scholarly journals Assessing Snow Phenology and Its Environmental Driving Factors in Northeast China

2022 ◽  
Vol 14 (2) ◽  
pp. 262
Author(s):  
Hui Guo ◽  
Xiaoyan Wang ◽  
Zecheng Guo ◽  
Siyong Chen
Keyword(s):  
Snow Cover ◽  
Northeast China ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Water Cycle ◽  
Water Source ◽  
Driving Factors ◽  
Change Rate ◽  
Mean Temperature ◽  
Forested Areas

Snow cover is an important water source and even an Essential Climate Variable (ECV) as defined by the World Meteorological Organization (WMO). Assessing snow phenology and its driving factors in Northeast China will help with comprehensively understanding the role of snow cover in regional water cycle and climate change. This study presents spatiotemporal variations in snow phenology and the relative importance of potential drivers, including climate, geography, and the normalized difference vegetation index (NDVI), based on the MODIS snow products across Northeast China from 2001 to 2018. The results indicated that the snow cover days (SCD), snow cover onset dates (SCOD) and snow cover end dates (SCED) all showed obvious latitudinal distribution characteristics. As the latitude gradually increases, SCD becomes longer, SCOD advances and SCED delays. Overall, there is a growing tendency in SCD and a delayed trend in SCED across time. The variations in snow phenology were driven by mean temperature, followed by latitude, while precipitation, aspect and slope all had little effect on the SCD, SCOD and SCED. With decreasing temperature, the SCD and SCED showed upward trends. The mean temperature has negatively correlation with SCD and SCED and positively correlation with SCOD. With increasing latitude, the change rate of the SCD, SCOD and SCED in the whole Northeast China were 10.20 d/degree, −3.82 d/degree and 5.41 d/degree, respectively, and the change rate of snow phenology in forested areas was lower than that in nonforested areas. At the same latitude, the snow phenology for different underlying surfaces varied greatly. The correlations between the snow phenology and NDVI were mainly positive, but weak correlations accounted for a large proportion.

scholarly journals Consideration of NDVI thematic changes in density analysis and floristic composition of Wadi Yalamlam, Saudi Arabia

2018 ◽  
Vol 7 (4) ◽  
pp. 297-306 ◽  
Author(s):  
Amal Y. Aldhebiani ◽  
Mohamed Elhag ◽  
Ahmad K. Hegazy ◽  
Hanaa K. Galal ◽  
Norah S. Mufareh
Keyword(s):  
Saudi Arabia ◽  
Vegetation Cover ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Remote Sensing Data ◽  
Floristic Composition ◽  
Water Source ◽  
Shannon Index ◽  
Landsat 8 ◽  
Main Stream

Abstract. Wadi Yalamlam is known as one of the significant wadis in the west of Saudi Arabia. It is a very important water source for the western region of the country. Thus, it supplies the holy places in Mecca and the surrounding areas with drinking water. The floristic composition of Wadi Yalamlam has not been comprehensively studied. For that reason, this work aimed to assess the wadi vegetation cover, life-form presence, chorotype, diversity, and community structure using temporal remote sensing data. Temporal datasets spanning 4 years were acquired from the Landsat 8 sensor in 2013 as an early acquisition and in 2017 as a late acquisition to estimate normalized difference vegetation index (NDVI) changes. The wadi was divided into seven stands. Stands 7, 1, and 3 were the richest with the highest Shannon index values of 2.98, 2.69, and 2.64, respectively. On the other hand, stand 6 has the least plant biodiversity with a Shannon index of 1.8. The study also revealed the presence of 48 different plant species belonging to 24 families. Fabaceae (17 %) and Poaceae (13 %) were the main families that form most of the vegetation in the study area, while many families were represented by only 2 % of the vegetation of the wadi. NDVI analysis showed that the wadi suffers from various types of degradation of the vegetation cover along with the wadi main stream.

scholarly journals Developing Active Canopy Sensor-Based Precision Nitrogen Management Strategies for Maize in Northeast China

