Aerodynamic resistance and Bowen ratio explain the biophysical effects of forest cover on understory air and soil temperatures at the global scale

<p>Although interest in peatland environments, especially in terms of their carbon storage, has gained momentum in response to a heightened awareness of the climate emergency; significant gaps remain in the geographical coverage of our knowledge of mires, including some major wetland systems. This paucity has implications, not only for our understanding of their development and functioning, but also for adequately predicting future changes and thus providing effective mire environmental management. Our INTERACT-supported study provides radiometrically dated, well-characterised millennial scale peat records from two contrasting undisturbed and impacted (ditched) ombrotrophic sites in the Great Vasyugan Mire (GVM) near Tomsk, Siberia and two additional mesotrophic sites to the east of the Ob river. In addition, the geochemical record was complemented by multiproxy palaeoecological characterisation (pollen, charcoal, stable isotopes, testate amoeba). We identified both natural (lithogenic) and anthropogenic geochemical signals recording human impacts with site specific variations. Elevated trace element concentrations in the peat profiles align with the region&#8217;s wider agricultural and economic development following the colonisation of Siberia by Russia (from ca. 1600 AD) when pollen assemblages indicate the decline of forest cover and an increase in human disturbance, including the use for fire. Trace element concentrations peak with the subsequent, post WWII industrialisation of regional centres in southern Siberia (after 1950 AD). On a global scale, our sites, together with evidence from the few other comparable studies in the region, suggest that the region&#8217;s peatlands are relatively uncontaminated by human activities with a mean lead (Pb) level of < 5 mg/kg. However, via lithogenic elements including Rb, Ti and Zr, we detected both a geochemical signal as a result of historical land cover changes enhancing mineral dust deposition following disturbance, as well as fossil fuel derived pollutants as relatively elevated, subsurface As and Pb concentrations of ca. 10 and 25 mg/kg respectively with the development of industry in the region. Nevertheless, the potential significance of local factors on the sites&#8217; geochemical profile is also highlighted. For example, we identify the effects of past peat drainage for afforestation (ca. 1960s) and the scheme&#8217;s subsequent abandonment. Although the region&#8217;s mire systems are remote and vast, they appear to hold a legacy of human activity that can be detected as a geochemical signal supporting the inferences of other palaeoenvironmental proxies. Such geochemical peat core records, from Eurasia in particular, remain relatively scarce in the international scientific literature and therefore, as yet, inadequately characterised and quantified compared to other regions.</p>

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Tree Cover Estimation in Global Drylands from Space Using Deep Learning

Remote Sensing ◽

10.3390/rs12030343 ◽

2020 ◽

Vol 12 (3) ◽

pp. 343 ◽

Cited By ~ 3

Author(s):

Emilio Guirado ◽

Domingo Alcaraz-Segura ◽

Javier Cabello ◽

Sergio Puertas-Ruíz ◽

Francisco Herrera ◽

...

Keyword(s):

Forest Cover ◽

Global Scale ◽

Classification Model ◽

Tree Cover ◽

System Modelling ◽

Earth System ◽

Visual Interpretation ◽

Food And Agriculture ◽

Food And Agriculture Organization ◽

The Cost

Accurate tree cover mapping is of paramount importance in many fields, from biodiversity conservation to carbon stock estimation, ecohydrology, erosion control, or Earth system modelling. Despite this importance, there is still uncertainty about global forest cover, particularly in drylands. Recently, the Food and Agriculture Organization of the United Nations (FAO) conducted a costly global assessment of dryland forest cover through the visual interpretation of orthoimages using the Collect Earth software, involving hundreds of operators from around the world. Our study proposes a new automatic method for estimating tree cover using artificial intelligence and free orthoimages. Our results show that our tree cover classification model, based on convolutional neural networks (CNN), is 23% more accurate than the manual visual interpretation used by FAO, reaching up to 79% overall accuracy. The smallest differences between the two methods occurred in the driest regions, but disagreement increased with the percentage of tree cover. The application of CNNs could be used to improve and reduce the cost of tree cover maps from the local to the global scale, with broad implications for research and management.

