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Remote Sensing of Savannas and Woodlands II

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Forest Remote Sensing".

Deadline for manuscript submissions: 15 September 2025 | Viewed by 6550

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Special Issue Editors

Dr. Karine Adeline

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Guest Editor

ONERA (The French Aerospace Lab.), Optics and Associated Techniques Department (DOTA), 2 Avenue Edouard Belin, F-31000 Toulouse, France
Interests: imaging spectroscopy; multiscale analysis; radiative transfer modeling; vegetation structural and functional traits; anthropic and natural wooded landscapes

Dr. Ana Andreu

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Guest Editor

Fluvial Dynamics and Hydrology Research Group, Department of Agronomy, Unit of Excellence María de Maeztu (DAUCO), University of Córdoba, 14014 Córdoba, Spain
Interests: hydrology; water management; evapotranspiration modeling; mediterranean semiarid ecosystems

Dr. Vicente Burchard-Levine

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Guest Editor

Institute of Agricultural Sciences, Spanish National Research Council (CSIC), 28006 Madrid, Spain
Interests: surface energy balance modeling; evapotranspiration; precision agriculture; ecohydrology
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Special Issue Information

Dear Colleagues,

Savannas, woodlands, and other tree-grass ecosystems comprise nearly 1/6th of Earth’s surface in a wide range of climates while being biodiversity hotspots. These transitory landscapes play a dominant role in global biogeochemical cycles, and are one of the most sensitive to global climate change. Indeed, these issues, combined with increasing pressures from agricultural land conversion, livestock grazing, and wildfires, require better characterization of these ecosystems. Especially, the performance of traditional remote sensing algorithms and physical modeling tend to have greater uncertainties in these landscapes due to the poor representation of both (i) the vertical multiple-layered vegetation strata (i.e., overstory with tree/shrub canopies over a herbaceous understory) and (ii) the openness of the horizontally distributed high vegetation, causing inherent pixel heterogeneity at the conventional satellite scale (e.g., >10 m). Besides, these vegetation types have distinct physiological, structural, and phenological traits that need to be better discriminated for better monitoring.

This Special Issue aims to gather papers focused on novel methodological advances to improve the characterization of savannas and woodlands integrating remote sensing. The main focus, but not limited, is towards the use of multispectral/hyperspectral and thermal infrared data to tackle the (i) vertical and (ii) horizontal variability of these ecosystems. This includes exploring sensor synergies at different spatial, spectral and temporal scales, and understanding the 3D architecture of these open forests.

Potential topics for this special issue can cover the following themes:

Estimation of vegetation functional/structural/spectral traits, and non-photosynthetic vegetation,
Ecological diversity, phenology, species classification,
Processing of mixed pixels from empirical, physics-based and hybrid approaches,
Energy balance modeling, water balance modeling, and water stress quantification, biogeochemical modeling,
Inversion of radiative transfer modeling (1D vs. 3D)
Sensor fusion (VSWIR, TIR, SIF, LiDAR, Microwave)
New hyperspectral (PRISMA, ENMAP, CHIME, SBG, ...) and TIR missions (SBG, ECOSTRESS, LSTM, TRISHNA, ...)

Dr. Karine Adeline
Dr. Ana Andreu
Dr. Vicente Burchard-Levine
Guest Editors

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

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Keywords

savannas and woodlands
functional traits and diversity
evapotranspiration and water stress
radiative transfer modeling
energy/water balance modeling
pixel unmixing
imaging spectroscopy
thermal infrared

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Published Papers (4 papers)

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Research

27 pages, 11161 KiB

Open AccessArticle

Quantifying Tree Structural Change in an African Savanna by Utilizing Multi-Temporal TLS Data

by Tasiyiwa Priscilla Muumbe, Jussi Baade, Pasi Raumonen, Corli Coetsee, Jenia Singh and Christiane Schmullius

