Remote Sensing

Assessment of the Temporal and Spatial Variations of Urban Development Using RS and GIS: A Case Study—Yasuj, Iran Abstract The temporal-spatial change of cities is one of the main challenges of managers and decision makers in urban development. In the present study, a parameter related to the evaluation of the capability of Yasuj City was selected, classed, and weighed to determine a desired location for urban development. Accordingly, in order to determine fuzzy maps, all factors and sub-factors were weighed using the AHP and hierarchical technique. Then, the maps of each sub-factor were standardized in IDRIS environment and the considered layers were combined in GIS environment to determine the land suitability for urban development. Next, in order to evaluate the trend of temporal-spatial variation of land use around Yasuj City, the prepared images of the Landsat satellite as well as TM, ETM+ and OLI sensors in Envin 5.1 software related to 1986, 2001, and 2016, respectively, were employed. Finally, based on the changes in land use of Yasuj City, the residential or urban sections widely changed. The changes included different uses such as forests, grasslands, agriculture, and water corpuses. In 1986–2016, the results of changes in land use around the Yasuj City indicated 5% increase in the residential section and 3, 2 and 1% decrease in rangelands, agricultural lands, and water bodies, respectively. Forests, which are located in the outer and inner sections of Yasuj City, have a lot of ecological functions. With the high speed of city development, the ecological functions of forests are decreasing, both quantitatively and qualitatively, and therefore, should be taken into consideration. The results of this study, and the similar studies, indicated that spatial data, especially data related to human settlements (since they are highly changeable), are a good foundation for detailed planning. Such kind of data is also used since they are suitable tools for research purposes.

Understanding Spatio-temporal Pattern of Grassland Phenology in the western Indian Himalayan State Abstract The present study has analysed grassland phenology: start of greening (SOG), end of greening (EOG) and length of greening (LOG), and their rate of change in the western Himalaya in India (Himachal Pradesh) using MODIS NDVI time series data (2001–2015). These metrics were inspected at different stratification levels: state, elevation, climatic zones and bio-geographic provinces. Delayed SOG was observed over 44.87% (P < 0.1), and delayed EOG over 63.3% (P < 0.1) of grassland grids. LOG was shortened in 24.37% (P < 0.1) and extended in 58.04% (P < 0.1) of the grids. At the state level, when statistically significant pixels (SSP) and all the pixels (AP) are used (given as SSP:AP), SOG is delayed by 20.27:6.28 days year−15, while EOG is delayed by 38.02:14.97 days year−15 and LOG is extended by 35.07:8.70 year−15 days. Extended LOG is observed over the temperate and cold arid regions, and shortened LOG is observed over sub-alpine and alpine regions. Variations in SOG and EOG are not uniform across different climatic and bio-geographic regions. However, in the sub-alpine and alpine zones, SOG and EOG followed elevation gradients, i.e. late SOG with early EOG over higher elevations, and early SOG with late EOG over lower elevations. Our study has revealed an interesting pattern of translational phenology (i.e. late SOG and late EOG) of grasslands which hints towards shifting winter period. Overall, it is observed that variations in timing of snowfall and snow cover extent are the reasons for inter-annual variations in the grassland phenology.

Short-Term Variability of Physico-Chemical Properties and pCO 2 fluxes off Dhamra estuary from north-eastern India Abstract Short-term variability in physico-chemical properties of Dhamra estuarine system located in north-eastern India was investigated to understand the inter-seasonal variability. The oxygen data show 89–92% saturation in winter months compared to 60–70% during summer. Overall, the nitrate ranged between < 1.0 and 22.0 µmol l−1; however, phosphate concentrations never exceeded 1 µmol l−1 during the whole study period. In general, the lowest nutrient values were recorded during March and December irrespective of the year sampled. Pearson correlation matrix shows poor relationships between inorganic nitrate and phosphate suggesting decoupling in the Dhamra estuary. However, relationship between surface chlorophyll and nitrate was significant highlighting modest control on phytoplankton population. Interestingly, pCO2(air) exhibited considerable monthly variability during the sampling period, thereby accentuating the sea–air CO2 gradient. The pCO2(air) varied between 370 and 421 µatm, whereas pCO2(water) ranged between 146 and 751 µatm. The ΔpCO2 therefore showed monthly fluctuation and acted as a weak to moderate source to the immediate atmosphere. Our observation from Dhamra estuary suggests large inter-annual variability which therefore necessitates the need for near real-time measurements which is now a possibility with emerging coastal biogeochemical buoys.

