Forest expansion in abandoned agricultural lands has limited effect to offset carbon emissions from Central-North Spain

Producción CientíficaWe assessed the process of carbon (C) accumulation as a consequence of forest expansion in abandoned agricultural lands over the period 1977–2017 in a vast (9.4 million ha) area of Mediterranean continental environment in Central-North Spain. We achieved this objective, through obtaining AGC and BGC estimations based on direct field measurements taken in 30 plots (25 m × 25 m), and extrapolating to the landscape using Synthetic Aperture Radar (SAR) satellite data from 2018. Using aerial photographs and forest maps, we found that 145,193 ha of agricultural land in 1957 (1.54% of the study regions’ total area) has since then regenerated naturally to forests and woodlands. Although mean AGC and BGC densities were modest (i.e. 18.04 and 6.78 Mg C ha−1), they reached relatively large maximum values (i.e. 60 and 21 Mg C ha−1). The BGC stock was also very large, representing 37.3% of the total C stock (10 Tg) accumulated. However, we detected a mean annual C sink of 0.25 Tg C·year−1 which barely offset 1.22% of the total regional CO2 emissions. Our findings point to a smaller sequestration potential under Mediterranean continental than under temperate-cold conditions. Nonetheless, the area affected by this process could be larger than detected and many of the recovering lands might have not still reached their C uptake peak. If such lands are to be used to store C, we strongly advocate for the application of active forest management measures to increase their CO2 sequestration potential.European Union’s Horizon 2020 research and innovation program. grant agreement no. 799885.Publicación en abierto financiada por el Consorcio de Bibliotecas Universitarias de Castilla y León (BUCLE), con cargo al Programa Operativo 2014ES16RFOP009 FEDER 2014-2020 DE CASTILLA Y LEÓN, Actuación:20007-CL - Apoyo Consorcio BUCL

Missed carbon emissions from forests: comparing countries' estimates submitted to UNFCCC to biophysical estimates

Reducing forest loss has the potential to reduce global carbon emissions, but paying countries to do so will only work if activities are targeting areas with rapid deforestation or high threat. As of December 2017, 25 countries reported their benchmark greenhouse gas emissions from forests (‘reference levels’) under the United Nations Framework Convention on Climate Change, with the aim of receiving payments if they end up releasing less or removing more. There remains however a question as to whether the eventual emission trajectories compared to these reference levels represent real emission reductions, as the benchmarks rely on a variety of different methods and limited datasets. To examine whether the forest areas historically associated with significant emissions are targeted in the reference levels, we compared the forest area estimates submitted by seven countries in Asia and the Pacific (Cambodia, Indonesia, Malaysia, Nepal, Papua New Guinea, Sri Lanka, and Vietnam) with forest area estimates using the Global Forest Change v1.4 (GFC) dataset from 2000–2016, processed to closely match national forest definitions. GFC provides standardised tree cover change data based on biophysical characteristics using an extensive collection of satellite images. We found consistent differences, with most countries reporting considerably less forest loss than the GFC-based analysis. These differences are due to the countries’ selection of activities to report, as well as their choice of forest types and land use, defining the forest areas to be monitored. Our study highlights an urgent need to address the gap between the forests monitored by countries and those sources of emissions. The current approaches, even successfully implemented, may not lead to emission reductions, thereby challenging the effectiveness of carbon payments

Human Impacts Flatten Rainforest-Savanna Gradient and Reduce Adaptive Diversity in a Rainforest Bird

Ecological gradients have long been recognized as important regions for diversification and speciation. However, little attention has been paid to the evolutionary consequences or conservation implications of human activities that fundamentally change the environmental features of such gradients. Here we show that recent deforestation in West Africa has homogenized the rainforest-savanna gradient, causing a loss of adaptive phenotypic diversity in a common rainforest bird, the little greenbul (Andropadus virens). Previously, this species was shown to exhibit morphological and song divergence along this gradient in Central Africa. Using satellite-based estimates of forest cover, recent morphological data, and historical data from museum specimens collected prior to widespread deforestation, we show that the gradient has become shallower in West Africa and that A. virens populations there have lost morphological variation in traits important to fitness. In contrast, we find no loss of morphological variation in Central Africa where there has been less deforestation and gradients have remained more intact. While rainforest deforestation is a leading cause of species extinction, the potential of deforestation to flatten gradients and inhibit rainforest diversification has not been previously recognized. More deforestation will likely lead to further flattening of the gradient and loss of diversity, and may limit the ability of species to persist under future environmental conditions

