method ◽

Global Scale ◽

Likelihood Method ◽

Unused Land ◽

Remote Sensing Techniques ◽

The One

Change detection is one of the most important applications of remote sensing techniques due to its capability of repetitive acquisition imageries with consistent image quality, at short intervals, on a global scale, and during complete seasonal cycles. This paper uses two Landsat ETM+ imageries acquired in 2000 and 2002 respectively to detect change of Guangzhou in southern China during two years using post classification comparison method. Firstly, two remote sensing data are precision geometrically corrected to UTM projection with a root mean square error (RMSE) of 0.3 pixels, and then they are classfied using Maximum Likelihood method respectively. Images are classified into four classes which are water, forest, grass or crop and building，soil or unused land. Sencondly, two classified images are calculated by band geometric algorithm pixel by pixel using programming. The class value of pixel in different year is the same, and then the processed pixel is zero, whereas the processed pixel is assigned to a certain value which represents change from the one land cover type to another during two years. Finally, statistic analyses of change information during two years are computed and the post classification comparison change detection image is outputted. It concludes that the largest change areas are exchanges of building, soil or unused land with grass land, and land covers in Baiyun district are changed mostly from 2000 to 2002.

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Association Analysis of NDVI Changes and Topographic Factors

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.333-335.1205 ◽

2013 ◽

Vol 333-335 ◽

pp. 1205-1208

Author(s):

De Li Liu ◽

Ya Shuang Zhang ◽

Nan Lin

Keyword(s):

Remote Sensing ◽

Spatial Analysis ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Remote Sensing Data ◽

Topographic Factors ◽

Gis Software ◽

Relationship Of ◽

The Relationship ◽

Slope Direction

Based on the TM remote sensing data of the Huadian city in 1991 and 2011 and based on the DEM data,using the normalized difference vegetation index (NDVI) change classification method,to Extraction the elevation,slope,slope direction data and the vegetation index data of the study area.Then using the spatial analysis function of GIS software to overlay the two different period NDVI data and analysis the NDVI change of area and spatial. Using the same method to overlay and analysis the relationship of NDVI data and elevation,slope,slope direction.Research shows that the variation of NDVI in the study area has relationship with the topographic factors change.

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