Explore a growing portfolio and publications of climate and sustainability projects. Each publication represents a journey of research, collaboration, and real-world impact, made accessible, transparent, and actionable for all.
DEPWISEN
Bushra Haq, Muhammad Ali Jamshed, Kamran Ali, Bakhtiar Kasi, Saira Arshad, Mumraiz Khan Kasi, Imran Ali, Aqsa Shabbir, Qammer H. Abbasi, and Masood Ur-Rehman
IEEE Internet of Things Journal, 2024
Deforestation poses a significant global environmental challenge with far-reaching consequences for biodiversity, climate change, and livelihoods. In this context, applying advanced technologies, such as the Internet of Things (IoT) and artificial intelligence (AI), holds immense promise. This article aims to comprehensively review and analyze the role of IoT, AI, and remote sensing technologies in monitoring, detecting, predicting, and preventing deforestation. By providing real-time data and enabling early detection, these technologies contribute to addressing activities like illegal logging, plant diseases, and forest fires. This review presents an overview of the advantages and limitations of these technologies, accompanied by an analysis of their current state and future potential. Key technologies covered include IoT, satellite imagery, drones, and AI algorithms, with each offering unique applications. Importantly, this article underscores the significance of these technologies in protecting forests and the diverse species they support. The findings discussed herein aim to inform ongoing debates and provide a foundation for further research in this crucial domain. Ultimately, the knowledge gained from this research has the potential to guide practical interventions and policies for effective forest conservation.
Mozina Afzal, Kamran Ali, Mumraiz Khan Kasi, Masood Ur Rehman, Mohammad Ali Khoshkholgh, Bushra Haq, and Syed Ahmed Shah
2023 IEEE 22nd International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)
The detection of changes in land cover and land use (LCLU) is crucial for various geospatial applications, including urban development and environmental management. One vital aspect of LCLU research involves identifying modifications in impervious surface cover, which has significantly increased due to global economic growth and the rising urban population in many parts of the world. This investigation employs Landsat 9 OLI- 2/TIRS-2 imagery with a 30m spatial resolution to map structures in the Ziarat District of Pakistan, encompassing forests, water bodies, and barren land. It aims to detect changes in tree cover and canopy height. A time series of Landsat 9 OLI-2/TIRS-2 images were utilized to create change detection and land cover maps. The Analysis of Land Cover and Land Use (LCLU) for the Ziarat District was conducted using the GEE platform. The satellite images were classified into broad land cover classes, which include impervious surfaces, forest/tree cover, grassland/cropland, and water. The generated change detection map facilitates the identification of locations that have undergone modifications due to new constructions, offering valuable insights for the implementation of urban development policies and disaster management on a global scale.
Abdul Haleem Butt, Muhammad Ali Jamshed, Ata Ur Rahman, Faiz Alam, Manoj Shakya, Ahmad S. Almadhor, and Masoor Ur-Rehman
Computational Intelligence and Neuroscience, 2023
Describing the processes leading to deforestation is essential for the development and implementation of the forest policies. In this work, two different learning models were developed in order to identify the best possible model for the assessment of the deforestation causes and trends. We developed autoregressive integrated moving average (ARIMA) model and long short‐term memory (LSTM) independently in order to see the trend between tree cover loss and carbon dioxide emission. This study includes the twenty‐year data of Pakistan on tree cover loss and carbon emission from the Global Forest Watch (GFW) platform, a known platform to get numerical data. Minimum mean absolute error (MAE) for the prediction of tree cover loss and carbon emission obtained through ARIMA model is 0.89 and 0.95, respectively. The minimum MAE given by LSTM model is 0.33 and 0.43, respectively. There is no such kind of study conducted in order to identify the increase in carbon emission due to tree cover loss most specifically in Pakistan. The results endorsed that one of the main causes of increase in the pollution in the environment in terms of carbon emission is due to tree cover loss.
Intelligent computing based forecasting of deforestation using fire alerts: A deep learning approach
Muhammad Ali Jamshed, Charalambos Theodorou, Tahera Kalsoom, Nadeem Anjum, Qammer H. Abbasi, and Masood Ur-Rehman
Physical Communication, 2022
Deforestation is depletion of the forest cover and degradation in forest quality mainly through repeated fires, over-exploitation, and diseases. In a forest ecosystem, occurrence of wildfires is a natural phenomena. The curse of global warming and man-made interventions have made the wildfires increasingly extreme and widespread. Though, extremely challenging due to rapidly changing climate, accurate prediction of these fire events can significantly improve forestation worldwide. In this paper, we have addressed this issue by proposing a deep learning (DL) framework using long short term memory (LSTM) model. The proposed mechanism accurately forecasts weekly fire alerts and associated burnt area (ha) utilizing historical fire data provided by GLOBAL FOREST WATCH. Pakistan is taken as a case study since its deforestation rate is among the highest in the world while having one of the lowest forest covers. Number of epochs, dense layers, hidden layers and hidden layer units are varied to optimize the model for high estimation accuracy and low root mean square error (RMSE). Simulation results show that the proposed method can predict the forest fire occurrences with 95% accuracy by employing a suitable hyperparameter tuning.
Muhammad Ali Jamshed, Kamran Ali, Qammer H. Abbasi, Muhammad Ali Imran, and Masood Ur-Rehman
IEEE Sensors Journal, 2022
The addition of massive machine type communication (mMTC) as a category of Fifth Generation (5G) of mobile communication, have increased the popularity of Internet of Things (IoT). The sensors are one of the critical component of any IoT device. Although the sensors posses a well-known historical existence, but their integration in wireless technologies and increased demand in IoT applications have increased their importance and the challenges in terms of design, integration, etc. This survey presents a holistic (historical as well as architectural) overview of wireless sensor (WS) nodes, providing a classical definition, in-depth analysis of different modules involved in the design of a WS node, and the ways in which they can be used to measure a system performance. Using the definition and analysis of a WS node, a more comprehensive classification of WS nodes is provided. Moreover, the need to form a wireless sensor network (WSN), their deployment, and communication protocols is explained. The applications of WS nodes in various use cases have been discussed. Additionally, an overlook of challenges and constraints that these WS nodes face in various environments and during the manufacturing process, are discussed. Their main existing developments which are expected to augment the WS nodes, to meet the requirements of the emerging systems, are also presented.