scholarly journals Exploring Climate Change Effects on Vegetation Phenology by MOD13Q1 Data: The Piemonte Region Case Study in the Period 2001–2019

Agronomy ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 555
Author(s):  
Filippo Sarvia ◽  
Samuele De Petris ◽  
Enrico Borgogno-Mondino
Keyword(s):  
Climate Change ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Arable Land ◽  
Observation Data ◽  
Climate Change Effects ◽  
Earth Observation Data ◽  
Nw Italy ◽  
Terra Modis ◽  
Phenological Metrics

Rising temperature, rainfall, and wind regime changes, increasing of frequency and intensity of extreme events are only some of the effects of climate change affecting the agro-forestry sector. Earth Observation data from satellite missions (often available for free) can certainly support analysis of climate change effects on vegetation, making possible to improve land management in space and time. Within this context, the present work aims at investigating natural and agricultural vegetation, as mapped by Corine Land Cover (CLC) dataset, focusing on phenological metrics trends that can be possibly conditioned by the ongoing climate-change. The study area consists of the entire Piemonte region (NW-Italy). MOD13Q1-v6 dataset from TERRA MODIS mission was used to describe pluri-annual (2001–2019) phenological behavior of vegetation focusing on the following CLC classes: Non-irrigated arable land, Vineyards, Pastures, and Forests. After computing and mapping some phenological metrics as derivable from the interpretation of at-pixel level NDVI (Normalized Difference Vegetation Index) temporal profile, we found that the most significant one was the maximum annual NDVI (MaxNDVI). Consequently, its trend was analyzed at CLC class level for the whole Piemonte region. Natural and semi-natural vegetation classes (Pastures and Forests) were furtherly investigated testing significance of the Percent Total Variation (TV %) of MaxNDVI in the period 2001–2019 for different altitude classes. Results proved that Non-irrigated arable land showed a not significant trend of MaxNDVI; differently, vineyards and forests showed a significant increasing one. Concerning TV %, it was found that it increases with altitude for the Forests CLC class, while it decreases with altitude for the pastures class.

scholarly journals Vegetation Change and Its Response to Climate Change between 2000 and 2016 in Marshes of the Songnen Plain, Northeast China

2020 ◽  
Vol 12 (9) ◽  
pp. 3569 ◽  
Author(s):  
Yanji Wang ◽  
Xiangjin Shen ◽  
Ming Jiang ◽  
Xianguo Lu
Keyword(s):  
Climate Change ◽  
Vegetation Change ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Growing Season ◽  
Maximum Temperature ◽  
Climate Data ◽  
Songnen Plain ◽  
Asymmetric Effects ◽  
Climate Change Effects

Songnen Plain is a representative semi-arid marshland in China. The Songnen Plain marshes have undergone obvious loss during the past decades. In order to protect and restore wetland vegetation, it is urgent to investigate the vegetation change and its response to climate change in the Songnen Plain marshes. Based on the normalized difference vegetation index (NDVI) and climate data, we investigated the spatiotemporal change of vegetation and its relationship with temperature and precipitation in the Songnen Plain marshes. During 2000–2016, the growing season mean NDVI of the Songnen Plain marshes significantly (p < 0.01) increased at a rate of 0.06/decade. For the climate change effects on vegetation, the growing season precipitation had a significant positive effect on the growing season NDVI of marshes. In addition, this study first found asymmetric effects of daytime maximum temperature (Tmax) and nighttime minimum temperature (Tmin) on NDVI of the Songnen Plain marshes: The growing season NDVI correlated negatively with Tmax but positively with Tmin. Considering the global asymmetric warming of Tmax and Tmin, more attention should be paid to these asymmetric effects of Tmax and Tmin on the vegetation of marshes.

scholarly journals Contributions of Vegetation Greening and Climate Change to Evapotranspiration Trend after Large-Scale Vegetation Restoration on the Loess Plateau, China

Water ◽  
2021 ◽  
Vol 13 (13) ◽  
pp. 1755
Author(s):  
Shuo Wang ◽  
Chenfeng Cui ◽  
Qin Dai
Keyword(s):  
Climate Change ◽  
Loess Plateau ◽  
Large Scale ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Potential Evapotranspiration ◽  
Development Trend ◽  
Future Trend ◽  
Natural Ecosystem ◽  
The Loess Plateau

