scholarly journals A Novel Approach for Forest Fragmentation Susceptibility Mapping and Assessment: A Case Study from the Indian Himalayan Region

2021 ◽  
Vol 13 (20) ◽  
pp. 4090
Author(s):  
Amit Kumar Batar ◽  
Hideaki Shibata ◽  
Teiji Watanabe
Keyword(s):  
Forest Fragmentation ◽  
Integrated Model ◽  
Himalayan Region ◽  
Geographical Information ◽  
Susceptibility Map ◽  
Forest Landscape ◽  
Indian Himalayan Region ◽  
Proposed Model ◽  
Causative Factors ◽  
The Future

An estimation of where forest fragmentation is likely to occur is critically important for improving the integrity of the forest landscape. We prepare a forest fragmentation susceptibility map for the first time by developing an integrated model and identify its causative factors in the forest landscape. Our proposed model is based upon the synergistic use of the earth observation data, forest fragmentation approach, patch forests, causative factors, and the weight-of-evidence (WOE) method in a Geographical Information System (GIS) platform. We evaluate the applicability of the proposed model in the Indian Himalayan region, a region of rich biodiversity and environmental significance in the Indian subcontinent. To obtain a forest fragmentation susceptibility map, we used patch forests as past evidence of completely degraded forests. Subsequently, we used these patch forests in the WOE method to assign the standardized weight value to each class of causative factors tested by the Variance Inflation Factor (VIF) method. Finally, we prepare a forest fragmentation susceptibility map and classify it into five levels: very low, low, medium, high, and very high and test its validity using 30% randomly selected patch forests. Our study reveals that around 40% of the study area is highly susceptible to forest fragmentation. This study identifies that forest fragmentation is more likely to occur if proximity to built-up areas, roads, agricultural lands, and streams is low, whereas it is less likely to occur in higher altitude zones (more than 2000 m a.s.l.). Additionally, forest fragmentation will likely occur in areas mainly facing south, east, southwest, and southeast directions and on very gentle and gentle slopes (less than 25 degrees). This study identifies Himalayan moist temperate and pine forests as being likely to be most affected by forest fragmentation in the future. The results suggest that the study area would experience more forest fragmentation in the future, meaning loss of forest landscape integrity and rich biodiversity in the Indian Himalayan region. Our integrated model achieved a prediction accuracy of 88.7%, indicating good accuracy of the model. This study will be helpful to minimize forest fragmentation and improve the integrity of the forest landscape by implementing forest restoration and reforestation schemes.

scholarly journals Spatial Analysis of the Drivers, Characteristics, and Effects of Forest Fragmentation

2021 ◽  
Vol 13 (6) ◽  
pp. 3246
Author(s):  
Zoe Slattery ◽  
Richard Fenner
Keyword(s):  
Forest Fragmentation ◽  
Forest Fires ◽  
Geographical Information ◽  
Agricultural Expansion ◽  
Forest Patches ◽  
Mato Grosso ◽  
Remote Imaging ◽  
Landscape Characteristics ◽  
High Level

Building on the existing literature, this study examines whether specific drivers of forest fragmentation cause particular fragmentation characteristics, and how these characteristics can be linked to their effects on forest-dwelling species. This research uses Landsat remote imaging to examine the changing patterns of forests. It focuses on areas which have undergone a high level of a specific fragmentation driver, in particular either agricultural expansion or commodity-driven deforestation. Seven municipalities in the states of Rondônia and Mato Grosso in Brazil are selected as case study areas, as these states experienced a high level of commodity-driven deforestation and agricultural expansion respectively. Land cover maps of each municipality are created using the Geographical Information System software ArcGIS Spatial Analyst extension. The resulting categorical maps are input into Fragstats fragmentation software to calculate quantifiable fragmentation metrics for each municipality. To determine the effects that these characteristics are likely to cause, this study uses a literature review to determine how species traits affect their responses to forest fragmentation. Results indicate that, in areas that underwent agricultural expansion, the remaining forest patches became more complex in shape with longer edges and lost a large amount of core area. This negatively affects species which are either highly dispersive or specialist to core forest habitat. In areas that underwent commodity-driven deforestation, it was more likely that forest patches would become less aggregated and create disjunct core areas. This negatively affects smaller, sedentary animals which do not naturally travel long distances. This study is significant in that it links individual fragmentation drivers to their landscape characteristics, and in turn uses these to predict effects on species with particular traits. This information will prove useful for forest managers, particularly in the case study municipalities examined in this study, in deciding which species require further protection measures. The methodology could be applied to other drivers of forest fragmentation such as forest fires.

Buddhist Business and Benevolence in Leh, Ladakh

2020 ◽  
Vol 27 (1) ◽  
pp. 60-71
Author(s):  
Elizabeth Williams-Oerberg
Keyword(s):  
Financial Support ◽  
Business Owners ◽  
Economic Transformation ◽  
Tourism Industry ◽  
Economic Resources ◽  
Himalayan Region ◽  
Indian Himalayan Region ◽  
Leading Role ◽  
The Social ◽  
Donor System

