The method compensating the saturation effect of normalized differential vegetation index in remote sensing

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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Effect of Slope Mesorelief on the Spatial Variability of Soil Properties and Vegetation Index Based on Remote Sensing Data

Izvestiya Atmospheric and Oceanic Physics ◽

10.1134/s0001433819090202 ◽

2019 ◽

Vol 55 (9) ◽

pp. 1329-1337

Author(s):

N. V. Gopp ◽

T. V. Nechaeva ◽

O. A. Savenkov ◽

N. V. Smirnova ◽

V. V. Smirnov

Keyword(s):

Remote Sensing ◽

Spatial Variability ◽

Soil Properties ◽

Vegetation Index ◽

Remote Sensing Data ◽

Sensing Data

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Análisis del efecto de variables ambientales en la estimación de la erosionabilidad (Factor K) = Analysis of the Effect of Environmental Variables on the Soil Erodibility Estimation (K Factor)

Espacio Tiempo y Forma Serie VI Geografía ◽

10.5944/etfvi.11.2018.20187 ◽

2018 ◽

pp. 195

Author(s):

Pedro Perez Cutillas ◽

Gonzalo G. Barberá ◽

Carmelo Conesa García

Keyword(s):

Remote Sensing ◽

Environmental Variables ◽

Input Data ◽

Vegetation Index ◽

Mineralogical Composition ◽

Soil Erodibility ◽

Estimation Algorithms ◽

K Factor ◽

Remote Sensing Techniques

El objetivo principal de este trabajo se centra en la determinación y análisis de las variables ambientales que influyen en las divergencias de las estimaciones de erosionabilidad a partir de dos métodos, aplicando tres algoritmos de estimación del Factor K. La exploración de esta información permite conocer el peso que ejerce el origen de los datos de entrada a los modelos en el cómputo de erosionabilidad y qué importancia tiene en función del algoritmo elegido para la estimación del Factor K. Los resultados muestran que las pendientes, así como los índices de vegetación (NDVI) y de composición mineralógico (IOI) obtenidos mediantes técnicas de teledetección han mostrado los valores de asociación más elevados entre ambos métodos.The main goal of this work is to determine and analyze the influence of environmental variables on the changes of two erodibility methods, through the application of three estimation algorithms of K Factor. The analysis of this information allows knowing the significance of the input data to the models in the erodibility estimation, and likewise the consequence of the algorithm selected for the estimation of K Factor. The results show that the slopes, as well as the vegetation index (NDVI) and the mineralogical composition index (IOI), generated both by remote sensing techniques, have shown the highest values of association between methods.

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Quantification of the Environmental Impacts of Highway Construction Using Remote Sensing Approach

Remote Sensing ◽

10.3390/rs13071340 ◽

2021 ◽

Vol 13 (7) ◽

pp. 1340

Author(s):

Shuailong Feng ◽

Shuguang Liu ◽

Lei Jing ◽

Yu Zhu ◽

Wende Yan ◽

...

Keyword(s):

Remote Sensing ◽

Environmental Impacts ◽

Land Surface ◽

Vegetation Index ◽

Highway Construction ◽

Temporal Changes ◽

Moisture Index ◽

Buffer Zones ◽

Wide Range ◽

Spatial And Temporal Changes

Highways provide key social and economic functions but generate a wide range of environmental consequences that are poorly quantified and understood. Here, we developed a before–during–after control-impact remote sensing (BDACI-RS) approach to quantify the spatial and temporal changes of environmental impacts during and after the construction of the Wujing Highway in China using three buffer zones (0–100 m, 100–500 m, and 500–1000 m). Results showed that land cover composition experienced large changes in the 0–100 m and 100–500 m buffers while that in the 500–1000 m buffer was relatively stable. Vegetation and moisture conditions, indicated by the normalized difference vegetation index (NDVI) and the normalized difference moisture index (NDMI), respectively, demonstrated obvious degradation–recovery trends in the 0–100 m and 100–500 m buffers, while land surface temperature (LST) experienced a progressive increase. The maximal relative changes as annual means of NDVI, NDMI, and LST were about −40%, −60%, and 12%, respectively, in the 0–100m buffer. Although the mean values of NDVI, NDMI, and LST in the 500–1000 m buffer remained relatively stable during the study period, their spatial variabilities increased significantly after highway construction. An integrated environment quality index (EQI) showed that the environmental impact of the highway manifested the most in its close proximity and faded away with distance. Our results showed that the effect distance of the highway was at least 1000 m, demonstrated from the spatial changes of the indicators (both mean and spatial variability). The approach proposed in this study can be readily applied to other regions to quantify the spatial and temporal changes of disturbances of highway systems and subsequent recovery.

