Study on Extraction Methods of Saline-Alkali Land Based on Remote Sensing Image and Influencing Factors

2015 ◽  
Vol 1092-1093 ◽  
pp. 1307-1312
Author(s):  
Jing Chen ◽  
Wei Quan Zhao ◽  
Li Hua Zhao ◽  
Lei Huo
Keyword(s):  
Remote Sensing ◽  
Soil Type ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Urban Expansion ◽  
Image Interpretation ◽  
Remote Sensing Image ◽  
Extraction Methods ◽  
Unused Land ◽  
The Relationship

Remote sensing image interpretation is one of the commonly methods to extract saline-alkali land. But it can only extract saline-alkali land of unused land, can not divide the types of slightly saline-alkali land, moderately saline-alkali land and severely saline-alkali land. On the basis of remote sensing image interpretation of 2010, the paper calculated the relationship between saline-alkali land degrees and the normalized difference vegetation index (NDVI), elevation, soil type, groundwater, river, urban expansion. Use AHP and Delphi to assign weights, calculate values, and overlay the layers, then concluded the degrees of saline-alkali land.

scholarly journals Assessing water demand with remote sensing for two coriander varieties

2018 ◽  
Vol 36 (3) ◽  
pp. 266-273
Author(s):  
Euseppe Ortiz ◽  
Enrique A. Torres
Keyword(s):  
Remote Sensing ◽  
Water Balance ◽  
Water Demand ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Digital Camera ◽  
Crop Coefficient ◽  
Remote Sensors ◽  
The Relationship ◽  
Important Basis

The use of remote sensing to determine water needs has been successfully applied by several authors to different crops, maintaining, as an important basis, the relationship between the normalized difference vegetation index (NDVI) and biophysical variables, such as the fraction of coverage (fc) and the basal crop coefficient (Kcb). Therefore, this study quantified the water needs of two varieties of coriander (UNAPAL Laurena CL and UNAPAL Precoso CP) based on the response of fc and Kcb, using remote sensors and a water balance according to the FAO-56 methodology. A Campbell Scientific meteorological station, a commercial digital camera and a portable spectro radiometer were used to obtain information on the environmental conditions and the crop. By means of remote sensing associated with a water balance, it was found that the water demand was 156 mm for CL and 151 mm for CP until the foliage harvest (41 d after sowing); additionally, the initial Kcb was 0.14, the mean Kcb was 1.16 (approximately) and the final Kcb was 0.71 (approximately).

Acquisitions of Vegetation Coverage and Cultivated Land Occupation Ratio of Taiyuan Valley Plain for Example Using CBERS-02B CCD Image

2012 ◽  
Vol 518-523 ◽  
pp. 5663-5667
Author(s):  
Shi Wei Li ◽  
Ji Long Zhang ◽  
Jian Sheng Yang
Keyword(s):  
Remote Sensing ◽  
Water Conservation ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Remote Sensing Image ◽  
Google Earth ◽  
Vegetation Coverage ◽  
Cultivated Land ◽  
Land Occupation ◽  
Coverage Ratio

Vegetation covering situation is very important for the quality of air quality, soil and water conservation ability and soil forming in an area. By using the remote sensing image of Taiyuan Valley Plain, the application of Normalized Difference Vegetation Index (NDVI) and unsupervised classification, the vegetation coverage map which includes non-cultivated land disposition and cultivated land disposition was obtained using ERDAS Imagine software. To evaluate the accuracy of the results, 200 points were sampled randomly, the high spatial resolution remote sensing image from Google Earth was used as the reference. The overall classification accuracy is 82%, with the Kappa statistic of 0.81. By counting the totally pixel acreage, it was gotten that the vegetation coverage was 46% and the cultivated land coverage ratio was 31% in the study area.

scholarly journals REMOTE SENSING AND GIS TECHNIQUES FOR URBAN GROWTH MONITORING OF BASARAH CITY

2011 ◽  
Vol 7 (1) ◽  
Author(s):  
Salah A. H. Saleh
Keyword(s):  
Economic Growth ◽  
Remote Sensing ◽  
Spatial Distribution ◽  
Urban Growth ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Urban Expansion ◽  
Remote Sensing And Gis ◽  
Growth Patterns ◽  
Growth Monitoring

Basarah city has experienced a rapid urban expansion over the last decades dueto accelerated economic growth. This paper reports an investigation into the application ofthe integration of remote sensing and geographic information systems (GIS) for detectingurban built up growth for the period 1973 - 2002, and evaluate its impact on theenvironmental situation of Basarah city by analyzing the spatial distribution of urbanexpansion according to land cover types and normalized difference vegetation index(NDVI). The integration of remote sensing and GIS was found to be effective inmonitoring and analyzing urban growth patterns and in evaluating urbanization impact onsurface conditions of Baghdad area.

