A Synthesis of Spatial Forest Assessment Studies Using Remote Sensing Data and Techniques in Pakistan

This paper synthesizes research studies on spatial forest assessment and mapping using remote sensing data and techniques in Pakistan. The synthesis states that 73 peer-reviewed research articles were published in the past 28 years (1993–2021). Out of all studies, three were conducted in Azad Jammu & Kashmir, one in Balochistan, three in Gilgit-Baltistan, twelve in Islamabad Capital Territory, thirty-one in Khyber Pakhtunkhwa, six in Punjab, ten in Sindh, and the remaining seven studies were conducted on national/regional scales. This review discusses the remote sensing classification methods, algorithms, published papers' citations, limitations, and challenges of forest mapping in Pakistan. The literature review suggested that the supervised image classification method and maximum likelihood classifier were among the most frequently used image classification and classification algorithms. The review also compared studies before and after the 18th constitutional amendment in Pakistan. Very few studies were conducted before this constitutional amendment, while a steep increase was observed afterward. The image classification accuracies of published papers were also assessed on local, regional, and national scales. The spatial forest assessment and mapping in Pakistan were evaluated only once using active remote sensing data (i.e., SAR). Advanced satellite imageries, the latest tools, and techniques need to be incorporated for forest mapping in Pakistan to facilitate forest stakeholders in managing the forests and undertaking national projects like UN’s REDD+ effectively.

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A supervised Multi-Spectral Image Classification for Remote Sensing Data

Proceedings of the 2015 International Conference on Recent Advances in Computer Systems ◽

10.2991/racs-15.2016.20 ◽

2016 ◽

Author(s):

Akram Zeki ◽

Muhsin Zaid

Keyword(s):

Remote Sensing ◽

Image Classification ◽

Remote Sensing Data ◽

Spectral Image ◽

Sensing Data

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Remote Sensing Data and Image Classification Algorithms in the Identification of Beach Patterns

Beach Management Tools - Concepts, Methodologies and Case Studies - Coastal Research Library ◽

10.1007/978-3-319-58304-4_28 ◽

2017 ◽

pp. 579-587 ◽

Cited By ~ 1

Author(s):

Ana Cláudia Teodoro ◽

Francisco Gutierres ◽

Pedro Gomes ◽

Jorge Rocha

Keyword(s):

Remote Sensing ◽

Image Classification ◽

Remote Sensing Data ◽

Classification Algorithms ◽

Sensing Data

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Convolutional Neural Networks for High Spatial Resolution Remote Sensing Image Classification

10.21203/rs.3.rs-953414/v1 ◽

2021 ◽

Author(s):

Rajagopal T K P ◽

Sakthi G ◽

Prakash J

Keyword(s):

Remote Sensing ◽

Neural Networks ◽

Image Classification ◽

Convolutional Neural Networks ◽

Spatial Resolution ◽

High Spatial Resolution ◽

Remote Sensing Data ◽

Remote Sensing Image ◽

Sensing Data

Abstract Hyperspectral remote sensing based image classification is found to be a very widely used method employed for scene analysis that is from a remote sensing data which is of a high spatial resolution. Classification is a critical task in the processing of remote sensing. On the basis of the fact that there are different materials with reflections in a particular spectral band, all the traditional pixel-wise classifiers both identify and also classify all materials on the basis of their spectral curves (or pixels). Owing to the dimensionality of the remote sensing data of high spatial resolution along with a limited number of labelled samples, a remote sensing image of a high spatial resolution tends to suffer from something known as the Hughes phenomenon which can pose a serious problem. In order to overcome such a small-sample problem, there are several methods of learning like the Support Vector Machine (SVM) along with the other methods that are kernel based and these were introduced recently for a remote sensing classification of the image and this has shown a good performance. For the purpose of this work, an SVM along with Radial Basis Function (RBF) method was proposed. But, a feature learning approach for the classification of the hyperspectral image is based on the Convolutional Neural Networks (CNNs). The results of the experiment that were based on various image datasets that were hyperspectral which implies that the method proposed will be able to achieve a better performance of classification compared to other traditional methods like the SVM and the RBF kernel and also all conventional methods based on deep learning (CNN).

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The Comparison of Landscape Metrics Derived from Remote Sensing Data before and after Wenchuan Earthquake

2009 First International Workshop on Education Technology and Computer Science ◽

10.1109/etcs.2009.270 ◽

2009 ◽

Author(s):

Xingping Wen ◽

Xiaofeng Yang

Keyword(s):

Remote Sensing ◽

Wenchuan Earthquake ◽

Landscape Metrics ◽

Remote Sensing Data ◽

Sensing Data ◽

Before And After

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THE IMPACT OF MUNICIPAL MERGERS ON LOCAL PUBLIC SPENDING: EVIDENCE FROM REMOTE-SENSING DATA

Journal of East Asian Studies ◽

10.1017/jea.2020.1 ◽

2020 ◽

Vol 20 (2) ◽

pp. 243-266

Author(s):

Steve Pickering ◽

Seiki Tanaka ◽

Kyohei Yamada

Keyword(s):

Remote Sensing ◽

Large Scale ◽

Public Spending ◽

Remote Sensing Data ◽

Sensing Data ◽

Before And After ◽

Administrative Units ◽

Political Units ◽

Political Leverage ◽

The Impact

AbstractHow are resources distributed when administrative units merge? We take advantage of recent, large-scale municipal mergers in Japan to systematically study the impact of municipal mergers within merged municipalities and, in particular, what politicians do when their districts and constituencies suddenly change. We argue that when rural and sparsely populated municipalities merge with more urban and densely populated municipalities, residents of the former are likely to see a reduced share of public spending because they lost political leverage through the merger. Our empirical analyses detect changes in public spending before and after the municipal mergers with remote sensing data, which allows for flexible units of analysis and enables us to proxy for spending within merged municipalities. Overall, our results show that politicians tend to reduce benefits allocated to areas where there are a small number of voters, while increasing the allocation to more populous areas. The micro-foundation of our argument is also corroborated by survey data. The finding suggests that, all things being equal, the quantity rather than quality of electorates matters for politicians immediately after political units change.

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