The extent of forest in dryland biomes Bastin, Jean-François; Berrahmouni, Nora; Grainger, Alan ...
Science,
05/2017, Letnik:
356, Številka:
6338
Journal Article, Web Resource
Recenzirano
Odprti dostop
Dryland biomes cover two-fifths of Earth’s land surface, but their forest area is poorly known. Here, we report an estimate of global forest extent in dryland biomes, based on analyzing more than ...210,000 0.5-hectare sample plots through a photo-interpretation approach using large databases of satellite imagery at (i) very high spatial resolution and (ii) very high temporal resolution, which are available through the Google Earth platform. We show that in 2015, 1327 million hectares of drylands had more than 10% tree-cover, and 1079 million hectares comprised forest. Our estimate is 40 to 47% higher than previous estimates, corresponding to 467 million hectares of forest that have never been reported before. This increases current estimates of global forest cover by at least 9%.
Unlike satellite images, which are typically acquired and processed in near-real-time, global land cover products have historically been produced on an annual basis, often with substantial lag times ...between image processing and dataset release. We developed a new automated approach for globally consistent, high resolution, near real-time (NRT) land use land cover (LULC) classification leveraging deep learning on 10 m Sentinel-2 imagery. We utilize a highly scalable cloud-based system to apply this approach and provide an open, continuous feed of LULC predictions in parallel with Sentinel-2 acquisitions. This first-of-its-kind NRT product, which we collectively refer to as Dynamic World, accommodates a variety of user needs ranging from extremely up-to-date LULC data to custom global composites representing user-specified date ranges. Furthermore, the continuous nature of the product's outputs enables refinement, extension, and even redefinition of the LULC classification. In combination, these unique attributes enable unprecedented flexibility for a diverse community of users across a variety of disciplines.
A humid tropical forest disturbance alert using Sentinel-1 radar data is presented for the Congo Basin. Radar satellite signals can penetrate through clouds, allowing Sentinel-1 to provide gap-free ...observations for the tropics consistently every 6-12 days at 10 m spatial scale. In the densely cloud covered Congo Basin, this represents a major advantage for the rapid detection of small-scale forest disturbances such as subsistence agriculture and selective logging. Alerts were detected with latest available Sentinel-1 images and results are presented from January 2019 to July 2020. We mapped 4 million disturbance events during this period, totalling 1.4 million ha with nearly 80% of events smaller than 0.5 ha. Monthly distribution of alert totals varied widely across the Congo Basin countries and can be linked to regional differences in wet and dry season cycles, with more forest disturbances in the dry season. Results indicated high user's and producer's accuracies and the rapid confirmation of alerts within a few weeks. Our disturbance alerts provide confident detection of events larger than or equal to 0.2 ha but do not include smaller events, which suggests that disturbance rates in the Congo Basin are even higher than presented in this study. The new alert product can help to better study the forest dynamics in the Congo Basin with improved spatial and temporal detail and near real-time detections, and highlights the value of dense Sentinel-1 time series data for large-area tropical forest monitoring. The research contributes to the Global Forest Watch initiative in providing timely and accurate information to support a wide range of stakeholders in sustainable forest management and law enforcement. The alerts are available via the https://www.globalforestwatch.org and http://radd-alert.wur.nl.
A Landsat-based humid tropical forest disturbance alert was implemented for Peru, the Republic of Congo and Kalimantan, Indonesia. Alerts were mapped on a weekly basis as new terrain-corrected ...Landsat 7 and 8 images were made available; results are presented for all of 2014 and through September 2015. The three study areas represent different stages of the forest land use transition, with all featuring a variety of disturbance dynamics including logging, smallholder agriculture, and agroindustrial development. Results for Peru were formally validated and alerts found to have very high user's accuracies and moderately high producer's accuracies, indicating an appropriately conservative product suitable for supporting land management and enforcement activities. Complete pan-tropical coverage will be implemented during 2016 in support of the Global Forest Watch initiative. To date, Global Forest Watch produces annual global forest loss area estimates using a comparatively richer set of Landsat inputs. The alert product is presented as an interim update of forest disturbance events between comprehensive annual updates. Results from this study are available for viewing and download at http://glad.geog.umd.edu/forest-alerts and www.globalforestwatch.org.
