scholarly journals Study on Ecological Threshold of Groundwater in Typical Salinization Area of Qian’an County

Water ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 856
Author(s):  
Zhiwei Qi ◽  
Changlai Xiao ◽  
Ge Wang ◽  
Xiujuan Liang
Keyword(s):  
Groundwater Level ◽  
Vegetation Index ◽  
Capillary Rise ◽  
Threshold Value ◽  
Research Area ◽  
Field Investigation ◽  
Soil Salinization ◽  
Gis Technology ◽  
Ecological Threshold ◽  
Mineralization Degree

A suitable groundwater level is an important condition to maintain the stability of the vegetation community, especially in arid and semi-arid areas. The surface of Qian’an County in Western Jilin Province is often accompanied by salinization due to the influence of natural and human factors. In order to maintain the healthy development of ecological vegetation and reduce the risk of soil salinization, the concept of an ecological threshold of groundwater level is proposed, and two methods are used to determine the reasonable ecological threshold of groundwater. (1) Based on field investigation and indoor experiment, the data layer of soil texture, land use type and groundwater mineralization degree in the research area was established by using remote sensing technology and GIS technology. According to the thickness of vegetation root layer and the height of capillary rise of different soil and water types, the influence of groundwater salinity is considered, and the sum of the two is taken as the ecological threshold of groundwater in the study area. The reasonable threshold value of suitable growth of various vegetation crops is 3.76~5.66 m. (2) According to the relationship between the normalized vegetation index (NDVI) and the groundwater buried depth and phreatic salt, the groundwater buried depth and the mineralization degree under the best vegetation cover are analyzed as follows: the buried depth of groundwater is between 4.8 m and 6.1 m, and the salinity of groundwater is between 0.37 and 1.25 g/L, which are reasonable groundwater properties in the study area of the ecological threshold. This result not only enriches and broadens the content of groundwater research, but also helps to predict the prospect of water resource development.

scholarly journals Mapping the Groundwater Level and Soil Moisture of a Montane Peat Bog Using UAV Monitoring and Machine Learning

2021 ◽  
Vol 13 (5) ◽  
pp. 907
Author(s):  
Theodora Lendzioch ◽  
Jakub Langhammer ◽  
Lukáš Vlček ◽  
Robert Minařík
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Spatial Data ◽  
Groundwater Level ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Sampling Strategy ◽  
Peat Bog ◽  
Strong Impact ◽  
Ground Truth Data

One of the best preconditions for the sufficient monitoring of peat bog ecosystems is the collection, processing, and analysis of unique spatial data to understand peat bog dynamics. Over two seasons, we sampled groundwater level (GWL) and soil moisture (SM) ground truth data at two diverse locations at the Rokytka Peat bog within the Sumava Mountains, Czechia. These data served as reference data and were modeled with a suite of potential variables derived from digital surface models (DSMs) and RGB, multispectral, and thermal orthoimages reflecting topomorphometry, vegetation, and surface temperature information generated from drone mapping. We used 34 predictors to feed the random forest (RF) algorithm. The predictor selection, hyperparameter tuning, and performance assessment were performed with the target-oriented leave-location-out (LLO) spatial cross-validation (CV) strategy combined with forward feature selection (FFS) to avoid overfitting and to predict on unknown locations. The spatial CV performance statistics showed low (R2 = 0.12) to high (R2 = 0.78) model predictions. The predictor importance was used for model interpretation, where temperature had strong impact on GWL and SM, and we found significant contributions of other predictors, such as Normalized Difference Vegetation Index (NDVI), Normalized Difference Index (NDI), Enhanced Red-Green-Blue Vegetation Index (ERGBVE), Shape Index (SHP), Green Leaf Index (GLI), Brightness Index (BI), Coloration Index (CI), Redness Index (RI), Primary Colours Hue Index (HI), Overall Hue Index (HUE), SAGA Wetness Index (TWI), Plan Curvature (PlnCurv), Topographic Position Index (TPI), and Vector Ruggedness Measure (VRM). Additionally, we estimated the area of applicability (AOA) by presenting maps where the prediction model yielded high-quality results and where predictions were highly uncertain because machine learning (ML) models make predictions far beyond sampling locations without sampling data with no knowledge about these environments. The AOA method is well suited and unique for planning and decision-making about the best sampling strategy, most notably with limited data.