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Detecting Forest Degradation in the Three-North Forest Shelterbelt in China from Multi-Scale Satellite Images

Remote Sensing ◽

10.3390/rs13061131 ◽

2021 ◽

Vol 13 (6) ◽

pp. 1131

Author(s):

Tao Yu ◽

Pengju Liu ◽

Qiang Zhang ◽

Yi Ren ◽

Jingning Yao

Keyword(s):

Remote Sensing ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Net Primary Production ◽

Remote Sensing Data ◽

Forest Degradation ◽

Observation Data ◽

Area Index ◽

Sensing Data ◽

Multi Scale

Detecting forest degradation from satellite observation data is of great significance in revealing the process of decreasing forest quality and giving a better understanding of regional or global carbon emissions and their feedbacks with climate changes. In this paper, a quick and applicable approach was developed for monitoring forest degradation in the Three-North Forest Shelterbelt in China from multi-scale remote sensing data. Firstly, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Ratio Vegetation Index (RVI), Leaf Area Index (LAI), Fraction of Photosynthetically Active Radiation (FPAR) and Net Primary Production (NPP) from remote sensing data were selected as the indicators to describe forest degradation. Then multi-scale forest degradation maps were obtained by adopting a new classification method using time series MODerate Resolution Imaging Spectroradiometer (MODIS) and Landsat Enhanced Thematic Mapper Plus (ETM+) images, and were validated with ground survey data. At last, the criteria and indicators for monitoring forest degradation from remote sensing data were discussed, and the uncertainly of the method was analyzed. Results of this paper indicated that multi-scale remote sensing data have great potential in detecting regional forest degradation.

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Remote Sensing for Plant Water Content Monitoring: A Review

Remote Sensing ◽

10.3390/rs13112088 ◽

2021 ◽

Vol 13 (11) ◽

pp. 2088

Author(s):

Carlos Quemada ◽

José M. Pérez-Escudero ◽

Ramón Gonzalo ◽

Iñigo Ederra ◽

Luis G. Santesteban ◽

...

Keyword(s):

Remote Sensing ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Continuous Wave ◽

Technological Development ◽

Water Status ◽

Irrigation Management ◽

Coefficient Of Determination ◽

Plant Water ◽

Monitoring Methods

This paper reviews the different remote sensing techniques found in the literature to monitor plant water status, allowing farmers to control the irrigation management and to avoid unnecessary periods of water shortage and a needless waste of valuable water. The scope of this paper covers a broad range of 77 references published between the years 1981 and 2021 and collected from different search web sites, especially Scopus. Among them, 74 references are research papers and the remaining three are review papers. The different collected approaches have been categorized according to the part of the plant subjected to measurement, that is, soil (12.2%), canopy (33.8%), leaves (35.1%) or trunk (18.9%). In addition to a brief summary of each study, the main monitoring technologies have been analyzed in this review. Concerning the presentation of the data, different results have been obtained. According to the year of publication, the number of published papers has increased exponentially over time, mainly due to the technological development over the last decades. The most common sensor is the radiometer, which is employed in 15 papers (20.3%), followed by continuous-wave (CW) spectroscopy (12.2%), camera (10.8%) and THz time-domain spectroscopy (TDS) (10.8%). Excluding two studies, the minimum coefficient of determination (R2) obtained in the references of this review is 0.64. This indicates the high degree of correlation between the estimated and measured data for the different technologies and monitoring methods. The five most frequent water indicators of this study are: normalized difference vegetation index (NDVI) (12.2%), backscattering coefficients (10.8%), spectral reflectance (8.1%), reflection coefficient (8.1%) and dielectric constant (8.1%).

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Phenological Analysis of Sub-Alpine Forest on Jeju Island, South Korea, Using Data Fusion of Landsat and MODIS Products

Forests ◽

10.3390/f12030286 ◽

2021 ◽

Vol 12 (3) ◽

pp. 286

Author(s):

Sang-Jin Park ◽

Seung-Gyu Jeong ◽

Yong Park ◽

Sang-hyuk Kim ◽

Dong-kun Lee ◽

...

Keyword(s):

Climate Change ◽

Remote Sensing ◽

Data Fusion ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Fusion Model ◽

Fusion Algorithm ◽

Study Region ◽

Modis Imagery ◽

Increasing Temperature

Climate change poses a disproportionate risk to alpine ecosystems. Effective monitoring of forest phenological responses to climate change is critical for predicting and managing threats to alpine populations. Remote sensing can be used to monitor forest communities in dynamic landscapes for responses to climate change at the species level. Spatiotemporal fusion technology using remote sensing images is an effective way of detecting gradual phenological changes over time and seasonal responses to climate change. The spatial and temporal adaptive reflectance fusion model (STARFM) is a widely used data fusion algorithm for Landsat and MODIS imagery. This study aims to identify forest phenological characteristics and changes at the species–community level by fusing spatiotemporal data from Landsat and MODIS imagery. We fused 18 images from March to November for 2000, 2010, and 2019. (The resulting STARFM-fused images exhibited accuracies of RMSE = 0.0402 and R2 = 0.795. We found that the normalized difference vegetation index (NDVI) value increased with time, which suggests that increasing temperature due to climate change has affected the start of the growth season in the study region. From this study, we found that increasing temperature affects the phenology of these regions, and forest management strategies like monitoring phenology using remote sensing technique should evaluate the effects of climate change.