Significance Our paper is significant in a number of respects. First, we expand the literature on quasi-experimental evaluation of the causal impact of conservation measures to include agricultural ...concessions. Second, our report is rare in that we use panel data and techniques in a literature on spatially explicit land-use change econometrics that has necessarily relied upon cross-sectional analyses because of data-availability constraints. Third, our report is rare among land-use change scenario analyses in that we calibrate the effect of land-use designations empirically, rather than assuming idealized perfect effectiveness of conservation measures or complete conversion without such measures. Finally, we compare the effectiveness of place-based policies with alternative price-based instruments for climate-change mitigation within a globally significant landscape.
To reduce greenhouse gas emissions from deforestation, Indonesia instituted a nationwide moratorium on new license areas (“concessions”) for oil palm plantations, timber plantations, and logging activity on primary forests and peat lands after May 2011. Here we indirectly evaluate the effectiveness of this policy using annual nationwide data on deforestation, concession licenses, and potential agricultural revenue from the decade preceding the moratorium. We estimate that on average granting a concession for oil palm, timber, or logging in Indonesia increased site-level deforestation rates by 17–127%, 44–129%, or 3.1–11.1%, respectively, above what would have occurred otherwise. We further estimate that if Indonesia’s moratorium had been in place from 2000 to 2010, then nationwide emissions from deforestation over that decade would have been 241–615 MtCO ₂e (2.8–7.2%) lower without leakage, or 213–545 MtCO ₂e (2.5–6.4%) lower with leakage. As a benchmark, an equivalent reduction in emissions could have been achieved using a carbon price-based instrument at a carbon price of $3.30–7.50/tCO ₂e (mandatory) or $12.95–19.45/tCO ₂e (voluntary). For Indonesia to have achieved its target of reducing emissions by 26%, the geographic scope of the moratorium would have had to expand beyond new concessions (15.0% of emissions from deforestation and peat degradation) to also include existing concessions (21.1% of emissions) and address deforestation outside of concessions and protected areas (58.7% of emissions). Place-based policies, such as moratoria, may be best thought of as bridge strategies that can be implemented rapidly while the institutions necessary to enable carbon price-based instruments are developed.
Recent advances in Landsat archive data processing and characterization enhanced our capacity to map land cover and land use globally with higher precision, temporal frequency, and thematic detail. ...Here, we present the first results from a project aimed at annual multidecadal land monitoring providing critical information for tracking global progress towards sustainable development. The global 30-m spatial resolution dataset quantifies changes in forest extent and height, cropland, built-up lands, surface water, and perennial snow and ice extent from the year 2000 to 2020. Landsat Analysis Ready Data served as an input for land cover and use mapping. Each thematic product was independently derived using locally and regionally calibrated machine learning tools. Thematic maps validation using a statistical sample of reference data confirmed their high accuracy (user’s and producer’s accuracies above 85% for all land cover and land use themes, except for built-up lands). Our results revealed dramatic changes in global land cover and land use over the past 20 years. The bitemporal dataset is publicly available and serves as a first input for the global land monitoring system.
Our society faces the pressing challenge of increasing agricultural production while minimizing negative consequences on ecosystems and the global climate. Indonesia, which has pledged to reduce ...greenhouse gas (GHG) emissions from deforestation while doubling production of several major agricultural commodities, exemplifies this challenge. Here we focus on palm oil, the world's most abundant vegetable oil and a commodity that has contributed significantly to Indonesia's economy. Most oil palm expansion in the country has occurred at the expense of forests, resulting in significant GHG emissions. We examine the extent to which land management policies can resolve the apparently conflicting goals of oil palm expansion and GHG mitigation in Kalimantan, a major oil palm growing region of Indonesia. Using a logistic regression model to predict the locations of new oil palm between 2010 and 2020 we evaluate the impacts of six alternative policy scenarios on future emissions. We estimate net emissions of 128.4-211.4 MtCO2 yr(-1) under business as usual expansion of oil palm plantations. The impact of diverting new plantations to low carbon stock land depends on the design of the policy. We estimate that emissions can be reduced by 9-10% by extending the current moratorium on new concessions in primary forests and peat lands, 35% by limiting expansion on all peat and forestlands, 46% by limiting expansion to areas with moderate carbon stocks, and 55-60% by limiting expansion to areas with low carbon stocks. Our results suggest that these policies would reduce oil palm profits only moderately but would vary greatly in terms of cost-effectiveness of emissions reductions. We conclude that a carefully designed and implemented oil palm expansion plan can contribute significantly towards Indonesia's national emissions mitigation goal, while allowing oil palm area to double.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Forest cover is an important input variable for assessing changes to carbon stocks, climate and hydrological systems, biodiversity richness, and other sustainability science disciplines. Despite ...incremental improvements in our ability to quantify rates of forest clearing, there is still no definitive understanding on global trends. Without timely and accurate forest monitoring methods, policy responses will be uninformed concerning the most basic facts of forest cover change. Results of a feasible and cost-effective monitoring strategy are presented that enable timely, precise, and internally consistent estimates of forest clearing within the humid tropics. A probability-based sampling approach that synergistically employs low and high spatial resolution satellite datasets was used to quantify humid tropical forest clearing from 2000 to 2005. Forest clearing is estimated to be 1.39% (SE 0.084%) of the total biome area. This translates to an estimated forest area cleared of 27.2 million hectares (SE 2.28 million hectares), and represents a 2.36% reduction in area of humid tropical forest. Fifty-five percent of total biome clearing occurs within only 6% of the biome area, emphasizing the presence of forest clearing "hotspots." Forest loss in Brazil accounts for 47.8% of total biome clearing, nearly four times that of the next highest country, Indonesia, which accounts for 12.8%. Over three-fifths of clearing occurs in Latin America and over one-third in Asia. Africa contributes 5.4% to the estimated loss of humid tropical forest cover, reflecting the absence of current agro-industrial scale clearing in humid tropical Africa.
