Analysis Of Remote Sensing Data Research Articles

Remote Sensing Technology for Observing Tree Mortality and Its Influences on Carbon–Water Dynamics

Trees are indispensable to ecosystems, yet mortality rates have been increasing due to the abnormal changes in forest growth environments caused by frequent extreme weather events associated with global climate warming. Consequently, the need to monitor, assess, and predict tree mortality has become increasingly urgent to better address climate change and protect forest ecosystems. Over the past few decades, remote sensing has been widely applied to vegetation mortality observation due to its significant advantages. Here, we reviewed and analyzed the major research advancements in the application of remote sensing for tree mortality monitoring, using the Web of Science Core Collection database, covering the period from 1998 to the first half of 2024. We comprehensively summarized the use of different platforms (satellite and UAV) for data acquisition, the application of various sensors (multispectral, hyperspectral, and radar) as image data sources, the primary indicators, the classification models used in monitoring tree mortality, and the influence of tree mortality. Our findings indicated that satellite-based optical remote sensing data were the primary data source for tree mortality monitoring, accounting for 80% of existing studies. Time-series optical remote sensing data have emerged as a crucial direction for enhancing the accuracy of vegetation mortality monitoring. In recent years, studies utilizing airborne LiDAR have shown an increasing trend, accounting for 48% of UAV-based research. NDVI was the most commonly used remote sensing indicator, and most studies incorporated meteorological and climatic factors as environmental variables. Machine learning was increasingly favored for remote sensing data analysis, with Random Forest being the most widely used classification model. People are more focused on the impacts of tree mortality on water and carbon. Finally, we discussed the challenges in monitoring and evaluating tree mortality through remote sensing and offered perspectives for future developments.

Assessing mangrove forest changes using vegetation index algorithm in southern west Lombok

Abstract The availability of multi-temporal remote sensing data, particularly satellite imagery combined with Geographic Information Systems (GIS), enables effective monitoring of changes in the condition and extent of mangrove forests. This study aims to analyze temporal changes in mangrove forests and evaluate their condition through a vegetation index analysis in southern West Lombok. The research methodology involves field observations and remote sensing data analysis, employing the Normalized Difference Vegetation Index (NDVI) as the primary vegetation index algorithm. The multi-temporal analysis focuses on three key years: 2001, 2008, and 2022, revealing significant changes in the mangrove ecosystem based on the classification of vegetation index values derived from Landsat satellite imagery. The findings indicate that by 2008, the mangrove ecosystem had experienced severe degradation, primarily due to land conversion for activities such as aquaculture and settlement development, as well as extensive logging by local communities for firewood and other uses.

Bayesian modeling of incompatible spatial data: A case study involving Post-Adrian storm forest damage assessment

Modeling incompatible spatial data, i.e., data with different spatial resolutions, is a pervasive challenge in remote sensing data analysis. Typical approaches to addressing this challenge aggregate information to a common coarse resolution, i.e., compatible resolutions, prior to modeling. Such pre-processing aggregation simplifies analysis, but potentially causes information loss and hence compromised inference and predictive performance. To avoid losing potential information provided by finer spatial resolution data and improve predictive performance, we propose a new Bayesian method that constructs a latent spatial process model at the finest spatial resolution. This model is tailored to settings where the outcome variable is measured on a coarser spatial resolution than predictor variables—a configuration seen increasingly when high spatial resolution remotely sensed predictors are used in analysis. A key contribution of this work is an efficient algorithm that enables full Bayesian inference using finer resolution data while optimizing computational and storage costs. The proposed method is applied to a forest damage assessment for the 2018 Adrian storm in Carinthia, Austria, that uses high-resolution laser imaging detection and ranging (LiDAR) measurements and relatively coarse resolution forest inventory measurements. Extensive simulation studies demonstrate the proposed approach substantially improves inference for small prediction units.