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Remote Sensing

Environmental Science ◽

10.1093/obo/9780199363445-0018 ◽

2015 ◽

Author(s):

Frank D. Eckardt

Keyword(s):

Remote Sensing ◽

Real Time ◽

Vegetation Index ◽

Forest Cover ◽

Normalized Difference Vegetation Index ◽

Laser Pulses ◽

Global Scale ◽

Dynamic Topography ◽

Forest Cover Change ◽

Atmospheric Remote Sensing

This article on remote sensing or earth observation focuses on mapping and monitoring systems that produce global-scale data sets which are easily accessible to the wider public. It makes particular reference to low-earth-orbiting remote sensing platforms and sensors and associated image archives such as provided by the Landsat and Moderate-Resolution Imaging Spectroradiometer (MODIS) programs. It also draws attention to handheld space photography, synthetic aperture radar (SAR), and the high-spatial-resolution capability obtained from the commercial remote sensing sector. This entry examines applications that are of global interest and are facilitated through image and data portals. Particular emphasis is placed on products such as the normalized difference vegetation index, real-time fire mapping, forest cover change, geomorphology, and global elevation data as well as actual true- and false-color imagery. All of these can be readily imported as shape or raster files into a Geographic Information System (GIS). Key papers dealing with the global monitoring of the biosphere, dynamic topography, and gravity are being cited. Special emphasis is placed on current capabilities in monitoring recent and ongoing changes in the tropics as well as Arctic and Antarctic environment. Numerous remote sensing systems capture the state and dynamics of rainforests, ice caps, glaciers, and shelf and sea ice, some of which are available in near-real-time trend analysis. Not all sensors produce images; some measure passive microwaves, send laser pulses, or detect small fluctuations in gravitational attraction. Nevertheless, all instruments measure changes in earth’s surface state, indicative of seasonal cycles and long-term trends as well as human impact. This article also makes reference to historic developments, social benefits, and ethical considerations in remote sensing as well as the modern role of aerial photography and airborne platforms. Most people will never get to see a satellite or its instruments, they might not even get to see the available data or imagery, but these systems are directly informing the masses or indirectly shaping the perception of a changing and dynamic world. Future revisions to this article will consider oceanographic and atmospheric remote sensing capabilities.

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A simple topography-driven and calibration-free runoff generation module

Hydrology and Earth System Sciences ◽

10.5194/hess-23-787-2019 ◽

2019 ◽

Vol 23 (2) ◽

pp. 787-809 ◽

Cited By ~ 10

Author(s):

Hongkai Gao ◽

Christian Birkel ◽

Markus Hrachowitz ◽

Doerthe Tetzlaff ◽

Chris Soulsby ◽

...

Keyword(s):

Land Surface ◽

Forest Cover ◽

Storage Capacity ◽

Root Zone ◽

Global Scale ◽

Runoff Generation ◽

The Us ◽

Long Time ◽

Calibration Free ◽

Rainfall Runoff Model

Abstract. Reading landscapes and developing calibration-free runoff generation models that adequately reflect land surface heterogeneities remains the focus of much hydrological research. In this study, we report a novel and simple topography-driven runoff generation parameterization – the HAND-based Storage Capacity curve (HSC), which uses a topographic index (HAND, Height Above the Nearest Drainage) to identify hydrological similarity and the extent of saturated areas in catchments. The HSC can be used as a module in any conceptual rainfall–runoff model. Further, coupling the HSC parameterization with the mass curve technique (MCT) to estimate root zone storage capacity (SuMax), we developed a calibration-free runoff generation module, HSC-MCT. The runoff generation modules of HBV and TOPMODEL were used for comparison purposes. The performance of these two modules (HSC and HSC-MCT) was first checked against the data-rich Bruntland Burn (BB) catchment in Scotland, which has a long time series of field-mapped saturation area extent. We found that HSC, HBV and TOPMODEL all perform well to reproduce the hydrograph, but the HSC module performs better in reproducing saturated area variation, in terms of correlation coefficient and spatial pattern. The HSC and HSC-MCT modules were subsequently tested for 323 MOPEX catchments in the US, with diverse climate, soil, vegetation and geological characteristics. In comparison with HBV and TOPMODEL, the HSC performs better in both calibration and validation, particularly in the catchments with gentle topography, less forest cover, and arid climate. Despite having no calibrated parameters, the HSC-MCT module performed comparably well with calibrated modules, highlighting the robustness of the HSC parameterization to describe the spatial distribution of the root zone storage capacity and the efficiency of the MCT method to estimate SuMax. This novel and calibration-free runoff generation module helps to improve the prediction in ungauged basins and has great potential to be generalized at the global scale.