A APLICAÇÃO DO ÍNDICE DE VEGETAÇÃO POR DIFERENÇA NORMALIZADA (NDVI) PARA ANÁLISE DA DEGRADAÇÃO AMBIENTAL DA ÁREA DE INFLUÊNCIA DIRETA DO AÇUDE CASTANHÃO

2019 ◽  
Vol 21 (2) ◽  
pp. 674-685
Author(s):  
Amanda Menezes De Albuquerque ◽  
José Robério Cabral Ribeiro ◽  
Marta Celina Linhares Sales
Keyword(s):  
Remote Sensing ◽  
Environmental Degradation ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Soil Cover ◽  
Spatial Location ◽  
Biophysical Parameters ◽  
Spectral Image ◽  
Vegetation Indexes ◽  
The Relationship

O aumento da degradação ambiental de terras secas vem conduzindo à erosão dos solos e desertificação, o uso intenso e predatório dos recursos naturais nessas áreas acaba impossibilitando a sobrevivência das comunidades que vivem nessas regiões. O estado do Ceará tem cerca de 92% de seu território inserido no semiárido, a pesquisa foi desenvolvida na Área de Influência Direta do Açude Castanhão – AIC. A através do registro de imagens, tornou-se possível às análises de relacionamento entre localização espacial de alvos do meio ambiente, variação espectral da imagem e variação da cobertura vegetal dos solos. A utilização do sensoriamento remoto e de índices de vegetação como o Índice de Vegetação da Diferença Normalizada (NDVI), facilita a obtenção e modelagem de parâmetros biofísicos das plantas, como a área foliar, biomassa e porcentagem de cobertura do solo, fornecendo importantes informações sobre a Degradação Ambiental da área.Palavras-chave: Degradação; Sensoriamento Remoto; Cobertura Vegetal. ABSTRACTThe increased environmental degradation of dry lands has led to soil erosion and desertification, the intense and predatory use of natural resources in these areas makes it impossible to survive the communities living in these regions. The state of Ceará has about 92% of its territory inserted in the semi-arid, the research was developed in the Area of Direct Influence of Castanhão - AIC. A through image registration, it became possible to analyze the relationship between spatial location of environmental targets, spectral image variation and variation of soil cover. The use of remote sensing and vegetation indexes such as the Normalized Difference Vegetation Index (NDVI) facilitates the obtaining and modeling of plant biophysical parameters such as leaf area, biomass and percentage of soil cover, providing important information on the Environmental Degradation of the area.Keywords:Degradation; Remote Sensing; Vegetal Cover.

scholarly journals Landslide Extraction from High-Resolution Remote Sensing Imagery Using Fully Convolutional Spectral–Topographic Fusion Network

2021 ◽  
Vol 13 (24) ◽  
pp. 5116
Author(s):  
Wei Xia ◽  
Jun Chen ◽  
Jianbo Liu ◽  
Caihong Ma ◽  
Wei Liu
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
High Reliability ◽  
Image Data ◽  
Remote Sensing Image ◽  
Tibet Plateau ◽  
Convolutional Network ◽  
Source Data

Considering the complexity of landslide hazards, their manual investigation lacks efficiency and is time-consuming, especially in high-altitude plateau areas. Therefore, extracting landslide information using remote sensing technology has great advantages. In this study, comprehensive research was carried out on the landslide features of high-resolution remote sensing images on the Mangkam dataset. Based on the idea of feature-driven classification, the landslide extraction model of a fully convolutional spectral–topographic fusion network (FSTF-Net) based on a deep convolutional neural network of multi-source data fusion is proposed, which takes into account the topographic factor (slope and aspect) and the normalized difference vegetation index (NDVI) as multi-source data input by which to train the model. In this paper, a high-resolution remote sensing image classification method based on a fully convolutional network was used to extract the landslide information, thereby realizing the accurate extraction of the landslide and surrounding ground-object information. With Mangkam County in the southeast of the Qinghai–Tibet Plateau China as the study area, the proposed method was evaluated based on the high-precision digital elevation model (DEM) generated from stereoscopic images of Resources Satellite-3 and multi-source high-resolution remote sensing image data (Beijing-2, Worldview-3, and SuperView-1). Results show that our method had a landslide detection precision of 0.85 and an overall classification accuracy of 0.89. Compared with the latest DeepLab_v3+, our model increases the landslide detection precision by 5%. Thus, the proposed FSTF-Net model has high reliability and robustness.