Carbon Stocks Estimation Using the Stock Difference Method of Various Land Use Systems Based on Geospatial in Kualan Watershed

Indonesia controls 75%-80% of the world's carbon stocks, so the amount of carbon stocks must be utilized optimally. This study aims to determine carbon stocks, potential emissions, and economic value of carbon stocks in each land use. The method used is secondary data analysis and field checking. The data collected were Sentinel 2A acquisitions in 2020 and 2022, Digital Elevation Model (DEM), and land use land cover in 2020-2023. Data analysis used SNAP and ArcGIS 10.8. The tool used for data analysis is spatial analysis map algebra. The results showed mixed dryland agriculture has the most extensive carbon stock, at 2,614,178 tons/ha, with potential emissions of 9,585,320 tons/ha. The most minor carbon stock is in mining land use, which is 0 tons/ha with potential emissions of 0 tons/ha. The highest C02 value in USD is the forest land use group. In the Secondary Dryland Forest, Secondary Swamp Forest, and Plantation Forest groups, it is 17,517,400.50 USD, while the lowest is mining land use, which is 0 USD. Overall, the CO2 value of land use in the study area is 34,246,314.45 USD. Integrating remote sensing data analysis and field surveys in geospatial technology is one of the new approaches to studying carbon stocks and CO2 emissions in topsoil from various land uses. By utilizing geospatial technology, efforts to estimate carbon stocks on the surface are easier and faster.

Morphometric Inferences of the Euphrates River Islands and the Possibility of their Development through the Analysis of Remote Sensing Data and Geographic Information Systems

Morphometric Inferences of the Euphrates River Islands and the Possibility of their Development through the Analysis of Remote Sensing Data and Geographic Information Systems

Navigating environmental fragility: (Mal)coping and adaptation strategies in the socio-environmental system of the Mtendeli Refugee Camp, Tanzania

The study conducts a systems analysis of coping and adaptation strategies in Tanzanian refugee camps, focusing on the Mtendeli camp as a case study. It explores the environmental changes during the stages of the camp's establishment, development, and closure, along with the responses of local actors. Employing qualitative interviews and remote-sensing data analysis, the research reveals a spectrum of strategies employed by the camp management, host community, and refugees. The findings highlight the interdependence of these strategies and stress the need to address both structural limitations and individual agency, considering the concepts of structural ambivalence and temporal dynamics. Notably, the concept of ‘(mal)coping’ is introduced to describe coping strategies that have short-term benefits but contribute to long-term environmental degradation. The study contributes to a deeper understanding of coping and adaptation dynamics in refugee camp environments.

Dynamic mode decomposition of GRACE satellite data

Advancements in satellite technology yield environmental data with ever improving spatial coverage and temporal resolution. This necessitates the development of techniques to discern actionable information from large amounts of such data. We explore the potential of dynamic mode decomposition (DMD) to discover the dynamics of spatially correlated structures present in global-scale data, specifically in observations of total water storage anomalies provided by GRACE satellite missions. Our results demonstrate that DMD enables data compression and extrapolation from a reduced set of dominant spatiotemporal structures. The accuracy of its predictions of global system dynamics is preserved in its reconstruction of local time series. These findings suggest potential uses of DMD in analysis of remote-sensing data for hydrologic applications.

Remote Sensing and Field Data Analysis to Evaluate the Impact of Stone Bunds on Rainfed Agriculture in West Africa