scholarly journals ESTIMASI PRODUKSI PADI DENGAN ANALISIS CITRA SATELIT LANDSAT 8 DI KABUPATEN KLUNGKUNG PROVINSI BALI

2018 ◽  
Vol 12 (1) ◽  
pp. 93
Author(s):  
Made Arya Bhaskara Putra ◽  
I Wayan Nuarsa ◽  
I Wayan Sandi Adnyana
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Satellite Image ◽  
Image Data ◽  
Research Area ◽  
Rice Production ◽  
Landsat 8 ◽  
Estimation Model ◽  
Accuracy Test ◽  
Accuracy Level

Rice crop is one of the important commodities that must always be available, so estimation of rice production becomes very important to do before harvesting time to know the food availability. The technology that can be used is remote sensing technology using Landsat 8 Satellite. The aims of this study were (1) to obtain the model of estimation of rice production with Landsat 8 image analysis, and (2) to know the accuracy of the model that obtained by Landsat 8. The research area is located in three sub-districts in Klungkung regency. Analysis in this research was conducted by single band analysis and analysis of vegetation index of satellite image of Landsat 8. Estimation model of rice production was developed by finding the relationship between satellite image data and rice production data. The final stage is the accuracy test of the rice production estimation model, with t test and regression analysis. The results showed: (1) estimation of rice production can be calculated between 67 to 77 days after planting; (2) there was a positive correlation between NDVI (Normalized Difference Vegetation Index) vegetation index value with rice yield; (3) the model of rice production estimation is y = 2.0442e1.8787x (x is NDVI value of Landsat 8 and y is rice production); (4) The results of the model accuracy test showed that the obtained model is suitable to predict rice production with accuracy level is 89.29% and standard error of production estimation is + 0.443 ton/ha. Based on research results, it can be concluded that Landsat 8 Satellite image can be used to estimate rice production and the accuracy level is 89.29%. The results are expected to be a reference in estimating rice production in Klungkung Regency.

scholarly journals Research on critical groundwater level under the threshold value of land subsidence in the typical region of Beijing

2015 ◽  
Vol 372 ◽  
pp. 507-510
Author(s):  
Y. Jiang ◽  
J.-R. Liu ◽  
Y. Luo ◽  
Y. Yang ◽  
F. Tian ◽  
...  
Keyword(s):  
Water Level ◽  
Water Table ◽  
Land Subsidence ◽  
Groundwater Level ◽  
Threshold Value ◽  
Scientific Basis ◽  
Good Time ◽  
Linear Regression Models ◽  
Control Plan ◽  
Time Space

Abstract. Groundwater in Beijing has been excessively exploited in a long time, causing the groundwater level continued to declining and land subsidence areas expanding, which restrained the economic and social sustainable development. Long years of study show good time-space corresponding relationship between groundwater level and land subsidence. To providing scientific basis for the following land subsidence prevention and treatment, quantitative research between groundwater level and settlement is necessary. Multi-linear regression models are set up by long series factual monitoring data about layered water table and settlement in the Tianzhu monitoring station. The results show that: layered settlement is closely related to water table, water level variation and amplitude, especially the water table. Finally, according to the threshold value in the land subsidence prevention and control plan of China (45, 30, 25 mm), the minimum allowable layered water level in this region while settlement achieving the threshold value is calculated between −18.448 and −10.082 m. The results provide a reasonable and operable control target of groundwater level for rational adjustment of groundwater exploited horizon in the future.

scholarly journals Dynamics of human-induced lakes and their impact on land surface temperature in Toshka Depression, Western Desert, Egypt

2021 ◽  
Author(s):  
Rasha Abou Samra
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Vegetation Index ◽  
Research Area ◽  
Western Desert ◽  
Severe Drought ◽  
Land Use Land Cover

