Abstract
Since the collapse of the Soviet Union and transition to a new forest inventory system, Russia has reported almost no change in growing stock (+ 1.8%) and biomass (+ 0.6%). Yet remote ...sensing products indicate increased vegetation productivity, tree cover and above-ground biomass. Here, we challenge these statistics with a combination of recent National Forest Inventory and remote sensing data to provide an alternative estimate of the growing stock of Russian forests and to assess the relative changes in post-Soviet Russia. Our estimate for the year 2014 is 111 ± 1.3 × 10
9
m
3
, or 39% higher than the value in the State Forest Register. Using the last Soviet Union report as a reference, Russian forests have accumulated 1163 × 10
6
m
3
yr
-1
of growing stock between 1988–2014, which balances the net forest stock losses in tropical countries. Our estimate of the growing stock of managed forests is 94.2 × 10
9
m
3
, which corresponds to sequestration of 354 Tg C yr
-1
in live biomass over 1988–2014, or 47% higher than reported in the National Greenhouse Gases Inventory.
•We review observed and projected climate change impacts on Russia’s boreal forest.•Substantial changes in productivity, vegetation distribution, disturbance regimes and carbon budget are already ...observed.•Future changes may intensify these patterns, but their interaction is subject to uncertainty.•There is an, as of yet unquantified, risk that tipping points may occur, related to permafrost melting and forest dieback.•Climate change impacts are strongly interconnected with socio-economic and forest management changes.
Russia’s boreal forests provide numerous important ecosystem functions and services, but they are being increasingly affected by climate change. This review presents an overview of observed and potential future climate change impacts on those forests with an emphasis on their aggregate carbon balance and processes driving changes therein. We summarize recent findings highlighting that radiation increases, temperature-driven longer growing seasons and increasing atmospheric CO2 concentrations generally enhance vegetation productivity, while heat waves and droughts tend to decrease it. Estimates of major carbon fluxes such as net biome production agree that the Russian forests as a whole currently act as a carbon sink, but these estimates differ in terms of the magnitude of the sink due to different methods and time periods used. Moreover, models project substantial distributional shifts of forest biomes, but they may overestimate the extent to which the boreal forest will shift poleward as past migration rates have been slow. While other impacts of current climate change are already substantial, and projected impacts could be both large-scale and disastrous, the likelihood for a tipping point behavior of Russia’s boreal forest is still unquantified. Other substantial research gaps include the large-scale effect of (climate-driven) disturbances such as fires and insect outbreaks, which are expected to increase in the future. We conclude that the impacts of climate change on Russia’s boreal forest are often superimposed by other environmental and societal changes in a complex way, and the interaction of these developments could exacerbate both existing and projected future challenges. Hence, development of adaptation and mitigation strategies for Russia’s forests is strongly advised.
Local climate zones (LCZs) divide the urban landscape into homogeneous types based on urban structure (i.e., morphology of streets and buildings), urban cover (i.e., permeability of surfaces), ...construction materials, and human activities (i.e., anthropogenic heat). This classification scheme represents a standardized way of capturing the basic urban form of cities and is currently being applied globally as part of the world urban database and portal tools (WUDAPT) initiative. This paper assesses the transferability of the LCZ concept to two Ukrainian cities, i.e., Kyiv and Lviv, which differ in urban form and topography, and considers three ways to validate and verify this classification scheme. An accuracy of 64% was achieved for Kyiv using an independent validation dataset while a comparison of the LCZ maps with the GlobeLand30 land cover map resulted in a match that was greater than 75% for both cities. There was also good correspondence between the urban classes in the LCZ maps and the urban points of interest in OpenStreetMap (OSM). However, further research is still required to produce a standardized validation protocol that could be used on a regular basis by contributors to WUDAPT to help produce more accurate LCZ maps in the future.
AIM: To infer a forest carbon density map at 0.01° resolution from a radar remote sensing product for the estimation of carbon stocks in Northern Hemisphere boreal and temperate forests. LOCATION: ...The study area extends from 30° N to 80° N, covering three forest biomes – temperate broadleaf and mixed forests (TBMF), temperate conifer forests (TCF) and boreal forests (BFT) – over three continents (North America, Europe and Asia). METHODS: This study is based on a recently available growing stock volume (GSV) product retrieved from synthetic aperture radar data. Forest biomass and spatially explicit uncertainty estimates were derived from the GSV using existing databases of wood density and allometric relationships between biomass compartments (stem, branches, roots, foliage). We tested the resultant map against inventory‐based biomass data from Russia, Europe and the USA prior to making intercontinent and interbiome carbon stock comparisons. RESULTS: Our derived carbon density map agrees well with inventory data at regional scales (r² = 0.70–0.90). While 40.7 ± 15.7 petagram of carbon (Pg C) are stored in BFT, TBMF and TCF contain 24.5 ± 9.4 Pg C and 14.5 ± 4.8 Pg C, respectively. In terms of carbon density, we found 6.21 ± 2.07 kg C m⁻² retained in TCF and 5.80 ± 2.21 kg C m⁻² in TBMF, whereas BFT have a mean carbon density of 4.00 ± 1.54 kg C m⁻². Indications of a higher carbon density in Europe compared with the other continents across each of the three biomes could not be proved to be significant. MAIN CONCLUSIONS: The presented carbon density and corresponding uncertainty map give an insight into the spatial patterns of biomass and stand as a new benchmark to improve carbon cycle models and carbon monitoring systems. In total, we found 79.8 ± 29.9 Pg C stored in northern boreal and temperate forests, with Asian BFT accounting for 22.1 ± 8.3 Pg C.