This study evaluates the effectiveness of stone bunds in enhancing soil moisture, vegetation health, and crop yields in Ghana’s semi-arid Upper East Region, an important area for agricultural productivity in West Africa. In this region, agricultural practices are heavily impacted by erratic rainfall and poor soil moisture retention, threatening food security. Despite the known benefits of traditional soil conservation practices like stone bunds, their effectiveness in this context has not been fully quantified. Field and remote sensing data were used to evaluate the influence of stone bunds on soil moisture dynamics, vegetation growth, and crop yield. Experimental plots with and without stone bunds were monitored for climate, soil water infiltration, and soil moisture and analyzed using the NDVI from Sentinel-2 satellite imagery over two growing seasons under sorghum production (2022–2023). The results indicated that stone bunds enhanced soil moisture retention and increased infiltration rates. The NDVI analysis consistently revealed higher vegetation health and growth in the plots with stone bunds, particularly during critical growth periods. The intermediate results of the conducted experiment indicated that stone bunds increased sorghum yields by over 35% compared to the control plots. The substantial agronomic benefits of stone bunds as a soil and water conservation strategy were evident, improving soil water infiltration, water retention, vegetation health, and crop yields. The findings support the broader adoption of stone bunds in semi-arid regions to enhance agricultural productivity and resilience against climate variability. Further research is recommended to explore the long-term impacts and the integration of stone bunds with other sustainable farming practices to optimize rainfed agricultural outcomes.

Post-fire vegetation dynamic patterns and drivers in Greater Hinggan Mountains: Insights from long-term remote sensing data analysis

Fire has become a major disturbing factor in boreal forests, and giant forest disturbances play a vital role in regulating the climate under global warming. Therefore, it is essential to investigate the spatiotemporal patterns and main drivers of post-fire vegetation recovery for forest ecological research and post-fire recovery management. However, previous studies have focused on the post-fire forest change within the entire fire perimeter, lacking separate analysis and comparison of the burned zone (BZ) and unburned zone (UNBZ). Here, we propose the utilization of Moderate Resolution Imaging Spectroradiometer land cover type and vegetation index data to monitor vegetation dynamics and explore its drivers after the most serious forest fire in the history of P.R. China in the Greater Hinggan Mountains (GHM). The temporal and spatial patterns of vegetation recovery in the BZ/UNBZ in the GHM were analyzed using the Sen & Mann-Kendall method, Hurst index and coefficient of variation, and their driving mechanisms were explored using GeoDetector and geographically weighted regression. The results showed that there were significant differences in the spatial distribution and fluctuation of vegetation between the BZ and UNBZ, and that the BZ exhibited higher productivity and vigor. Vegetation recovery was influenced by different dominant factors and changed over time, in which land surface temperature and precipitation dominated all the time, whereas topographic relief and elevation had a more significant contribution to vegetation recovery in the BZ and UNBZ, respectively. This study provides a scientific basis for the protection and management of vegetation in disturbed forested areas, particularly after fires.

Towards Robust Pansharpening: A Large-Scale High-Resolution Multi-Scene Dataset and Novel Approach

Pansharpening, a pivotal task in remote sensing, involves integrating low-resolution multispectral images with high-resolution panchromatic images to synthesize an image that is both high-resolution and retains multispectral information. These pansharpened images enhance precision in land cover classification, change detection, and environmental monitoring within remote sensing data analysis. While deep learning techniques have shown significant success in pansharpening, existing methods often face limitations in their evaluation, focusing on restricted satellite data sources, single scene types, and low-resolution images. This paper addresses this gap by introducing PanBench, a high-resolution multi-scene dataset containing all mainstream satellites and comprising 5898 pairs of samples. Each pair includes a four-channel (RGB + near-infrared) multispectral image of 256 × 256 pixels and a mono-channel panchromatic image of 1024 × 1024 pixels. To avoid irreversible loss of spectral information and achieve a high-fidelity synthesis, we propose a Cascaded Multiscale Fusion Network (CMFNet) for pansharpening. Multispectral images are progressively upsampled while panchromatic images are downsampled. Corresponding multispectral features and panchromatic features at the same scale are then fused in a cascaded manner to obtain more robust features. Extensive experiments validate the effectiveness of CMFNet.

