Environmental Footprints Forecasts Using Remote Sensing, Information Technology and Artificial Intelligence Methods

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closed (31 October 2024)

Manuscript submission deadline

closed (31 December 2024)

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Topic Information

Dear Colleagues,

Environmental footprints have emerged and been widely applied in environmental impact assessments to show the appropriation of natural resources by humans. Footprints are divided into environmental, economic, and social footprints, and combined environmental, social, and/or economic footprints. The concept of a “footprint” originates from the idea of the ecological footprint, EF. Each footprint indicates particular classes of pressures associated with process, product, or activity from the life cycle perspective.

Several footprints are identified as key footprints because they are essential for sustainability and sustainable development. The recognized footprints are carbon (CF), water (WF), nitrogen (NF), phosphorus (PF), biodiversity (BF), and land (LF).

The goal of this Special Issue is to offer a platform for the quick, open publication of peer-reviewed research on environmental footprints estimations using RS, GIS, and artificial modes in semiarid and arid regions. The topics that will be covered in this issue include, but are not limited to, the following:

• Environmental resource management;
• Environmental risk assessment;
• Water/groundwater footprint;
• Gas emissions
• Water scarcity;
• Advanced artificial intelligence;
• Remote sensing applications;
• Rainfall harvesting;
• Information technology;
• Spatial planning and governance for climate adaptation.

Dr. Ahmed Elbeltagi
Prof. Dr. Quanhua Hou
Prof. Dr. Bin He
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Agriculture agriculture	3.3	4.9	2011	19.2 Days	CHF 2600
Climate climate	3.0	5.5	2013	19.7 Days	CHF 1800
Forests forests	2.4	4.4	2010	16.2 Days	CHF 2600
Remote Sensing remotesensing	4.2	8.3	2009	23.9 Days	CHF 2700
Water water	3.0	5.8	2009	17.5 Days	CHF 2600
Earth earth	2.1	3.3	2020	23.7 Days	CHF 1200

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Published Papers (3 papers)

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All Agriculture Climate Forests Remote Sensing Water Earth

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30 pages, 8264 KiB  

Open AccessArticle

Parameterization before Meta-Analysis: Cross-Modal Embedding Clustering for Forest Ecology Question-Answering

by Rui Tao, Meng Zhu, Haiyan Cao and Hong-E Ren

Forests 2024, 15(9), 1670; https://doi.org/10.3390/f15091670 - 22 Sep 2024

Viewed by 1053

Abstract

In the field of forestry ecology, image data capture factual information, while literature is rich with expert knowledge. The corpus within the literature can provide expert-level annotations for images, and the visual information within images naturally serves as a clustering center for the [...] Read more.

In the field of forestry ecology, image data capture factual information, while literature is rich with expert knowledge. The corpus within the literature can provide expert-level annotations for images, and the visual information within images naturally serves as a clustering center for the textual corpus. However, both image data and literature represent large and rapidly growing, unstructured datasets of heterogeneous modalities. To address this challenge, we propose cross-modal embedding clustering, a method that parameterizes these datasets using a deep learning model with relatively few annotated samples. This approach offers a means to retrieve relevant factual information and expert knowledge from the database of images and literature through a question-answering mechanism. Specifically, we align images and literature across modalities using a pair of encoders, followed by cross-modal information fusion, and feed these data into an autoregressive generative language model for question-answering with user feedback. Experiments demonstrate that this cross-modal clustering method enhances the performance of image recognition, cross-modal retrieval, and cross-modal question-answering models. Our method achieves superior performance on standardized tasks in public datasets for image recognition, cross-modal retrieval, and cross-modal question-answering, notably achieving a 21.94% improvement in performance on the cross-modal question-answering task of the ScienceQA dataset, thereby validating the efficacy of our approach. Essentially, our method targets cross-modal information fusion, combining perspectives from multiple tasks and utilizing cross-modal representation clustering of images and text. This approach effectively addresses the interdisciplinary complexity of forestry ecology literature and the parameterization of unstructured heterogeneous data encapsulating species diversity in conservation images. Building on this foundation, intelligent methods are employed to leverage large-scale data, providing an intelligent research assistant tool for conducting forestry ecological studies on larger temporal and spatial scales. Full article

(This article belongs to the Topic Environmental Footprints Forecasts Using Remote Sensing, Information Technology and Artificial Intelligence Methods)

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43 pages, 24204 KiB  

Open AccessArticle

Support Vector Machine Algorithm for Mapping Land Cover Dynamics in Senegal, West Africa, Using Earth Observation Data

by Polina Lemenkova

Earth 2024, 5(3), 420-462; https://doi.org/10.3390/earth5030024 - 6 Sep 2024

Cited by 4 | Viewed by 1992

Abstract

This paper addresses the problem of mapping land cover types in Senegal and recognition of vegetation systems in the Saloum River Delta on the satellite images. Multi-seasonal landscape dynamics were analyzed using Landsat 8-9 OLI/TIRS images from 2015 to 2023. Two image classification [...] Read more.

