Hydrological model parameterization using NDVI values to account for the effects of land cover change on the rainfall–runoff response

Abstract Classic rainfall–runoff models usually use historical data to estimate model parameters and mean values of parameters are considered for predictions. However, due to climate changes and human effects, model parameters change temporally. To overcome this problem, normalized difference vegetation index (NDVI) derived from remotely sensed data was used in this study to investigate the effect of land cover variations on hydrological response of watersheds using a conceptual rainfall–runoff model. The study area consists of two sub-watersheds (Hervi and Lighvan) with varied land cover conditions. Obtained results show that the one-parameter model generates runoff forecasts with acceptable level of the considered criteria. Remote sensing data were employed to relate land cover properties of the watershed to the model parameter. While a power form of the regression equation could be best fitted to the parameter values using available images of Hervi sub-watershed, for the Lighvan sub-watershed the fitted equation shows somewhat lower correlation due to higher fluctuations of the model parameter. The average values of the Nash–Sutcliffe efficiency criterion of the model were obtained as 0.87 and 0.55, respectively, for Hervi and Lighvan sub-watersheds. Applying this methodology, the model's parameters might be determined using temporal NDVI values.

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Use of remote sensing to understand the terroir of the Niagara Peninsula. Applications in a Riesling vineyard

OENO One ◽

10.20870/oeno-one.2015.49.1.97 ◽

2015 ◽

Vol 49 (1) ◽

pp. 1 ◽

Cited By ~ 2

Author(s):

Matthieu Marciniak ◽

Ralph Brown ◽

Andrew Reynolds ◽

Marilyne Jollineau

Keyword(s):

Remote Sensing ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Water Status ◽

Remote Sensing Data ◽

Titratable Acidity ◽

Weather Conditions ◽

Remotely Sensed ◽

Remotely Sensed Data ◽

Berry Composition

Aim: The purpose of this study was to determine if multispectral high spatial resolution airborne imagery could be used to segregate zones in vineyards to target fruit of highest quality for premium winemaking. We hypothesized that remotely sensed data would correlate with vine size and leaf water potential (ψ), as well as with yield and berry composition.Methods and results: Hypotheses were tested in a 10-ha Riesling vineyard [Thirty Bench Winemakers, Beamsville (Ontario)]. The vineyard was delineated using GPS and 519 vines were geo-referenced. Six sub-blocks were delineated for study. Four were identified based on vine canopy size (low, high) with remote sensing in 2005. Airborne images were collected with a four-band digital camera every 3-4 weeks over 3 seasons (2007-2009). Normalized difference vegetation index (NDVI) values (NDVI-red, green) and greenness ratio were calculated from the images. Single-leaf reflectance spectra were collected to compare vegetation indices (VIs) obtained from ground-based and airborne remote-sensing data. Soil moisture, leaf ψ, yield components, vine size, and fruit composition were also measured. Strong positive correlations were observed between VIs and vine size throughout the growing season. Vines with higher VIs during average to dry years had enhanced fruit maturity (higher °Brix and lower titratable acidity). Berry monoterpenes always had the same relationship with remote sensing variables regardless of weather conditions.Conclusions: Remote sensing images can assist in delineating vineyard zones where fruit will be of different maturity levels, or will have different concentrations of aroma compounds. Those zones could be considered as sub-blocks and processed separately to make wines that reflect those terroir differences. Strongest relationships between remotely sensed VIs and berry composition variables occurred when images were taken around veraison.Significance and impact of the study: Remote sensing may be effective to quantify spatial variation in grape flavour potential within vineyards, in addition to characteristics such as water status, yield, and vine size. This study was unique by employing remote sensing in cover-cropped vineyards and using protocols for excluding spectral reflectance contributed by inter-row vegetation.