Timely and accurate data on forest change within Indonesia is required to provide government, private and civil society interests with the information needed to improve forest management. The forest ...clearing rate in Indonesia is among the highest reported by the United Nations Food and Agriculture Organization (FAO), behind only Brazil in terms of forest area lost. While the rate of forest loss reported by FAO was constant from 1990 through 2005 (1.87 Mhayr−1), the political, economic, social and environmental drivers of forest clearing changed at the close of the last century. We employed a consistent methodology and data source to quantify forest clearing from 1990 to 2000 and from 2000 to 2005. Results show a dramatic reduction in clearing from a 1990s average of 1.78 Mhayr−1 to an average of 0.71Mhayr−1 from 2000 to 2005. However, annual forest cover loss indicator maps reveal a near-monotonic increase in clearing from a low in 2000 to a high in 2005. Results illustrate a dramatic downturn in forest clearing at the turn of the century followed by a steady resurgence thereafter to levels estimated to exceed 1 Mhayr−1 by 2005. The lowlands of Sumatra and Kalimantan were the site of more than 70% of total forest clearing within Indonesia for both epochs; over 40% of the lowland forests of these island groups were cleared from 1990 to 2005. The method employed enables the derivation of internally consistent, national-scale changes in the rates of forest clearing, results that can inform carbon accounting programs such as the Reducing Emissions from Deforestation and Forest Degradation in Developing Countries (REDD) initiative.
Scattered trees outside of dense, closed-canopy forests are very important for carbon sequestration, supporting livelihoods, maintaining ecosystem integrity, and climate change adaptation and ...mitigation. In contrast to trees inside of closed-canopy forests, not much is known about the spatial extent and distribution of scattered trees at a global scale. Due to the cost of high-resolution satellite imagery, global monitoring systems rely on medium-resolution satellites to monitor land use and land use change. However, detecting and monitoring scattered trees with an open canopy using medium-resolution satellites is difficult because individual trees often cover a smaller footprint than the satellites' resolution. Additionally, the variable background land uses and canopy shapes of trees cause a high variability in their spectral signatures. Here we present a globally consistent method to identify trees with canopy diameters greater than 3 m with medium-resolution optical and radar imagery. Biweekly cloud-free, pan-sharpened 10 metres Sentinel-2 optical imagery and Sentinel-1 radar imagery are used to train a fully convolutional network, consisting of a convolutional gated recurrent unit layer and a feature pyramid attention layer. Tested across more than 215 thousand Sentinel-1 and Sentinel-2 pixels distributed from - 60
to +60
latitude, the proposed model exceeds 75% user's and producer's accuracy identifying trees in hectares with a low to medium density (
) of tree cover, and 95% user's and producer's accuracy in hectares with dense (
) tree cover. In comparison with common remote-sensing classification methods, the proposed method increases the accuracy of monitoring tree presence in areas with sparse and scattered tree cover (
) by as much as 20%, and reduces commission and omission error in mountainous and very cloudy regions by nearly half. When applied across large, heterogeneous landscapes, the results demonstrate potential to map trees in high detail and consistent accuracy over diverse landscapes across the globe. This information is important for understanding current land cover and can be used to detect changes in land cover such as agroforestry, buffer zones around biological hotspots, and expansion or encroachment of forests.
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