Estimation of Leaf Chlorophyll Concentration in Turmeric ( Curcuma longa ) Using High-Resolution Unmanned Aerial Vehicle Imagery Based on Kernel Ridge Regression Abstract High-resolution information is needed for precision agriculture to achieve precise management of inputs. High spatial and temporal resolution is requisite to get the actionable information for the timely response. The objective of the present study is to estimate the leaf chlorophyll Concentration using high-resolution (2 cm) images captured from UAV-mounted multispectral sensors for crop health monitoring. In this study, a hexacopter was flown at an elevation of 25 m to capture the images in green, red, red edge and NIR bands of turmeric plots grown at ICAR Research Complex, Northeast Hilly Region, India. A handheld SVC spectroradiometer having spectral range from 350 to 2500 nm was also used to collect the spectra of sample plants to support the UAV study. We evaluated an advanced machine learning algorithm kernel ridge regression combined with spectral information and ground-truth chlorophyll data to model the chlorophyll estimation. The multivariate analysis was also applied on spectroradiometer and UAV data, which recommended red band for chlorophyll prediction with R2 value greater than 0.6. We also found that kernel ridge regression is a robust method for developing chlorophyll estimation model with lesser training time. The results indicate that kernel ridge regression with a radial basis kernel function with four multispectral input bands can be utilized to evaluate the leaf chlorophyll concentration with an root mean squared error RMSE = 0.10 mg/g and regression coefficient R2 = 0.7452. However, this study is site specific and needs to be practiced in different crop sites in order to generalize this method for precision agriculture.

Detection of Urban Land Use Land Cover Dynamics Using GIS and Remote Sensing: A Case Study of Axum Town, Northern Ethiopia Abstract The present study was conducted in the Northern Ethiopia, the most ancient town, Axum to detect urban expansion of for the last 3 decades and predict the trend of urban expansion for the year of 2025. To do so, multi-temporal satellite images of 1985, 2000 and 2015 with other datasets were considered and analyzed to scrutinize level of urban expansion. Maximum likelihood algorithm of supervised classification was applied for each Landsat satellite images to generate land use and land cover map of the study area using ERDAS Imagine software. The levels of accuracy of these classifications were evaluated using confusion matrix to derive the overall accuracy. The finding of this study indicated that in the base year (1985), the largest LULC was occupied by agriculture covered, 60% of the entire area in 1985, 48% in 2000 and 38% in 2015, while the urban area increased by 10%, 19% and 39% in 1985, 2000 and 2015, respectively. The decline of the agricultural area was the fast conversion of the agricultural land into the urban areas. The vegetated area was 19% in 1985, 16% in 2000 and 20% in 2015, and also barren lands were possessed 11% in 1985, 17% in 2000 and 3% in 2015 of the entire study area. Even though many changes have observed among the LULC in the year between 1985 and 2015, the highest rate of changes was observed in urban land which increased by 9.9% in every year and vegetated areas by 0.1% per year. Besides, agricultural and barren lands are also decreased in size by 1.18% and 2.38%, respectively, in 2015 when compared with 1985. Therefore, the built-up area was under fast growing currently and some of the causative factors include population growth of the town and rural to urban migration. Finally, the forecast urban expansion of Axum Town for the year, 2025 was found 1497.9 ha Therefore, urban planning authorities and urban planners should think about the future impact of horizontal urban expansion and population growth of the town in line with possible infrastructures development.