Due to a burgeoning tourism industry in the Indian Himalayan region of Ladakh, Buddhist monasteries now have lucrative means for generating income through tourism-related business and financial support from international sponsors and local business owners. Where previously Buddhist monasteries were dependent on the donations and labour of the lay community, currently, with the accumulation of surplus wealth, many Buddhist leaders of prominent monasteries have begun flipping this donor system around. Throughout this article, I look at how Buddhist monastic leaders have invested surplus economic resources into philanthropic projects as a way to ‘give back’ to the wider community. I argue that the philanthropic initiatives by Buddhist leaders in Ladakh help to position Buddhist monastics as taking a leading role in the social and economic transformation of the region, thus working to push back against processes of secularization that threaten to decrease the influence of Buddhist monastic institutions.

scholarly journals Agroforestry land suitability analysis in the Eastern Indian Himalayan region

2021 ◽  
pp. 100199
Author(s):  
Arun Jyoti Nath ◽  
Rakesh Kumar ◽  
N. Bijayalaxmi Devi ◽  
Pebam Rocky ◽  
Krishna Giri ◽  
...  
Keyword(s):  
Land Suitability ◽  
Himalayan Region ◽  
Suitability Analysis ◽  
Indian Himalayan Region ◽  
Land Suitability Analysis

scholarly journals Incorporating Landslide Spatial Information and Correlated Features among Conditioning Factors for Landslide Susceptibility Mapping

2021 ◽  
Vol 13 (11) ◽  
pp. 2166
Author(s):  
Xin Yang ◽  
Rui Liu ◽  
Mei Yang ◽  
Jingjue Chen ◽  
Tianqiang Liu ◽  
...  
Keyword(s):  
Land Use ◽  
Hybrid Model ◽  
Landslide Susceptibility ◽  
Spatial Information ◽  
Landslide Susceptibility Mapping ◽  
Ratio Method ◽  
Support Vector ◽  
Susceptibility Map ◽  
Conditioning Factors ◽  
Proposed Model

This study proposed a new hybrid model based on the convolutional neural network (CNN) for making effective use of historical datasets and producing a reliable landslide susceptibility map. The proposed model consists of two parts; one is the extraction of landslide spatial information using two-dimensional CNN and pixel windows, and the other is to capture the correlated features among the conditioning factors using one-dimensional convolutional operations. To evaluate the validity of the proposed model, two pure CNN models and the previously used methods of random forest and a support vector machine were selected as the benchmark models. A total of 621 earthquake-triggered landslides in Ludian County, China and 14 conditioning factors derived from the topography, geological, hydrological, geophysical, land use and land cover data were used to generate a geospatial dataset. The conditioning factors were then selected and analyzed by a multicollinearity analysis and the frequency ratio method. Finally, the trained model calculated the landslide probability of each pixel in the study area and produced the resultant susceptibility map. The results indicated that the hybrid model benefitted from the features extraction capability of the CNN and achieved high-performance results in terms of the area under the receiver operating characteristic curve (AUC) and statistical indices. Moreover, the proposed model had 6.2% and 3.7% more improvement than the two pure CNN models in terms of the AUC, respectively. Therefore, the proposed model is capable of accurately mapping landslide susceptibility and providing a promising method for hazard mitigation and land use planning. Additionally, it is recommended to be applied to other areas of the world.

Forecast of the efficiency confidence interval of decision-making units in data envelopment analysis

2020 ◽  
Vol 33 (02) ◽  
pp. 431-445
Author(s):  
Azarnoosh Kafi ◽  
Behrouz Daneshian ◽  
Mohsen Rostamy-Malkhalifeh ◽  
Mohsen Rostamy-Malkhalifeh
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Confidence Interval ◽  
Data Envelopment ◽  
Data Set ◽  
Interval Model ◽  
Time Period ◽  
Proposed Model ◽  
The Future ◽  
Decision Making Units

Data Envelopment Analysis (DEA) is a well-known method for calculating the efficiency of Decision-Making Units (DMUs) based on their inputs and outputs. When the data is known and in the form of an interval in a given time period, this method can calculate the efficiency interval. Unfortunately, DEA is not capable of forecasting and estimating the efficiency confidence interval of the units in the future. This article, proposes a efficiency forecasting algorithm along with 95% confidence interval to generate interval data set for the next time period. What’s more, the manager’s opinion inserts and plays its role in the proposed forecasting model. Equipped with forecasted data set and with respect to data set from previous periods, the efficiency for the future period can be forecasted. This is done by proposing a proposed model and solving it by the confidence interval method. The proposed method is then implemented on the data of an automotive industry and, it is compared with the Monte Carlo simulation methods and the interval model. Using the results, it is shown that the proposed method works better to forecast the efficiency confidence interval. Finally, the efficiency and confidence interval of 95% is calculated for the upcoming period using the proposed model.

High-altitude meteorology of Indian Himalayan Region: complexities, effects, and resolutions

2021 ◽  
Vol 193 (10) ◽  
Author(s):  
Jairam Singh Yadav ◽  
Sameer K. Tiwari ◽  
Anshuman Misra ◽  
Santosh K. Rai ◽  
Ravi K. Yadav
Keyword(s):  
High Altitude ◽  
Himalayan Region ◽  
Indian Himalayan Region

scholarly journals Muslims in France: features of the integrated model

2012 ◽  
pp. 98-102
Author(s):  
A. V. Veretevskaya
Keyword(s):  
Integrated Model ◽  
Integration Model ◽  
Muslim Immigrants ◽  
The Future ◽  
Modern Status

The article deals with an acute problem of integration of Muslim immigrants and their descendants in France. The author follows the problem throughout its history and analyzes its modern status. The article provides thorough analysis of the French Integration Model. The author concludes with a prospect on its use in the future.

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