scholarly journals Normalized difference vegetation index as the dominant predicting factor of groundwater recharge in phreatic aquifers: case studies across Iran

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Esmaeel Parizi ◽  
Seiyed Mossa Hosseini ◽  
Behzad Ataie-Ashtiani ◽  
Craig T. Simmons
Keyword(s):  
Groundwater Recharge ◽  
Arid Regions ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Data Availability ◽  
Mean Annual Temperature ◽  
Recharge Rate ◽  
Predicting Factors ◽  
Phreatic Aquifers ◽  
Semi Arid

Abstract The estimation of long-term groundwater recharge rate ($${GW}_{r}$$ GW r ) is a pre-requisite for efficient management of groundwater resources, especially for arid and semi-arid regions. Precise estimation of $${GW}_{r}$$ GW r is probably the most difficult factor of all measurements in the evaluation of GW resources, particularly in semi-arid regions in which the recharge rate is typically small and/or regions with scarce hydrogeological data. The main objective of this study is to find and assess the predicting factors of $${GW}_{r}$$ GW r at an aquifer scale. For this purpose, 325 Iran’s phreatic aquifers (61% of Iran’s aquifers) were selected based on the data availability and the effect of eight predicting factors were assessed on $${GW}_{r}$$ GW r estimation. The predicting factors considered include Normalized Difference Vegetation Index (NDVI), mean annual temperature ($$T$$ T ), the ratio of precipitation to potential evapotranspiration ($${P/ET}_{P}$$ P / E T P ), drainage density ($${D}_{d}$$ D d ), mean annual specific discharge ($${Q}_{s}$$ Q s ), Mean Slope ($$S$$ S ), Soil Moisture ($${SM}_{90}$$ SM 90 ), and population density ($${Pop}_{d}$$ Pop d ). The local and global Moran’s I index, geographically weighted regression (GWR), and two-step cluster analysis served to support the spatial analysis of the results. The eight predicting factors considered are positively correlated to $${GW}_{r}$$ GW r and the NDVI has the greatest influence followed by the $$P/{ET}_{P}$$ P / ET P and $${SM}_{90}$$ SM 90 . In the regression model, NDVI solely explained 71% of the variation in $${GW}_{r}$$ GW r , while other drivers have only a minor modification (3.6%). The results of this study provide new insight into the complex interrelationship between $${GW}_{r}$$ GW r and vegetation density indicated by the NDVI. The findings of this study can help in better estimation of $${GW}_{r}$$ GW r especially for the phreatic aquifers that the hydrogeological ground-data requisite for establishing models are scarce.

scholarly journals Relating surface backscatter response from TRMM Precipitation Radar to soil moisture: results over a semi-arid region

2009 ◽  
Vol 6 (5) ◽  
pp. 6425-6454
Author(s):  
H. Stephen ◽  
S. Ahmad ◽  
T. C. Piechota ◽  
C. Tang
Keyword(s):  
Soil Moisture ◽  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Arid Regions ◽  
Vegetation Index ◽  
Temporal Trends ◽  
Model Parameters ◽  
Precipitation Radar ◽  
Semi Arid

Abstract. The Tropical Rainfall Measuring Mission (TRMM) carries aboard the Precipitation Radar (TRMMPR) that measures the backscatter (σ°) of the surface. σ° is sensitive to surface soil moisture and vegetation conditions. Due to sparse vegetation in arid and semi-arid regions, TRMMPR σ° primarily depends on the soil water content. In this study we relate TRMMPR σ° measurements to soil water content (ms) in Lower Colorado River Basin (LCRB). σ° dependence on ms is studied for different vegetation greenness values determined through Normalized Difference Vegetation Index (NDVI). A new model of σ° that couples incidence angle, ms, and NDVI is used to derive parameters and retrieve soil water content. The calibration and validation of this model are performed using simulated and measured ms data. Simulated ms is estimated using Variable Infiltration Capacity (VIC) model whereas measured ms is acquired from ground measuring stations in Walnut Gulch Experimental Watershed (WGEW). σ° model is calibrated using VIC and WGEW ms data during 1998 and the calibrated model is used to derive ms during later years. The temporal trends of derived ms are consistent with VIC and WGEW ms data with correlation coefficient (R) of 0.89 and 0.74, respectively. Derived ms is also consistent with the measured precipitation data with R=0.76. The gridded VIC data is used to calibrate the model at each grid point in LCRB and spatial maps of the model parameters are prepared. The model parameters are spatially coherent with the general regional topography in LCRB. TRMMPR σ° derived soil moisture maps during May (dry) and August (wet) 1999 are spatially similar to VIC estimates with correlation 0.67 and 0.76, respectively. This research provides new insights into Ku-band σ° dependence on soil water content in the arid regions.