Abstract Land surface temperature (LST) is a significant environmental variable that is appreciably influenced by land use /land cover changes. The main goal of this research was to quantify the impacts of land use/land cover change (LULC) from the drying of Toshka Lakes on LST by remote sensing and GIS techniques. Landsat series TM and OLI satellite images were used to estimate LST from 2001 to 2019. Automated Water Extraction Index (AWEI) was applied to extract water bodies from the research area. Optimized Soil-Adjusted Vegetation Index (OSAVI) was utilized to predict the reclaimed land in the Toshka region until 2019. The results indicated a decrease in the lakes by about 1517.79 km2 with an average increase in LST by about 25.02 °C between 2001 and 2019. It was observed that the dried areas of the lakes were converted to bare soil and are covered by salt crusts. The results indicated that the land use change was a significant driver for the increased LST. The mean annual LST increased considerably by 0.6 °C/y between 2001 and 2019. A strong negative correlation between LST and Toshka Lakes area (R-square = 0.98) estimated from regression analysis implied that Toshka Lakes drying considerably affected the microclimate of the study area. Severe drought conditions, soil degradation, and many environmental issues were predicted due to the rise of LST in the research area. There is an urgent need to develop favorable strategies for sustainable environmental management in the Toshka region.

scholarly journals The soil-adjusted vegetation index for soil salinity assessment in Uzbekistan

2020 ◽  
Vol 26 (3) ◽  
pp. 324-333
Author(s):  
Nozimjon Teshaev ◽  
Bunyod Mamadaliyev ◽  
Azamjon Ibragimov ◽  
Sayidjakhon Khasanov
Keyword(s):  
Soil Salinity ◽  
Information Technologies ◽  
Vegetation Index ◽  
Arable Land ◽  
Salt Content ◽  
Secondary Data ◽  
Soil Surface ◽  
Soil Salinization ◽  
Arid Zones ◽  
Agronomic Practices

Soil salinization, as one of the threats of land degradation, is the main environmental issue in Uzbekistan due to its aridic climate. One of the most vulnerable areas to soil salinization is Sirdarya province in Uzbekistan. The main human-induced causes of soil salinization are the insufficient operation of drainage and irrigation systems, irregular observations of the agronomic practices, and non-efficient on-farm water use. All of these causes considerably interact with the level of the groundwater, leading to an increase in the level of soil salinity. The availability of historical data on actual soil salinity in agricultural lands helps in formulating validated generic state-of-the-art approaches to control and monitor soil salinization by remote sensing and geo-information technologies. In this paper, we hypothesized that the Soil-Adjusted Vegetation Index-based results in soil salinity assessment give statistically valid illustrations and salinity patterns. As a study area, the Mirzaabad district was taken to monitor soil salinization processes since it is the most susceptible territory of Sirdarya province to soil salinization and provides considerably less agricultural products. We mainly formulated this paper based on the secondary data, as we downloaded satellite images and conducted an experiment against the in-situ method of soil salinity assessment using the Soil-Adjusted Vegetation Index. As a result, highly saline areas decreased by a factor of two during the studied period (2005–2014), while non-saline areas increased remarkably from a negligible value to over 10 000 ha. Our study showed that arable land suitability for agricultural purposes has been improving year by year, and our research held on this district also proved that there was a gradual decrease in high salt contents on the soil surface and land quality has been improved. The methodology has proven to be statistically valid and significant to be applied to other arid zones for the assessment of soil salinity. We assume that our methodology is surely considered as a possible vegetation index to evaluate salt content in arable land of either Uzbekistan or other aridic zones and our hypothesis is not rejected by this research.

scholarly journals Satellites reveal an increase in gross primary production in a greenlandic high arctic fen 1992–2008

2010 ◽  
Vol 7 (1) ◽  
pp. 1101-1129 ◽  
Author(s):  
T. Tagesson ◽  
M. Mastepanov ◽  
M. P. Tamstorf ◽  
L. Eklundh ◽  
P. Schubert ◽  
...  
Keyword(s):  
Primary Production ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Gross Primary Production ◽  
Field Measurements ◽  
High Arctic ◽  
Research Area ◽  
Strong Increase ◽  
Co2 Uptake ◽  
Flux Measurements

Abstract. Arctic wetlands play a key role in the terrestrial carbon cycle. Recent studies have shown a greening trend and indicated an increase in CO2 uptake in boreal and sub- to low-arctic areas. Our aim was to combine satellite-based normalized difference vegetation index (NDVI) with ground-based flux measurements of CO2 to investigate a possible greening trend and potential changes in gross primary production (GPP) between 1992 and 2008 in a high arctic fen area. The study took place in Rylekaerene in the Zackenberg Research Area (74°28' N 20°34' W), located in the National park of North Eastern Greenland. We estimated the light use efficiency (ε) for the dominant vegetation types from field measured fractions of photosynthetic active radiation (FAPAR) and ground-based flux measurements of GPP. Measured FAPAR were correlated to satellite-based NDVI. The FAPAR-NDVI relationship in combination with ε was applied to satellite data to model GPP 1992–2008. The model was evaluated against field measured GPP. The model was a useful tool for up-scaling GPP and all basic requirements for the model were well met, e.g., FAPAR was well correlated to NDVI and modeled GPP was well correlated to field measurements. The studied high arctic fen area has experienced a strong increase in GPP between 1992 and 2008. The area has during this period also experienced a substantial increase in local air temperature. Consequently, the observed greening trend is most likely due to ongoing climatic change possibly in combination with CO2 fertilization, due to increasing atmospheric concentrations of CO2.