Since the early 2000s, the vegetation cover of the Loess Plateau (LP) has increased significantly, which has been fully recorded. However, the effects on relevant eco-hydrological processes are still unclear. Here, we made an investigation on the changes of actual evapotranspiration (ETa) during 2000–2018 and connected them with vegetation greening and climate change in the LP, based on the remote sensing data with correlation and attribution analysis. Results identified that the average annual ETa on the LP exhibited an obvious increasing trend with the value of 9.11 mm yr−1, and the annual ETa trend was dominated by the changes of ETa in the third quarter (July, August, and September). The future trend of ETa was predicted by the Hurst exponent. Partial correlation analysis indicated that annual ETa variations in 87.8% regions of the LP were controlled by vegetation greening. Multiple regression analysis suggested that the relative contributions of potential evapotranspiration (ETp), precipitation, and normalized difference vegetation index (NDVI), to the trend of ETa were 5.7%, −26.3%, and 61.4%, separately. Vegetation greening has a close relationship with the Grain for Green (GFG) project and acts as an essential driver for the long-term development trend of water consumption on the LP. In this research, the potential conflicts of water demanding between the natural ecosystem and social-economic system in the LP were highlighted, which were caused by the fast vegetation expansion.

scholarly journals Detecting Forest Degradation in the Three-North Forest Shelterbelt in China from Multi-Scale Satellite Images

2021 ◽  
Vol 13 (6) ◽  
pp. 1131
Author(s):  
Tao Yu ◽  
Pengju Liu ◽  
Qiang Zhang ◽  
Yi Ren ◽  
Jingning Yao
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Net Primary Production ◽  
Remote Sensing Data ◽  
Forest Degradation ◽  
Observation Data ◽  
Area Index ◽  
Sensing Data ◽  
Multi Scale

Detecting forest degradation from satellite observation data is of great significance in revealing the process of decreasing forest quality and giving a better understanding of regional or global carbon emissions and their feedbacks with climate changes. In this paper, a quick and applicable approach was developed for monitoring forest degradation in the Three-North Forest Shelterbelt in China from multi-scale remote sensing data. Firstly, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Ratio Vegetation Index (RVI), Leaf Area Index (LAI), Fraction of Photosynthetically Active Radiation (FPAR) and Net Primary Production (NPP) from remote sensing data were selected as the indicators to describe forest degradation. Then multi-scale forest degradation maps were obtained by adopting a new classification method using time series MODerate Resolution Imaging Spectroradiometer (MODIS) and Landsat Enhanced Thematic Mapper Plus (ETM+) images, and were validated with ground survey data. At last, the criteria and indicators for monitoring forest degradation from remote sensing data were discussed, and the uncertainly of the method was analyzed. Results of this paper indicated that multi-scale remote sensing data have great potential in detecting regional forest degradation.

scholarly journals A Possible Role of Copernicus Sentinel-2 Data to Support Common Agricultural Policy Controls in Agriculture

Agronomy ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 110
Author(s):  
Filippo Sarvia ◽  
Elena Xausa ◽  
Samuele De Petris ◽  
Gianluca Cantamessa ◽  
Enrico Borgogno-Mondino
Keyword(s):  
Agricultural Policy ◽  
Common Agricultural Policy ◽  
Observation Data ◽  
Key Factor ◽  
Ndvi Time Series ◽  
Earth Observation Data ◽  
Four Levels ◽  
Nw Italy ◽  
Sentinel 2

Farmers that intend to access Common Agricultural Policy (CAP) contributions must submit an application to the territorially competent Paying Agencies (PA). Agencies are called to verify consistency of CAP contributions requirements through ground campaigns. Recently, EU regulation (N. 746/2018) proposed an alternative methodology to control CAP applications based on Earth Observation data. Accordingly, this work was aimed at designing and implementing a prototype of service based on Copernicus Sentinel-2 (S2) data for the classification of soybean, corn, wheat, rice, and meadow crops. The approach relies on the classification of S2 NDVI time-series (TS) by “user-friendly” supervised classification algorithms: Minimum Distance (MD) and Random Forest (RF). The study area was located in the Vercelli province (NW Italy), which represents a strategic agricultural area in the Piemonte region. Crop classes separability proved to be a key factor during the classification process. Confusion matrices were generated with respect to ground checks (GCs); they showed a high Overall Accuracy (>80%) for both MD and RF approaches. With respect to MD and RF, a new raster layer was generated (hereinafter called Controls Map layer), mapping four levels of classification occurrences, useful for administrative procedures required by PA. The Control Map layer highlighted that only the eight percent of CAP 2019 applications appeared to be critical in terms of consistency between farmers’ declarations and classification results. Only for these ones, a GC was warmly suggested, while the 12% must be desirable and the 80% was not required. This information alone suggested that the proposed methodology is able to optimize GCs, making possible to focus ground checks on a limited number of fields, thus determining an economic saving for PA and/or a more effective strategy of controls.