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Rainfall anomalies are a significant driver of cropland expansion

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1910719117 ◽

2020 ◽

Vol 117 (19) ◽

pp. 10225-10233 ◽

Cited By ~ 3

Author(s):

Esha Zaveri ◽

Jason Russ ◽

Richard Damania

Keyword(s):

Developing Countries ◽

Forest Cover ◽

Global Scale ◽

Alternative Measure ◽

Productivity Shocks ◽

Small Holder Farmers ◽

Rainfall Anomalies ◽

The Impact ◽

The Relationship ◽

Grid Level

Rainfall anomalies have long occupied center stage in policy discussions, and understanding their impacts on agricultural production has become more important as climate change intensifies. However, the global scale of rainfall-induced productivity shocks on changes in cropland is yet to be quantified. Here we identify how rainfall anomalies impact observed patterns of cropped areas at a global scale by leveraging locally determined unexpected variations in rainfall. Employing disaggregated panel data at the grid level, we find that repeated dry anomalies lead to an increase in cropland expansion in developing countries. No discernible effects are detected from repeated wet events. That these effects are confined to developing countries, which are often dominated by small-holder farmers, implies that they may be in response to reduced yields. The estimates suggest that overall, in developing countries, dry anomalies account for ∼9% of the rate of cropland expansion over the past two decades. We perform several tests to check for consistency and robustness of this relationship. First, using forest cover as an alternative measure, we find comparable reductions in forest cover in the same regions where cropland expands due to repeated dry anomalies. Second, we test the relationship in regions where yields are buffered from rainfall anomalies by irrigation infrastructure and find that the impact on cropland expansion is mitigated, providing further support for our results. Since cropland expansion is a significant driver of deforestation, these results have important implications for forest loss and environmental services.

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A geochemical peat record from the Great Vasyugan Mire, Tomsk, Siberia evidencing a regionally coherent pattern of human impact over the last five centuries.

10.5194/egusphere-egu2020-11170 ◽

2020 ◽

Author(s):

Simon Hutchinson ◽

Andrei Diaconu ◽

Sergey Kirpotin ◽

Angelica Feurdean

Keyword(s):

Trace Element ◽

Forest Cover ◽

Human Impacts ◽

Time Frame ◽

Global Scale ◽

Climatic Conditions ◽

Dust Deposition ◽

Peat Core ◽

Element Concentrations ◽

Spatial Coverage

<p>Interest in peatland environments, especially in terms of their carbon storage, has increased markedly in response to the heightened awareness of future, global climatic conditions. However, significant gaps remain in the spatial coverage of our knowledge of mires; including some major wetland systems. This paucity has implications, not only for our understanding of their origins, development and functioning, but also for adequately predicting future changes and providing scientifically based recommendations for mire environmental management. Our INTERACT-supported study provides a radiometrically dated, well-characterised millennial-scale peat record from two contrasting undisturbed and impacted (ditched) sites, respectively in the Great Vasyugan Mire (GVM) near Tomsk, Siberia, which is reputedly the largest peat system in the world. In addition to their palaeoecological characterisation, we identified both natural (lithogenic) and anthropogenic geochemical signals recording human impacts with site-specific variations. Elevated trace element concentrations in both peat profiles align with the time frame of the region&#8217;s wider agricultural and economic development with the annexation of Siberia by Russia (from ca. 1600 AD) when pollen assemblage characteristics suggest a decline in forest cover and an increase in herbaceous plants associated with human disturbance. Trace element concentrations peak with the subsequent industrialisation of centres around the Ob river (after ca. 1950 AD). On a global scale, our sites, together with evidence from the few other comparable studies in the region, suggest that the GVM is relatively uncontaminated by human activities with a mean lead (Pb) level of < 4 mg/kg. However, via lithogenic elements including Rb, Ti and Zr we detected both a geochemical signal as a result of historical land cover changes, which enhanced mineral dust deposition following disturbance, as well as fossil fuel derived pollutants, as relatively elevated, subsurface As and Pb concentrations of ca. 10 and 25 mg/kg respectively, with the development of industry in the region. Moreover, we identify the local effects of drainage for afforestation (ca. 1960s) on the peat profile. At the impacted site, which was ditched, but subsequently abandoned, the influence of arrested peat growth on the site&#8217;s geochemical depth profile highlights the potential significance of local factors. Although relatively remote and vast, the GVM appears to hold a legacy of human activity that can be detected as a geochemical signal supporting the inferences of other palaeoenvironmental proxies. Such geochemical peat core records, from Eurasia in particular, remain relatively scarce in the international scientific literature. Therefore, our study contributes to an understanding of a less well known and, as yet, inadequately characterised and quantified region.&#160;</p>