scholarly journals Evaluation of Water Indices for Surface Water Extraction in a Landsat 8 Scene of Nepal

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2580 ◽  
Author(s):  
Tri Acharya ◽  
Anoj Subedi ◽  
Dong Lee
Keyword(s):  
Surface Water ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Pure Water ◽  
Confusion Matrix ◽  
Threshold Value ◽  
Environmental Noise ◽  
Water Extraction ◽  
Landsat 8 ◽  
Water Indices

Accurate and frequent updates of surface water have been made possible by remote sensing technology. Index methods are mostly used for surface water estimation which separates the water from the background based on a threshold value. Generally, the threshold is a fixed value, but can be challenging in the case of environmental noise, such as shadow, forest, built-up areas, snow, and clouds. One such challenging scene can be found in Nepal where no such evaluation has been done. Taking that in consideration, this study evaluates the performance of the most widely used water indices: Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Modified NDWI (MNDWI), and Automated Water Extraction Index (AWEI) in a Landsat 8 scene of Nepal. The scene, ranging from 60 m to 8848 m, contains various types of water bodies found in Nepal with different forms of environmental noise. The evaluation was conducted based on measures from a confusion matrix derived using validation points. Comparing visually and quantitatively, not a single method was able to extract surface water in the entire scene with better accuracy. Upon selecting optimum thresholds, the overall accuracy (OA) and kappa coefficient (kappa) was improved, but not satisfactory. NDVI and NDWI showed better results for only pure water pixels, whereas MNDWI and AWEI were unable to reject snow cover and shadows. Combining NDVI with NDWI and AWEI with shadow improved the accuracy but inherited the NDWI and AWEI characteristics. Segmenting the test scene with elevations above and below 665 m, and using NDVI and NDWI for detecting water, resulted in an OA of 0.9638 and kappa of 0.8979. The accuracy can be further improved with a smaller interval of categorical characteristics in one or multiple scenes.

scholarly journals Response of a Coastal Groundwater System to Natural and Anthropogenic Factors: Case Study on East Coast of Laizhou Bay, China

2020 ◽  
Vol 17 (14) ◽  
pp. 5204
Author(s):  
Ya Sun ◽  
Shiguo Xu ◽  
Qin Wang ◽  
Suduan Hu ◽  
Guoshuai Qin ◽  
...  
Keyword(s):  
Human Factors ◽  
Groundwater Level ◽  
Human Activities ◽  
Research Area ◽  
Laizhou Bay ◽  
Natural Background ◽  
Groundwater System ◽  
Irrigated Area ◽  
Groundwater Environment ◽  
Groundwater Samples

With a shifting climate pattern and enhancement of human activities, coastal areas are exposed to threats of groundwater environmental issues. This work takes the eastern coast of Laizhou Bay as a research area to study the response of a coastal groundwater system to natural and human impacts with a combination of statistical, hydrogeochemical, and fuzzy classification methods. First, the groundwater level dynamics from 1980 to 2017 were analyzed. The average annual groundwater level dropped 13.16 m with a descent rate of 0.379 m/a. The main external environmental factors that affected the groundwater level were extracted, including natural factors (rainfall and temperature), as well as human activities (irrigated area, water-saving irrigated area, sown area of high-water-consumption crops, etc.). Back-propagation artificial neural network was used to model the response of groundwater level to the above driving factors, and sensitivity analysis was conducted to measure the extent of impact of these factors on groundwater level. The results verified that human factors including irrigated area and water-saving irrigated area were the most important influencing factors on groundwater level dynamics, followed by annual precipitation. Further, groundwater samples were collected over the study area to analyze the groundwater hydrogeochemical signatures. With the hydrochemical diagrams and ion ratios, the formation of groundwater, the sources of groundwater components, and the main hydrogeochemical processes controlling the groundwater evolution were discussed to understand the natural background of groundwater environment. The fuzzy C-means clustering method was adopted to classify the groundwater samples into four clusters based on their hydrochemical characteristics to reveal the spatial variation of groundwater quality in the research area. Each cluster was spatially continuous, and there were great differences in groundwater hydrochemical and pollution characteristics between different clusters. The natural and human factors resulted in this difference were discussed based on the natural background of the groundwater environment, and the types and intensity of human activity.