Remote Sens. 2025, 17(5), 757; https://doi.org/10.3390/rs17050757 - 22 Feb 2025

Viewed by 514

Abstract

Structural changes in savanna trees vary spatially and temporally because of both biotic and abiotic drivers, as well as the complex interactions between them. Given this complexity, it is essential to monitor and quantify woody structural changes in savannas efficiently. We implemented a non-destructive approach based on Terrestrial Laser Scanning (TLS) and Quantitative Structure Models (QSMs) that offers the unique advantage of investigating changes in complex tree parameters, such as volume and branch length parameters that have not been previously reported for savanna trees. Leaf-off multi-scan TLS point clouds were acquired during the dry season, using a Riegl VZ1000 TLS, in September 2015 and October 2019 at the Skukuza flux tower in Kruger National Park, South Africa. These three-dimensional (3D) data covered an area of 15.2 ha with an average point density of 4270 points/m² (0.015°) and 1600 points/m² (0.025°) for the 2015 and 2019 clouds, respectively. Individual tree segmentation was applied on the two clouds using the comparative shortest-path algorithm in LiDAR 360(v5.4) software. We reconstructed optimized QSMs and assessed tree structural parameters such as Diameter at Breast Height (DBH), tree height, crown area, volume, and branch length at individual tree level. The DBH, tree height, crown area, and trunk volume showed significant positive correlations (R² > 0.80) between scanning periods regardless of the difference in the number of points of the matched trees. The opposite was observed for total and branch volume, total number of branches, and 1st-order branch length. As the difference in the point densities increased, the difference in the computed parameters also increased (R² < 0.63) for a high relative difference. A total of 45% of the trees present in 2015 were identified in 2019 as damaged/felled (75 trees), and the volume lost was estimated to be 83.4 m³. The results of our study showed that volume reconstruction algorithms such as TreeQSMs and high-resolution TLS datasets can be used successfully to quantify changes in the structure of savanna trees. The results of this study are key in understanding savanna ecology given its complex and dynamic nature and accurately quantifying the gains and losses that could arise from fire, drought, herbivory, and other abiotic and biotic disturbances. Full article

(This article belongs to the Special Issue Remote Sensing of Savannas and Woodlands II)

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25 pages, 24844 KiB

Open AccessArticle

Individual Tree Crown Delineation Using Airborne LiDAR Data and Aerial Imagery in the Taiga–Tundra Ecotone

by Yuanyuan Lin, Hui Li, Linhai Jing, Haifeng Ding and Shufang Tian

Remote Sens. 2024, 16(21), 3920; https://doi.org/10.3390/rs16213920 - 22 Oct 2024

Cited by 1 | Viewed by 1514

Abstract

The circumpolar Taiga–Tundra Ecotone significantly influences the feedback mechanism of global climate change. Achieving large-scale individual tree crown (ITC) extraction in the transition zone is crucial for estimating vegetation biomass in the transition zone and studying plants’ response to climate change. This study employed aerial images and airborne LiDAR data covering several typical transitional zone regions in northern Finland to explore the ITC delineation method based on deep learning. First, this study developed an improved multi-scale ITC delineation method to enable the semi-automatic assembly of the ITC sample collection. This approach led to the creation of an individual tree dataset containing over 20,000 trees in the transitional zone. Then, this study explored the ITC delineation method using the Mask R-CNN model. The accuracies of the Mask R-CNN model were compared with two traditional ITC delineation methods: the improved multi-scale ITC delineation method and the local maxima clustering method based on point cloud distribution. For trees with a height greater than 1.3 m, the Mask R-CNN model achieved an overall recall rate (

A_{r}

) of 96.60%. Compared to the two conventional ITC delineation methods, the

A_{r}

of Mask R-CNN showed an increase of 1.99 and 5.52 points in percentage, respectively, indicating that the Mask R-CNN model can significantly improve the accuracy of ITC delineation. These results highlight the potential of Mask R-CNN in extracting low trees with relatively small crowns in transitional zones using high-resolution aerial imagery and low-density airborne point cloud data for the first time. Full article

(This article belongs to the Special Issue Remote Sensing of Savannas and Woodlands II)

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27 pages, 3720 KiB

Open AccessArticle

Hierarchical Analysis of Miombo Woodland Spatial Dynamics in Lualaba Province (Democratic Republic of the Congo), 1990–2024: Integrating Remote Sensing and Landscape Ecology Techniques

by Yannick Useni Sikuzani, Médard Mpanda Mukenza, John Kikuni Tchowa, Delphin Kabamb Kanyimb, François Malaisse and Jan Bogaert

Remote Sens. 2024, 16(20), 3903; https://doi.org/10.3390/rs16203903 - 21 Oct 2024