Mapping of Soil Salinity Using the Landsat 8 Image and Direct Field Measurements: A Case Study of the Tadla Plain, Morocco Abstract Soil salinization from arid to semiarid climate is a serious environmental problem. In some countries, salinization is considered a real threat to food security and food quality because it lowers crop yields and can irretrievably damage land. In the irrigated area of the plain of Tadla (Central Morocco), the intensive use of groundwater and saline surfaces lead to the degradation of soil quality. Experimental methods of monitoring soil salinity by direct real-time measurements are in high demand, but also very limited in terms of spatial coverage. The objective of this paper is to map soil salinity in the arid and semiarid zone of the Tadla plain in Morocco, based on optical remote sensing data and field measurements of sodium absorption ratio. The first results of work were devoted to the evaluation and validation of salinity models in the study area. Observations on the site, correlation, verification and validation of the model enabled us to map the salinity. All these use the soil salinity map based on non-saline soil content, light saline soil, moderate saline soil and highly saline soil. The map obtained from our model allowed us to identify the distribution of salinity in the study area. The values of the electrical conductivity in the study area range from less than 2 ds/m (non-saline soil) to more than 8 ds/m (highly saline soil) with a significant variation between the different levels of soil salinity in the study area.

Using GIS to Develop a Model for Forest Fire Risk Mapping Abstract Forests are the most beautiful natural resources around the world and play a pivotal role in preserving environmental balance. An important measure taken for managing and protecting forest areas as well as for decreasing the potential damages caused by the fire is the detection of regions susceptible to forest fire through forest fire risk mapping with different models and methods. In recent years, a geographic information system (GIS)-based multi-criteria decision analyses (MCDA) have been successfully applied in the production of forest fire risk maps. In this study, GIS-based analytical network process as MCDA method was employed in order to provide the fire risk map of Noshahr Forests (North Iran) using slope, slope aspect, altitude, land cover, normalized difference vegetation index, annual rainfall, annual temperature, distance to settlements, and distance to road as input data. Furthermore, to prepare the map of the distribution of occurred fires, MODIS fire product and wide-field observations were used. Thereafter, each of these subcriteria of the utilized factors was standardized according to their significance in a forest fire and then with the extracted coefficients in the analytical network process model merged in ArcGIS software. Finally, the fire risk map was generated. Evaluation of the results obtained using receiver operating characteristic curve indicated that the designed model has good accuracy with a value of under curve area of 0.783. According to the map prepared, 57.45% of the study area (1034.41 km2) is located in the high and very high-risk classes.

Pol-InSAR for Forest Biomass Estimation with the Transformation of the Polarization Basis Abstract An improved method that uses polarimetric and Pol-InSAR information at the L-band and P-band was proposed in this study for estimating forest biomass. In the first phase, various polarimetric and polarimetric interferometry indicators were extracted via transformation of the polarization basis. In the second phase, the particle swarm optimization method is applied to identify optimal parameter values for biomass estimation. According to the results, the correlations between the polarimetric indicators and the biomass were stronger at the P-band compared to the L-band. Polarimetric indicators that involve HV and HH–VV show the maximum correlation with biomass prior to optimization. It is demonstrated that changing the polarization basis can significantly improve the correlations of estimators with the biomass, especially at the P-band. The globally optimal estimators involve the same scattering mechanisms (volume and double-bounce scattering), and the optimal polarization bases differ slightly among most of the cases, due to the adaptation to the forest natural geometry and/or the acquisition configuration. Regarding Pol-InSAR, several tree height retrieval estimators that are based on various assumptions have been analyzed under multiple polarization basis rotations. According to the results, the RVoG phase method, which represents the forest as a low-extinction structured volume, yielded the best accuracy, with R = 0.73 and RMSE = 4.30 m. The identification of the optimum polarization indicators via binary PSO could improve the biomass estimation accuracy by 2% and 6% at the P-band and L-band, respectively. It is demonstrated that such an approach can be employed to accurately estimate biomass via extrapolation of in situ measurements over an entire region.