There is an increasing evidence that smallholder farms contribute substantially to food production globally, yet spatially explicit data on agricultural field sizes are currently lacking. Automated ...field size delineation using remote sensing or the estimation of average farm size at subnational level using census data are two approaches that have been used. However, both have limitations, for example, automatic field size delineation using remote sensing has not yet been implemented at a global scale while the spatial resolution is very coarse when using census data. This paper demonstrates a unique approach to quantifying and mapping agricultural field size globally using crowdsourcing. A campaign was run in June 2017, where participants were asked to visually interpret very high resolution satellite imagery from Google Maps and Bing using the Geo‐Wiki application. During the campaign, participants collected field size data for 130 K unique locations around the globe. Using this sample, we have produced the most accurate global field size map to date and estimated the percentage of different field sizes, ranging from very small to very large, in agricultural areas at global, continental, and national levels. The results show that smallholder farms occupy up to 40% of agricultural areas globally, which means that, potentially, there are many more smallholder farms in comparison with the two different current global estimates of 12% and 24%. The global field size map and the crowdsourced data set are openly available and can be used for integrated assessment modeling, comparative studies of agricultural dynamics across different contexts, for training and validation of remote sensing field size delineation, and potential contributions to the Sustainable Development Goal of Ending hunger, achieve food security and improved nutrition and promote sustainable agriculture.
This paper demonstrates a unique approach to quantifying and mapping agricultural field size globally using crowdsourcing. A campaign was run in June 2017 where participants were asked to visually interpret very high resolution satellite imagery from Google Maps and Bing using the Geo‐Wiki application. The results show that smallholder farms occupy up to 40% of agricultural areas globally, which means that, potentially, there are many more smallholder farms in comparison with the two different current global estimates of 12% and 24%. The global field size map and the crowdsourced data set are openly available.
Forests contribute to climate change mitigation through carbon storage and uptake, but the extent to which this carbon pool varies in space and time is still poorly known. Several Earth Observation ...missions have been specifically designed to address this issue, for example, NASA's GEDI, NASA‐ISRO's NISAR and ESA's BIOMASS. Yet, all these missions' products require independent and consistent validation. A permanent, global, in situ, site‐based forest biomass reference measurement system relying on ground data of the highest possible quality is therefore needed. Here, we have assembled a list of almost 200 high‐quality sites through an in‐depth review of the literature and expert knowledge. In this study, we explore how representative these sites are in terms of their coverage of environmental conditions, geographical space and biomass‐related forest structure, compared to those experienced by forests worldwide. This work also aims at identifying which sites are the most representative, and where to invest to improve the representativeness of the proposed system. We show that the environmental coverage of the system does not seem to improve after at least the 175 most representative sites are included, but geographical and structural coverages continue to improve as more sites are added. We highlight the areas of poor environmental, geographical, or structural coverage, including, but not limited to, Canada, the western half of the USA, Mexico, Patagonia, Angola, Zambia, eastern Russia, and tropical and subtropical highlands (e.g. in Colombia, the Himalayas, Borneo, Papua). For the proposed system to succeed, we stress that (1) data must be collected and processed applying the same standards across all countries and continents; (2) system establishment and management must be inclusive and equitable, with careful consideration of working conditions; and (3) training and site partner involvement in downstream activities should be mandatory.
A permanent, global, in situ, site‐based forest biomass reference measurement system relying on ground data of the highest possible quality is needed to validate products from biomass‐related Earth Observation missions. Here, we have assembled a list of almost 200 high‐quality sites. We explore how representative these sites are in terms of their coverage of environmental conditions, geographical space and biomass‐related forest structure, compared to those experienced by forests worldwide.
