Analysis of Agricultural Drought Monitoring Based on Vegetation Health Index in East Java Indonesia Using MODIS Satellite Data | Chapter 7 | Novel Perspectives of Geography, Environment and Earth Sciences Vol. 6

 The land sector is very susceptible to drought, laboriously relying on precipitation. This study used the Vegetation Health Index (VHI) of MODIS Data as a dryness indicator to monitor dryness in East Java, Indonesia. The VHI combines the vegetation index and land surface hotness, both fault-finding satellite commodity for remote thinking of drought. The VHI describes plants health utilizing the TCI and VCI indices. The study establish that the distribution of dryness areas increased by 3.69% betwixt 2017 and 2018, with the best distribution happening between July and October. On average, 22.24% of the paddy field district was affected by drought in 2017 and 25.93% in 2018, from a total extent of 1.174,586 km2. Moderate drought was the main type of drought in the land area of East Java, established the VHI values obtained 'tween 2017 and 2018.

Author(s) Details:

A. P. Kirana,
Information Technology Department, State Polytechnic of Malang, Indonesia.

R. Ariyanto,
Information Technology Department, State Polytechnic of Malang, Indonesia.

A. R. T. H. Ririd,
Information Technology Department, State Polytechnic of Malang, Indonesia.

E. L. Amalia,
Information Technology Department, State Polytechnic of Malang, Indonesia.

A. Bhawiyuga,
Faculty of Computer Science, University of Brawijaya, Indonesia.

Please see the link here: https://stm.bookpi.org/NPGEES-V6/article/view/10104

Effects of Unloading Groundwater on Aquifer Sorage and Water Availability | Chapter 6 | Current Advances in Geography, Environment and Earth Science Vol. 1

 Excessive groundwater withdrawal has long been known to cause soil subsidence [1]. Northern China is increasingly exploiting its water resources, fueled by decades of vigorous food self-sufficiency efforts [2]. Large areas of groundwater depletion cone and land subsidence are related with exceptional water resource mining in this agro-politically sensitive region [3]. However, studies in the region on water storage depletion and/or land subsidence are mostly statistical and fragmentary [3,4]. The anomaly trends in GRACE (Gravity Recovery and Climate Experiment) and InSAR (Interferometric Synthetic Aperture Radar) satellite observations, GLDAS (Global Land Data Assimilation System) model products, and measured groundwater depth data are used to estimate crustal unloading-controlled land deformation in this study. For the study period of 2002 to 2009, estimated land subsidence was 17.74.7 cm, which is the equivalent of 13.60.3 mm in groundwater storage loss or 104.6 mm in aquifer depletion, based on the average specific yield of 0.13. Annual storage depletion is estimated at 63.89.3 mm (53.87.8 km3) for total water storage, 58.35.3 mm (49.64.5 km3) for groundwater storage, and 3.40.6 mm (2.80.5 km3) for soil water storage for the 843 000 km2 research area. The expected overall water storage depletion exceeds the South-North Water Diversion Project's projected yearly water delivery of 45 km3 in 2050. Water storage depletion, in combination with ground subsidence in the region, might have negative consequences for the country's agricultural, industrial, socioeconomic, and political stability. It is vital for farmers and other stakeholders to implement effective water conservation techniques. To replenish local water resources, such initiatives should be accompanied with the tapping of alternate water sources (such as the South-North Water Diversion Project). Such measures not only prevent future pumping-related problems and disruptions in food production, supply, and security, but also maintain stable socioeconomic growth.

Author(S) Details

Juana P. Moiwo
Department of Agricultural Engineering, School of Technology, Njala University, Njala Campus, Sierra Leone.

Yahaya K. Kawa
Department of Chemistry, School of Environmental Sciences, Njala University, Njala Campus, Freetown, Sierra Leone.

Alhaji M. H. Conteh
Department of Mathematics and Statistics, School of Technology, Njala University, Njala Campus, Sierra Leone.

John P. Kaisam
Department of Chemistry, School of Environmental Sciences, Njala University, Njala Campus, Freetown, Sierra Leone.

View Book:- https://stm.bookpi.org/CAGEES-V1/article/view/5187

A Forest Change Detection Using Auto Regressive Model-based Kernel Fuzzy Clustering: Advanced Study | Chapter 13 | Emerging Trends in Engineering Research and Technology Vol. 4

This chapter focuses on the use of satellite images for the forest change detection, forest cover management. In this chapter, the vegetation indices play a major role in extracting the useful information from the satellite images. Also analysis was done on the imagery data from the remote sensing satellites for detecting the changes in the forest over the year’s 2007-2017 using the pixelbased Bhattacharya distance. The indices from the satellite images are fed to the automatic segmentation model using the proposed Kernel Fuzzy Auto regressive (KFAR) model, which is the modified Kernel Fuzzy C-Means (KFCM) Clustering algorithm with the Conditional Autoregressive Value at Risk (CAVIAR). The forest change detection using the pixel-based Bhattacharya distance follows the segmentation and the experimentation reveals that the proposed method acquired the minimal Mean Square Error (MSE) and maximal accuracy of 0.0581 and 0.9211.

Author(s) Details

Ms Madhuri B. Mulik
Department of Electronics and Telecommunication, Sharad Institute of Technology, College of Engineering Ichalkaranji, India.

Dr. (MRS) V. Jayashree
Department of Electronics Engineering, DKTE ‘s College of Engineering, Ichalkaranji, India.

Dr. P. N. Kulkarni
Department of Electronics and Communication Engineering, Bagalkot, Visvesvaraya Technical University Belgawi, India.

View Book :- http://bp.bookpi.org/index.php/bpi/catalog/book/180