Conference Agenda

Overview and details of the sessions of this conference. Please select a date or location to show only sessions at that day or location. Please select a single session for detailed view (with abstracts and downloads if available).

Session Overview
301R: Multi-Source Land Imaging for Land-Cover/Land-Use Change Studies: Prospects for Early Career Scientists
Thursday, 25/Apr/2019:
3:15pm - 4:45pm

Session Chair: Garik Gutman
Location: UniS-A -122
UniS Building, room A -122, basement, 72 seats
Session Topics:
How do we support transformation?

Session Abstract

A synergistic use of spectral data with moderate to high spatial resolution from more than one source is getting momentum due to successful launches under the ESA Sentinel program. Landsat and Sentinel-2 optical data are now used synergistically by many researchers, often combined with Sentinel-1 radar data. Efficient and synergistic use of these sensor data increases the number of observations available for studies. The proposed session aims at bringing together early career scientists (within 5 years from getting their PhD) who work on innovative data-fusion approaches to study LCLUC issues by applying high-to-medium (1-30m) resolution data on various landscapes. On the other hand, social and economic science research plays an important role in land-change science and includes analyses of the impacts of changes in human behavior at various levels on land use, studies of the resultant impacts of land-use change on society, or how the social and economic aspects of land-use systems adapt to climate change. Novel ideas on synergistic use of data from various sensors, including thermal IR and hyperspectral, to make breakthrough advances to the next level of understanding the LCLUC underpinning science are welcome.

Session Organizer: Garik Gutman

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Full talk
ID: 478 / 301R: 1
301R Multi-source land imaging for land-cover/land-use change studies: prospects for early career scientists
Keywords: Planet, Sentinel, smallholder farm, India, food security

Using Planet and Sentinel data to map smallholder farms and close yield gaps in eastern India

Meha Jain

University of Michigan, United States of America

Climate change, natural resource degradation, and growing populations are challenging food security across the globe. This is particularly true in smallholder systems, which will face some of the greatest food security issues over the coming decades. In order to increase food production, satellite data can be used to quantify existing yield gaps, identify low yielding regions, and estimate yield responses to environmental change. Yet, to date, it has been challenging to map characteristics of smallholder farms given the small size (< 0.3 ha) of individual fields and the heterogeneous management across farms, which lead to issues of mixed pixels when using moderate resolution imagery (e.g., 30 m to 250 m). We use new higher-resolution satellite products (3 m to 10 m), including Sentinel-1, Sentinel-2, and Planet, to map field and sub-field level characteristics of smallholder farms in India. Specifically, we map crop type, sow date, and yield across central Bihar in eastern India, and use these data to identify the causes of wheat yield gaps and the extent to which changes in management can increase production. In addition, we show how these high-resolution datasets can be used to target yield increasing interventions to the right locations, doubling yield gain for the same intervention cost and effort. This work highlights the ability of new high-resolution satellite sensors to map field-level characteristics of smallholder farms and how these data can be used to target the right interventions to the right locations to increase the efficacy of sustainable intensification interventions.

Full talk
ID: 517 / 301R: 2
301R Multi-source land imaging for land-cover/land-use change studies: prospects for early career scientists
Keywords: Biodiversity loss, deforestation, Cameroon, Ebo Forest, Equatorial Africa, logging, oil palm, roads, wildlife

Geospatial Analysis of land-cover change threats to tropical forests and biodiversity in Littoral Region of Cameroon

Mahmoud Ibrahim Mahmoud1, Mason Campbell2, William Laurance3

1National Oil Spill Detection and Response Agency (NOSDRA); 2James Cook University (JCU) Cairns; 3James Cook University (JCU) Cairns