This paper presents and assesses spatially explicit estimates of forest growing stock volume (GSV) of the northern hemisphere (north of 10°N) from hyper-temporal observations of Envisat Advanced ...Synthetic Aperture Radar (ASAR) backscattered intensity using the BIOMASAR algorithm. Approximately 70,000 ASAR images at a pixel size of 0.01° were used to estimate GSV representative for the year 2010. The spatial distribution of the GSV across four ecological zones (polar, boreal, temperate, subtropical) was well captured by the ASAR-based estimates. The uncertainty of the retrieved GSV was smallest in boreal and temperate forest (<30% for approximately 80% of the forest area) and largest in subtropical forest. ASAR-derived GSV averages at the level of administrative units were mostly in agreement with inventory-derived estimates. Underestimation occurred in regions of very high GSV (>300m3/ha) and fragmented forest landscapes. For the major forested countries within the study region, the relative RMSE between ASAR-derived GSV averages at provincial level and corresponding values from National Forest Inventory was between 12% and 45% (average: 29%).
•Wall-to-wall estimates of forest growing stock volume (GSV) north of 10°N•Spatial distribution of GSV well reproduced in four biomes•Percent error of ASAR-derived GSV averages at provincial level: between 12% and 45%.•Underestimation for areas with GSV>300m3/ha and in fragmented forest landscapes
The last few decades have seen an explosion in the availability of remotely sensed and geospatial big data, which are defined by the 3 Vs: a large volume of data; a variety of different forms of ...data; and the rapid velocity of data arrival ...
Abstract
Key message
We propose a framework to derive the direct loss of aboveground carbon stocks after the 2020 wildfire in forests of the Chornobyl Exclusion Zone using optical and radar Sentinel ...satellite data. Carbon stocks were adequately predicted using stand-wise inventory data and local combustion factors where new field observations are impossible. Both the standalone Sentinel-1 backscatter delta (before and after fire) indicator and radar-based change model reliably predicted the associated carbon loss.
Context
The Chornobyl Exclusion Zone (CEZ) is a mosaic forest landscape undergoing dynamic natural disturbances. Local forests are mostly planted and have low ecosystem resilience against the negative impact of global climate and land use change. Carbon stock fluxes after wildfires in the area have not yet been quantified. However, the assessment of this and other ecosystem service flows is crucial in contaminated (both radioactively and by unexploded ordnance) landscapes of the CEZ.
Aims
The aim of this study was to estimate carbon stock losses resulting from the catastrophic 2020 fires in the CEZ using satellite data, as field visitations or aerial surveys are impossible due to the ongoing war.
Methods
The aboveground carbon stock was predicted in a wall-to-wall manner using random forest modelling based on Sentinel data (both optical and synthetic aperture radar or SAR). We modelled the carbon stock loss using the change in Sentinel-1 backscatter before and after the fire events and local combustion factors.
Results
Random forest models performed well (root-mean-square error (RMSE) of 22.6 MgC·ha
−1
or 37% of the mean) to predict the pre-fire carbon stock. The modelled carbon loss was estimated to be 156.3 Gg C (9.8% of the carbon stock in burned forests or 1.5% at the CEZ level). The standalone SAR backscatter delta showed a higher RMSE than the modelled estimate but better systematic agreement (0.90 vs. 0.73). Scots pine (
Pinus sylvestris
L.)-dominated stands contributed the most to carbon stock loss, with 74% of forests burned in 2020.
Conclusion
The change in SAR backscatter before and after a fire event can be used as a rough proxy indicator of aboveground carbon stock loss for timely carbon map updating. The model using SAR backscatter change and backscatter values prior to wildfire is able to reliably estimate carbon emissions when on-ground monitoring is impossible.
Since 24 February 2022, Ukraine has experienced full-scale military aggression initiated by the Russian Federation. The war has had a major negative impact on vegetation cover of war-affected ...regions. We explored interactions between pre-war forest management and the impacts of military activities in three of the most forested Ukrainian areas of interest (AOI), affected by the war. These were forests lying between Kharkiv and Luhansk cities (AOI 'East'), forests along the Dnipro River delta (AOI 'Kherson'), and those of the Chornobyl Exclusion Zone (AOI CEZ). We used Sentinel satellite imagery to create damaged forest cover masks for the year 2022. We mapped forests with elevated fire hazard, which was defined as a degree of exposure to the fire-supporting land use (mostly an agricultural land, a common source of ignitions in Ukraine). We evaluated the forest disturbance rate in 2022, as compared to pre-war rates. We documented significant increases in non-stand replacing disturbances (low severity fires and non-fire disturbances) for all three of the AOIs. Damaged forest cover varied among the AOIs (24,180 ± 4,715 ha, or 9.3% ± 1.8% in the 'East' AOI; 7,293 ± 1,925 ha, or 15.7% ± 4.1% in the 'Kherson' AOI; 7,116 ± 1,274 ha, or 5.0% ± 0.9% in the CEZ AOI). Among the forests damaged in 2022, the 'Kherson' AOI will likely have the highest proportion of an area with elevated fire hazard in the coming decades, as compared to other regions (89% vs. 70% in the 'East' and CEZ AOIs respectively). Future fire risks and extensive war-related disturbance of forest cover call for forest management to develop strategies explicitly addressing these factors.
PDF
