Urbanization in India – Spatiotemporal analysis using remote sensing data

The wide availability of satellite data from many distributors in different domains of science has provided the opportunity for the development of new and improved methodologies to aid the analysis of environmental problems and to support more reliable estimations and forecasts. Moreover, the rapid development of specialized technologies in satellite instruments provides the opportunity to obtain a wide spectrum of various measurements. The purpose of this research is to use publicly available remote sensing product data computed from geostationary, polar and near-polar satellites and radar to improve space–time modeling and prediction of precipitation on Crete island in Greece. The proposed space–time kriging method carries out the fusion of remote sensing data with data from ground stations that monitor precipitation during the hydrological period 2009/10–2017/18. Precipitation observations are useful for water resources, flood and drought management studies. However, monitoring stations are usually sparse in regions with complex terrain, are clustered in valleys, and often have missing data. Satellite precipitation data are an attractive alternative to observations. The fusion of the datasets in terms of the space–time residual kriging method exploits the auxiliary satellite information and aids in the accurate and reliable estimation of precipitation rates at ungauged locations. In addition, it represents an alternative option for the improved modeling of precipitation variations in space and time. The obtained results were compared with the outcomes of similar works in the study area.

Download Full-text

Quantifying the costs of soil constraints to Australian agriculture: a case study of wheat in north-eastern Australia

Soil Research ◽

10.1071/sr15007 ◽

2016 ◽

Vol 54 (6) ◽

pp. 700 ◽

Cited By ~ 4

Author(s):

Y. P. Dang ◽

P. W. Moody

Keyword(s):

Remote Sensing ◽

Hybrid Approach ◽

Remote Sensing Data ◽

Spatiotemporal Analysis ◽

Yield Potential ◽

Wheat Grain ◽

Sensing Data ◽

Soil Constraints ◽

Eastern Australia ◽

The Cost

Soil salinity, sodicity, acidity and alkalinity, elemental toxicities, such as boron, chloride and aluminium, and compaction are important soil constraints to agricultural sustainability in many soils of Australia. There is considerable variation in the existing information on the costs of each of the soil constraints to Australian agriculture. Determination of the cost of soil constraints requires measuring the magnitude and causes of yield gap (Yg) between yield potential and actual yield. We propose a ‘hybrid approach’ consisting of determining the magnitude of Yg and the cause(s) of Yg for spatiotemporal representation of Yg that can be apportioned between management and soil constraint effects, thereby allowing a better estimate of the cost of mitigation of the constraints. The principles of this approach are demonstrated using a 2820-ha wheat-growing farm over a 10-year period to quantify the costs of the proportion of forfeited Yg due to soil constraints. Estimated Yg over the whole farm varied annually from 0.6 to 2.4Mgha–1, with an average of 1.4Mgha–1. A multiyear spatiotemporal analysis of remote sensing data identified that 44% of the farm was consistently poor performing, suggesting the potential presence of at least one soil constraint. The percentage decrease in productivity due to soil constraints varied annually from 5% to 24%, with an average estimated annual loss of wheat grain production of 182 Mg per year on 1069ha. With the 2015 season’s average wheat grain price (A$0.29kg–1), the estimated annual value of lost agricultural production due to soil constraints was estimated at A$52780 per year. For successful upscaling of the hybrid approach to regional or national scale, Australia has reliable data on the magnitude of Yg. The multiyear spatiotemporal analysis of remote sensing data would identify stable, consistently poor performing areas at a similar scale to Yg. Soil maps could then be used to identify the most-limiting soil constraints in the consistently poor performing areas. The spatial distribution of soil constraint at similar scale could be used to obtain the cost of lost production using soil constraint–grain yield models.

Download Full-text

The Spatiotemporal Analysis of Urban Sprawl Using Remote Sensing Data and Geographic Information System in Shenzhen, China

2009 International Conference on Computational Intelligence and Software Engineering ◽

10.1109/cise.2009.5364301 ◽

2009 ◽

Author(s):

Zhi-qiang Lv ◽

Zhi-feng Wu ◽

Jian-bin Wei ◽

Zheng Sun ◽

Ya Wen

Keyword(s):

Remote Sensing ◽

Information System ◽

Geographic Information System ◽

Urban Sprawl ◽

Remote Sensing Data ◽

Spatiotemporal Analysis ◽

Geographic Information ◽

Sensing Data

Download Full-text

Spatiotemporal analysis of urban growth in three African capital cities: A grid-cell-based analysis using remote sensing data

Journal of African Earth Sciences ◽

10.1016/j.jafrearsci.2016.08.014 ◽

2016 ◽

Vol 123 ◽

pp. 381-391 ◽

Cited By ~ 14

Author(s):

Hao Hou ◽

Ronald C. Estoque ◽

Yuji Murayama

Keyword(s):

Remote Sensing ◽

Urban Growth ◽

Grid Cell ◽

Remote Sensing Data ◽

Spatiotemporal Analysis ◽

Capital Cities ◽

Sensing Data

Download Full-text

Forecasting landslides using a spatiotemporal analysis of remote sensing data

10.5194/egusphere-egu2020-17972 ◽

2020 ◽

Author(s):

Carlos Alfredo Mesa Zuluaga ◽

German Ricardo Santos Granados ◽

Gerald Augusto Corzo Perez

Keyword(s):

Remote Sensing ◽

High Performance ◽

Remote Sensing Data ◽

Weather Conditions ◽

Spatiotemporal Analysis ◽

Google Earth ◽

Water Volume ◽

Ongoing Research ◽

Maximum Rainfall ◽

Sensing Data

Forecasting landslides is highly dependent in the weather conditions and the land-soil characteristics and its state. The uncertainty present in the evaluation of precipitation and its continuous variation is always a challenge for having accurate forecast, of primary importance for risks reduction. Currently, the landslides generate an impact on the imbalance of ecosystems and their occurrence is increasing which leads to an increase in the vulnerability of man on earth. The complexity of the landslide systems requires detailed analysis of the highly dynamic information of the rain and in turn the form as the hydrology response. Being able to combine hydrological models forced by satellite information systems and put them with a soil cohesion analysis system could help improve monitoring and in a particular case forecast landslide events.The Combeima river located at the village of Juntas with canyon type land relief currently, faces a vital challenge in the face of winter times where precipitation threaten urban zones. Current researchers have explored risk factors, however, results still are quite far from optimal.This study develops a methodology to identify the water volume that can cause landslides over the canyon type land relief, and use it as a trigger for forecasting. Remote sensing data at the present time and projected from past data will be used to simulate forecasting situations (hidcasting). A coupled Mike SHE models and data from Google earth platform are used to analyze a period of twenty years. Local information from events and its analysis in the satellite images are used to validate the events. Finally, the results of past conditions that led to the generation of floods are used to identify the state of the soil and the volumes. A calibration and validation of a neural network model is done feeding the volumes and states. The results of the model allow us to specifically characterize the saturation limits of the soil and the maximum rainfall intensities that a soil may contain before collapsing. With this information a high performance and a design of a system to forecast in real time was proposed. This work is part of an ongoing research and partial results will be presented.

Download Full-text