2019 ◽  
Vol 11 (3) ◽  
pp. 706 ◽  
Author(s):  
Xinbing Wang ◽  
Yuxin Miao ◽  
Rui Dong ◽  
Zhichao Chen ◽  
Yanjie Guan ◽  
...  
Keyword(s):  
Optimization Algorithm ◽  
Northeast China ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Field Experiments ◽  
Management Strategies ◽  
N Fertilizer ◽  
Yield Potential ◽  
Maize Production ◽  
N Management

Precision nitrogen (N) management (PNM) strategies are urgently needed for the sustainability of rain-fed maize (Zea mays L.) production in Northeast China. The objective of this study was to develop an active canopy sensor (ACS)-based PNM strategy for rain-fed maize through improving in-season prediction of yield potential (YP0), response index to side-dress N based on harvested yield (RIHarvest), and side-dress N agronomic efficiency (AENS). Field experiments involving six N rate treatments and three planting densities were conducted in three growing seasons (2015–2017) in two different soil types. A hand-held GreenSeeker sensor was used at V8-9 growth stage to collect normalized difference vegetation index (NDVI) and ratio vegetation index (RVI). The results indicated that NDVI or RVI combined with relative plant height (NDVI*RH or RVI*RH) were more strongly related to YP0 (R2 = 0.44–0.78) than only using NDVI or RVI (R2 = 0.26–0.68). The improved N fertilizer optimization algorithm (INFOA) using in-season predicted AENS optimized N rates better than the N fertilizer optimization algorithm (NFOA) using average constant AENS. The INFOA-based PNM strategies could increase marginal returns by 212 $ ha−1 and 70 $ ha−1, reduce N surplus by 65% and 62%, and improve N use efficiency (NUE) by 4%–40% and 11%–65% compared with farmer’s typical N management in the black and aeolian sandy soils, respectively. It is concluded that the ACS-based PNM strategies have the potential to significantly improve profitability and sustainability of maize production in Northeast China. More studies are needed to further improve N management strategies using more advanced sensing technologies and incorporating weather and soil information.

scholarly journals Teleconnections between Snow Cover Change over Siberia and Crop Growth in Northeast China

2020 ◽  
Vol 12 (18) ◽  
pp. 7632
Author(s):  
Cong Guan ◽  
Lingxue Yu ◽  
Fengqin Yan ◽  
Shuwen Zhang
Keyword(s):  
Snow Cover ◽  
Northeast China ◽  
Vegetation Index ◽  
Regional Climate ◽  
Growing Season ◽  
Crop Growth ◽  
Maximum Temperature ◽  
Satellite Measurement ◽  
Agricultural Ecosystem ◽  
Snow Cover Area

Snow cover is a sensitive indicator of climate change, and the variations in snow cover can influence the global climate system and terrestrial water cycling. However, the teleconnections between snow cover changes of the northern hemisphere and the crop growth of Northeast China (NEC) are less documented. In this study, we estimated the correlations between spring snow cover area over Siberia (SSCA) and the regional climate, as well as the crop growth in NEC based on both satellite measurement and observational climate records from 1982 to 2015. The local temperature, including minimum temperature (Tmin) in May–June, maximum temperature (Tmax), and Tmin in July–August, showed significant negative correlations with SSCA. SSCA is found to be negatively correlated to rainfall during the beginning of the growing season, while positively correlated to rainfall during the peak growing season for the agricultural ecosystem of NEC. The remote responses of the normalized difference vegetation index (NDVI) to SSCA varied across different climate zones and different growing periods. The NDVI variations over cold and dry cultivated regions exhibit negative correlations with SSCA in May–June, which is opposite for the wetter areas. The negative correlation between NDVI over the agricultural ecosystem and SSCA during the peak growing season was also detected, implying the variations in SSCA might be an essential driving factor in affecting the crop growth through modifying the regional climate of NEC. In the future, more in situ observations and model simulations should be conducted to verify our results described here, which would have significant implications for maintaining regional food security and sustainable development in Northeast China under the changing climate background.

