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        <title>Frontiers in Remote Sensing | Forest and Ecosystem Remote Sensing section | New and Recent Articles</title>
        <link>https://www.frontiersin.org/journals/remote-sensing/sections/forest-and-ecosystem-remote-sensing</link>
        <description>RSS Feed for Forest and Ecosystem Remote Sensing section in the Frontiers in Remote Sensing journal | New and Recent Articles</description>
        <language>en-us</language>
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        <pubDate>2026-05-14T21:22:34.262+00:00</pubDate>
        <ttl>60</ttl>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2026.1815646</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2026.1815646</link>
        <title><![CDATA[The impacts of climate variability and agricultural expansion on ecosystem functions in Xinjiang drylands]]></title>
        <pubdate>2026-04-29T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Dechao Zhai</author><author>Yuhao Jiang</author><author>Zhifang Wang</author><author>Zhicheng Huang</author><author>Naijie Peng</author><author>Qunchao He</author><author>Wenjie Fan</author>
        <description><![CDATA[Climate variability and agricultural expansion are fundamentally reshaping ecosystem functions (EFs) in drylands. Xinjiang Region, a vast and typical dryland area in China, also faces dual pressures of natural and anthropogenic disturbances. Numerous local studies in Xinjiang have quantified individual EFs using biophysical models. However, research remains limited on the region-wide spatial distribution of long-term EF transitions, and on how nature-human interactions shape EF transitions across all of Xinjiang. In this paper, these gaps are addressed by using an EF classification framework to assess dominant EF patterns and reveal the ecosystem responses across Xinjiang, China from 2000 to 2024. A suite of multi-year EF maps with high accuracies (0.864–0.882) were produced using a two-level classification scheme with a random forest model, and Landsat imagery. EF maps showed that the water retention (WR) regions are concentrated in mountainous areas, the sandstorm prevention (SP) regions primarily occur in oasis–desert transition zones, and the agricultural production (AP) regions are dispersed in the desert (DS) periphery. Three major transition trajectories were identified: two climate-driven pathways, namely, greening (“DS–SP–WR”) and browning (“WR–SP–DS”) induced mainly by the water changes (R = 0.594 and −0.553 with annual precipitation, respectively), and a policy-driven cropland expansion pathway (“DS–AP”) resulting from aggressive agricultural expansion, particularly cotton cultivation (R = 0.831). This “DS–AP” functional transition in Xinjiang may potentially introduce multiple ecological challenges, including biodiversity loss and increased water demand. These findings reveal how climate variability and agricultural intensification reshape dryland landscapes, with implications for sustainability across water-limited ecosystems globally. Our results underscore the urgent need for adaptive management strategies that balance agricultural development with ecosystem resilience in Earth’s expanding drylands.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2026.1812294</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2026.1812294</link>
        <title><![CDATA[Assessment of seasonal variations in teal carbon of the palustrine wetland in the Grassland Biome of South Africa using remote sensing]]></title>
        <pubdate>2026-04-14T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Sisipho Ngebe</author><author>Laven Naidoo</author><author>Heidi van Deventer</author><author>Philemon Tsele</author><author>Mcebisi Qabaqaba</author>
        <description><![CDATA[Quantifying carbon stocks from the above-ground biomass (AGB) of wetland vegetation across seasons is crucial for assessing ecosystem resilience to anthropogenic and climate pressures. This study aimed to assess differences between summer and winter in aboveground carbon (AGC) of palustrine wetland vegetation using Sentinel-1 and Sentinel-2 data. The Random Forest (RF) and Support Vector Regression (SVR) were implemented with variable importance selection to develop an optimal model from remote sensing derived modelling scenario combinations. Modelling scenarios included field measured Leaf Area index and different combinations of (i) Sentinel-2 derived variables namely vegetation indices (VIs) and reflectance bands, and (ii) Sentinel-1 grey-level co-occurrence matrices, backscatter band ratios, and backscatter channels. Results indicated significant seasonal variation (p < 0.05) with higher total teal carbon in summer (155.1 g C/m2) than winter (115.8 g C/m2). Large macrophytes particularly Phragmites australis stored the highest carbon (93.04 g C/m2 in summer; 78.37 g C/m2 in winter). Sentinel-1-derived models outperformed Sentinel-2-based models for both seasons, achieving R2 of 0.7–0.8, RMSE of 39.9–69.6 g·m-2, and relative RMSE of 17.3%–21.3%. RF consistently performed better than SVR. Thus, seasonal monitoring of teal carbon provide valuable insights of wetlands vegetation contribution in carbon accounting and sequestration.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2025.1724950</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2025.1724950</link>