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Application of Earth Observation Data for Estimation of Changes in Land Trajectories in Varanasi District, India

Journal of Landscape Ecology ◽

10.1515/jlecol-2017-0017 ◽

2018 ◽

Vol 11 (1) ◽

pp. 5-18 ◽

Cited By ~ 1

Author(s):

Sunita Singh ◽

Praveen Kumar Rai

Keyword(s):

Land Cover ◽

Supervised Classification ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Urban Expansion ◽

Remote Sensing Data ◽

Water Bodies ◽

Landsat 8 ◽

Observation Data ◽

Agriculture Land

Abstract Digital change detection is the process that helps in shaping the changes associated with land use land cover (LULC) properties with reference to geo-registered multi-temporal remote sensing data. In this study different methods of analyzing satellite images are presented, with the aim to identify changes in land cover in a certain period of time (1980-2016). The methods represented in this study are vegetation indices, image differencing and supervised classification. These methods gave different results in terms of land cover area. Urban expansion has brought serious losses of agriculture land, vegetation and water bodies. The present study demonstrates changes in land trajectories of Varanasi district, India using Landsat MSS (1980), TM (1990 and 2010), ETM+ (2000) and Landsat-8 OLI data (2016). The LULC classes in the study area are divided into eight categories using supervised classification method. Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI) are also calculated to estimate the changes in LULC classes during these time periods. Major changes are seen from 2000 to 2016 for the built-up, agriculture land, water bodies and wasteland.

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MONITORING MODEL OF LAND COVER CHANGE FOR THE INDICATION OF DEVEGETATION AND REVEGETATION USING SENTINEL-2

International Journal of Remote Sensing and Earth Sciences (IJReSES) ◽

10.30536/j.ijreses.2020.v17.a3385 ◽

2021 ◽

Vol 17 (2) ◽

pp. 163

Author(s):

Samsul Arifin ◽

Tatik Kartika

Keyword(s):

Land Cover ◽

Land Cover Change ◽

Environmental Sustainability ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Remote Sensing Data ◽

Threshold Value ◽

Monitoring Model ◽

Land Changes ◽

Sentinel 2

IInformation on land cover change is very important for various purposes, including the monitoring of changes for environmental sustainability. The objective of this study is to create a monitoring model of land cover change for the indication of devegetation and revegetation usingdata fromSentinel-2 from 2017 to 2018 of the Brantas watershed.This is one of the priority watersheds in Indonesia, so it is necessary to observe changes in its environment, including land cover change. Such change can be detected using remote sensing data. The method used is a hybrid between Normalized Difference Vegetation Index(NDVI) and Normalized Burn Ratio (NBR) which aims to detect land changes with a focus on devegetationand revegetation by determining the threshold value for vegetation index (ΔNDVI) and open land index (ΔNBR).The study found that the best thresholds to detect revegetation were ΔNDVI > 0.0309 and ΔNBR < 0.0176 and to detect devegetation ΔNDVI < -0.0206 and ΔNBR > 0.0314.It is concluded that Sentinel-2 data can be used to monitor land changes indicating devegetation and revegetation with established NDVI and NBR threshold conditions.

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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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Consideration of NDVI thematic changes in density analysis and floristic composition of Wadi Yalamlam, Saudi Arabia

Geoscientific Instrumentation Methods and Data Systems ◽

10.5194/gi-7-297-2018 ◽

2018 ◽

Vol 7 (4) ◽

pp. 297-306 ◽

Cited By ~ 4

Author(s):

Amal Y. Aldhebiani ◽

Mohamed Elhag ◽

Ahmad K. Hegazy ◽

Hanaa K. Galal ◽

Norah S. Mufareh

Keyword(s):

Saudi Arabia ◽

Vegetation Cover ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Remote Sensing Data ◽

Floristic Composition ◽

Water Source ◽

Shannon Index ◽

Landsat 8 ◽

Main Stream

Abstract. Wadi Yalamlam is known as one of the significant wadis in the west of Saudi Arabia. It is a very important water source for the western region of the country. Thus, it supplies the holy places in Mecca and the surrounding areas with drinking water. The floristic composition of Wadi Yalamlam has not been comprehensively studied. For that reason, this work aimed to assess the wadi vegetation cover, life-form presence, chorotype, diversity, and community structure using temporal remote sensing data. Temporal datasets spanning 4 years were acquired from the Landsat 8 sensor in 2013 as an early acquisition and in 2017 as a late acquisition to estimate normalized difference vegetation index (NDVI) changes. The wadi was divided into seven stands. Stands 7, 1, and 3 were the richest with the highest Shannon index values of 2.98, 2.69, and 2.64, respectively. On the other hand, stand 6 has the least plant biodiversity with a Shannon index of 1.8. The study also revealed the presence of 48 different plant species belonging to 24 families. Fabaceae (17 %) and Poaceae (13 %) were the main families that form most of the vegetation in the study area, while many families were represented by only 2 % of the vegetation of the wadi. NDVI analysis showed that the wadi suffers from various types of degradation of the vegetation cover along with the wadi main stream.