Mass movement tendencies and interaction with climate change in the Northern Chilean Patagonia

2020 ◽  
Author(s):  
Marcelo Somos-Valenzuela ◽  
Ivo Fustos-Toribio ◽  
Elizabeth Lizama-Montecinos ◽  
Bastián Morales-Vargas ◽  
Nataly Manque-Roa
Keyword(s):  
Climate Change ◽  
Remote Sensing ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Landsat Imagery ◽  
Mass Movement ◽  
Conditioning Factors ◽  
Mass Removal ◽  
Chilean Patagonia ◽  
The Relationship

<p>Mass movement processes correspond to one of the most dangerous geological events, mainly where human settlements are present, due to their destructive power and unpredictable nature. Chilean Patagonia has experienced important mass removal events in recent years. In this work, we are seeking to detect trends in the occurrence of these events and the relationship with long-term and short-term dispositions driven mainly by hydrometeorological events and the geology of the study area.</p><p>In the Chilean Patagonia, the Chilean Geological Survey (Sernageomin) has detected more than 713 landslides events in the Chilean Northern Patagonia (~42.7ºS, ~72.4ºW)” alone, a small area compared to the Chilean Patagonia. However, there is a lack of understanding of the triggers and mechanisms that control such events, and further studies need to be carried in order to understand the evolution of these events, linkages to climate change or anthropogenic changes, and to understand where they potentially can affect village directly destroying houses and taking human lives.</p><p>In this study, we use remote sensing to detect mass removals, fieldwork data collection to understand the geological predisposition to enable mass removal, and the analysis of hydrometeorological information to statistically establish relationships between the events and the potential triggers. For the remote sensing, we use Google Engine to create an exhaustive dataset of mass removal of 35 years in the study area. We apply the Normalized Difference Vegetation Index (NDVI) and the Grain Size Index (GSI) in Landsat Imagery. We will use the Sernageomin dataset and fieldwork to validate the methodology. For the geology, we analyze the conditioning factors associated with the geomorphological, structural, and lithological characteristics of the area. Finally, we used ERA5 data to determine the relationship between climate and mass removal events, analyzing, for example, the total annual precipitation patterns (TP) and extreme indicators as the maximum number of consecutive dry days (CDD) as well as annual temperatures and heatwaves.</p><p>The results of this research sought to provide the foundations for a complete risk assessment in the Chilean Patagonia and to increase awareness and preparedness in the region.</p>

Association Analysis of NDVI Changes and Topographic Factors

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.

Pemetaan Pulau Kecil Gelasa Kabupaten Bangka Tengah

2019 ◽  
Vol 2 (1) ◽  
pp. 11-14
Author(s):  
Wahyu Adi
Keyword(s):  
Coral Reefs ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Image Interpretation ◽  
Vegetation Coverage ◽  
Small Island ◽  
Shallow Waters ◽  
Small Islands ◽  
Seagrass Beds ◽  
Cover Density

Pulau Kecil Gelasa merupakan daerah yang belum banyak diteliti. Pemetaan ekosistem di pulau kecil dilakukan dengan bantuan citra Advanced Land Observing Satellite (ALOS). Penelitian terdahulu diketahui bahwa ALOS memiliki kemampuan memetakan terumbu karang dan padang lamun di perairan dangkal serta mampu memetakan kerapatan penutupan vegetasi. Metode interpretasi citra menggunakan alogaritma indeks vegetasi pada citra ALOS yaitu NDVI (Normalized Difference Vegetation Index), serta pendekatan Lyzengga untuk mengkoreksi kolom perairan. Hasil penelitian didapatkan luasan Padang Lamun di perairan dangkal 41,99 Ha, luasan Terumbu Karang 125,57 Ha. Hasil NDVI di daratan/ pulau kecil Gelasa untuk Vegetasi Rapat seluas 47,62 Ha; luasan penutupan Vegetasi Sedang 105,86 Ha; dan penutupan Vegetasi Jarang adalah 34,24 Ha.   Small Island Gelasa rarely studied. Mapping ecosystems on small islands with the image of Advanced Land Observing Satellite (ALOS). Previous research has found that ALOS has the ability to map coral reefs and seagrass beds in shallow water, and is able to map vegetation cover density. The method of image interpretation uses the vegetation index algorithm in the ALOS image, NDVI (Normalized Difference Vegetation Index), and the Lyzengga approach to correct the water column. The results of the study were obtained in the area of Seagrass Padang in the shallow waters of 41.99 ha, the area of coral reefs was 125.57 ha. NDVI results on land / small islands Gelasa for dense vegetation of 47.62 ha; area of Medium Vegetation coverage 105.86 Ha; and the coverage of Rare Vegetation is 34.24 Ha.