        <title><![CDATA[Satellite-based mapping of annual canopy height and aboveground biomass in African dense forests]]></title>
        <pubdate>2025-12-19T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Liang Wan</author><author>Philippe Ciais</author><author>Aurélien de Truchis</author><author>Ewan Sean</author><author>Fabian Jörg Fischer</author><author>David Purnell</author><author>Gabriel Belouze</author><author>Ibrahim Fayad</author><author>Martin Schwartz</author><author>Yidi Xu</author><author>Yang Su</author><author>Maxime Réjou-Méchain</author><author>Nicolas Barbier</author><author>Paul Tresson</author><author>Jean-François Bastin</author><author>Jan Bogaert</author><author>Arthur Vander Linden</author><author>Antoine Plumacker</author><author>Bhely Angoboy Ilondea</author><author>Dieu-Merci Assumani</author><author>Thales de Haulleville</author><author>Le Bienfaiteur Sagang</author><author>Laurent Durieux</author><author>Youngryel Ryu</author><author>Tackang Yang</author><author>Conan Vassily Obame</author><author>Thomas Bossy</author><author>Frédéric Frappart</author><author>Marc Peaucelle</author><author>Jean-Pierre Wigneron</author><author>Jerome Chave</author><author>Aida Cuni-Sanchez</author><author>Wannes Hubau</author><author>Hans Verbeeck</author><author>Pascal Boeckx</author><author>Jean-Remy Makana</author><author>Corneille Ewango</author><author>Elizabeth Kearsley</author><author>Bonaventure Sonké</author><author>Moses Libalah</author><author>Pierre Ploton</author>
        <description><![CDATA[Accurate maps of canopy height (CH) and aboveground biomass (AGB) are needed for monitoring forests over large regions. Producing such data is particularly challenging over the complex, diverse and dense humid tropical forests of Africa where signal saturation observed from optical and radar satellites and complex responses in LiDAR data require advanced mapping techniques to capture high biomass and tall height values. Here, we trained a deep learning (U-Net) model to generate the first annual maps (2019–2022) of top CH at 10 m resolution over the African dense forest region, using Sentinel-1/-2 images trained on LiDAR-derived height data from the Global Ecosystem Dynamics Investigation mission (GEDI). To predict AGB from CH on a 30-m grid, we calibrated allometric models combining AGB data from field inventories, CH from our map, and wood density from a new high-resolution (1 km) map. The CH map has a mean absolute error (MAE) of 4.54 m and an underestimation bias of 1.54 m compared to independent airborne LiDAR data (5.93 m and 1.40 m compared to independent GEDI data). Evaluation of the AGB map against independent measurements from field sites suggests an improved accuracy (MAE = 79.65 Mg/ha, bias = 6.47 Mg/ha) compared to recent datasets such as ESA-CCI, NCEO, and GEDI L4B. Our map also captures the large-scale spatial gradients of AGB across African dense forests, as observed in a comprehensive dataset of forest concession measurements aggregated at a 1-km scale. Interpretable machine learning was used to assess the contribution of ancillary variables (e.g., climate, soil, forest type) to biomass prediction. While some variables were relevant, their inclusion failed to improve AGB estimates in high and low biomass extremes and introduced spatial artifacts, limiting their utility for consistent annual mapping. Together, our annual CH and AGB maps offer an open, scalable tool for monitoring forest disturbances and interannual biomass dynamics. Future work will focus on refining biomass–height relationships to further improve AGB estimation.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2025.1532280</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2025.1532280</link>
        <title><![CDATA[State of the art in remote sensing monitoring of carbon dynamics in African tropical forests]]></title>
        <pubdate>2025-03-17T00:00:00Z</pubdate>
        <category>Review</category>
        <author>Thomas Bossy</author><author>Philippe Ciais</author><author>Solène Renaudineau</author><author>Liang Wan</author><author>Bertrand Ygorra</author><author>Elhadi Adam</author><author>Nicolas Barbier</author><author>Marijn Bauters</author><author>Nicolas Delbart</author><author>Frédéric Frappart</author><author>Tawanda Winmore Gara</author><author>Eliakim Hamunyela</author><author>Suspense Averti Ifo</author><author>Gabriel Jaffrain</author><author>Philippe Maisongrande</author><author>Maurice Mugabowindekwe</author><author>Theodomir Mugiraneza</author><author>Cassandra Normandin</author><author>Conan Vassily Obame</author><author>Marc Peaucelle</author><author>Camille Pinet</author><author>Pierre Ploton</author><author>Le Bienfaiteur Sagang</author><author>Martin Schwartz</author><author>Valentine Sollier</author><author>Bonaventure Sonké</author><author>Paul Tresson</author><author>Aurélien De Truchis</author><author>An Vo Quang</author><author>Jean-Pierre Wigneron</author>