scholarly journals Soil Moisture and Irrigation Mapping in A Semi-Arid Region, Based on the Synergetic Use of Sentinel-1 and Sentinel-2 Data

2018 ◽  
Vol 10 (12) ◽  
pp. 1953 ◽  
Author(s):  
Safa Bousbih ◽  
Mehrez Zribi ◽  
Mohammad El Hajj ◽  
Nicolas Baghdadi ◽  
Zohra Lili-Chabaane ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Arid Region ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Cloud Model ◽  
Remotely Sensed Data ◽  
Semi Arid Region ◽  
Semi Arid ◽  
Sentinel 2 ◽  
Reference Fields

This paper presents a technique for the mapping of soil moisture and irrigation, at the scale of agricultural fields, based on the synergistic interpretation of multi-temporal optical and Synthetic Aperture Radar (SAR) data (Sentinel-2 and Sentinel-1). The Kairouan plain, a semi-arid region in central Tunisia (North Africa), was selected as a test area for this study. Firstly, an algorithm for the direct inversion of the Water Cloud Model (WCM) was developed for the spatialization of the soil water content between 2015 and 2017. The soil moisture retrieved from these observations was first validated using ground measurements, recorded over 20 reference fields of cereal crops. A second method, based on the use of neural networks, was also used to confirm the initial validation. The results reported here show that the soil moisture products retrieved from remotely sensed data are accurate, with a Root Mean Square Error (RMSE) of less than 5% between the two moisture products. In addition, the analysis of soil moisture and Normalized Difference Vegetation Index (NDVI) products over cultivated fields, as a function of time, led to the classification of irrigated and rainfed areas on the Kairouan plain, and to the production of irrigation maps at the scale of individual fields. This classification is based on a decision tree approach, using a combination of various statistical indices of soil moisture and NDVI time series. The resulting irrigation maps were validated using reference fields within the study site. The best results were obtained with classifications based on soil moisture indices only, with an accuracy of 77%.

scholarly journals Mapping Periodic Patterns of Global Vegetation Based on Spectral Analysis of NDVI Time Series

2019 ◽  
Vol 11 (21) ◽  
pp. 2497
Author(s):  
Laura Recuero ◽  
Javier Litago ◽  
Jorge E. Pinzón ◽  
Margarita Huesca ◽  
Maria C. Moyano ◽  
...  
Keyword(s):  
Time Series ◽  
Spectral Analysis ◽  
Land Surface ◽  
Arid Regions ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Periodic Patterns ◽  
Annual Cycles ◽  
Ndvi Time Series ◽  
Global Vegetation