Green space coverage versus air pollution: a cloud-based remote sensing data analysis in Sichuan, Western China

ABSTRACT Air pollution is the greatest health risk to human health, as acknowledged in several Sustainable Development Goals (SDG), such as SDG-1, SDG-3, SDG-7, and SDG-11. Despite global comprehension of the positive effect of Green Space Coverage (GSC) on mitigating air pollution, investigating the impact of different GSC types has received little attention. Here, we utilized multiple air pollution data and a cloud-computing platform to examine the role of different GSC types in mitigating NO2 and PM2.5 pollutants across 2019 and 2022 in Sichuan Province, China. We classified GSC areas into tall GSC and short GSC classes, taking into account the recognized importance of vegetation height in prior studies. Our analysis revealed that tall GSCs exhibit lower pollutant levels across all areas studied, indicating a potential correlation between GSC height and pollution mitigation efficacy. Furthermore, in high human activity areas, while tall GSC emerged as effective sinks for PM 2.5 compared to short GSC (25% and 20% lower average annual in 2019 and 2022, respectively), their performance in reducing NO2 pollutant levels was relatively limited (9% and 4% lower average annual in 2019 and 2022, respectively). These findings can contribute to urban planning and environmental management.

Big Disaster from Small Watershed: Insights into the Failure and Disaster-Causing Mechanism of a Debris Flow on 25 September 2021 in Tianquan, China

The occurrence of debris flow events in small-scale watersheds with dense vegetation in mountainous areas that result in significant loss of life and missing individuals challenges our understanding and expertise in investigating and preventing these disasters. This has raised concerns about the occurrence of large debris flow disasters from small watersheds. This study focused on a catastrophic debris flow that took place in Longtou Gully (0.45 km2) in Tianquan County, Ya’an City on 25 September 2021, which resulted in 14 deaths and missing individuals. Through comprehensive field investigations, high-precision remote sensing data analyses, and numerical simulations, we analyzed the triggering mechanisms and dynamic processes of this event. Our results indicate that the convergence hollow at the channel head exhibited higher hydraulic conditions during rainfall compared to gentle slopes and convex terrains, leading to the instability of colluvial soil due to the expansion of the saturated zone near the soil–bedrock interface. The entrainment of material eroded from the channel resulted in an approximately 4.7 times increase in volume, and the channel scarp with a height of about 200 m amplified the destructive power of the debris flow. We emphasize the need to take seriously the possibility of catastrophic debris flows in small-scale watersheds, with colluvial deposits in hollows at the channel head under vegetation cover that serve as precursor material sources, and the presence of channel scarps formed by changes in the incision rate of the main river, which is common in the small watershed on both sides. This study provides insights for risk assessment of debris flows in small-scale catchments with dense vegetation cover in mountainous areas, highlighting the importance of vigilance in addressing disasters in small-scale catchments, particularly in regions with increasing human–environment conflicts.

Assessing Ecological Impacts and Recovery in Coal Mining Areas: A Remote Sensing and Field Data Analysis in Northwest China

In the coal-rich provinces of Shanxi, Shaanxi, and Inner Mongolia, the landscape bears the scars of coal extraction—namely subsidence and deformation—that disrupt both the terrain and the delicate ecological balance. This research delves into the transformative journey these mining regions undergo, from pre-mining equilibrium, through the tumultuous phase of extraction, to the eventual restoration of stability post-reclamation. By harnessing a suite of analytical tools, including sophisticated remote sensing, UAV aerial surveys, and the meticulous ground-level sampling of flora and soil, the study meticulously measures the environmental toll of mining activities and charts the path to ecological restoration. The results are promising, indicating that the restoration initiatives are effectively healing the landscapes, with proactive interventions such as seeding, afforestation, and land rehabilitation proving vital in the swift ecological turnaround. Remote sensing technology, in particular, emerges as a robust ally in tracking ecological shifts, supporting sustainable practices and guiding ecological management strategies. This study offers a promising framework for assessing geological environmental shifts, which may guide policymakers in shaping the future of mining rehabilitation in arid and semi-arid regions.