        <description><![CDATA[African tropical forests play a crucial role in global carbon dynamics, biodiversity conservation, and climate regulation, yet monitoring their structure, diversity, carbon stocks and changes remains challenging. Remote sensing techniques, including multi-spectral data, lidar-based canopy height and vertical structure detection, and radar interferometry, have significantly improved our ability to map forest composition, estimate height and biomass, and detect degradation and deforestation features at a finer scale. Machine learning approaches further enhance these capabilities by integrating multiple data sources to produce improved maps of forest attributes and track changes over time. Despite these advancements, uncertainties remain due to limited ground-truth validation, and the structural complexity and large spatial heterogeneity of African forests. Future developments in remote sensing should examine how multi-sensor integration of high-resolution data from instruments such as Planet, Tandem-X, SPOT and improved AI methods can refine forest composition, carbon storage and function maps, enhance large-scale monitoring of tree height and biomass dynamics, and improve forest degradation and deforestation detection down to tree level. These advancements will be essential for supporting science-based decision-making in forest conservation and climate mitigation.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2024.1432577</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2024.1432577</link>
        <title><![CDATA[Combining satellite images with national forest inventory measurements for monitoring post-disturbance forest height growth]]></title>
        <pubdate>2024-08-22T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Agnès Pellissier-Tanon</author><author>Philippe Ciais</author><author>Martin Schwartz</author><author>Ibrahim Fayad</author><author>Yidi Xu</author><author>François Ritter</author><author>Aurélien de Truchis</author><author>Jean-Michel Leban</author>
        <description><![CDATA[Introduction: The knowledge about forest growth, influenced by factors such as tree species, tree age, and environmental conditions, is a key for future forest preservation. Height and age data can be combined to describe forest growth and used to infer known environmental effects.Methods: In this study, we built 14 height growth curves for stands composed of monospecific or mixed species using ground measurements and satellite data. We built a random forest height model from tree species, age, area of disturbance, and 125 environmental parameters (climate, altitude, soil composition, geology, stand ownership, and proximity to road and urban areas). Using feature elimination and SHapley Additive exPlanations (SHAP) analysis, we identified six key features explaining the forest growth and investigated how they affect the height.Results: The agreement between satellite and ground data justifies their simultaneous exploitation. Age and tree species are the main predictors of tree height (49% and 10%, respectively). The disturbed patch area, revealing the regeneration method, impacts post-disturbance growth at 19%. The soil pH, altitude, and climatic water budget in summer impact tree height differently depending on the age and tree species.Discussion: Methods integrating satellite and field data show promise for analyzing future forest evolution.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2024.1416550</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2024.1416550</link>
        <title><![CDATA[A near-real-time tropical deforestation monitoring algorithm based on the CuSum change detection method]]></title>
        <pubdate>2024-07-26T00:00:00Z</pubdate>
        <category>Methods</category>
        <author>Bertrand Ygorra</author><author>Frédéric Frappart</author><author>Jean-Pierre Wigneron</author><author>Thibault Catry</author><author>Benjamin Pillot</author><author>Antoine Pfefer</author><author>Jonas Courtalon</author><author>Serge Riazanoff</author>
        <description><![CDATA[Tropical forests are currently under pressure from increasing threats. These threats are mostly related to human activities. Earth observations (EO) are increasingly used for monitoring forest cover, especially synthetic aperture radar (SAR), that is less affected than optical sensors by atmospheric conditions. Since the launch of the Sentinel-1 satellites, numerous methods for forest disturbance monitoring have been developed, including near real-time (NRT) operational algorithms as systems providing early warnings on deforestation. These systems include Radar for Detecting Deforestation (RADD), Global Land Analysis and Discovery (GLAD), Real Time Deforestation Detection System (DETER), and Jica-Jaxa Forest Early Warning System (JJ-FAST). These algorithms provide online disturbance maps and are applied at continental/global scales with a Minimum Mapping Unit (MMU) ranging from 0.1 ha to 6.25 ha. For local operators, these algorithms are hard to customize to meet users’ specific needs. Recently, the Cumulative sum change detection (CuSum) method has been developed for the monitoring of forest disturbances from long time series of Sentinel-1 images. Here, we present the development of a NRT version of CuSum with a MMU of 0.03 ha. The values of the different parameters of this NRT CuSum algorithm were determined to optimize the detection of changes using the F1-score. In the best configuration, 68% precision, 72% recall, 93% accuracy and 0.71 F1-score were obtained.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2024.1338618</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2024.1338618</link>
