Summary of the 2017 South Southeast Research Initiative (SARI) Agricultural Workshop

South/Southeast Asian countries are growing rapidly in terms of population, industrialization, andurbanization. As a result of this growth, one of the key policy challenges facing the region is foodsecuritythat is, those conditions when all people, at all times, have physical and economic access tosufficient, safe and nutritious food that meets their dietary needs and food preferences for an active andhealthy life.1 Although total food production has increased in the region since 1960 due to land areahaving been converted to agricultural use, more recently it has decreased, mostly due to loss ofproductive agricultural land due to urbanization and industrial development. Furthermore, the region isexperiencing variability in the timing of the monsoon and extreme weather events, resulting in droughtor flooding, which impact agricultural production. Monitoring crop production in a timely manner isessential to predict and prepare for disruptions in the food supply. To achieve such timely monitoringrequires improved and uptodate information on agricultural landuse practices.Although there has been significant progress in remote sensing and geospatial technologies over thepast few decades, there has been little emphasis placed on developing robust methods for operationalmapping and monitoring of areas devoted to crops. In South/Southeast Asia generally, most mappingefforts to date have focused on the broader classification of land cover types and generalized croplandareas into a single or limited number of thematic classes. Only a few countries have access to uptodatecrop type information. There is an urgent need to make this nearrealtime information morereadily available to stakeholders and to enhance national and regional operational systems formonitoring agricultural crops.

The South/Southeast Asia Research Initiative (SARI) Update and Meeting Objectives

Land Use/Cover Change (LU/CC) is one of the most important types of environmental change in South and Southeast Asian countries. Several studies suggest that LU/CC in these countries is in large part driven by population growth and economic development. In the region, changes that are most common include urban expansion, agricultural land loss, land abandonment, deforestation, logging, reforestation, etc. To address the research needs and priorities in the region, a regional initiative entitled South Southeast Asia Regional Initiative (SARI) has been developed involving US and regional scientists. The initiative is funded by NASA Land Cover, Land Use Change program. The goal of SARI is to integrate state-of-the-art remote sensing, natural sciences, engineering and social sciences to enrich LU/CC science in South Southeast Asian countries. In the presentation, LU/CC change research in SARI countries will be highlighted including the drivers of change. For example, in South Asia, forest cover has been increasing in countries like India, Nepal and Bhutan due to sustainable afforestation measures; whereas, large-scale deforestation in Southeast Asian countries is still continuing, due to oil palm plantation expansion driven by the international market demand in Malaysia and Indonesia. With respect to urbanization, South and Southeast Asian countries contain 23 megacities, each with more than 10 million people. Rapid urbanization is driving agricultural land loss and agricultural intensification has been increasing due to less availability of land for growing food crops such as in India, Vietnam, and Thailand. The drivers of LUCC vary widely in the region and include such factors as land tenure, local economic development, government policies, inappropriate land management, land speculation, improved road networks, etc. In addition, variability in the weather, climate, and socioeconomic factors also drive LU/CC resulting in disruptions of biogeochemical cycles, radiation and the surface energy balance of the atmosphere. The presentation will also highlight SARI collaborative activities with space agencies, universities and non-government organizations including data sharing mechanisms in the region

Evaluation of Sentinel-1A Data For Above Ground Biomass Estimation in Different Forests in India