Vegetation seasonality assessment through remote sensing data is crucial to understand ecosystem responses to climatic variations and human activities at large-scales. Whereas the study of the timing of phenological events showed significant advances, their recurrence patterns at different periodicities has not been widely study, especially at global scale. In this work, we describe vegetation oscillations by a novel quantitative approach based on the spectral analysis of Normalized Difference Vegetation Index (NDVI) time series. A new set of global periodicity indicators permitted to identify different seasonal patterns regarding the intra-annual cycles (the number, amplitude, and stability) and to evaluate the existence of pluri-annual cycles, even in those regions with noisy or low NDVI. Most of vegetated land surface (93.18%) showed one intra-annual cycle whereas double and triple cycles were found in 5.58% of the land surface, mainly in tropical and arid regions along with agricultural areas. In only 1.24% of the pixels, the seasonality was not statistically significant. The highest values of amplitude and stability were found at high latitudes in the northern hemisphere whereas lowest values corresponded to tropical and arid regions, with the latter showing more pluri-annual cycles. The indicator maps compiled in this work provide highly relevant and practical information to advance in assessing global vegetation dynamics in the context of global change.

Development of a neural network to calculate groundwater recharge in karstified aquifers

2020 ◽  
Author(s):  
Tomy-Minh Trùòng ◽  
Márk Rudolf Somogyvári ◽  
Martin Sauter ◽  
Reinhard Hinkelmann ◽  
Irina Engelhardt
Keyword(s):  
Neural Network ◽  
Groundwater Recharge ◽  
Input Signal ◽  
Arid Regions ◽  
Time Frame ◽  
West Germany ◽  
Model Parameters ◽  
Karst Aquifers ◽  
Semi Arid ◽  
Infiltration Processes

<p>Groundwater resources are expected to be affected by climate change and population growth and thus sophisticated water resources management strategies are of importance especially in arid and semi-arid regions. A better understanding of groundwater recharge and infiltration processes will allow us to consider not only water availability but also the sustainable yield of karst aquifers.</p><p>Because of the thin or frequently absent soil cover and thick vadose zones the assessment of groundwater recharge in fractured rock aquifers is highly complex. Furthermore, in (semi)-arid regions, precipitation is highly variable in space and time and frequently characterized by data scarcity. Therefore, classical methods are often not directly applicable.</p><p>This is especially the case for karstic aquifers, where i) the surface is characterized by depressions and dry valleys, ii) the vadose zone by complex infiltration processes, and iii) the saturated zone by high hydraulic conductivity and low storage capacity. Furthermore, epikarst systems display their own hydraulic dynamics affecting spatial and temporal distribution of infiltration rates. The superposition of all these hydraulic effects and characteristics of all compartments generates a complex groundwater recharge input signal.</p><p>Artificial neural networks (ANN) have the advantage, that they do not require knowledge about the underlying physical processes or the structure of the system, nor do they need prior hydrogeological information and therefore no model parameters, usually difficult to obtain. Groundwater recharge shows a high dependency on precipitation history and therefore the ANN to be chosen should be capable to reproduce some memory effects. This is considered by a standard multilayer perceptron (MLP) ANN, which uses a time frame as an input signal, as well as a recurrent ANN. For both large data sets are desirable. Because of the delay between input (precipitation, temperature, pumping) and output (spring discharge) signals, the data have to be analyzed in a geostatistical framework to determine the time lag between the input and the corresponding output as well as the input time frame for the MLP.</p><p>Two models are set up, one for the Lez catchment, located in the South of France, and one for the catchment of the Gallusquelle spring, located in South-West Germany. Both catchments aquifers are characterized by different degrees of karstification. While in the Lez catchment flow is dominated by conduit network, the Gallusquelle aquifer shows a lower degree of karstification with a stronger influence of the aquifer matrix. Additionally, the two climates differ, with the Lez catchment displaying a Mediterranean type of climate while the Gallusquelle catchment is characterized by oceanic to continental climatic conditions.</p><p>Our goal is to find neural network architecture(s) capable of reproducing the general system behaviour of the two karst aquifers possibly transferable to other karst systems. Therefore, the networks will be trained for the two different locations and compared to analyze similarities and differences.</p>

Recurrence Quantification Techniques of vegetation time-series indices in semiarid grasslands

