Improved methods for estimating monthly and growing season ET using METRIC applied to moderate resolution satellite imagery

<p><strong>Abstract.</strong> Accurate information about the built-up area in a city or town is essential for urban planners for proper planning of urban infrastructure facilities and other basic amenities. The normalized difference indices available in literature for built-up area extraction are mostly based on moderate resolution images such as Landsat Thematic Mapper (TM) and enhanced TM (ETM+) and may not be applicable for high resolution images such as Sentinel-2A. In the present study, an attempt has been made to extract the built-up area from Sentinel-2A satellite data of Chennai, India using normalized difference index (NDI) with different band combinations. It was found that the built-up area was clearly distinguishable when the index value ranges between &minus;0.29 and &minus;0.09 in blue and near-infrared (NIR) band combination. Post extraction editing using Google satellite imagery was also attempted to improve the extraction results. The results showed an overall accuracy of 90% and Kappa value of 0.785. Same approach when applied for another area also yields good results with overall accuracy of 92% and Kappa value of 0.83. As the proposed approach is simple to understand, yields accurate results and requires only open source data, the same can be used for extracting the built-up area using Sentinel-2A and Google satellite imagery.</p>

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Forest Phenology Dynamics to Climate Change and Topography in a Geographic and Climate Transition Zone: The Qinling Mountains in Central China

Forests ◽

10.3390/f10111007 ◽

2019 ◽

Vol 10 (11) ◽

pp. 1007 ◽

Cited By ~ 11

Author(s):

Haoming Xia ◽

Yaochen Qin ◽

Gary Feng ◽

Qingmin Meng ◽

Yaoping Cui ◽

...

Keyword(s):

Climate Change ◽

Vegetation Index ◽

Climate Change Impacts ◽

Growing Season ◽

Biological Indicators ◽

Central China ◽

Qinling Mountains ◽

Moderate Resolution ◽

Moderate Resolution Imaging Spectroradiometer ◽

Climate Transition

Forest ecosystems in an ecotone and their dynamics to climate change are growing ecological and environmental concerns. Phenology is one of the most critical biological indicators of climate change impacts on forest dynamics. In this study, we estimated and visualized the spatiotemporal patterns of forest phenology from 2001 to 2017 in the Qinling Mountains (QMs) based on the enhanced vegetation index (EVI) from MODerate-resolution Imaging Spectroradiometer (MODIS). We further analyzed this data to reveal the impacts of climate change and topography on the start of the growing season (SOS), end of the growing season (EOS), and the length of growing season (LOS). Our results showed that forest phenology metrics were very sensitive to changes in elevation, with a 2.4 days delayed SOS, 1.4 days advanced EOS, and 3.8 days shortened LOS for every 100 m increase in altitude. During the study period, on average, SOS advanced by 0.13 days year−1, EOS was delayed by 0.22 days year−1, and LOS increased by 0.35 day year−1. The phenological advanced and delayed speed across different elevation is not consistent. The speed of elevation-induced advanced SOS increased slightly with elevation, and the speed of elevation-induced delayed EOS shift reached a maximum value of 1500 m from 2001 to 2017. The sensitivity of SOS and EOS to preseason temperature displays that an increase of 1 °C in the regionally averaged preseason temperature would advance the average SOS by 1.23 days and delay the average EOS by 0.72 days, respectively. This study improved our understanding of the recent variability of forest phenology in mountain ecotones and explored the correlation between forest phenology and climate variables in the context of the ongoing climate warming.

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Analysis of Drought Impact on Croplands from Global to Regional Scale: A Remote Sensing Approach

Remote Sensing ◽

10.3390/rs12244030 ◽

2020 ◽

Vol 12 (24) ◽

pp. 4030

Author(s):

Gohar Ghazaryan ◽

Simon König ◽

Ehsan Eyshi Rezaei ◽

Stefan Siebert ◽

Olena Dubovyk

Keyword(s):

Remote Sensing ◽

Crop Production ◽

Regional Scale ◽

Wheat Yield ◽

Growing Season ◽

Early Warning Systems ◽

Agricultural Drought ◽

Eastern Africa ◽

Stress Index ◽

Moderate Resolution

Drought is one of the extreme climatic events that has a severe impact on crop production and food supply. Our main goal is to test the suitability of remote sensing-based indices to detect drought impacts on crop production from a global to regional scale. Moderate resolution imaging spectroradiometer (MODIS) based imagery, spanning from 2001 to 2017 was used for this task. This includes the normalized difference vegetation index (NDVI), land surface temperature (LST), and the evaporative stress index (ESI), which is based on the ratio of actual to potential evapotranspiration. These indices were used as indicators of drought-induced vegetation conditions for three main crops: maize, wheat, and soybean. The start and end of the growing season, as observed at 500 m resolution, were used to exclude the time steps that are outside of the growing season. Based on the three indicators, monthly standardized anomalies were estimated, which were used for both analyses of spatiotemporal patterns of drought and the relationship with yield anomalies. Anomalies in the ESI had higher correlations with maize and wheat yield anomalies than other indices, indicating that prolonged periods of low ESI during the growing season are highly correlated with reduced crop yields. All indices could identify past drought events, such as the drought in the USA in 2012, Eastern Africa in 2016–2017, and South Africa in 2015–2016. The results of this study highlight the potential of the use of moderate resolution remote sensing-based indicators combined with phenometrics for drought-induced crop impact monitoring. For several regions, droughts identified using the ESI and LST were more intense than the NDVI-based results. We showed that these indices are relevant for agricultural drought monitoring at both global and regional scales. They can be integrated into drought early warning systems, process-based crop models, as well as can be used for risk assessment and included in advanced decision-support frameworks.