Multimodal deep learning for mapping forest dominant height by fusing GEDI with earth observation data

The integration of multisource remote sensing data and deep learning models offers new possibilities for accurately mapping high spatial resolution forest height. We found that GEDI relative heights (RH) metrics exhibited strong correlation with the mean of the top 10 highest trees (dominant height) measured in situ at the corresponding footprint locations. Consequently, we proposed a novel deep learning framework termed the multi-modal attention remote sensing network (MARSNet) to estimate forest dominant height by extrapolating dominant height derived from GEDI, using Setinel-1 data, ALOS-2 PALSAR-2 data, Sentinel-2 optical data and ancillary data. MARSNet comprises separate encoders for each remote sensing data modality to extract multi-scale features, and a shared decoder to fuse the features and estimate height. Using individual encoders for each remote sensing imagery avoids interference across modalities and extracts distinct representations. To focus on the efficacious information from each dataset, we reduced the prevalent spatial and band redundancies in each remote sensing data by incorporating the extended spatial and band reconstruction convolution modules in the encoders. MARSNet achieved commendable performance in estimating dominant height, with an R2 of 0.62 and RMSE of 2.82 m, outperforming the widely used random forest approach which attained an R2 of 0.55 and RMSE of 3.05 m. Finally, we applied the trained MARSNet model to generate wall-to-wall maps at 10 m resolution for Jilin, China. Through independent validation using field measurements, MARSNet demonstrated an R2 of 0.58 and RMSE of 3.76 m, compared to 0.41 and 4.37 m for the random forest baseline. Our research demonstrates the effectiveness of a multimodal deep learning approach fusing GEDI with SAR and passive optical imagery for enhancing the accuracy of high resolution dominant height estimation

Sentinel-1 Shadows Used to Quantify Canopy Loss from Selective Logging in Gabon

Selective logging is a major cause of forest degradation in the tropics, but its precise scale, location and timing are not known as wide-area, automated remote sensing methods are not yet available at this scale. This limits the abilities of governments to police illegal logging, or monitor (and thus receive payments for) reductions in degradation. Sentinel-1, a C-band Synthetic Aperture Radar satellite mission with a 12-day repeat time across the tropics, is a promising tool for this due to the known appearance of shadows in images where canopy trees are removed. However, previous work has relied on optical satellite data for calibration and validation, which has inherent uncertainties, leaving unanswered questions about the minimum magnitude and area of canopy loss this method can detect. Here, we use a novel bi-temporal LiDAR dataset in a forest degradation experiment in Gabon to show that canopy gaps as small as 0.02 ha (two 10 m × 10 m pixels) can be detected by Sentinel-1. The accuracy of our algorithm was highest when using a timeseries of 50 images over 20 months and no multilooking. With these parameters, canopy gaps in our study site were detected with a false alarm rate of 6.2%, a missed detection rate of 12.2%, and were assigned disturbance dates that were a good qualitative match to logging records. The presence of geolocation errors and false alarms makes this method unsuitable for confirming individual disturbances. However, we found a linear relationship (r2=0.74) between the area of detected Sentinel-1 shadow and LiDAR-based canopy loss at a scale of 1 hectare. By applying our method to three years’ worth of imagery over Gabon, we produce the first national scale map of small-magnitude canopy cover loss. We estimate a total gross canopy cover loss of 0.31 Mha, or 1.3% of Gabon’s forested area, which is a far larger area of change than shown in currently available forest loss alert systems using Landsat (0.022 Mha) and Sentinel-1 (0.019 Mha). Our results, which are made accessible through Google Earth Engine, suggest that this approach could be used to quantify the magnitude and timing of degradation more widely across tropical forests

