Intra-annual reflectance composites from Sentinel-2 and Landsat for national-scale crop and land cover mapping

Remote Sensing of Environment, Journal Year: 2018, Volume and Issue: 220, P. 135 - 151

Published: Nov. 6, 2018

Many applications that target dynamic land surface processes require a temporal observation frequency is not easily satisfied using data from single optical sensor. Sentinel-2 and Landsat provide observations of similar nature offer the opportunity to combine both sources increase time-series at high spatial resolution. Multi-sensor image compositing one way for performing pixel-level integration has many advantages processing frameworks, especially if analyses over larger areas are targeted. Our approach optimized narrow temporal-intervals allows derivation consistent reflectance composites capture field level phenologies. We processed more than year's worth imagery acquired by Sentinel-2A MSI Landsat-8 OLI as available NASA Harmonized Landsat-Sentinel dataset. used all Germany integrated into three defined intervals (10-day, monthly seasonal). includes generation proxy values in red edge bands gap filling on 10-day time-series. then derive national scale crop type cover map compare our results spatially explicit agricultural reference federal states recent census entire country. The resulting successfully captures distribution across 30 m resolution achieves 81% overall accuracy 12 classes which was available. mapping performance most highest discriminated with class specific accuracies >80%. For several crops, such cereals, maize rapeseed mapped acreages very well official average differences between area 11%, 2% 3%, respectively. Other (grapevine forest classes) perform slightly less well, likely, because does fully variability these Germany. inclusion improved cases classes. Similarly, procedure led when compared nongap-filled features. Overall, demonstrate valuable potential approaches utilize detailed assessments other land-uses large areas.

Language: Английский

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Subnational distribution of average farm size and smallholder contributions to global food production

Environmental Research Letters, Journal Year: 2016, Volume and Issue: 11(12), P. 124010 - 124010

Published: Nov. 29, 2016

Smallholder farming is the most prevalent form of agriculture in world, supports many planet's vulnerable populations, and coexists with some its diverse threatened landscapes. However, there little information about location small farms, making it difficult both to estimate their numbers implement effective agricultural, development, land use policies. Here, we present a map mean agricultural area, classified by amount per household, at subnational resolutions across three key global regions using novel integration household microdata landscape data. This approach provides number, average size, contribution farms much developing world. By our estimates, 918 units 83 countries Latin America, sub-Saharan Africa, South East Asia less than five hectares household. These smallholder-dominated systems are home more 380 million households, make up roughly 30% produce 70% food calories produced these regions, responsible for half globally, as well production several major crops. direct greater percentage toward human consumption, consumed food, compared 55% globally. Our ability disaggregate populations from non-farming providing accurate picture households on has previously been available. data meet critical need, improved understanding prevalence distribution smallholder essential policy development security, poverty reduction, conservation agendas.

Language: Английский

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How much of the world's food do smallholders produce?

Global Food Security, Journal Year: 2018, Volume and Issue: 17, P. 64 - 72

Published: May 22, 2018

The widely reported claim that smallholders produce 70–80% of the world's food has been a linchpin agricultural development policy despite limited empirical evidence. Recent attempts to reinvestigate this number have lacked raw data on how much produce, and relied model assumptions with unknown biases spatial commodity coverage. We examine variations in crop production by farm size using newly-compiled global sample subnational level microdata censuses covering more countries (n=55) types (n=154) than assessed date. estimate farms under 2ha globally 28–31% total 30–34% supply 24% gross area. Farms devote greater proportion their food, account for diversity, while over 1000ha greatest post-harvest loss.

Language: Английский

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Fifty years of Landsat science and impacts

Remote Sensing of Environment, Journal Year: 2022, Volume and Issue: 280, P. 113195 - 113195

Published: July 28, 2022

Since 1972, the Landsat program has been continually monitoring Earth, to now provide 50 years of digital, multispectral, medium spatial resolution observations. Over this time, data were crucial for many scientific and technical advances. Prior program, detailed, synoptic depictions Earth's surface rare, ability acquire work with large datasets was limited. The early delivered a series technological breakthroughs, pioneering new methods, demonstrating capacity digital satellite imagery, creating template other global Earth observation missions programs. Innovations driven by have paved way subsequent science, application, policy support activities. economic value knowledge gained through long recognized, despite periods funding uncertainty, resulted in program's continuity, as well substantive ongoing improvements payload mission performance. Free open access data, enacted 2008, unprecedented substantially increased usage led proliferation science application opportunities. Here, we highlight key developments over past that influenced changed our understanding system. Major programmatic impacts realized areas agricultural crop mapping water use, climate change drivers impacts, ecosystems land cover monitoring, changing human footprint. introduction collection processing, coupled free policy, facilitated transition away from single images towards time analyses fostered widespread use science-grade data. launch Landsat-9 on September 27, 2021, advanced planning its successor mission, Landsat-Next, underscore sustained institutional program. Such commitment continuity is recognition both historic impact future potential build upon Landsat's remarkable 50-year legacy.