scholarly journals Phenological Analysis of Sub-Alpine Forest on Jeju Island, South Korea, Using Data Fusion of Landsat and MODIS Products

Forests ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 286
Author(s):  
Sang-Jin Park ◽  
Seung-Gyu Jeong ◽  
Yong Park ◽  
Sang-hyuk Kim ◽  
Dong-kun Lee ◽  
...  
Keyword(s):  
Climate Change ◽  
Remote Sensing ◽  
Data Fusion ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Fusion Model ◽  
Fusion Algorithm ◽  
Study Region ◽  
Modis Imagery ◽  
Increasing Temperature

Climate change poses a disproportionate risk to alpine ecosystems. Effective monitoring of forest phenological responses to climate change is critical for predicting and managing threats to alpine populations. Remote sensing can be used to monitor forest communities in dynamic landscapes for responses to climate change at the species level. Spatiotemporal fusion technology using remote sensing images is an effective way of detecting gradual phenological changes over time and seasonal responses to climate change. The spatial and temporal adaptive reflectance fusion model (STARFM) is a widely used data fusion algorithm for Landsat and MODIS imagery. This study aims to identify forest phenological characteristics and changes at the species–community level by fusing spatiotemporal data from Landsat and MODIS imagery. We fused 18 images from March to November for 2000, 2010, and 2019. (The resulting STARFM-fused images exhibited accuracies of RMSE = 0.0402 and R2 = 0.795. We found that the normalized difference vegetation index (NDVI) value increased with time, which suggests that increasing temperature due to climate change has affected the start of the growth season in the study region. From this study, we found that increasing temperature affects the phenology of these regions, and forest management strategies like monitoring phenology using remote sensing technique should evaluate the effects of climate change.

scholarly journals Relationship between MODIS Derived NDVI and Yield of Cereals for Selected European Countries

Agronomy ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 340
Author(s):  
Ewa Panek ◽  
Dariusz Gozdowski
Keyword(s):  
Linear Regression ◽  
Grain Yield ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Arable Land ◽  
Early Spring ◽  
European Countries ◽  
Yield Prediction ◽  
Country Level ◽  
Using Data

In this study, the relationships between normalized difference vegetation index (NDVI) obtained based on MODIS satellite data and grain yield of all cereals, wheat and barley at a country level were analyzed. The analysis was performed by using data from 2010–2018 for 20 European countries, where percentage of cereals is high (at least 35% of the arable land). The analysis was performed for each country separately and for all of the collected data together. The relationships between NDVI and cumulative NDVI (cNDVI) were analyzed by using linear regression. Relationships between NDVI in early spring and grain yield of cereals were very strong for Croatia, Czechia, Germany, Hungary, Latvia, Lithuania, Poland and Slovakia. This means that the yield prediction for these countries can be as far back as 4 months before the harvest. The increase of NDVI in early spring was related to the increase of grain yield by about 0.5–1.6 t/ha. The cumulative of averaged NDVI gives more stable prediction of grain yield per season. For France and Belgium, the relationships between NDVI and grain yield were very weak.

Alpine sparsely vegetated areas in the eastern Qilian Mountains shrank with climate warming in the past 30 years

2018 ◽  
Vol 42 (4) ◽  
pp. 415-430 ◽  
Author(s):  
Biao Zeng ◽  
Fuguang Zhang ◽  
Taibao Yang ◽  
Jiaguo Qi ◽  
Mihretab G Ghebrezgabher
Keyword(s):  
Climate Change ◽  
Climate Warming ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Regional Scale ◽  
Qilian Mountains ◽  
Visual Interpretation ◽  
Hydrothermal Conditions ◽  
The Past ◽  
Distribution Ranges