Tropical forest regions in equatorial Africa are threatened with degradation, deforestation and biodiversity loss as a result of land-cover change. We investigated historical land-cover dynamics in unprotected forested areas of the Littoral Region in south-western Cameroon during 1975–2017, to detect changes that may influence this important biodiversity and wildlife area. Processed historical multi-temporal and multi-sensor Landsat imagery in synergy with Google Earth and Planet Labs high-resolution images were used to map and monitor changes in land use and land cover. During 1975–2017 the area of high-value forest landscapes decreased by ~420,000 ha, and increasing forest fragmentation caused a decline of 12% in the largest patch index. Conversely, disturbed vegetation, cleared areas and urban areas all expanded in extent, by 32% (~400,000 ha), 5.6% (~26,800 ha) and 6.6% (~78,631 ha), respectively. The greatest increase was in the area converted to oil palm plantations (~26,893 ha), followed by logging and land clearing (~34,838 ha), all of which were the major factors driving deforestation in the study area. Our findings demonstrate the role of multi-source land imaging for land-cover/land-use change studies. This remote sensing study highlights the increasing threats facing the wider Littoral Region, which includes Mount Nlonako and Ebo Forest, both of which are critical areas for regional conservation and the latter a proposed National Park and the only sizable area of intact forest in the region. Intact forest in the Littoral Region, and in particular at Ebo, merits urgent protection.

Full talk
ID: 309 / 301R: 3
333R Mapping land system through coupling the biophysical and socioeconomic attributes based on remote sensing and big data approaches
Keywords: cropping intensity, land use, agricultural intensification, crop phenology, data fusion, uncertainty

From multi-cropping index to multi-cropping frequency: observing cropland use intensity at a finer scale

Qiangyi Yu, Wenbin Wu, Mingtao Xiang

Institute of Agriculture Resource and Regional Planning, Chinese Academy of Agricultural Sciences, China, People's Republic of

Accurate observations on multi-cropping practices are required to better understand the status and potential of cropland use intensity. However, previous studies largely relied on multi-cropping index (MCI), which only measures the average state for an administrative unit. In this study we use various satellite images to observe the multi-cropping frequency (MCF), in order to know how MCF adds spatial information on MCI, and how temporal and spatial resolution affects the observation of MCF. We apply the NDVI time-series curve to observe cropland phenology, and subsequently to estimate the MCF by counting the number of peaks. Three MCF maps are developed for the experimental region (Jinxian County, Poyang Lake Plain, South China) in the year 2015 based on MODIS, GF (GF-1/WFV) and GF-MODIS fusion, which represent a character of higher temporal resolution, higher spatial resolution, and higher temporal-spatial resolution, respectively. All these maps have detected multi-cropping patterns, including various single-, double- and triple-cropping grids: 90.38%, 9.53%, and 0.08% from the MODIS map, 70.32%, 29.27%, and 0.41% from the GF map, and 64.85%, 33.62%, and 1.53% from the GF-MODIS map. The confusion matrix containing 161 field samples shows that the overall accuracy and Kappa coefficient are 62.11% and 0.34, 78.88% and 0.65, and 90.06% and 0.84, for the MODIS, GF, and GF-MODIS maps, respectively. Moreover, the statistics show that the county-level MCI is 1.42, while the aggregated MCI for these maps are 1.10, 1.30, and 1.37, respectively. Our study indicates that the GF-MODIS map not only has highest accuracy but also has a closest estimation on MCI. It implies that higher spatial resolution is the first necessity for mapping the MCF in the landscape fragmented region. Higher temporal resolution is also important to distinguish the nuances on MCF induced by crop rotation.

Full talk
ID: 424 / 301R: 4
333R Mapping land system through coupling the biophysical and socioeconomic attributes based on remote sensing and big data approaches
Keywords: land-use changes, spatiotemporal characteristics, remote sensing, Major Function-oriented Zones, China

National land cover/use change in china from 1990 to 2015

Wenhui Kuang, Shuwen Zhang, Tao Pan, Wenfeng Chi, Guoming Du, Fanhao Meng, Xiaoyong Li, Gencheng Su, Zherui Yin

Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, China, People's Republic of