Using portable RapidSCAN active canopy sensor for rice nitrogen status diagnosis

2017 ◽  
Vol 8 (2) ◽  
pp. 349-352 ◽  
Author(s):  
J. Lu ◽  
Y. Miao ◽  
W. Shi ◽  
J. Li ◽  
J. Wan ◽  
...  
Keyword(s):  
Northeast China ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Stem Elongation ◽  
Vegetation Indices ◽  
Sanjiang Plain ◽  
N Nutrition ◽  
Red Edge ◽  
Edge Based ◽  
On Farm

The objective of this study was to determine how much improvement red edge-based vegetation indices (VIs) obtained with the RapidSCAN sensor would achieve for estimating rice nitrogen (N) nutrition index (NNI) at stem elongation stage (SE) as compared with commonly used normalized difference vegetation index (NDVI) and ratio vegetation index (RVI) in Northeast China. Sixteen plot experiments and seven on-farm experiments were conducted from 2014 to 2016 in Sanjiang Plain, Northeast China. The results indicated that the performance of red edge-based VIs for estimation of rice NNI was better than NDVI and RVI. N sufficiency index calculated with RapidSCAN VIs (NSI_VIs) (R2=0.43–0.59) were more stable and more strongly related to NNI than the corresponding VIs (R2=0.12–0.38).

scholarly journals Dynamics of Nocturnal Evapotranspiration and Its Biophysical Controls over a Desert Shrubland of Northwest China

Forests ◽  
2021 ◽  
Vol 12 (10) ◽  
pp. 1296
Author(s):  
Xiaonan Guo ◽  
Guofei Shang ◽  
Yun Tian ◽  
Xin Jia ◽  
Tianshan Zha ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Water Cycle ◽  
Northwest China ◽  
Summer Drought ◽  
Desert Ecosystems ◽  
Growing Seasons ◽  
Daily Evapotranspiration ◽  
Eddy Covariance Measurements ◽  
Biophysical Controls

Knowledge about the dynamics and biophysical controlling mechanism of nocturnal evapotranspiration (ETN) in desert-dwelling shrub ecosystem is still lacking. Using the eddy covariance measurements of latent heat flux in a dried shrubland in northwest China, we examined the dynamics of ETN and its biophysical controls at multiple timescales during growing-seasons from 2012 to 2014. The ETN was larger in the mid-growing season (usually in mid-summer) than in spring and autumn. The maximum daily ETN was 0.21, 0.17, and 0.14 mm night−1 in years 2012–2014, respectively. At the diel scale, ETN decreased from 21:00 to 5:00, then began to increase. ETN were mainly controlled by soil volumetric water content at 30 cm depth (VWC30), by vapor pressure deficit (VPD) and normalized difference vegetation index (NDVI) at leaf expanding and expanded stage, and by air temperature (Ta) and wind speed (Ws) at the leaf coloring stage. At the seasonal scale, variations of ETN were mainly driven by Ta, VPD, and VWC10. Averaged annual ETN was 4% of daytime ET. The summer drought in 2013 and the spring drought in 2014 caused the decline of daily evapotranspiration (ET). The present results demonstrated that ETN is a significant part of the water cycle and needs to be seriously considered in ET and related studies. The findings here can help with the sustainable management of water in desert ecosystems undergoing climate change.

scholarly journals Ecohydrological Optimality in Northeast China Transect

2017 ◽  
Author(s):  
Zhentao Cong ◽  
Qinshu Li ◽  
Kangle Mo ◽  
Lexin Zhang
Keyword(s):  
Water Balance ◽  
Optimality Theory ◽  
Northeast China ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Precipitation Amount ◽  
Canopy Cover ◽  
Sensitivity Analyses ◽  
Leaf Angle ◽  
Vegetation Distribution