        <title><![CDATA[Global carbon balance of the forest: satellite-based L-VOD results over the last decade]]></title>
        <pubdate>2024-05-10T00:00:00Z</pubdate>
        <category>Review</category>
        <author>Jean-Pierre Wigneron</author><author>Philippe Ciais</author><author>Xiaojun Li</author><author>Martin Brandt</author><author>Josep G. Canadell</author><author>Feng Tian</author><author>Huan Wang</author><author>Ana Bastos</author><author>Lei Fan</author><author>Gabriel Gatica</author><author>Rahul Kashyap</author><author>Xiangzhuo Liu</author><author>Stephen Sitch</author><author>Shengli Tao</author><author>Xiangming Xiao</author><author>Hui Yang</author><author>Jhan Carlo Espinoza Villar</author><author>Frederic Frappart</author><author>Wei Li</author><author>Yuanwei Qin</author><author>Aurélien De Truchis</author><author>Rasmus Fensholt</author>
        <description><![CDATA[Monitoring forest carbon (C) stocks is essential to better assess their role in the global carbon balance, and to better model and predict long-term trends and inter-annual variability in atmospheric CO2 concentrations. On a national scale, national forest inventories (NFIs) can provide estimates of forest carbon stocks, but these estimates are only available in certain countries, are limited by time lags due to periodic revisits, and cannot provide spatially continuous mapping of forests. In this context, remote sensing offers many advantages for monitoring above-ground biomass (AGB) on a global scale with good spatial (50–100 m) and temporal (annual) resolutions. Remote sensing has been used for several decades to monitor vegetation. However, traditional methods of monitoring AGB using optical or microwave sensors are affected by saturation effects for moderately or densely vegetated canopies, limiting their performance. Low-frequency passive microwave remote sensing is less affected by these saturation effects: saturation only occurs at AGB levels of around 400 t/ha at L-band (frequency of around 1.4 GHz). Despite its coarse spatial resolution of the order of 25 km × 25 km, this method based on the L-VOD (vegetation optical depth at L-band) index has recently established itself as an essential approach for monitoring annual variations in forest AGB on a continental scale. Thus, L-VOD has been applied to forest monitoring in many continents and biomes: in the tropics (especially in the Amazon and Congo basins), in boreal regions (Siberia, Canada), in Europe, China, Australia, etc. However, no reference study has yet been published to analyze L-VOD in detail in terms of capabilities, validation and results. This paper fills this gap by presenting the physical principles of L-VOD calculation, analyzing the performance of L-VOD for monitoring AGB and reviewing the main applications of L-VOD for tracking the carbon balance of global vegetation over the last decade (2010–2019).]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2024.1332728</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2024.1332728</link>
        <title><![CDATA[Recent advances and challenges in monitoring and modeling of disturbances in tropical moist forests]]></title>
        <pubdate>2024-03-14T00:00:00Z</pubdate>
        <category>Mini Review</category>
        <author>Jiaying He</author><author>Wei Li</author><author>Zhe Zhao</author><author>Lei Zhu</author><author>Xiaomeng Du</author><author>Yidi Xu</author><author>Minxuan Sun</author><author>Jiaxin Zhou</author><author>Philippe Ciais</author><author>Jean-Pierre Wigneron</author><author>Ronggao Liu</author><author>Guanghui Lin</author><author>Lei Fan</author>
        <description><![CDATA[Tropical moist forests have been severely affected by natural and anthropogenic disturbances, leading to substantial changes in global carbon cycle and climate. These effects have received great attention in scientific research and debates. Here we review recent progress on drivers and ecological impacts of tropical moist forest disturbances, and their monitoring and modeling methods. Disturbances in tropical moist forests are primarily driven by clearcutting, selective logging, fire, extreme drought, and edge effects. Compound disturbances such as fire and edge effects aggravate degradation in the edge forests. Drought can result in terrestrial carbon loss via physiological impacts. These disturbances lead to direct carbon loss, biophysical warming and microclimate change. Remote sensing observations are promising for monitoring forest disturbances and revealing mechanisms, which will be useful for implementing disturbance processes in dynamic vegetation models. Yet, constrained spatiotemporal coverages and resolutions limit the application of these data in process-based models. It is also challenging to represent physical processes derived from fine-resolution remote sensing data in coarse-resolution models. We highlight the need to continuously integrate new datasets and physical processes in forest disturbance modeling to advance understanding of disturbance patterns and impacts. Interactions and impacts of climate change and anthropogenic activities should also be considered for modeling and assessing feedbacks of tropical moist forest disturbances.]]></description>
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