scholarly journals Lessons to Be Learned: Groundwater Depletion in Chile’s Ligua and Petorca Watersheds through an Interdisciplinary Approach

Water ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 2446
Author(s):  
Iongel Duran-Llacer ◽  
Juan Munizaga ◽  
José Arumí ◽  
Christopher Ruybal ◽  
Mauricio Aguayo ◽  
...  
Keyword(s):  
Water Management ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Standardized Precipitation Index ◽  
Primary Source ◽  
Interdisciplinary Approach ◽  
Anthropogenic Factors ◽  
Groundwater Depletion ◽  
Aquifer Depletion ◽  
Semi Arid

Groundwater (GW) is the primary source of unfrozen freshwater on the planet and in many semi-arid areas, it is the only source of water available during low-water periods. In north-central Chile, there has been GW depletion as a result of semi-arid conditions and high water demand, which has unleashed major social conflicts, some due to drought and others due to agribusiness practices against the backdrop of a private water management model. The Ligua and Petorca watersheds in the Valparaíso Region were studied in order to analyze the influence of climatic and anthropogenic factors on aquifer depletion using an interdisciplinary approach that integrates hydroclimatic variables, remote sensing data techniques, and GW rights data to promote sustainable GW management. The Standardized Precipitation Index (SPI) and Normalized Difference Vegetation Index (NDVI) were calculated and the 2002–2017 land-use change was analyzed. It was shown that GW decreased significantly (in 75% of the wells) and that the hydrological drought was moderate and prolonged (longest drought in the last 36 years). The avocado-growing area in Ligua increased significantly—by 2623 ha—with respect to other agricultural areas (higher GW decrease), while in Petorca, it decreased by 128 ha. In addition, GW-rainfall correlations were low and GW rights were granted continuously despite the drought. The results confirmed that aquifer depletion was mostly influenced by human factors due to overexploitation by agriculture and a lack of water management.

scholarly journals A Study of Desertification Using Remote Sensing Techniques in Basra Governorate, South Iraq

2021 ◽  
pp. 912-926
Author(s):  
Fadel Abbas Zwain ◽  
Thair Thamer Al-Samarrai ◽  
Younus I. Al-Saady
Keyword(s):  
Remote Sensing ◽  
Land Degradation ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Arid Zone ◽  
Sand Dunes ◽  
Risk Index ◽  
Anthropogenic Factors ◽  
Landsat Images

Iraq territory as a whole and south of Iraq in particular encountered rapid desertification and signs of severe land degradation in the last decades. Both natural and anthropogenic factors are responsible for the extent of desertification. Remote sensing data and image analysis tools were employed to identify, detect, and monitor desertification in Basra governorate. Different remote sensing indicators and image indices were applied in order to better identify the desertification development in the study area, including the Normalized difference vegetation index (NDVI), Normalized Difference Water Index (NDWI), Salinity index (SI), Top Soil Grain Size Index (GSI) , Land Surface Temperature (LST) , Land Surface Soil Moisture (LSM), and Land Degradation Risk Index (LDI) which was used for the assessment of degradation severity .Three Landsat images, acquired in 1973, 1993, and 2013, were used to evaluate the potential of using remote sensing analysis in desertification monitoring. The approach applied in this study for evaluating this phenomenon was proven to be an effective tool for the recognition of areas at risk of desertification. The results indicated that the arid zone of Basra governorate encounters substantial changes in the environment, such as decreasing surface water, degradation of agricultural lands (as palm orchards and crops), and deterioration of marshlands. Additional changes include increased salinization with the creeping of sand dunes to agricultural areas, as well as the impacts of oil fields and other facilities.

scholarly journals Irrigation Optimization Under a Limited Water Supply by the Integration of Modern Approaches into Traditional Water Management on the Cotton Fields

2019 ◽  
Vol 11 (18) ◽  
pp. 2127
Author(s):  
Polinova ◽  
Salinas ◽  
Bonfante ◽  
Brook
Keyword(s):  
Water Management ◽  
Water Supply ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Irrigation Schedule ◽  
Irrigation Scheduling ◽  
Future Climate Change ◽  
Weather Forecasts ◽  
Red Edge ◽  
Cotton Fields

The ability to effectively develop agriculture with limited water resources is an important strategic objective to face future climate change and to achieve the Sustainable Development Goal 2 (SDG2) of the United Nations. Since new conditions increasingly point to a limited water supply, the aim of modern irrigation management is to be sure to maximize the crop yield and minimize water use. This study aims to explore the advantages of the traditional agronomic approach, agro-hydrological model and field feedback obtained by spectroscopy, to optimize irrigation water management in the example of a cotton field. The study was conducted for two summer growing seasons in 2015 and 2016 in Kibbutz Hazorea, near Haifa, Israel. The irrigation schedule was developed by farmers using weather forecasts and corrected by the results of field inspections. The Soil Water Atmosphere Plant (SWAP) model was applied to optimize seasonal water distribution based on different criteria (critical soil pressure head and allowable daily stress). A new optimization algorithm for irrigation schedules by weather forecasts and vegetation indices was developed and presented in this paper. A few indices related to physical parameters and plant health (Normalized Difference Vegetation Index, Red Edge Normalized Difference Vegetation Index, Modified Chlorophyll Absorption Ratio Index 2, and Photochemical Reflectance Index) were considered. Red Edge Normalized Difference Vegetation Index proves itself as a suitable parameter for monitoring crop state due to its clear-cut response to irrigation treatments and was introduced in the developed algorithm. The performance of the considered irrigation scheduling approaches was assessed by a simulation model application for cotton fields in 2016. The results show, that the irrigation schedule developed by farmers did not compensate for the absence of precipitation in spring, which led to long-term lack of water during crop development. The optimization developed by SWAP allows determining the minimal amount of water which ensures appropriate yield. However, this approach could not take into account the non-linear effect of the lack of water at specific phenological stages on the yield. The new algorithm uses the minimal sufficient seasonal amount of water obtained from SWAP optimization. The approach designed allows one to prevent critical stress in cotton and distribute water in conformity with agronomic practice.