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Object-Based Classification of Forest Disturbance Types in the Conterminous United States

Remote Sensing ◽

10.3390/rs11050477 ◽

2019 ◽

Vol 11 (5) ◽

pp. 477 ◽

Cited By ~ 12

Author(s):

Lian-Zhi Huo ◽

Luigi Boschetti ◽

Aaron Sparks

Keyword(s):

United States ◽

Forest Cover ◽

Forest Disturbance ◽

Global Scale ◽

Management Policy ◽

Forest Loss ◽

Forest Cover Change ◽

Forest Change ◽

Goods And Services ◽

Moderate Resolution Imaging Spectroradiometer

Forest ecosystems provide critical ecosystem goods and services, and any disturbance-induced changes can have cascading impacts on natural processes and human socioeconomic systems. Forest disturbance frequency, intensity, and spatial and temporal scale can be altered by changes in climate and human activity, but without baseline forest disturbance data, it is impossible to quantify the magnitude and extent of these changes. Methodologies for quantifying forest cover change have been developed at the regional-to-global scale via several approaches that utilize data from high (e.g., IKONOS, Quickbird), moderate (e.g., Landsat) and coarse (e.g., Moderate Resolution Imaging Spectroradiometer (MODIS)) spatial resolution satellite imagery. While detection and quantification of forest cover change is an important first step, attribution of disturbance type is critical missing information for establishing baseline data and effective land management policy. The objective here was to prototype and test a semi-automated methodology for characterizing high-magnitude (>50% forest cover loss) forest disturbance agents (stress, fire, stem removal) across the conterminous United States (CONUS) from 2003–2011 using the existing University of Maryland Landsat-based Global Forest Change Product and Web-Enabled Landsat Data (WELD). The Forest Cover Change maps were segmented into objects based on temporal and spatial adjacency, and object-level spectral metrics were calculated based on WELD reflectance time series. A training set of objects with known disturbance type was developed via high-resolution imagery and expert interpretation, ingested into a Random Forest classifier, which was then used to attribute disturbance type to all 15,179,430 forest loss objects across CONUS. Accuracy assessments of the resulting classification was conducted with an independent dataset consisting of 4156 forest loss objects. Overall accuracy was 88.1%, with the highest omission and commission errors observed for fire (32.8%) and stress (31.9%) disturbances, respectively. Of the total 172,686 km2 of forest loss, 83.75% was attributed to stem removal, 10.92% to fire and 5.33% to stress. The semi-automated approach described in this paper provides a promising framework for the systematic characterization and monitoring of forest disturbance regimes.

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Global cooling induced by biophysical effects of bioenergy crop cultivation

Nature Communications ◽

10.1038/s41467-021-27520-0 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Jingmeng Wang ◽

Wei Li ◽

Philippe Ciais ◽

Laurent Z. X. Li ◽

Jinfeng Chang ◽

...

Keyword(s):

Air Temperature ◽

Large Scale ◽

Aerodynamic Resistance ◽

Bioenergy Crops ◽

Climate Mitigation ◽

Bioenergy Crop ◽

Crop Cultivation ◽

Continental Scale ◽

Cooling Effects ◽

Biophysical Effects

AbstractBioenergy crop with carbon capture and storage (BECCS) is a key negative emission technology to meet carbon neutrality. However, the biophysical effects of widespread bioenergy crop cultivation on temperature remain unclear. Here, using a coupled atmosphere-land model with an explicit representation of lignocellulosic bioenergy crops, we find that after 50 years of large-scale bioenergy crop cultivation following plausible scenarios, global air temperature decreases by 0.03~0.08 °C, with strong regional contrasts and interannual variability. Over the cultivated regions, woody crops induce stronger cooling effects than herbaceous crops due to larger evapotranspiration rates and smaller aerodynamic resistance. At the continental scale, air temperature changes are not linearly proportional to the cultivation area. Sensitivity tests show that the temperature change is robust for eucalypt but more uncertain for switchgrass among different cultivation maps. Our study calls for new metrics to take the biophysical effects into account when assessing the climate mitigation capacity of BECCS.