Spatiotemporal Assessment of Water Pollution for Beira Lake, Sri Lanka

Beira Lake, located in Colombo, Sri Lanka, has suffered severe anthropogenic impacts, with previous restoration attempts failing due to a limited understanding of pollutant dynamics. Aiming to fill this gap, a comprehensive study was conducted during dry and wet seasons to assess the spatiotemporal water pollution of Beira Lake, employing key physicochemical parameters, numerical indices, and remote sensing analysis. The water pollution index (WPI) results categorize Beira Lake as highly polluted, with WPI values ranging from 2.38 ± 0.92 in the wet season to 2.53 ± 1.32 in the dry season. Comparatively higher COD levels recorded in the Beira Lake network, especially for Gangarama Lake show significant pollution levels during both the dry and wet seasons, e.g., the highest COD levels, at 306.40 mg/L, were observed during the wet season. The Trophic State Index (TSI) results indicate eutrophic and hypereutrophic conditions in Beira Lake, which are particularly pronounced during the wet season. The heavy metal pollution index (HPI) results suggest elevated heavy metal concentrations in Beira Lake, especially in the wet season. Combined with field investigation results, a remote sensing data analysis between 2016 and 2023 reveals significant improvements in water transparency, suggesting positive effects of recent management interventions. Parameters demanding attention include COD, nitrate, and total phosphate levels due to their consistent exceedance of permissible limits. The PCA results of indices correlations between wet and dry seasons offer valuable insights into the complex dynamics of Beira Lake’s water quality. The study makes recommendations for restoring Beira Lake, including stringent pollution controls, regular dredging, green infrastructure implementation, implementing new rules and regulations, and community engagement.

Multi-Scale Fusion Siamese Network Based on Three-Branch Attention Mechanism for High-Resolution Remote Sensing Image Change Detection

Remote sensing image change detection (CD) is an important means in remote sensing data analysis tasks, which can help us understand the surface changes in high-resolution (HR) remote sensing images. Traditional pixel-based and object-based methods are only suitable for low- and medium-resolution images, and are still challenging for complex texture features and detailed image detail processing in HR images. At present, the method based on deep learning has problems such as inconsistent fusion and difficult model training in the combination of the difference feature information of the deep and shallow layers and the attention mechanism, which leads to errors in the distinction between the changing region and the invariant region, edge detection and small target detection. In order to solve the above problems of inconsistent fusions of feature information aggregation and attention mechanisms, and indistinguishable change areas, we propose a multi-scale feature fusion Siamese network based on attention mechanism (ABMFNet). To tackle the issues of inconsistent fusion and alignment difficulties when integrating multi-scale fusion and attention mechanisms, we introduce the attention-based multi-scale feature fusion module (AMFFM). This module not only addresses insufficient feature fusion and connection between different-scale feature layers, but also enables the model to automatically learn and prioritize important features or regions in the image. Additionally, we design the cross-scale fusion module (CFM) and the difference feature enhancement pyramid structure (DEFPN) to assist the AMFFM module in integrating differential information effectively. These modules bridge the spatial disparity between low-level and high-level features, ensuring efficient connection and fusion of spatial difference information. Furthermore, we enhance the representation and inference speed of the feature pyramid by incorporating a feature enhancement module (FEM) into DEFPN. Finally, the BICD dataset proposed by the laboratory and public datasets LEVIR-CD and BCDD are compared and tested. We use F1 score and MIoU values as evaluation metrics. For AMBMFNet, the F1 scores on the three datasets are 77.69%, 81.57%, and 77.91%, respectively, while the MIoU values are 84.65%, 85.84%, and 84.54%, respectively. The experimental results show that ABMFNet has better effectiveness and robustness.