2020 ◽  
Author(s):  
Andres Almeida-Ñauñay ◽  
Rosa M. Benito ◽  
Miguel Quemada ◽  
Juan Carlos Losada ◽  
Ana Maria Tarquis
Keyword(s):  
Time Series ◽  
Recurrent Events ◽  
Vegetation Index ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Dynamic Properties ◽  
Distinct Pattern ◽  
Climatic Variables ◽  
Recurrence Quantification ◽  
Semi Arid

<p>Grassland ecosystems are extremely complex and set up intricate structures, whose characteristics and dynamic properties are greatly influenced by climate and meteorological patterns. Climate change and global warming are factors that could impact negatively in the quality and productivity of these ecosystems.</p><p>Remote sensing techniques have been demonstrated as a powerful tool for monitoring extensive areas. In this study, two semi-arid grassland plots were selected in the centre of Spain. This region is characterized by low precipitation and moderate productivity per unit. Through scientific research, spectral vegetation indices (VIs) have been developed to characterize vegetation cover. The most common VI is the Normalized Difference Vegetation Index (NDVI). However, in vegetation scarcity conditions, bare soil reflectance is increased, and the feasibility of NDVI is reduced. This study aims to perform a method to compare soil and agro-climatic variables effect on vegetation time-series indices.</p><p>The construction of the time series was based on multispectral images of MODIS TERRA (MOD09A1.006) product acquired from 2002 till 2018. Three pixels with a temporal resolution of 8 days and a spatial resolution of 500 x 500 m were chosen in each area. To estimate and analyse VIs series, Red (620-670 nm) and Near Infrared (841-876 nm) channels were extracted and filtered by the quality of pixel. All spectral bands showed statistically significant differences confirming that both areas presented different soil properties. Moreover, average annual precipitation was different in each area of study.</p><p>NDVI calculation is only based on NIR and RED bands. To improve the estimation of vegetation in semi-arid areas, several indices have been developed to minimize the soil effect. Each one of them incorporates soil influence in a different way, i.e., Soil Adjusted Vegetation Index (SAVI) adds a constant soil adjustment factor (L), whereas, MSAVI, incorporate an L variable and dependant on soil characteristics.</p><p>Recurrence plots (RP) and recurrence quantification analysis (RQA) were computed to characterize the influence of agro-climatic variables in vegetation index dynamics. Characterization was based on various RQA measures, such as Determinism (DET), average diagonal length (LT) or entropy (ENT).</p><p>Our results showed different RPs depending on the area, VI utilized and precipitation. MSAVI patterns were further distinct, meanwhile, NDVI showed a noisy pattern. LT values in MSAVI were higher than in SAVI implying that MSAVI recurrent events are much longer than SAVI. Simultaneously, LT and DET values in ZSO, with a higher rain, were above ZEA values in MSAVI.</p><p>This indicates that incorporating more detailed information of soil and precipitation reinforce vegetation index estimation and allow to obtain a more distinct pattern of the time series. Therefore, in arid-semiarid grasslands, they should be considered.</p><p><strong>ACKNOWLEDGEMENTS</strong></p><p>The authors acknowledge support from Project No. PGC2018-093854-B-I00 of the Spanish <em>Ministerio de Ciencia Innovación y Universidades</em> of Spain and the funding from the Comunidad de Madrid (Spain) and Structural Funds 2014-2020 512 (ERDF and ESF), through project AGRISOST-CM S2018/BAA-4330, are highly appreciated.</p>

scholarly journals LAND USE/LAND COVER CHANGES IN SEMI-ARID MOUNTAIN LANDSCAPE IN SOUTHERN INDIA: A GEOINFORMATICS BASED MARKOV CHAIN APPROACH

2017 ◽  
Vol XLII-1/W1 ◽  
pp. 231-237 ◽  
Author(s):  
S. A. Rahaman ◽  
S. Aruchamy ◽  
K. Balasubramani ◽  
R. Jegankumar
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Vegetation Index ◽  
Forest Cover ◽  
Normalized Difference Vegetation Index ◽  
Southern India ◽  
Vegetation Changes ◽  
Land Use Land Cover ◽  
Mountain Landscape ◽  
Semi Arid