Use of remote sensing data for mapping and monitoring of forest biomass across large spatial scales can aid in addressing uncertainties in carbon cycle. Earlier, several researchers reported on the use of Synthetic Aperture Radar (SAR) data for characterizing forest structural parameters and the above ground biomass estimation. However, these studies cannot be generalized and the algorithms cannot be applied to all types of forests without additional information on the forest physiognomy, stand structure and biomass characteristics. The radar backscatter signal also saturates as forest parameters such as biomass and the tree height increase. It is also not clear how different polarizations (VV versus VH) impact the backscatter retrievals in different forested regions. Thus, it is important to evaluate the potential of SAR data in different landscapes for characterizing forest structural parameters. In this study, the SAR data from Sentinel-1A has been used to characterize forest structural parameters including the above ground biomass from tropical forests of India. Ground based data on tree density, basal area and above ground biomass data from thirty-eight different forested sites has been collected to relate to SAR data. After the pre-processing of Sentinel 1-A data for radiometric calibration, geo-correction, terrain correction and speckle filtering, the variability in the backscatter signal in relation tree density, basal area and above biomass density has been investigated. Results from the curve fitting approach suggested exponential model between the Sentinel-1A backscatter versus tree density and above ground biomass whereas the relationship was almost linear with the basal area in the VV polarization mode. Of the different parameters, tree density could explain most of the variations in backscatter. Both VV and VH backscatter signals could explain only thirty and thirty three percent of variation in above biomass in different forest sites of India. Results also suggested saturation of the Sentinel-1A backscatter signal around hundred tonnes per hectare for VV polarization and one hundred and forty five tonnes per hectare for VH polarization. The presentation will highlight the above results in addition to potentials and limitations of Sentinel-1A data for retrieving forest structural parameters. Also, background information on different forest types of India, biomass variations and forest type mapping efforts in the region will be presented

The South/Southeast Asia Research Initiative (SARI) - Priorities and Progress

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The South Southeast Asia Research Initiative (SARI)

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Atmospheric Correction of Sentinel 2A Data and Influence on Winter Wheat Phenology in Kabul Region, Afghanistan

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A Comparison of Spectral Angle Mapper and Artificial Neural Network Classifiers Combined with Landsat TM Imagery Analysis for Obtaining Burnt Area Mapping

Satellite remote sensing, with its unique synoptic coverage capabilities, can provide accurate and immediately valuable information on fire analysis and post-fire assessment, including estimation of burnt areas. In this study the potential for burnt area mapping of the combined use of Artificial Neural Network (ANN) and Spectral Angle Mapper (SAM) classifiers with Landsat TM satellite imagery was evaluated in a Mediterranean setting. As a case study one of the most catastrophic forest fires, which occurred near the capital of Greece during the summer of 2007, was used. The accuracy of the two algorithms in delineating the burnt area from the Landsat TM imagery, acquired shortly after the fire suppression, was determined by the classification accuracy results of the produced thematic maps. In addition, the derived burnt area estimates from the two classifiers were compared with independent estimates available for the study region, obtained from the analysis of higher spatial resolution satellite data. In terms of the overall classification accuracy, ANN outperformed (overall accuracy 90.29%, Kappa coefficient 0.878) the SAM classifier (overall accuracy 83.82%, Kappa coefficient 0.795). Total burnt area estimates from the two classifiers were found also to be in close agreement with the other available estimates for the study region, with a mean absolute percentage difference of ∼1% for ANN and ∼6.5% for SAM. The study demonstrates the potential of the examined here algorithms in detecting burnt areas in a typical Mediterranean setting

Scenario-led habitat modelling of land use change impacts on key species

© 2015 Gearyet al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Accurate predictions of the impacts of future land use change on species of conservation concern can help to inform policy-makers and improve conservation measures. If predictions are spatially explicit, predicted consequences of likely land use changes could be accessible to land managers at a scale relevant to their working landscape. We introduce a method, based on open source software, which integrates habitat suitability modelling with scenario-building, and illustrate its use by investigating the effects of alternative land use change scenarios on landscape suitability for black grouse Tetrao tetrix. Expert opinion was used to construct five near-future (twenty years) scenarios for the 800 km 2 study site in upland Scotland. For each scenario, the cover of different land use types was altered by 5-30% from 20 random starting locations and changes in habitat suitability assessed by projecting a MaxEnt suitability model onto each simulated landscape. A scenario converting grazed land to moorland and open forestry was the most beneficial for black grouse, and 'increased grazing' (the opposite conversion) the most detrimental. Positioning of new landscape blocks was shown to be important in some situations. Increasing the area of opencanopy forestry caused a proportional decrease in suitability, but suitability gains for the 'reduced grazing' scenario were nonlinear. 'Scenario-led' landscape simulation models can be applied in assessments of the impacts of land use change both on individual species and also on diversity and community measures, or ecosystem services. A next step would be to include landscape configuration more explicitly in the simulation models, both to make them more realistic, and to examine the effects of habitat placement more thoroughly. In this example, the recommended policy would be incentives on grazing reduction to benefit black grouse