Viewed by 2060

Abstract

Lualaba Province, located in the southeastern Democratic Republic of the Congo (DRC), consists of five territories with varied dominant land uses: agriculture (Dilolo, Kapanga, and Musumba in the west) and mining (Mutshatsha and Lubudi in the east). The province also includes protected areas with significant governance challenges. The tropical dry forests that cover the unique Miombo woodland of Lualaba are threatened by deforestation, which poses risks to biodiversity and local livelihoods that depend on these forests for agriculture and forestry. To quantify the spatio-temporal dynamics of Lualaba’s landscape, we utilized Landsat images from 1990 to 2024, supported by a Random Forest Classifier. Landscape metrics were calculated at multiple hierarchical levels: province, territory, and protected areas. A key contribution of this work is its identification of pronounced deforestation trends in the unique Miombo woodlands, where the overall woodland cover has declined dramatically from 62.9% to less than 25%. This is coupled with a marked increase in landscape fragmentation, isolation of remaining woodland patches, and a shift toward more heterogeneous land use patterns, as evidenced by the Shannon diversity index. Unlike previous research, our study distinguishes between the dynamics in agricultural territories—which are particularly vulnerable to deforestation—and those in mining areas, where Miombo forest cover remains more intact but is still under threat. This nuanced distinction between land use types offers critical insights into the differential impacts of economic activities on the landscape. Our study also uncovers significant deforestation within protected areas, underscoring the failure of current governance structures to safeguard these critical ecosystems. This comprehensive analysis offers a novel contribution to the literature by linking the spatial patterns of deforestation to both agricultural and mining pressures while simultaneously highlighting the governance challenges that exacerbate landscape transformation. Lualaba’s Miombo woodlands are at a critical juncture, and without urgent, coordinated intervention from local and international stakeholders, the ecological and socio-economic foundations of the region will be irreversibly compromised. Urgent action is needed to implement land conservation policies, promote sustainable agricultural practices, strengthen Miombo woodland regulation enforcement, and actively support protected areas. Full article

(This article belongs to the Special Issue Remote Sensing of Savannas and Woodlands II)

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27 pages, 6924 KiB

Open AccessArticle

GPP of a Chinese Savanna Ecosystem during Different Phenological Phases Simulated from Harmonized Landsat and Sentinel-2 Data

by Xiang Zhang, Shuai Xie, Yiping Zhang, Qinghai Song, Gianluca Filippa and Dehua Qi

Remote Sens. 2024, 16(18), 3475; https://doi.org/10.3390/rs16183475 - 19 Sep 2024

Cited by 1 | Viewed by 1830

Abstract

Savannas are widespread biomes with highly valued ecosystem services. To successfully manage savannas in the future, it is critical to better understand the long-term dynamics of their productivity and phenology. However, accurate large-scale gross primary productivity (GPP) estimation remains challenging because of the high spatial and seasonal variations in savanna GPP. China’s savanna ecosystems constitute only a small part of the world’s savanna ecosystems and are ecologically fragile. However, studies on GPP and phenological changes, while closely related to climate change, remain scarce. Therefore, we simulated savanna ecosystem GPP via a satellite-based vegetation photosynthesis model (VPM) with fine-resolution harmonized Landsat and Sentinel-2 (HLS) imagery and derived savanna phenophases from phenocam images. From 2015 to 2018, we compared the GPP from HLS VPM (GPP_HLS-VPM) simulations and that from Moderate-Resolution Imaging Spectroradiometer (MODIS) VPM simulations (GPP_MODIS-VPM) with GPP estimates from an eddy covariance (EC) flux tower (GPP_EC) in Yuanjiang, China. Moreover, the consistency of the savanna ecosystem GPP was validated for a conventional MODIS product (MOD17A2). This study clearly revealed the potential of the HLS VPM for estimating savanna GPP. Compared with the MODIS VPM, the HLS VPM yielded more accurate GPP estimates with lower root-mean-square errors (RMSEs) and slopes closer to 1:1. Specifically, the annual RMSE values for the HLS VPM were 1.54 (2015), 2.65 (2016), 2.64 (2017), and 1.80 (2018), whereas those for the MODIS VPM were 3.04, 3.10, 2.62, and 2.49, respectively. The HLS VPM slopes were 1.12, 1.80, 1.65, and 1.27, indicating better agreement with the EC data than the MODIS VPM slopes of 2.04, 2.51, 2.14, and 1.54, respectively. Moreover, HLS VPM suitably indicated GPP dynamics during all phenophases, especially during the autumn green-down period. As the first study that simulates GPP involving HLS VPM and compares satellite-based and EC flux observations of the GPP in Chinese savanna ecosystems, our study enables better exploration of the Chinese savanna ecosystem GPP during different phenophases and more effective savanna management and conservation worldwide. Full article

(This article belongs to the Special Issue Remote Sensing of Savannas and Woodlands II)

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