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.

TEMPERATURA DE SUPERFÍCIE E A RELAÇÃO COM O ÍNDICE DE VEGETAÇÃO POR DIFERENÇA NORMALIZADA (NDVI) NA MICROBACIA DO RIO DA BATATEIRAS, CRATO-CE

2019 ◽  
Vol 21 (2) ◽  
pp. 1310-1320
Author(s):  
Cícera Celiane Januário da Silva ◽  
Vinicius Ferreira Luna ◽  
Joyce Ferreira Gomes ◽  
Juliana Maria Oliveira Silva
Keyword(s):  
Remote Sensing ◽  
Surface Temperature ◽  
Temperature Measurement ◽  
Rainy Season ◽  
Satellite Images ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Landsat 8 ◽  
Low Density ◽  
Difference Map

O objetivo do presente trabalho é fazer uma comparação entre a temperatura de superfície e o Índice de Vegetação por Diferença Normalizada (NDVI) na microbacia do rio da Batateiras/Crato-CE em dois períodos do ano de 2017, um chuvoso (abril) e um seco (setembro) como também analisar o mapa de diferença de temperatura nesses dois referidos períodos. Foram utilizadas imagens de satélite LANDSAT 8 (banda 10) para mensuração de temperatura e a banda 4 e 5 para geração do NDVI. As análises demonstram que no mês de abril a temperatura da superfície variou aproximadamente entre 23.2ºC e 31.06ºC, enquanto no mês correspondente a setembro, os valores variaram de 25°C e 40.5°C, sendo que as maiores temperaturas foram encontradas em locais com baixa densidade de vegetação, de acordo com a carta de NDVI desses dois meses. A maior diferença de temperatura desses dois meses foi de 14.2°C indicando que ocorre um aumento da temperatura proporcionado pelo período que corresponde a um dos mais secos da região, diferentemente de abril que está no período de chuvas e tem uma maior umidade, presença de vegetação e corpos d’água que amenizam a temperatura.Palavras-chave: Sensoriamento Remoto; Vegetação; Microbacia.                                                                                  ABSTRACTThe objective of the present work is to compare the surface temperature and the Normalized Difference Vegetation Index (NDVI) in the Batateiras / Crato-CE river basin in two periods of 2017, one rainy (April) and one (September) and to analyze the temperature difference map in these two periods. LANDSAT 8 (band 10) satellite images were used for temperature measurement and band 4 and 5 for NDVI generation. The analyzes show that in April the surface temperature varied approximately between 23.2ºC and 31.06ºC, while in the month corresponding to September, the values ranged from 25ºC and 40.5ºC, and the highest temperatures were found in locations with low density of vegetation, according to the NDVI letter of these two months. The highest difference in temperature for these two months was 14.2 ° C, indicating that there is an increase in temperature provided by the period that corresponds to one of the driest in the region, unlike April that is in the rainy season and has a higher humidity, presence of vegetation and water bodies that soften the temperature.Key-words: Remote sensing; Vegetation; Microbasin.RESUMENEl objetivo del presente trabajo es hacer una comparación entre la temperatura de la superficie y el Índice de Vegetación de Diferencia Normalizada (NDVI) en la cuenca Batateiras / Crato-CE en dos períodos de 2017, uno lluvioso (abril) y uno (Septiembre), así como analizar el mapa de diferencia de temperatura en estos dos períodos. Las imágenes de satélite LANDSAT 8 (banda 10) se utilizaron para la medición de temperatura y las bandas 4 y 5 para la generación de NDVI. Los análisis muestran que en abril la temperatura de la superficie varió aproximadamente entre 23.2ºC y 31.06ºC, mientras que en el mes correspondiente a septiembre, los valores oscilaron entre 25 ° C y 40.5 ° C, y las temperaturas más altas se encontraron en lugares con baja densidad de vegetación, según el gráfico NDVI de estos dos meses. La mayor diferencia de temperatura de estos dos meses fue de 14.2 ° C, lo que indica que hay un aumento en la temperatura proporcionada por el período que corresponde a uno de los más secos de la región, a diferencia de abril que está en la temporada de lluvias y tiene una mayor humedad, presencia de vegetación y cuerpos de agua que suavizan la temperatura.Palabras clave: Detección remota; vegetación; Cuenca.

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