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Wildfires Vegetation Recovery through Satellite Remote Sensing and Functional Data Analysis

Mathematics ◽

10.3390/math9111305 ◽

2021 ◽

Vol 9 (11) ◽

pp. 1305

Author(s):

Feliu Serra-Burriel ◽

Pedro Delicado ◽

Fernando M. Cucchietti

Keyword(s):

Remote Sensing ◽

Data Analysis ◽

Functional Data Analysis ◽

Functional Data ◽

Vegetation Index ◽

Vegetation Recovery ◽

Functional Responses ◽

Elapsed Time ◽

Synthetic Controls ◽

Available Information

In recent years, wildfires have caused havoc across the world, which are especially aggravated in certain regions due to climate change. Remote sensing has become a powerful tool for monitoring fires, as well as for measuring their effects on vegetation over the following years. We aim to explain the dynamics of wildfires’ effects on a vegetation index (previously estimated by causal inference through synthetic controls) from pre-wildfire available information (mainly proceeding from satellites). For this purpose, we use regression models from Functional Data Analysis, where wildfire effects are considered functional responses, depending on elapsed time after each wildfire, while pre-wildfire information acts as scalar covariates. Our main findings show that vegetation recovery after wildfires is a slow process, affected by many pre-wildfire conditions, among which the richness and diversity of vegetation is one of the best predictors for the recovery.

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Spatial and Temporal Changes in Vegetation in the Ruoergai Region, China

Forests ◽

10.3390/f12010076 ◽

2021 ◽

Vol 12 (1) ◽

pp. 76

Author(s):

Yahui Guo ◽

Jing Zeng ◽

Wenxiang Wu ◽

Shunqiang Hu ◽

Guangxu Liu ◽

...

Keyword(s):

Remote Sensing ◽

Vegetation Index ◽

Linear Trend ◽

Spectral Characteristics ◽

Growth Conditions ◽

Temporal Changes ◽

Vegetation Coverage ◽

Dynamic Changes ◽

Spatial And Temporal Changes ◽

Temporal And Spatial

Timely monitoring of the changes in coverage and growth conditions of vegetation (forest, grass) is very important for preserving the regional and global ecological environment. Vegetation information is mainly reflected by its spectral characteristics, namely, differences and changes in green plant leaves and vegetation canopies in remote sensing domains. The normalized difference vegetation index (NDVI) is commonly used to describe the dynamic changes in vegetation, but the NDVI sequence is not long enough to support the exploration of dynamic changes due to many reasons, such as changes in remote sensing sensors. Thus, the NDVI from different sensors should be scientifically combined using logical methods. In this study, the Global Inventory Modeling and Mapping Studies (GIMMS) NDVI from the Advanced Very High Resolution Radiometer (AVHRR) and Moderate-resolution Imaging Spectroradiometer (MODIS) NDVI are combined using the Savitzky–Golay (SG) method and then utilized to investigate the temporal and spatial changes in the vegetation of the Ruoergai wetland area (RWA). The dynamic spatial and temporal changes and trends of the NDVI sequence in the RWA are analyzed to evaluate and monitor the growth conditions of vegetation in this region. In regard to annual changes, the average annual NDVI shows an overall increasing trend in this region during the past three decades, with a linear trend coefficient of 0.013/10a, indicating that the vegetation coverage has been continuously improving. In regard to seasonal changes, the linear trend coefficients of NDVI are 0.020, 0.021, 0.004, and 0.004/10a for spring, summer, autumn, and winter, respectively. The linear regression coefficient between the gross domestic product (GDP) and NDVI is also calculated, and the coefficients are 0.0024, 0.0015, and 0.0020, with coefficients of determination (R2) of 0.453, 0.463, and 0.444 for Aba, Ruoergai, and Hongyuan, respectively. Thus, the positive correlation coefficients between the GDP and the growth of NDVI may indicate that increased societal development promotes vegetation in some respects by resulting in the planting of more trees or the promotion of tree protection activities. Through the analysis of the temporal and spatial NDVI, it can be assessed that the vegetation coverage is relatively large and the growth condition of vegetation in this region is good overall.

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