Abstract. Northeast China Transect (NECT) is one of International Geosphere-Biosphere Program (IGBP) terrestrial transects., where there is a significant precipitation gradient from east to west, as well as a vegetation transition of forest-grasslands-dessert. It is interesting to understand vegetation distribution and dynamics under water limitation in this transect. We take canopy cover (M), derived from Normalized Difference Vegetation Index (NDVI), as an index to describe the properties of vegetation distribution and dynamics in NECT. In Eagleson's ecohydrological optimality theory, the optimal canopy cover (M*) is determined by the trade-off of water supply depending on water balance and water demand depending on canopy transpiration. We apply Eagleson’s ecohydrological optimality method in NECT based on data from 2000 to 2013 to get M*, then compare with M from NDVI, furthermore to discuss the sensitivity of M* to vegetation properties and climate factors. The result indicates that the average M* fits the actual M well (for forest, M* = 0.822 while M = 0.826 for grassland, M* = 0.353 while M = 0.352; the correlation coefficient between M and M* is 0.81). The result of water balance also matches the field-measured data in references. The sensitivity analyses show that M* decreases with the increase of LAI, stem fraction, temperature, while increases with the increase of leaf angle and precipitation amount. The Eagleson's ecohydrological optimality method offers a quantitative way to understand the impacts of climate change to canopy cover quantitatively, and provides guidelines for eco-restoration projects.

scholarly journals Ecohydrological Optimality in Northeast China Transect

2016 ◽  
Author(s):  
Qinshu Li ◽  
Zhentao Cong ◽  
Kangle Mo ◽  
Lexin Zhang
Keyword(s):  
Optimality Theory ◽  
Vegetation Cover ◽  
Northeast China ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Precipitation Amount ◽  
Canopy Cover ◽  
Leaf Angle ◽  
Vegetation Transition ◽  
Impacts Of Climate Change

Abstract. Northeast China Transect (NECT) is one of International Geosphere-Biosphere Program (IGBP) terrestrial transects. In this transect area, there is a significant precipitation gradient from east to west, as well as a vegetation transition of forest-grasslands-dessert. In this paper, we use vegetation cover as an index to describe the properties of vegetation distribution and dynamics in NECT. Normalized Difference Vegetation Index (NDVI) is used to derive the actual vegetation cover M, while Eagleson's ecohydrological optimality theory is applied to calculate the optimal canopy cover M* along NECT. The result indicates that the theoretical M* fits the actual M well (for forest, M* = 0.822 while M = 0.826; for grassland, M* = 0.353 while M = 0.352; the correlation coefficient between M and M* is 0.81). Water balance are also calculated using Eagleson's theory. The result is compared to the field measured data and shows a relative good match, which further demonstrates the reliability of the ecohydrological optimality theory in this area. M* increases with the decrease of LAI, stem fraction, temperature, and the increase of leaf angle and precipitation amount. The ecohydrological optimality method offers a quantitative way to analyse the impacts of climate change to canopy cover quantitatively, thus providing advices for eco-restoration projects.

scholarly journals Advanced Method to Capture the Time-Lag Effects between Annual NDVI and Precipitation Variation Using RNN in the Arid and Semi-Arid Grasslands

Water ◽  
2019 ◽  
Vol 11 (9) ◽  
pp. 1789
Author(s):  
Taosuo Wu ◽  
Feng Feng ◽  
Qian Lin ◽  
Hongmei Bai
Keyword(s):  
Northeast China ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Time Lag ◽  
Time Lags ◽  
Lag Effects ◽  
Precipitation Variation ◽  
Arid Grasslands ◽  
Successful Capture ◽  
Semi Arid

The latest research indicates that there are time-lag effects between the normalized difference vegetation index (NDVI) and the precipitation variation. It is well known that the time-lags are different from region to region, and there are time-lags for the NDVI itself correlated to the precipitation. In the arid and semi-arid grasslands, the annual NDVI has proved not only to be highly dependent on the precipitation of the concurrent year and previous years, but also the NDVI of previous years. This paper proposes a method using recurrent neural network (RNN) to capture both time-lags of the NDVI with respect to the NDVI itself, and of the NDVI with respect to precipitation. To quantitatively capture these time-lags, 16 years of the NDVI and precipitation data are used to construct the prediction model of the NDVI with respect to precipitation. This study focuses on the arid and semi-arid Hulunbuir grasslands dominated by perennials in northeast China. Using RNN, the time-lag effects are captured at a 1 year time-lag of precipitation and a 2 year time-lag of the NDVI. The successful capture of the time-lag effects provides significant value for the accurate prediction of vegetation variation for arid and semi-arid grasslands.

Sign in / Sign up

Export Citation Format

Share Document