Nowadays land use/ land cover in mountain landscape is in critical condition; it leads to high risky and uncertain environments. These areas are facing multiple stresses including degradation of land resources; vagaries of climate and depletion of water resources continuously affect land use practices and livelihoods. To understand the Land use/Land cover (Lu/Lc) changes in a semi-arid mountain landscape, Kallar watershed of Bhavani basin, in southern India has been chosen. Most of the hilly part in the study area covers with forest, plantation, orchards and vegetables and which are highly affected by severe soil erosion, landslide, frequent rainfall failures and associated drought. The foothill regions are mainly utilized for agriculture practices; due to water scarcity and meagre income, the productive agriculture lands are converted into settlement plots and wasteland. Hence, land use/land cover change deduction; a stochastic processed based method is indispensable for future prediction. For identification of land use/land cover, and vegetation changes, Landsat TM, ETM (1995, 2005) and IRS P6- LISS IV (2015) images were used. Through CAMarkov chain analysis, Lu/Lc changes in past three decades (1995, 2005, and 2015) were identified and projected for (2020 and 2025); Normalized Difference Vegetation Index (NDVI) were used to find the vegetation changes. The result shows that, maximum changes occur in the plantation and slight changes found in forest cover in the hilly terrain. In foothill areas, agriculture lands were decreased while wastelands and settlement plots were increased. The outcome of the results helps to farmer and policy makers to draw optimal lands use planning and better management strategies for sustainable development of natural resources.

Application of normalized difference vegetation index (NDVI) to forecast rodent population abundance in smallholder agro-ecosystems in semi-arid areas in Tanzania

Mammalia ◽  
2020 ◽  
Vol 84 (2) ◽  
pp. 136-143 ◽  
Author(s):  
Davis J. Chidodo ◽  
Didas N. Kimaro ◽  
Proches Hieronimo ◽  
Rhodes H. Makundi ◽  
Moses Isabirye ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Landsat 8 ◽  
Electromagnetic Spectrum ◽  
Strong Positive Correlation ◽  
Arid Areas ◽  
Population Abundance ◽  
Rodent Population ◽  
Rodent Abundance ◽  
Semi Arid

AbstractThis study aimed to evaluate the potential use of normalized difference vegetation index (NDVI) from satellite-derived remote sensing data for monitoring rodent abundance in semi-arid areas of Tanzania. We hypothesized that NDVI could potentially complement rainfall in predicting rodent abundance spatially and temporally. NDVI were determined across habitats with different vegetation types in Isimani landscape, Iringa Region, in the southern highlands of Tanzania. Normalized differences in reflectance between the red (R) (0.636–0.673 mm) and near-infrared (NIR) (0.851–0.879 mm) channels of the electromagnetic spectrum from the Landsat 8 [Operational Land Imager (OLI)] sensor were obtained. Rodents were trapped in a total of 144 randomly selected grids each measuring 100 × 100 m2, for which the corresponding values of NDVI were recorded during the corresponding rodent trapping period. Raster analysis was performed by transformation to establish NDVI in study grids over the entire study area. The relationship between NDVI, rodent distribution and abundance both spatially and temporally during the start, mid and end of the dry and wet seasons was established. Linear regression model was used to evaluate the relationships between NDVI and rodent abundance across seasons. The Pearson correlation coefficient (r) at p ≤ 0.05 was carried out to describe the degree of association between actual and NDVI-predicted rodent abundances. The results demonstrated a strong linear relationship between NDVI and actual rodent abundance within grids (R2 = 0.71). NDVI-predicted rodent abundance showed a strong positive correlation (r = 0.99) with estimated rodent abundance. These results support the hypothesis that NDVI has the potential for predicting rodent population abundance under smallholder farming agro-ecosystems. Hence, NDVI could be used to forecast rodent abundance within a reasonable short period of time when compared with sparse and not widely available rainfall data.

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