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A spatio-temporal analysis of fire recurrence and extent for semi-arid savanna ecosystems in southern Africa using moderate-resolution satellite imagery

Journal of Environmental Management ◽

10.1016/j.jenvman.2012.01.024 ◽

2012 ◽

Vol 100 ◽

pp. 72-85 ◽

Cited By ~ 30

Author(s):

Narcisa G. Pricope ◽

Michael W. Binford

Keyword(s):

Satellite Imagery ◽

Southern Africa ◽

Temporal Analysis ◽

Fire Recurrence ◽

Moderate Resolution ◽

Spatio Temporal ◽

Semi Arid

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Evaluation of Satellite Imagery for Monitoring Pacific Walruses at a Large Coastal Haulout

Remote Sensing ◽

10.3390/rs13214266 ◽

2021 ◽

Vol 13 (21) ◽

pp. 4266

Author(s):

Anthony S. Fischbach ◽

David C. Douglas

Keyword(s):

Satellite Imagery ◽

Chukchi Sea ◽

Late Summer ◽

Visual Interpretation ◽

Odobenus Rosmarus Divergens ◽

Radar Imagery ◽

Moderate Resolution ◽

Satellite Sensors ◽

Sar Imagery ◽

Sentinel 2

Pacific walruses (Odobenus rosmarus divergens) are using coastal haulouts in the Chukchi Sea more often and in larger numbers to rest between foraging bouts in late summer and autumn in recent years, because climate warming has reduced availability of sea ice that historically had provided resting platforms near their preferred benthic feeding grounds. With greater numbers of walruses hauling out in large aggregations, new opportunities are presented for monitoring the population. Here we evaluate different types of satellite imagery for detecting and delineating the peripheries of walrus aggregations at a commonly used haulout near Point Lay, Alaska, in 2018–2020. We evaluated optical and radar imagery ranging in pixel resolutions from 40 m to ~1 m: specifically, optical imagery from Landsat, Sentinel-2, Planet Labs, and DigitalGlobe, and synthetic aperture radar (SAR) imagery from Sentinel-1 and TerraSAR-X. Three observers independently examined satellite images to detect walrus aggregations and digitized their peripheries using visual interpretation. We compared interpretations between observers and to high-resolution (~2 cm) ortho-corrected imagery collected by a small unoccupied aerial system (UAS). Roughly two-thirds of the time, clouds precluded clear optical views of the study area from satellite. SAR was unaffected by clouds (and darkness) and provided unambiguous signatures of walrus aggregations at the Point Lay haulout. Among imagery types with 4–10 m resolution, observers unanimously agreed on all detections of walruses, and attained an average 65% overlap (sd 12.0, n 100) in their delineations of aggregation boundaries. For imagery with ~1 m resolution, overlap agreement was higher (mean 85%, sd 3.0, n 11). We found that optical satellite sensors with moderate resolution and high revisitation rates, such as PlanetScope and Sentinel-2, demonstrated robust and repeatable qualities for monitoring walrus haulouts, but temporal gaps between observations due to clouds were common. SAR imagery also demonstrated robust capabilities for monitoring the Point Lay haulout, but more research is needed to evaluate SAR at haulouts with more complex local terrain and beach substrates.

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Response of net primary productivity to grassland phenological changes in Xinjiang, China

PeerJ ◽

10.7717/peerj.10650 ◽

2021 ◽

Vol 9 ◽

pp. e10650

Author(s):

Renping Zhang ◽

Jing Guo ◽

Gang Yin

Keyword(s):

Primary Productivity ◽

Net Primary Productivity ◽

Growing Season ◽

Casa Model ◽

Phenological Changes ◽

Moderate Resolution ◽

Resolution Imaging ◽

Moderate Resolution Imaging Spectroradiometer ◽

Phenological Metrics ◽

The Impact

Determining the relationship between net primary productivity (NPP) and grassland phenology is important for an in-depth understanding of the impact of climate change on ecosystems. In this study, the NPP of grassland in Xinjiang, China, was simulated using the Carnegie-Ames-Stanford approach (CASA) model with Moderate Resolution Imaging Spectroradiometer (MODIS) grassland phenological (MCD12Q2) data to study trends in phenological metrics, grassland NPP, and the relations between these factors from 2001–2014. The results revealed advancement of the start of the growing season (SOS) for grassland in most regions (55.2%) in Xinjiang. The percentage of grassland area in which the end of the growing season (EOS) was delayed (50.9%) was generally the same as that in which the EOS was advanced (49.1%). The percentage of grassland area with an increase in the length of the growing season (LOS) for the grassland area (54.6%) was greater than that with a decrease in the LOS (45.4%). The percentage of grassland area with an increase in NPP (61.6%) was greater than that with a decrease in NPP (38.4%). Warmer regions featured an earlier SOS and a later EOS and thus a longer LOS. Regions with higher precipitation exhibited a later SOS and an earlier EOS and thus a shorter LOS. In most regions, the SOS was earlier, and spring NPP was higher. A linear statistical analysis showed that at various humidity (K) levels, grassland NPP in all regions initially increased but then decreased with increasing LOS. At higher levels of K, when NPP gradually increased, the LOS gradually decreased.

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