This paper addresses the problem of mapping land cover types in Senegal and recognition of vegetation systems in the Saloum River Delta on the satellite images. Multi-seasonal landscape dynamics were analyzed using Landsat 8-9 OLI/TIRS images from 2015 to 2023. Two image classification methods were compared, and their performance was evaluated in the GRASS GIS software (version 8.4.0, creator: GRASS Development Team, original location: Champaign, Illinois, USA, currently multinational project) by means of unsupervised classification using the k-means clustering algorithm and supervised classification using the Support Vector Machine (SVM) algorithm. The land cover types were identified using machine learning (ML)-based analysis of the spectral reflectance of the multispectral images. The results based on the processed multispectral images indicated a decrease in savannas, an increase in croplands and agricultural lands, a decline in forests, and changes to coastal wetlands, including mangroves with high biodiversity. The practical aim is to describe a novel method of creating land cover maps using RS data for each class and to improve accuracy. We accomplish this by calculating the areas occupied by 10 land cover classes within the target area for six consecutive years. Our results indicate that, in comparing the performance of the algorithms, the SVM classification approach increased the accuracy, with 98% of pixels being stable, which shows qualitative improvements in image classification. This paper contributes to the natural resource management and environmental monitoring of Senegal, West Africa, through advanced cartographic methods applied to remote sensing of Earth observation data. Full article

(This article belongs to the Topic Environmental Footprints Forecasts Using Remote Sensing, Information Technology and Artificial Intelligence Methods)

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21 pages, 5309 KiB  

Open AccessArticle

Estimation of Surface Soil Nutrient Content in Mountainous Citrus Orchards Based on Hyperspectral Data

by Xuchao Jiao, Hui Liu, Weimu Wang, Jiaojiao Zhu and Hao Wang

Agriculture 2024, 14(6), 873; https://doi.org/10.3390/agriculture14060873 - 30 May 2024

Cited by 2 | Viewed by 965

Abstract

Monitoring soil conditions is of great significance for guiding fruit tree production and increasing yields. Achieving a rapid determination of soil physicochemical properties can more efficiently monitor soil conditions. Traditional sampling and survey methods suffer from slow detection speeds, low accuracy, limited coverage, [...] Read more.

Monitoring soil conditions is of great significance for guiding fruit tree production and increasing yields. Achieving a rapid determination of soil physicochemical properties can more efficiently monitor soil conditions. Traditional sampling and survey methods suffer from slow detection speeds, low accuracy, limited coverage, and require a large amount of manpower and resources. In contrast, the use of hyperspectral technology enables the precise and rapid monitoring of soil physicochemical properties, playing an important role in advancing precision agriculture. Yuxi City, Yunnan Province, was selected as the study area; soil samples were collected and analyzed for soil organic matter (SOM), total nitrogen (TN), total phosphorus (TP), and available nitrogen (AN) contents. Additionally, soil spectral reflectance was obtained using a portable spectroradiometer. Hyperspectral characteristic bands for soil nutrients were selected from different spectral preprocessing methods, and different models were used to predict soil nutrient content, identifying the optimal modeling approach. For SOM prediction, the second-order differentiation-multiple stepwise regression (SD-MLSR) model performed exceptionally well, with an R² value of 0.87 and RMSE of 6.61 g·kg⁻¹. For TN prediction, the logarithm of the reciprocal first derivative-partial least squares regression (LRD-PLSR) model had an R² of 0.77 and RMSE of 0.37 g·kg⁻¹. For TP prediction, the logarithmic second-order differentiation-multiple stepwise regression (LTSD-MLSR) model had an R² of 0.69 and RMSE of 0.04 g·kg⁻¹. For AN prediction, the logarithm of the reciprocal second derivative-partial least squares regression (LRSD-PLSR) model had an R² of 0.83 and RMSE of 24.12 mg·kg⁻¹. The results demonstrate the high accuracy of these models in predicting soil nutrient content. Full article

(This article belongs to the Topic Environmental Footprints Forecasts Using Remote Sensing, Information Technology and Artificial Intelligence Methods)

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