Land-use/land-cover changes (LUCCs) have links to both human and nature interactions. China’s Land-Use/cover Datasets (CLUDs) were updated regularly at 5-year intervals during 1990–2015, with standard procedures based on Landsat TMETM+OLI and GF-2 remote sensing images. A land-use dynamic regionalization method was proposed to analyze major land-use conversions. The spatiotemporal characteristics, differences, and causes of land-use changes at a national scale were then examined. The main findings are summarized as follows: land-use changes (LUCs) across China indicated a significant variation in spatial and temporal characteristics in the last 20 years (1990–2010). The area of cropland change decreased in the south and increased in the north, but the total area remained almost unchanged. The reclaimed cropland was shifted from the northeast to the northwest. The built-up lands expanded rapidly, were mainly distributed in the east, and gradually spread out to central and western China. Woodland decreased first, and then increased, but desert area was the opposite. Grassland continued decreasing. Different spatial patterns of LUCs in China were found between the late 20th century and the early 21st century. New characteristics of land-use changes emerged in different regions of China in 2010–2015. The built-up land in eastern China expanded continually, and the total area of cropland decreased, both at decreasing rates. The rates of built-up land expansion and cropland shrinkage were accelerated in central China. The rates of built-up land expansion and cropland growth increased in western China, while the decreasing rate of woodland and grassland accelerated. In northeastern China, built-up land expansion slowed continually, and cropland area increased slightly accompanied by the conversions between paddy land and dry land. Besides, woodland and grassland area decreased in northeastern China. The characteristics of land-use changes in eastern China were essentially consistent with the spatial govern and control requirements of the optimal development zones and key development zones according to the Major Function-oriented Zones Planning implemented during the 12th Five-Year Plan (2011–2015). It was a serious challenge for the central government of China to effectively protect the reasonable layout of land use types dominated with the key ecological function zones and agricultural production zones in central and western China. Furthermore, the local governments should take effective measures to strengthen the management of territorial development in future.

Full talk
ID: 839 / 301R: 5
316R High resolution remote sensing for understanding dynamics of forest and grassland systems
Keywords: Canopy chlorophyll, vegetation traits, scerin, sentinel, landsat, high temporal frequency, medium and high spatial and spectral resolution monitoring

Satellite monitoring of of vegetation traits and function within the South, Central and East European region (SCERIN) – Czech Republic

Jana Albrechtova1, Petr Lukes2, Zuzana Lhotakova1, Lucie Kupkova1, Eva Neuwirthova1, Lucie Cervena1, Ruzena Janoutova2, Lucie Homolova2, Marketa Potuckova1, Petya Campbell3,4

1Charles University, Faculty of Science, Czech Republic; 2Global Change Research Institute, Czech Academy of Sciences, Brno, Czech Republic; 3University of Maryland Baltimore County, United States of America; 4NASA Goddard Space Flight Center

Utilizing the NASA Multi-Source Land Imaging (MuSLI) Harmonized Landsat-8 and Sentinel-2 (HLS) high-frequency time series and other archive data (e.g. RapidEye, PlanetScope, possibly World View), a NASA coordinated research effort is aimed at developing canopy Chlorophyll (Chl) content maps, and assess the seasonal changes in chlorophyll content and other vegetation traits for various ecosystems. The main project goal is a complex evaluation of seasonal dynamics of vegetation physiological status (characterised by contents of photosynthetic pigments) in ecosystems with different species heterogeneity of plant communities. The method is utilizing phenological course of vegetation traits derived from remote sensing data with different spatial, spectral and temporal resolutions. We aim to produce consistent medium resolution (30m) Chl prototypes and robust algorithms that can be scaled reliably to regional and continental scales. To generate robust workflows, the algorithms is tested, refined and validated over established research areas and instrumented sites representing key ecosystems (different forests, crop agroecosytems, and altitudinal tundra). Our research sites include areas in the South Central and Eastern European (SCERIN) region,, particularly Czech Republic.

The study is based on NASA-funded project 17-LCLUC17-0013 (2018-2020) and the project INTER-ACTION of the Ministry of Education of Czech Republic . We acknowledge the role of GOFC-GOLD SCERIN in facilitating collaboration, which resulted in this research.

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