Alpine sparsely vegetated areas (ASVAs) in mountains are sensitive to climate change and rarely studied. In this study, we focused on the response of ASVA distribution to climate change in the eastern Qilian Mountains (EQLM) from the 1990s to the 2010s. The ASVA distribution ranges in the EQLM during the past three decades were obtained from the Thematic Mapper remote sensing digital images by using the threshold of normalized difference vegetation index (NDVI) and artificial visual interpretation. Results indicated that the ASVA shrank gradually in the EQLM and lost its area by approximately 11.4% from the 1990s to the 2010s. The shrunken ASVA with markedly more area than the expanded one was mainly located at altitudes from 3700 m to 4300 m, which were comparatively lower than the average altitude of the ASVA distribution ranges. This condition led to the low ASVA boundaries in the EQLM moving upwards at a significant velocity of 22 m/decade at the regional scale. This vertical zonal process was modulated by topography-induced differences in local hydrothermal conditions. Thus, the ASVA shrank mainly in its lower parts with mild and sunny slopes. Annual maximum NDVI in the transition zone increased significantly and showed a stronger positive correlation with significantly increasing temperature than insignificant precipitation variations during 1990–2015. The ASVA shrinkage and up-shifting of its boundary were attributed to climate warming, which facilitated the upper part of alpine meadow in the EQLM by releasing the low temperature limitation on vegetation growth.

scholarly journals Explicitly Identifying the Desertification Change in CMREC Area Based on Multisource Remote Data

2020 ◽  
Vol 12 (19) ◽  
pp. 3170
Author(s):  
Zemeng Fan ◽  
Saibo Li ◽  
Haiyan Fang
Keyword(s):  
Climate Change ◽  
Human Activities ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Net Primary Productivity ◽  
Regression Tree ◽  
Google Earth ◽  
Classification And Regression Tree ◽  
Support Vector ◽  
Driving Mechanisms

Explicitly identifying the desertification changes and causes has been a hot issue of eco-environment sustainable development in the China–Mongolia–Russia Economic Corridor (CMREC) area. In this paper, the desertification change patterns between 2000 and 2015 were identified by operating the classification and regression tree (CART) method with multisource remote sensing datasets on Google Earth Engine (GEE), which has the higher overall accuracy (85%) than three other methods, namely support vector machine (SVM), random forest (RF) and Albedo-normalized difference vegetation index (NDVI) models. A contribution index of climate change and human activities on desertification was introduced to quantitatively explicate the driving mechanisms of desertification change based on the temporal datasets and net primary productivity (NPP). The results show that the area of slight desertification land had increased from 719,700 km2 to 948,000 km2 between 2000 and 2015. The area of severe desertification land decreased from 82,400 km2 to 71,200 km2. The area of desertification increased by 9.68%, in which 69.68% was mainly caused by human activities. Climate change and human activities accounted for 68.8% and 27.36%, respectively, in the area of desertification restoration. In general, the degree of desertification showed a decreasing trend, and climate change was the major driving factor in the CMREC area between 2000 and 2015.

scholarly journals Revealing the Fingerprint of Climate Change in Interannual NDVI Variability among Biomes in Inner Mongolia, China

2020 ◽  
Vol 12 (8) ◽  
pp. 1332 ◽  
Author(s):  
Linghui Guo ◽  
Liyuan Zuo ◽  
Jiangbo Gao ◽  
Yuan Jiang ◽  
Yongling Zhang ◽  
...  
Keyword(s):  
Climate Change ◽  
Interannual Variability ◽  
Inner Mongolia ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Time Lag ◽  
Growing Season ◽  
Climate Fluctuations ◽  
Temperature And Precipitation ◽  
Lag Effect

An understanding of the response of interannual vegetation variations to climate change is critical for the future projection of ecosystem processes and developing effective coping strategies. In this study, the spatial pattern of interannual variability in the growing season normalized difference vegetation index (NDVI) for different biomes and its relationships with climate variables were investigated in Inner Mongolia during 1982–2015 by jointly using linear regression, geographical detector, and geographically weighted regression methodologies. The result showed that the greatest variability of the growing season NDVI occurred in typical steppe and desert steppe, with forest and desert most stable. The interannual variability of NDVI differed monthly among biomes, showing a time gradient of the largest variation from northeast to southwest. NDVI interannual variability was significantly related to that of the corresponding temperature and precipitation for each biome, characterized by an obvious spatial heterogeneity and time lag effect marked in the later period of the growing season. Additionally, the large slope of NDVI variation to temperature for desert implied that desert tended to amplify temperature variations, whereas other biomes displayed a capacity to buffer climate fluctuations. These findings highlight the relationships between vegetation variability and climate variability, which could be used to support the adaptive management of vegetation resources in the context of climate change.

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