Language: Английский

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Farming and the geography of nutrient production for human use: a transdisciplinary analysis

The Lancet Planetary Health, Journal Year: 2017, Volume and Issue: 1(1), P. e33 - e42

Published: April 1, 2017

BackgroundInformation about the global structure of agriculture and nutrient production its diversity is essential to improve present understanding national food patterns, agricultural livelihoods, chains, their linkages land use associated ecosystems services. Here we provide a plausible breakdown by farm size, also study associations between diversity, production. This analysis crucial design interventions that might be appropriately targeted promote healthy diets in face population growth, urbanisation, climate change.MethodsWe used existing spatially-explicit datasets estimate levels 41 major crops, seven livestock, 14 aquaculture fish products. From overall estimates, estimated vitamin A, B12, folate, iron, zinc, calcium, calories, protein. We relative contribution farms different sizes commodities nutrients, as well how based on number products grown per geographic pixel distribution within this (Shannon index [H]) changes with sizes.FindingsGlobally, small medium (≤50 ha) produce 51–77% nearly all nutrients examined here. However, important regional differences exist. Large (>50 dominate North America, South Australia New Zealand. In these regions, large contribute 75% 100% cereal, fruit production, pattern similar for other commodity groups. By contrast, (≤20 more than most sub-Saharan Africa, southeast Asia, south China. Europe, west Asia north central medium-size (20–50 substantially commodities. Very (≤2 are have local significance where they 30% The majority vegetables (81%), roots tubers (72%), pulses (67%), fruits (66%), livestock (60%), cereals (56%) produced diverse landscapes (H>1·5). Similarly, micronutrients (53–81%) protein (57%) sugar (73%) oil crops less ones (H≤1·5), which account calorie (56%). diminishes size increases. areas world higher irrespective size.InterpretationOur results show vary across regions key structural determinants need considered plans meet social, economic, environmental targets. At level, both roles nutrition security. Efforts maintain increase seem necessary viable, multifunctional, sustainable landscapes.FundingCommonwealth Scientific Industrial Research Organisation, Bill & Melinda Gates Foundation, CGIAR Programs Climate Change, Agriculture Food Security Nutrition Health funded Fund Council, Daniel Nina Carasso European Union, International Agricultural Development, Australian National Science Gordon Betty Moore Joint Programming Initiative Agriculture, Change—Belmont Forum.

Language: Английский

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What causes deforestation in Indonesia?

Environmental Research Letters, Journal Year: 2018, Volume and Issue: 14(2), P. 024007 - 024007

Published: Dec. 6, 2018

We investigate the causes of deforestation in Indonesia, a country with one highest rates primary natural forest loss tropics, annually between 2001 and 2016. use high spatial resolution imagery made available on Google Earth to characterize land cover types following random selection events, drawn from Global Forest Change dataset. Notorious region, large-scale oil palm timber plantations together contributed more than two-fifths nationwide over our study period, peak late aughts followed by notable decline up Conversion forests grasslands, which comprised an average one-fifth national deforestation, rose sharply dominance years periods considerable fire activity, particularly Small-scale agriculture small-scale also were dominant drivers outside major islands Indonesia. Although relatively small contributors total logging roads responsible for declining share mining activities increasing share, period. Direct Indonesia are thus spatially temporally dynamic, suggesting need conservation policy responses tailored at subnational level, new methods monitoring time.

Language: Английский

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Assessment of an Operational System for Crop Type Map Production Using High Temporal and Spatial Resolution Satellite Optical Imagery

Remote Sensing, Journal Year: 2015, Volume and Issue: 7(9), P. 12356 - 12379

Published: Sept. 22, 2015

Crop area extent estimates and crop type maps provide crucial information for agricultural monitoring management. Remote sensing imagery in general and, more specifically, high temporal spatial resolution data as the ones which will be available with upcoming systems, such Sentinel-2, constitute a major asset this kind of application. The goal paper is to assess what state-of-the-art supervised classification methods can applied multi-temporal optical produce accurate at global scale. Five concurrent strategies automatic map production have been selected benchmarked using SPOT4 (Take5) Landsat 8 over 12 test sites spread all globe (four Europe, four Africa, two America Asia). This variety tests allows one draw conclusions applicable wide landscapes systems. results show that random forest classifier operating on linearly temporally gap-filled images achieve overall accuracies above 80% most sites. Only showed low performances: Madagascar due presence fields smaller than pixel size Burkina Faso mix trees crops fields. approach based machine learning techniques, need situ collection training step, but fully automatic.

Language: Английский

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Mapping cropping intensity in China using time series Landsat and Sentinel-2 images and Google Earth Engine

Remote Sensing of Environment, Journal Year: 2019, Volume and Issue: 239, P. 111624 - 111624

Published: Dec. 30, 2019

Language: Английский

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Biodiversity at risk under future cropland expansion and intensification

Nature Ecology & Evolution, Journal Year: 2017, Volume and Issue: 1(8), P. 1129 - 1135

Published: July 12, 2017

Language: Английский

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Evaluating agricultural trade-offs in the age of sustainable development

Agricultural Systems, Journal Year: 2016, Volume and Issue: 163, P. 73 - 88

Published: Oct. 18, 2016

Language: Английский

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