Application of remote sensing and GIS for identification of potential ground water recharge sites in Semi-arid regions of Hard-rock terrain, in north Karnataka, South India

Bhagwat, Tejaswini Nikhil; Hegde, V. S.; Shetty, Amba

doi:10.1007/s40899-018-0244-6

Cited by 10 publications

(2 citation statements)

References 27 publications

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“…In another study conducted by Wu et al [19], GIS was successfully utilized for site selection for rainwater harvesting in Northeastern Guatemala. In the case study provided by Bhagwat et al [20], GIS and RS were employed to identify suitable sites for runoff harvesting and artificial recharge in India. Besides GIS and MCDM techniques, Analytic Hierarchy Process (AHP) and Soil Conservation Service Curve Number (SCS-CN) have been frequently cited for weighting thematic data layers and mapping runoff potential in site suitability analysis [21][22][23].…”

Section: Introductionmentioning

confidence: 99%

Runoff Harvesting Site Suitability Analysis for Wildlife in Sub-Desert Regions

Shalamzari

Zhang

Gholami

et al. 2019

Water

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Site selection for runoff harvesting at large scales is a very complex task. It requires inclusion and spatial analysis of a multitude of accurately measured parameters in a time-efficient manner. Compared with direct measurements of runoff, which is time consuming and costly, a combination of a Geographic Information System (GIS) and multi-criteria techniques have proven feasible to address this challenge. Although the accuracy of this new approach is lower than the direct method, conducting in-situ measurements over large scales is not feasible due to its financial issues, a lack of sufficient human resources, and time limitations. To achieve this purpose, climatic, topographic, and soil parameters were used to estimate a runoff coefficient and volume for a single event with the 33%-exceedance probability of maximum daily rainfall in the Kavir National Park of Iran. The main challenges ahead of this research have been a) the large area of the park and the inability to directly evaluate site suitability for runoff harvesting, b) the need for a quick and reliable site evaluation to implement water harvesting measures to address water scarcity, and c) the lack of discharge volume data from water streams (as there are no permanent water streams in the site) and the necessity of reliably estimating runoff in different parts of the park to design water harvesting structures which have been addressed by using GIS and a rainfall-runoff model (Soil Conservation Service Curve Number (SCS-CN)). Site suitability was evaluated for the natural territory of two important wildlife species of the park, namely Gazella dorcas and Ovis orientalis, as the main important food sources of an endangered species named Acinonyx jubatus, commonly known as Persian Cheetah. Saving Persian Cheetah from extinction is currently the top priority for the park managers, which is the main factor behind the species chosen for this research. The Analytic Hierarchical Process (AHP) and fuzzy membership functions were employed to assign weights and standardized thematic layers, respectively. The layers were then integrated using the weighted linear combination method (WLC) to obtain the final suitability map. Accordingly, 38% of the area (846 km2) is suitable or highly suitable for runoff harvesting, while 62% (2623 km2) has a very low potential for this purpose. Afterward, 11 suitable locations were identified to collect runoff. The results indicated that suitable catchments are mainly located on the southern slopes of the Mount Siahkouh as the only major elevation in the area. The storage capacity of the earth embankment in each catchment was estimated based on the upstream area of the catchment and runoff volume. Based on the population of the intended wildlife species and their average water requirement, there is a need for 6500 m3 of drinking water annually. In the best-case scenario and under the circumstance of receiving five rainstorm events a year, only 257 m3 is collectible from all runoff harvesting structures, which is only 4% of the total water demand.

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Section: Introductionmentioning

confidence: 99%

Runoff Harvesting Site Suitability Analysis for Wildlife in Sub-Desert Regions

Shalamzari

Zhang

Gholami

et al. 2019

Water

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“…Different from previous tasks, an RSI captioning task concentrates on generating high-level semantic information (a descriptive sentence) and has received a significant amount of attention [1], [11]- [13]. Many useful potential applications need to utilize the RSI captioning task, such as RSI retrieval, disaster assessment [14], [15], terrain scanning [16], [17], city planning, and military scout. The automatic caption generation can provide more semantic information about an RSI.…”

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confidence: 99%

Retrieval Topic Recurrent Memory Network for Remote Sensing Image Captioning

Wang

Zheng

2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Remote sensing image (RSI) captioning aims to generate sentences to describe the content of RSIs. Generally, five sentences are used to describe the RSI in caption datasets. Every sentence can just focus on part of images' contents due to the different attention parts of annotation persons. One annotated sentence may be ambiguous compared with other four sentences. However, previous methods, treating five sentences separately, may generate an ambiguous sentence. In order to consider five sentences together, a collection of words, which named topic words contained common information among five sentences, is jointly incorporated into a captioning model to generate a determinate sentence that covers common contents in RSIs. Instead of employing a naive recurrent neural network, a memory network in which topic words can be naturally included as memory cells is introduced to generate sentences. A novel retrieval topic recurrent memory network is proposed to utilize the topic words. First, a topic repository is built to record the topic words in training datasets. Then, the retrieval strategy is exploited to obtain the topic words for a test image from topic repository. Finally, the retrieved topic words are incorporated into a recurrent memory network to guide the sentence generation. In addition to getting topics through retrieval, the topic words of test images can also be edited manually. The proposed method sheds light on controllability of caption generation. Experiments are conducted on two caption datasets to evaluate the proposed method.

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