Storage and Analysis of Remote Sensing Data

Storage and Analysis of Remote Sensing Data

Quantifying the Influence of Climate Variables on Vegetation Through Remote Sensing and Multi-dimensional Data Analysis

Quantifying the Influence of Climate Variables on Vegetation Through Remote Sensing and Multi-dimensional Data Analysis

S3L: Spectrum Transformer for Self-Supervised Learning in Hyperspectral Image Classification

In the realm of Earth observation and remote sensing data analysis, the advancement of hyperspectral imaging (HSI) classification technology is of paramount importance. Nevertheless, the intricate nature of hyperspectral data, coupled with the scarcity of labeled data, presents significant challenges in this domain. To mitigate these issues, we introduce a self-supervised learning algorithm predicated on a spectral transformer for HSI classification under conditions of limited labeled data, with the objective of enhancing the efficacy of HSI classification. The S3L algorithm operates in two distinct phases: pretraining and fine-tuning. During the pretraining phase, the algorithm learns the spatial representation of HSI from unlabeled data, utilizing a masking mechanism and a spectral transformer, thereby augmenting the sequence dependence of spectral features. Subsequently, in the fine-tuning phase, labeled data is employed to refine the pretrained weights, thereby improving the precision of HSI classification. Within the comprehensive encoder–decoder framework, we propose a novel spectral transformer module specifically engineered to synergize spatial feature extraction with spectral domain analysis. This innovative module adeptly navigates the complex interplay among various spectral bands, capturing both global and sequential spectral dependencies. Uniquely, it incorporates a gated recurrent unit (GRU) layer within the encoder to enhance its ability to process spectral sequences. Our experimental evaluations across several public datasets reveal that our proposed method, distinguished by its spectral transformer, achieves superior classification performance, particularly in scenarios with limited labeled samples, outperforming existing state-of-the-art approaches.

A fast and robust cirrus removal method for Landsat 8/9 images

High-quality cirrus removal plays a crucial role in remote sensing data analysis. Cirrus parallaxes are commonly observed within the vicinity of cirrus clouds in the visible and near-infrared (VNIR) bands of Landsat 8/9 images. Cirrus parallaxes have a nonnegligible effect on cirrus removal, but the existing methods do not account for the correction of parallaxes. Meanwhile, large-scale image processing involves intensive computation that requires extensive computing time. To address the effect of cirrus parallaxes and the low processing efficiency, we propose a fast and robust cirrus removal (FRCR) method. FRCR has achieved the first realization of the statistics law of cirrus parallax between the VNIR and cirrus bands, thus realizing the cirrus parallax correction. In addition, FRCR introduces an automatic sampling method to obtain the regression samples for practicality. Then, a Compute Unified Device Architecture (CUDA) based Newton method with constraints is introduced to parallelize the computation, to improve the computational performance. Experiment results of various scenarios demonstrate that the FRCR method can achieve high-quality cirrus removal by eliminating cirrus parallaxes, and significantly improving computational performance.

Analysis of Surface Runoff and Remote Sensing Data to Identify Flood Potential in Simbang Sub-Watershed

Increased water runoff due to land use change phenomena has triggered flooding events. The objective is to identify flood potential in Simbang Sub-Watershed in Maros Regency using rational methods and remote sensing data. Potential flood hazards were analyzed using the weighting method with the parameters NDVI, MNDWI, NDSI, rainfall, and annual flow coefficient. The highest debit of runoff occurred in 2017, with a value of 113.36 m3/s, while the lowest occurred in 2019, with a value of 63.91 m3/s. The NDVI value is 0.37–1 with high vegetation covering an area of 3,089 ha, while the low-very low vegetation has value -0.03–0.25 with an area of 1,668 ha. The MNDWI value ranges from 0-0.33 with a moderate wetness level covering an area of 741 ha and an NDSI value ranging from -0.06–0.43 for bare land surrounding an area of 738 ha, which has an impact on reducing water catchment areas which can trigger an increase in surface water runoff discharge. The average rainfall is 2,965 mm/year, the area with low potential for flooding is 3,705 ha, and the area prone (moderate) to flooding is 1,450 ha. The rainfall factor is the main priority trigger for flood events with weight of 0.266, and the soil index is the lowest priority factor with weight of 0.145. Surface water runoff in the Simbang Sub-Watershed area makes a small contribution to the flood events that occurred in Maros Regency with an annual flow coefficient value of 0.23.