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Assessing and Simulating Impacts of Land Use Land Cover Changes on Land Surface Temperature in Mymensingh City, Bangladesh

Environment and Natural Resources Journal ◽

10.32526/ennrj/20/202100110 ◽

2021 ◽

Vol 20 (2) ◽

pp. 1-19

Author(s):

Tahmid Anam Chowdhury ◽

◽

Md. Saiful Islam ◽

Keyword(s):

Land Use ◽

Land Cover ◽

Surface Temperature ◽

Land Surface Temperature ◽

Land Surface ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Bare Soil ◽

Land Use Land Cover ◽

Remarkable Change

Urban developments in the cities of Bangladesh are causing the depletion of natural land covers over the past several decades. One of the significant implications of the developments is a change in Land Surface Temperature (LST). Through LST distribution in different Land Use Land Cover (LULC) and a statistical association among LST and biophysical indices, i.e., Urban Index (UI), Bare Soil Index (BI), Normalized Difference Builtup Index (NDBI), Normalized Difference Bareness Index (NDBaI), Normalized Difference Vegetation Index (NDVI), and Modified Normalized Difference Water Index (MNDWI), this paper studied the implications of LULC change on the LST in Mymensingh city. Landsat TM and OLI/TIRS satellite images were used to study LULC through the maximum likelihood classification method and LSTs for 1989, 2004, and 2019. The accuracy of LULC classifications was 84.50, 89.50, and 91.00 for three sampling years, respectively. From 1989 to 2019, the area and average LST of the built-up category has been increased by 24.99% and 7.6ºC, respectively. Compared to vegetation and water bodies, built-up and barren soil regions have a greater LST each year. A different machine learning method was applied to simulate LULC and LST in 2034. A remarkable change in both LULC and LST was found through this simulation. If the current changing rate of LULC continues, the built-up area will be 59.42% of the total area, and LST will be 30.05ºC on average in 2034. The LST in 2034 will be more than 29ºC and 31ºC in 59.64% and 23.55% areas of the city, respectively.

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Land Cover Change Assessment of Vaal Harts Irrigation Scheme using Multi-temporal Satellite Sata

Archives of Environmental Protection ◽

10.2478/aep-2013-0036 ◽

2013 ◽

Vol 39 (4) ◽

pp. 59-70 ◽

Cited By ~ 1

Author(s):

Fredrick Ao Otieno ◽

Olumuyiwa I Ojo ◽

George M. Ochieng

Keyword(s):

Land Use ◽

Land Use Change ◽

Land Cover ◽

Land Cover Change ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Management Program ◽

Land Use Land Cover ◽

Irrigation Scheme ◽

Multi Temporal

Abstract Land cover change (LCC) is important to assess the land use/land cover changes with respect to the development activities like irrigation. The region selected for the study is Vaal Harts Irrigation Scheme (VHS) occupying an area of approximately 36, 325 hectares of irrigated land. The study was carried out using Land sat data of 1991, 2001, 2005 covering the area to assess the changes in land use/land cover for which supervised classification technique has been applied. The Normalized Difference Vegetation Index (NDVI) index was also done to assess vegetative change conditions during the period of investigation. By using the remote sensing images and with the support of GIS the spatial pattern of land use change of Vaal Harts Irrigation Scheme for 15 years was extracted and interpreted for the changes of scheme. Results showed that the spatial difference of land use change was obvious. The analysis reveals that 37.86% of additional land area has been brought under fallow land and thus less irrigation area (18.21%). There is an urgent need for management program to control the loss of irrigation land and therefore reclaim the damaged land in order to make the scheme more viable.

