PMID- 33276312
OWN - NLM
STAT- MEDLINE
DCOM- 20210423
LR  - 20210423
IS  - 1873-6750 (Electronic)
IS  - 0160-4120 (Linking)
VI  - 146
DP  - 2021 Jan
TI  - Remote sensing metrics to assess exposure to residential greenness in 
      epidemiological studies: A population case study from the Eastern Mediterranean.
PG  - 106270
LID - S0160-4120(20)32225-X [pii]
LID - 10.1016/j.envint.2020.106270 [doi]
AB  - INTRODUCTION/AIMS: Application of remote sensing-based metrics of exposure to 
      vegetation in epidemiological studies of residential greenness is typically 
      limited to several standard products. The Normalized Difference Vegetation Index 
      (NDVI) is the most widely used, but its precision varies with vegetation density 
      and soil color/moisture. In areas with heterogeneous vegetation cover, the 
      Soil-adjusted Vegetation Index (SAVI) corrects for soil brightness. Linear 
      Spectral Unmixing (LSU), measures the relative contribution of different land 
      covers, and estimates percent of each over a unit area. We compared the precision 
      of NDVI, SAVI and LSU for quantifying residential greenness in areas with high 
      spatial heterogeneity in vegetation cover. METHODS: NDVI, SAVI, and LSU in a 
      300 m radius surrounding homes of 3,188 cardiac patients living in Israel 
      (Eastern Mediterranean) were derived from Landsat 30 m spatial resolution 
      imagery. Metrics were compared to assess shifts in exposure quartiles and 
      differences in vegetation detection as a function of overall greenness, climatic 
      zones, and population density, using NDVI as the reference method. RESULTS: For 
      the entire population, the dispersion (SD) of the vegetation values detected was 
      60% higher when greenness was measured using LSU compared to NDVI: mean (SD) 
      NDVI: 0.17 (0.05), LSU (%): 0.23 (0.08), SAVI: 0.12 (0.03). Importantly, with an 
      increase in population density, the sensitivity of LSU, compared to NDVI, 
      doubled: There was a 95% difference between the LSU and NDVI interquartile range 
      in the highest population density quartile vs 47% in the lowest quartile. 
      Compared to NDVI, exposures estimated by LSU resulted in 21% of patients changing 
      exposure quartiles. In urban areas, the shift in exposure quartile depended on 
      land cover characteristics. An upward shift occurred in dense urban areas, while 
      no shift occurred in high and low vegetated urban areas. CONCLUSIONS: LSU was 
      shown to outperform the commonly used NDVI in terms of accuracy and variability, 
      especially in dense urban areas. Therefore, LSU potentially improves exposure 
      assessment precision, implying reduced exposure misclassification.
CI  - Copyright (c) 2020 The Authors. Published by Elsevier Ltd.. All rights reserved.
FAU - Sadeh, Maya
AU  - Sadeh M
AD  - Department of Epidemiology and Preventive Medicine, School of Public Health, 
      Sackler School of Medicine, Tel Aviv University, Ramat Aviv, Israel. Electronic 
      address: maya.sadeh@gmail.com.
FAU - Brauer, Michael
AU  - Brauer M
AD  - School of Population & Public Health, University of British Columbia, Canada.
FAU - Dankner, Rachel
AU  - Dankner R
AD  - Department of Epidemiology and Preventive Medicine, School of Public Health, 
      Sackler School of Medicine, Tel Aviv University, Ramat Aviv, Israel; Unit for 
      Cardiovascular Epidemiology, the Gertner Institute for Epidemiology and Health 
      Policy Research, Sheba Medical Center, Tel Hashomer, Israel.
FAU - Fulman, Nir
AU  - Fulman N
AD  - Department of Geography and Human Environment, Porter School of the Environment 
      and Earth Sciences, Faculty of Exact Sciences, Tel Aviv University.
FAU - Chudnovsky, Alexandra
AU  - Chudnovsky A
AD  - AIR-O Lab, Porter School of Environment and Geosciences, Faculty of Exact 
      Sciences, Department of Geography and Human Environment, Tel Aviv University, 
      Israel. Electronic address: achudnov@tauex.tau.ac.il.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20201202
PL  - Netherlands
TA  - Environ Int
JT  - Environment international
JID - 7807270
SB  - IM
MH  - *Benchmarking
MH  - Epidemiologic Studies
MH  - Humans
MH  - Israel
MH  - Population Density
MH  - *Remote Sensing Technology
OTO - NOTNLM
OT  - Epidemiological studies
OT  - Exposure assessment
OT  - Linear spectral unmixing
OT  - Normalized difference vegetation index (NDVI)
OT  - Residential greenness
OT  - Spectral mixture analysis
EDAT- 2020/12/05 06:00
MHDA- 2021/04/24 06:00
CRDT- 2020/12/04 20:14
PHST- 2020/07/12 00:00 [received]
PHST- 2020/10/20 00:00 [revised]
PHST- 2020/11/05 00:00 [accepted]
PHST- 2020/12/05 06:00 [pubmed]
PHST- 2021/04/24 06:00 [medline]
PHST- 2020/12/04 20:14 [entrez]
AID - S0160-4120(20)32225-X [pii]
AID - 10.1016/j.envint.2020.106270 [doi]
PST - ppublish
SO  - Environ Int. 2021 Jan;146:106270. doi: 10.1016/j.envint.2020.106270. Epub 2020 
      Dec 2.