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%.

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 An Analysis of Vegetation Change Trends and Their Causes in Inner Mongolia, China from 1982 to 2006

2011 ◽  
Vol 2011 ◽  
pp. 1-8 ◽  
Author(s):  
Baolin Li ◽  
Wanli Yu ◽  
Juan Wang
Keyword(s):  
Vegetation Change ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Anthropogenic Factors ◽  
Simple Linear Regression ◽  
Ordos Plateau ◽  
Hetao Plain ◽  
Avhrr Data ◽  
The Relationship ◽  
Very High

This paper presents the vegetation change trends and their causes in the Inner Mongolian Autonomous Region, China from 1982 to 2006. We used National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) data to determine the vegetation change trends based on regression model by fitting simple linear regression through the time series of the integrated Normalized Difference Vegetation Index (NDVI) in the growing season for each pixel and calculating the slopes. We also explored the relationship between vegetation change trends and climatic and anthropogenic factors. This paper indicated that a large portion of the study area (17%) had experienced a significant vegetation increase at the 0.05 level from 1982 to 2006. The significant vegetation increase showed no positive link with precipitation and was mainly caused by human activities. In or to the south of Horqin Sandy Land, in the Hetao Plain, and at the northern foothills of the YinshanMountain, the significant NDVI increase trends were mainly caused by the increase of the millet yield per unit of cropland. In the east of Ordos Plateau, the significant NDVI increase trends were mainly determined by the fencing and planting of grasses and trees on grassland.

scholarly journals Drought Risk Assessment in the Khushab Region of Pakistan Using Satellite Remote Sensing and Geospatial Methods

2019 ◽  
Vol 10 (1) ◽  
pp. 48-56 ◽  
Author(s):  
Muhammad Khubaib Abuzar ◽  
Muhammad Shafiq ◽  
Syed Amer Mahmood ◽  
Muhammad Irfan ◽  
Tayyaba Khalil ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Social Life ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Standardized Precipitation Index ◽  
Temporal Analysis ◽  
Agricultural Drought ◽  
Natural Phenomenon ◽  
Drought Risk ◽  
Gis And Remote Sensing

Drought is a harmful and slow natural phenomenon that has significant effects on the economy, social life,agriculture and environment of the country. Due to its slow process it is difficult to study this phenomenon. RemoteSensing and GIS tools play a key role in studying different hazards like droughts. The main objective of the study wasto investigate drought risk by using GIS and Remote Sensing techniques in district Khushab, Pakistan. Landsat ETMimages for the year 2003, 2009 and 2015 were utilized for spatial and temporal analysis of agricultural andmeteorological drought. Normalized difference vegetation index (NDVI) Standardized Precipitation Index (SPI) andrainfall anomaly indices were calculated to identify the drought prone areas in the study area. To monitormeteorological drought SPI values were used and NDVI was calculated for agricultural drought. These indices wereintegrated to compute the spatial and temporal drought maps. Three zones; no drought, slight drought and moderatedrought were identified. Final drought map shows that 30.21% of the area faces moderate drought, 28.36% faces slightdrought while nearly 41.3% faces no drought situation. Drought prevalence and severity is present more in the southernpart of Khushab district than the northern part. Most of the northern part is not under any type of drought. Thus, anoverall outcome of this study shows that risk areas can be assessed appropriately by integration of various data sourcesand thereby management plans can be prepared to deal with the hazard.

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.

scholarly journals Time Varying Spatial Downscaling of Satellite-Based Drought Index

2021 ◽  
Vol 13 (18) ◽  
pp. 3693
Author(s):  
Hone-Jay Chu ◽  
Regita Faridatunisa Wijayanti ◽  
Lalu Muhamad Jaelani ◽  
Hui-Ping Tsai
Keyword(s):  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Standardized Precipitation Index ◽  
Environmental Data ◽  
Drought Monitoring ◽  
Drought Severity ◽  
Time Varying ◽  
Fine Resolution ◽  
Spatial Downscaling

Drought monitoring is essential to detect the presence of drought, and the comprehensive change of drought conditions on a regional or global scale. This study used satellite precipitation data from the Tropical Rainfall Measuring Mission (TRMM), but refined the data for drought monitoring in Java, Indonesia. Firstly, drought analysis was conducted to establish the standardized precipitation index (SPI) of TRMM data for different durations. Time varying SPI spatial downscaling was conducted by selecting the environmental variables, normalized difference vegetation index (NDVI), and land surface temperature (LST) that were highly correlated with precipitation because meteorological drought was associated with vegetation and land drought. This study used time-dependent spatial regression to build the relation among original SPI, auxiliary variables, i.e., NDVI and LST. Results indicated that spatial downscaling was better than nonspatial downscaling (overall RMSEs: 0.25 and 0.46 in spatial and nonspatial downscaling). Spatial downscaling was more suitable for heterogeneous SPI, particularly in the transition time (R: 0.863 and 0.137 in June 2019 for spatial and nonspatial models). The fine resolution (1 km) SPI can be composed of the environmental data. The fine-resolution SPI captured a similar trend of the original SPI. Furthermore, the detailed SPI maps can be used to understand the spatio-temporal pattern of drought severity.

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