scholarly journals Statistical analysis for satellite-index-based insurance to define damaged pasture thresholds

2019 ◽  
Vol 19 (8) ◽  
pp. 1685-1702 ◽  
Author(s):  
Juan José Martín-Sotoca ◽  
Antonio Saa-Requejo ◽  
Rubén Moratiel ◽  
Nicolas Dalezios ◽  
Ioannis Faraslis ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Damage Threshold ◽  
Threshold Value ◽  
Likelihood Method ◽  
Agricultural Insurance ◽  
Gev Distribution ◽  
Normal Distributions

Abstract. Vegetation indices based on satellite images, such as the normalized difference vegetation index (NDVI), have been used in countries like the USA, Canada and Spain for damaged pasture and forage insurance over the last few years. This type of agricultural insurance is called satellite-index-based insurance (SIBI). In SIBI, the occurrence of damage is defined as normal distributions. In this work a pasture area at the north of the Community of Madrid (Spain) has been delimited by means of Moderate Resolution Imaging Spectroradiometer (MODIS) images. A statistical analysis of NDVI histograms was applied to seek for alternative distributions using the maximum likelihood method and χ2 test. The results show that the normal distribution is not the optimal representation and the generalized extreme value (GEV) distribution presents a better fit through the year based on a quality estimator. A comparison between normal and GEV is shown with respect to the probability under a NDVI threshold value throughout the year. This suggests that an a priori distribution should not be selected and a percentile methodology should be used to define a NDVI damage threshold rather than the average and standard deviation, typically of normal distributions. Highlights. The GEV distribution provides better fit to the NDVI historical observations than the normal one. Differences between normal and GEV distributions are higher during spring and autumn, which are transition periods in the precipitation regimen. NDVI damage threshold shows evident differences using normal and GEV distributions both covering the same probability (24.20 %). NDVI damage threshold values based on percentile calculation are proposed as an improvement in the index-based insurance in damaged pasture.

scholarly journals Impact of Differences in Land Management on Natural Vegetation in Semi-Dry Areas: The Case Study of the Adi Zaboy Watershed in the Kilite Awlaelo District, Eastern Tigray Region, Ethiopia

Environments ◽  
2018 ◽  
Vol 6 (1) ◽  
pp. 2 ◽  
Author(s):  
Ryunosuke Ogawa ◽  
Masahiro Hirata ◽  
Birhane Gebremedhin ◽  
Satoshi Uchida ◽  
Toru Sakai ◽  
...  
Keyword(s):  
Land Management ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Satellite Image ◽  
Field Investigation ◽  
Sustainable Land Management ◽  
Forest Land ◽  
Grazing Land ◽  
Feed Resources ◽  
Use Of Resources

The search for a sustainable land management has become a universal issue. It is especially necessary to discuss sustainable land management and to secure a site with enough feed supply to improve the lives of the farmers in the Ethiopian Highlands. This research studied the Adi Zaboy watershed in Tigray in order to reveal the changes in land management, assess how the different forms of land management affected the vegetation through unsupervised classification and normalized difference vegetation index (NDVI) analysis with geographic information system (GIS) 10.5 using a WorldView-2 satellite image taken in September 2016 and field investigation, and consider how to allow both environmental preservation and sustainable use of feed resources. The land management types at the research site were classified as “seasonally-closed grazing land”, “prohibited grazing and protected forest land”, and “free grazing land”. On comparing the NDVI of each type of land management, it was found that the seasonally-closed grazing land makes it highly possible to secure and supply feed resources by limiting the grazing period. The expansion of the prohibited grazing and protected forest land is likely to tighten the restriction on the use of resources. Therefore, sustainable land management to secure feed resources may be possible by securing and actively using seasonally-closed grazing land, securing feed by a cut-and-carry, and using satellite images and GIS.

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