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Parameter interactions and sensitivity analysis for modelling carbon heat and water fluxes in a natural peatland, using CoupModel v5

Geoscientific Model Development ◽

10.5194/gmd-9-4313-2016 ◽

2016 ◽

Vol 9 (12) ◽

pp. 4313-4338 ◽

Cited By ~ 8

Author(s):

Christine Metzger ◽

Mats B. Nilsson ◽

Matthias Peichl ◽

Per-Erik Jansson

Keyword(s):

Aerodynamic Resistance ◽

Net Ecosystem Exchange ◽

Heat Fluxes ◽

Sensitivity Analyses ◽

Area Index ◽

Soil Temperatures ◽

Parameter Interactions ◽

And Performance ◽

Carbon Dioxide Co2 ◽

Parameter Ranges

Abstract. In contrast to previous peatland carbon dioxide (CO2) model sensitivity analyses, which usually focussed on only one or a few processes, this study investigates interactions between various biotic and abiotic processes and their parameters by comparing CoupModel v5 results with multiple observation variables. Many interactions were found not only within but also between various process categories simulating plant growth, decomposition, radiation interception, soil temperature, aerodynamic resistance, transpiration, soil hydrology and snow. Each measurement variable was sensitive to up to 10 (out of 54) parameters, from up to 7 different process categories. The constrained parameter ranges varied, depending on the variable and performance index chosen as criteria, and on other calibrated parameters (equifinalities). Therefore, transferring parameter ranges between models needs to be done with caution, especially if such ranges were achieved by only considering a few processes. The identified interactions and constrained parameters will be of great interest to use for comparisons with model results and data from similar ecosystems. All of the available measurement variables (net ecosystem exchange, leaf area index, sensible and latent heat fluxes, net radiation, soil temperatures, water table depth and snow depth) improved the model constraint. If hydraulic properties or water content were measured, further parameters could be constrained, resolving several equifinalities and reducing model uncertainty. The presented results highlight the importance of considering biotic and abiotic processes together and can help modellers and experimentalists to design and calibrate models as well as to direct experimental set-ups in peatland ecosystems towards modelling needs.

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Spatial and Temporal Variations of Forest Cover in Developing Countries

Sustainability ◽

10.3390/su11061517 ◽

2019 ◽

Vol 11 (6) ◽

pp. 1517 ◽

Cited By ~ 5

Author(s):

Qianwen Duan ◽

Minghong Tan

Keyword(s):

Developing Countries ◽

South America ◽

Forest Cover ◽

European Space Agency ◽

Binary Logistic Regression ◽

Temporal Variations ◽

Global Scale ◽

Forest Area ◽

Binary Logistic Regression Model ◽

African Countries

An understanding of changes in forest cover and the drivers of forest transition (FT) contributes to the sustainable management of global forests. In this paper, we used the latest global land cover data published by the European Space Agency (ESA) to investigate spatiotemporal variation characteristics of forest cover in developing countries from 1992 to 2015, and then analyzed causal factors of this variation using a binary logistic regression model. Existing studies on FT are mostly based on data from the Food and Agriculture Organization (FAO); this study improves our understanding of FT mechanisms through the use of a new dataset. The results indicate that the forest area in developing countries decreased from 21.8 to 21.3 million km2 from 1992–2015, and the rate of decline slowed after 2004. South America suffered the largest reduction in forest area (505,100 km2), whereas forest area in Africa increased slightly. By 2015, more than 80% of African countries had experienced FT, whereas only half of developing countries experienced forest expansion in South America. The variables affecting FT occurrence differed among continents. On the global scale, the remaining forest coverage and the proportion of forest exports negatively affected the likelihood of FT occurrence, whereas urbanization level had a positive effect.

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