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Impacts of temperature extremes on European vegetation during the growing season

10.5194/bg-2017-189 ◽

2017 ◽

Author(s):

Lukas Baumbach ◽

Jonatan F. Siegmund ◽

Magdalena Mittermeier ◽

Reik V. Donner

Keyword(s):

Land Cover ◽

Land Surface ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Extreme Temperature ◽

Terrestrial Ecosystems ◽

Growing Season ◽

Temperature Extremes ◽

Minor Effect ◽

European Continent

Abstract. Temperature is a key factor controlling plant growth and vitality in the temperate climates of the mid-latitudes like in vast parts of the European continent. Beyond the effect of average conditions, the timings and magnitudes of temperature extremes play a particularly crucial role, which needs to be better understood in the context of projected future rises in the frequency and/or intensity of such events. In this work, we employ event coincidence analysis (ECA) to quantify the likelihood of simultaneous occurrences of extremes in daytime land surface temperature anomalies and the normalized difference vegetation index (NDVI). We perform this analysis for entire Europe based upon remote sensing data, differentiating between three periods corresponding to different stages of plant development during the growing season. In addition, we analyze the typical elevation and land cover type of the regions showing significantly large event coincidences rates to identify the most severely affected vegetation types. Our results reveal distinct spatio-temporal impact patterns in terms of extraordinarily large co-occurrence rates between several combinations of temperature and NDVI extremes. Croplands are among the most frequently affected land cover types, while elevation is found to have only a minor effect on the spatial distribution of corresponding extreme weather impacts. These findings provide important insights into the vulnerability of European terrestrial ecosystems to extreme temperature events and demonstrate how event-based statistics like ECA can provide a valuable perspective on environmental nexuses.

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Parametric Methods to Multispectral Image Classification using Normalized Difference Vegetation Index

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.b1061.1292s19 ◽

2019 ◽

Vol 9 (2S) ◽

pp. 611-615

Keyword(s):

Land Cover ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Learning Algorithm ◽

Urban Region ◽

Landsat 8 ◽

Multispectral Images ◽

Barren Soil ◽

Day By Day ◽

Manual Verification

The key to proper governance of the municipal bodies lies in knowing the geography of the region. The land cover of the region changes with respect to time. Also, there are seasonal variation in the layout of the waterbodies. Manual verification and surveying of these things becomes very difficult for want of resources. Remote Sensing Images play a very important role in mapping the land cover. In this paper, we consider such remotely sensed Multispectral Images, taken from Landsat-8. Parametric Machine learning algorithm like Maximum Likelihood Classifier has been used on those images to classify the land cover. Normalized Difference Vegetation Index (NDVI) has been calculated and integrates with the classification process. Four basic land covers have been identified for the purpose namely Water, Vegetation, Built-up and Barren soil. The area of study is Bangalore urban region where we find that the water bodies are decreasing day by day. An overall efficiency of 82% with a kappa hat 0f 0.67 has been achieved with the method. The user and the producer accuracies have also been tabulated in the Results part. The results show the land cover changes in a temporal manner

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Assessment of the climate change-induced impact on forest vegetation in the Mediterranean Basin using an integrated approach

10.5194/egusphere-egu21-13511 ◽

2021 ◽

Author(s):

Maria Castellaneta ◽

Angelo Rita ◽

Jesus Julio Camarero ◽

Michele Colangelo ◽

Francesco Ripullone

Keyword(s):

Tree Mortality ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Integrated Approach ◽

Mediterranean Forests ◽

Climate Extreme ◽

Remotely Sensed Data ◽

Climate Anomalies ◽

Forest Sites ◽

Tree Ring Data

The recent increase in the frequency and severity of heat weaves and droughts has intensified efforts to understand their impact on forest productivity and tree vigor. These climate extreme events are expected to reduce productivity and increase the tree mortality rate, particularly in drought-prone Mediterranean forests. Thus, our goal is to quantify the impacts of hotter droughts on forests vulnerable to drought in the Italian and Iberian peninsulas by using remotely sensed data (NDVI, Normalized Difference Vegetation Index) to track vegetation changes and tree-ring data from forest sites showing dieback to assess tree&#8217;s growth trends. The survey involved the comparison of stands showing dieback where trees showed growth decline and high defoliation rates (decay) versus stands where trees showed low or no defoliation. Our outcomes will be discussed i) to describe the effects of climate anomalies on forest vulnerability in terms of resistance and resilience, and ii) to evaluate the existence of a correlation between vegetation response and &#8220;post-disturbance&#8221; recovery.

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