A review of machine learning and big data applications in addressing ecosystem service research gaps

Manley, Kyle; Nyelele, Charity; Egoh, Benis N.

doi:10.1016/j.ecoser.2022.101478

Cited by 39 publications

(21 citation statements)

References 66 publications

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“…Recent developments in the incorporation of cloud-based geospatial platforms (e.g., Google Earth Engine) and machine learning techniques also facilitate data collection, processing, and regional assessments. Machine learning technology (e.g., artificial neural networks, random forests, and support vector machines) and regression or classification (supervised or unsupervised) of ecosystems have been applied to extract indicators for environmental assessments for their capability to address the challenges of data gaps and uncertainty and to connect social and ecological factors [80][81][82]. For example, airborne particulates were successfully mapped by integrating data from ground measurement stations and machine learning techniques [80].…”

Section: Incorporating Spatial Big Data and Machine Learning Into The...mentioning

confidence: 99%

Assessing Regional Ecosystem Conditions Using Geospatial Techniques—A Review

Zhang

Wang

Yue

et al. 2023

Sensors

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Ecosystem conditions at the regional level are critical factors for environmental management, public awareness, and land use decision making. Regional ecosystem conditions may be examined from the perspectives of ecosystem health, vulnerability, and security, as well as other conceptual frameworks. Vigor, organization, and resilience (VOR) and pressure–stress–response (PSR) are two commonly adopted conceptual models for indicator selection and organization. The analytical hierarchy process (AHP) is primarily used to determine model weights and indicator combinations. Although there have been many successful efforts in assessing regional ecosystems, they remain affected by a lack of spatially explicit data, weak integration of natural and human dimensions, and uncertain data quality and analyses. In the future, regional ecosystem condition assessments may be advanced by incorporating recent improvements in spatial big data and machine learning to create more operative indicators based on Earth observations and social metrics. The collaboration between ecologists, remote sensing scientists, data analysts, and scientists in other relevant disciplines is critical for the success of future assessments.

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Section: Incorporating Spatial Big Data and Machine Learning Into The...mentioning

confidence: 99%

Assessing Regional Ecosystem Conditions Using Geospatial Techniques—A Review

Zhang

Wang

Yue

et al. 2023

Sensors

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“…To explore the relations between PULs and ES and how these landscapes might support sustainable development through the achievement of different SDGs, we analyzed a set of scientific articles based on a systematic review of the literature. Thus, to produce transparent and reliable scientific evidence, we largely considered the guidelines proposed by the collaboration for environmental evidence [24], which suggested a set of steps to be considered in a review, such as (1) the identification of evidence needs, (2) the planning of the review, (3) conducting the review, and (4) reporting the review (see as an example [25]).…”

Section: Data Collection and Selection Of Papersmentioning

confidence: 99%

Ecosystem Services Supply from Peri-Urban Landscapes and Their Contribution to the Sustainable Development Goals: A Global Perspective

Rozas-Vásquez

Spyra

Jorquera

et al. 2022

Land

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Peri-urban landscapes (PULs) play an important role in the supply of ecosystem services (ES), which support development and well-being in urban and rural areas. Understanding the impacts of policy actions on the supply of ES and how they might contribute to the achievement of a range of sustainable development goals (SDG) is a key challenge, especially for spatial planning. The aim of this article is to explore the links between the ES supply from PULs and how they support different SDGs from a global perspective. For this, we implemented a review of the literature oriented to identify (1) the most relevant ES provided by PULs at a global level, (2) how they support the achievement of different SDG, and (3) the type of policy interventions and actors related to PULs. We identified the supply of 17 different ES by PULs and a relationship to 12 SDGs. In 58% of the cases, the SDGs were related to two ES sections, where regulation and maintenance was dominant. Pure research was the main type of intervention, mainly at the municipal scale. Increasing the cooperation between science and policy and expanding the scale of analysis beyond municipal boundaries are critical aspects.

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“…Several factors have contributed to the popularity of utilizing algorithms for machine learning in today's society. Three primary aspects inspire the use of machine learning [1]: (a) computers have high performance and memory; (b) machine learning algorithms learn and train on behavioral patterns resembling the human brain; and (c) huge datasets available. Therefore, collecting environmental datasets is essential for monitoring environmental conditions and gaining knowledge of the environment's state in any country, including Saudi Arabia.…”

Section: Introductionmentioning

confidence: 99%

“…Humidity: The standard deviation (1.83) and variance (3.34) suggest moderate variability in humidity. The range (15) and IQR (1) indicate that most of the data points lie near the mean. The humidity values lie between 10 and 25 units.…”

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confidence: 97%

Temporal Variations Dataset for Indoor Environmental Parameters in Northern Saudi Arabia

2023

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The advancement of the Internet of Things applications (technologies and enabling platforms), consisting of software and hardware (e.g., sensors, actuators, etc.), allows healthcare providers and users to analyze and measure physical environments at home or hospital. The measured physical environment parameters contribute to improving healthcare in real time. Researchers in this domain require existing representative datasets to develop machine-learning techniques to learn physical variables from the surrounding environments. The available environmental datasets are rare and need too much effort to be generated. To our knowledge, it has been noticed that no datasets are available for some countries, including Saudi Arabia. Therefore, this paper presents one of the first environmental data generated in Saudi Arabia’s environment. The advantage of this dataset is to encourage researchers to investigate the effectiveness of machine learning in such an environment. The collected data will also help utilize the machine learning and deep learning algorithms in smart home and health care applications based on the Saudi Arabia environment. Saudi Arabia has a special environment in each session, especially in the northern area where we work, where it is too hot in the summer and cold in the winter. Therefore, environmental data measurements in both sessions are important for the research community, especially those working in smart and healthcare environments. The dataset is generated based on the indoor environment from six sensors (timestamps, light, temperature, humidity, pressure, and altitude sensors). The room data were collected for 31 days in July 2022, acquiring 8910 records. The datasets include six columns of different data types that represent sensor values. During the experiment, the sensors captured the data every 5 min, storing them in a comma-separated value file. The data are already validated and publicly available at PLOMS Press and can be applied for training, testing, and validating machine learning algorithms. This is the first dataset developed by the authors for the research community for such an environment, and other datasets will follow it in different environments and places.

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A review of machine learning and big data applications in addressing ecosystem service research gaps

Cited by 39 publications

References 66 publications

Assessing Regional Ecosystem Conditions Using Geospatial Techniques—A Review

Assessing Regional Ecosystem Conditions Using Geospatial Techniques—A Review

Ecosystem Services Supply from Peri-Urban Landscapes and Their Contribution to the Sustainable Development Goals: A Global Perspective

Temporal Variations Dataset for Indoor Environmental Parameters in Northern Saudi Arabia

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