Woody encroachment and forest degradation in sub-Saharan Africa's woodlands and savannas 1982-2006

We review the literature and find 16 studies from across Africa's savannas and woodlands where woody encroachment dominates. These small-scale studies are supplemented by an analysis of long-term continent-wide satellite data, specifically the Normalized Difference Vegetation Index (NDVI) time series from the Global Inventory Modeling and Mapping Studies (GIMMS) dataset. Using dry-season data to separate the tree and grass signals, we find 4.0% of non-rainforest woody vegetation in sub-Saharan Africa (excluding West Africa) significantly increased in NDVI from 1982 to 2006, whereas 3.52% decreased. The increases in NDVI were found predominantly to the north of the Congo Basin, with decreases concentrated in the Miombo woodland belt. We hypothesize that areas of increasing dry-season NDVI are undergoing woody encroachment, but the coarse resolution of the study and uncertain relationship between NDVI and woody cover mean that the results should be interpreted with caution; certainly, these results do not contradict studies finding widespread deforestation throughout the continent. However, woody encroachment could be widespread, and warrants further investigation as it has important consequences for the global carbon cycle and land–climate interactions

A New Field Protocol for Monitoring Forest Degradation

Forest degradation leads to the gradual reduction of forest carbon stocks, function, and biodiversity following anthropogenic disturbance. Whilst tropical degradation is a widespread problem, it is currently very under-studied and its magnitude and extent are largely unknown. This is due, at least in part, to the lack of developed and tested methods for monitoring degradation. Due to the relatively subtle and ongoing changes associated with degradation, which can include the removal of small trees for fuelwood or understory clearance for agricultural production, it is very hard to detect using Earth Observation. Furthermore, degrading activities are normally spatially heterogeneous and stochastic, and therefore conventional forest inventory plots distributed across a landscape do not act as suitable indicators: at best only a small proportion of plots (often zero) will actually be degraded in a landscape undergoing active degradation. This problem is compounded because the metal tree tags used in permanent forest inventory plots likely deter tree clearance, biasing inventories toward under-reporting change. We have therefore developed a new forest plot protocol designed to monitor forest degradation. This involves a plot that can be set up quickly, so a large number can be established across a landscape, and easily remeasured, even though it does not use tree tags or other obvious markers. We present data from a demonstration plot network set up in Jalisco, Mexico, which were measured twice between 2017 and 2018. The protocol was successful, with one plot detecting degradation under our definition (losing greater than 10% AGB but remaining forest), and a further plot being deforested for Avocado (Persea americana) production. Live AGB ranged from 8.4 Mg ha–1 to 140.8 Mg ha–1 in Census 1, and from 0 Mg ha–1 to 144.2 Mg ha–1 Census 2, with four of ten plots losing AGB, and the remainder staying stable or showing slight increases. We suggest this protocol has great potential for underpinning appropriate forest plot networks for degradation monitoring, potentially in combination with Earth Observation analysis, but also in isolation

An Effective Method for InSAR Mapping of Tropical Forest Degradation in Hilly Areas

Current satellite remote sensing methods struggle to detect and map forest degradation, which is a critical issue as it is likely a major and growing source of carbon emissions and biodiveristy loss. TanDEM-X InSAR phase height is a promising variable for measuring forest disturbances, as it is closely related to the mean canopy height, and thus should decrease if canopy trees are removed. However, previous research has focused on relatively flat terrains, despite the fact that much of the world's remaining tropical forests are found in hilly areas, and this inevitably introduces artifacts in sideways imaging systems. In this paper, we find a relationship between InSAR phase height and aboveground biomass change in four selectively logged plots in a hilly region of central Gabon. We show that minimising multilooking prior to the calculation of InSAR phase height on a pixel-by-pixel basis. This shows that TanDEM-X InSAR can measure the magnitude of degradation, and that topographic effects can be mitigated if data from multiple SAR viewing geometries are available