Identification of Groundwater Prospect in Bara Region of Allahabad District Based on Hydro-Geomorphological Analysis Using Satellite Imagery Abstract Water scarcity is a major problem for villagers' survival as it determines the population density and affects the migration pattern in Bara region. In the present study, groundwater resource endowment is studied and correlated with the distributional pattern of population, settlements and economic activity for analysing regional development in the study area. The study has been carried out using geospatial platforms, i.e. Erdas Imagine 2014 and ArcGis 10.2.2 software. Sentinel-2 satellite imagery and Cartosat-1 DEM data were the major data sources for extracting factor layers. Geomorphology and lineament maps of NRSC, District Resource Map of GSI, topographic maps and Google Earth images along with field surveys were ancillary database. Saaty's 9-point rating scale of analytical hierarchy process was used to extract the GWPI by integrating factor layers of geomorphology, lineament density, slope, geology, rainfall, drainage density and land use land cover according to their relative influence. Final map shows different zones of groundwater prospects in the study region, which is validated from aquifer thickness data. Result shows that 39.19% (291.41 km2) of the total area (743.64 km2) is classified as high-to-excellent GWP, whereas 27.96% (207.89 km2) of the area is under very poor-to-poor GWP. Areas having poor-to-poorest groundwater storage impact on population distribution, as 14% of the total population is lying over these zones. The descriptive statistical analysis showed that CGWPI and built-up area are significantly correlated (F = 18.024 > 4.41*, t = − 4.245 > 2.101, R2 = 50.03%). CGWPI is also correlated significantly with population density (F = 18.855 > 4.41*, t = -4.342 > 2.101, R2 = 50.16%). However, the relationship is not very high on linear regression model as expected since only 50% variations in population distribution can be attributed to CGWPI, for example, the Shankargarh town having the highest population density and the second highest built-up percentage in the whole study area in spite of being endowed with the lowest groundwater potential.

Estimation of Canal Water Deficit Using Satellite Remote Sensing and GIS: A Case Study in Lower Chenab Canal System Abstract The timely precise information of land use land cover (LULC) in the canal command area can help in managing irrigation water according to the crop water requirement. A study was conducted to map the LULC of irrigated command area of three distributaries in the lower Chenab canal system, Pakistan, namely Mungi, Killianwala and Khurrianwala for the estimation of canal water deficit (CWD). Multispectral images of LANDSAT-7 were used for Rabi season of 2009–2010 and 2010–2011. Normalized difference vegetation index-based unsupervised classification was performed for the formation of LULC of the commands area. During the initial classification, totally nine clusters were created with maximum likelihood. These clusters were then merged into final four classes on the basis of field knowledge. Accuracy assessment was performed using error matrix; producer and user accuracies were estimated for each class with overall accuracy of 84% and 86% for the Rabi season 2009–2010 and 2010–2011, respectively. For the assessment of the irrigation water demand, potential evapotranspiration was estimated using the Penman–Monteith equation. Crop water requirement was estimated based on the 10-day Kc value of the mapped crop from the LULC. Irrigation water demand for cropped area was estimated from the LULC and crop evapotranspiration. Canal water deficit was estimated from the available canal water supply and irrigation water requirement in the Rabi season. During the Rabi seasons, average CWD was 64%, 72% and 32% (2009–2010) and 33%, 46% and 36% (2010–2011) for Khurrianwala, Killianwala and Mungi distributary, respectively.

Alexandros Sfakianakis
Anapafseos 5 . Agios Nikolaos
Crete.Greece.72100
2841026182

6948891480

alsfakia@gmail.com