Causation Discovery of Weather and Vegetation Condition on Global Wildfire Using the PCMCI Approach

2021 ◽  
Author(s):  
Yuquan Qu ◽  
Carsten Montzka ◽  
Harry Vereecken
Keyword(s):  
Vegetation Condition

scholarly journals Inter-annual and seasonal trends of vegetation condition in the Upper Blue Nile (Abay) Basin: dual-scale time series analysis

2015 ◽  
Vol 6 (2) ◽  
pp. 617-636 ◽  
Author(s):  
E. Teferi ◽  
S. Uhlenbrook ◽  
W. Bewket
Keyword(s):  
Trend Analysis ◽  
Sustainable Land Use ◽  
Integrated Analysis ◽  
Positive Trend ◽  
Blue Nile ◽  
Seasonal Trends ◽  
Modis Ndvi ◽  
Vegetation Condition ◽  
Gimms Ndvi

Abstract. A long-term decline in ecosystem functioning and productivity, often called land degradation, is a serious environmental challenge to Ethiopia that needs to be understood so as to develop sustainable land use strategies. This study examines inter-annual and seasonal trends of vegetation cover in the Upper Blue Nile (UBN) or Abbay Basin. The Advanced Very High Resolution Radiometer (AVHRR)-based Global Inventory, Monitoring, and Modeling Studies (GIMMS) normalized difference vegetation index (NDVI) was used for long-term vegetation trend analysis at low spatial resolution. Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI data (MOD13Q1) were used for medium-scale vegetation trend analysis. Harmonic analyses and non-parametric trend tests were applied to both GIMMS NDVI (1981–2006) and MODIS NDVI (2001–2011) data sets. Based on a robust trend estimator (Theil–Sen slope), most parts of the UBN (~ 77 %) showed a positive trend in monthly GIMMS NDVI, with a mean rate of 0.0015 NDVI units (3.77 % yr−1), out of which 41.15 % of the basin depicted significant increases (p < 0.05), with a mean rate of 0.0023 NDVI units (5.59 % yr−1) during the period. However, the MODIS-based vegetation trend analysis revealed that about 36 % of the UBN showed a significant decreasing trend (p < 0.05) over the period 2001–2011 at an average rate of 0.0768 NDVI yr−1. This indicates that the greening trend of the vegetation condition was followed by decreasing trend since the mid-2000s in the basin, which requires the attention of land users and decision makers. Seasonal trend analysis was found to be very useful to identify changes in vegetation condition that could be masked if only inter-annual vegetation trend analysis was performed. Over half (60 %) of the Abay Basin was found to exhibit significant trends in seasonality over the 25-year period (1982–2006). About 17 and 16 % of the significant trends consisted of areas experiencing a uniform increase in NDVI throughout the year and extended growing season, respectively. These areas were found primarily in shrubland and woodland regions. The study demonstrated that integrated analysis of inter-annual and intra-annual trends based on GIMMS and MODIS enables a more robust identification of changes in vegetation condition.

scholarly journals Forecasting Vegetation Condition with a Bayesian Auto-regressive Distributed Lags (BARDL) Model

2021 ◽  
Author(s):  
Edward E. Salakpi ◽  
Peter D. Hurley ◽  
James M. Muthoka ◽  
Adam B. Barrett ◽  
Andrew Bowell ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Lead Time ◽  
Expert Knowledge ◽  
Research Work ◽  
Early Warning Systems ◽  
Ar Model ◽  
Lead Times ◽  
Vegetation Condition ◽  
Early Action ◽  
Auto Regressive

Abstract. Droughts form a large part of climate/weather-related disasters reported globally. In Africa, pastoralists living in the Arid and Semi-Arid Lands (ASALs) are the worse affected. Prolonged dry spells that cause vegetation stress in these regions have resulted in the loss of income and livelihoods. To curb this, global initiatives like the Paris Agreement and the United Nations recognised the need to establish Early Warning Systems (EWS) to save lives and livelihoods. Existing EWS use a combination of Satellite Earth Observation (EO) based biophysical indicators like the Vegetation Condition Index (VCI) and socio-economic factors to measure and monitor droughts. Most of these EWS rely on expert knowledge in estimating upcoming drought conditions without using forecast models. Recent research has shown that the use of robust algorithms like Auto-Regression, Gaussian Processes and Artificial Neural Networks can provide very skilled models for forecasting vegetation condition at short to medium range lead times. However, to enable preparedness for early action, forecasts with a longer lead time are needed. The objective of this research work is to develop models that forecast vegetation conditions at longer lead times on the premise that vegetation condition is controlled by factors like precipitation and soil moisture. To achieve this, we used a Bayesian Auto-Regressive Distributed Lag (BARDL) modelling approach which enabled us to factor in lagged information from Precipitation and Soil moisture levels into our VCI forecast model. The results showed a ∼2-week gain in the forecast range compared to the univariate AR model used as a baseline. The R2 scores for the Bayesian ARDL model were 0.94, 0.85 and 0.74, compared to the AR model's R2 of 0.88, 0.77 and 0.65 for 6, 8 and 10 weeks lead time respectively.

The more the merrier: using environmental flows to improve floodplain vegetation condition

2021 ◽  
Author(s):  
Cherie Joy Campbell ◽  
Fiona Linda Freestone ◽  
Richard P. Duncan ◽  
Will Higgisson ◽  
Sascha Jade Healy
Keyword(s):  
Environmental Flows ◽  
Floodplain Vegetation ◽  
Vegetation Condition

scholarly journals Drought identification in the eastern Baltic region using NDVI

2017 ◽  
Vol 8 (3) ◽  
pp. 627-637 ◽  
Author(s):  
Egidijus Rimkus ◽  
Edvinas Stonevicius ◽  
Justinas Kilpys ◽  
Viktorija Maciulyte ◽  
Donatas Valiukas
Keyword(s):  
Baltic Sea ◽  
Arable Land ◽  
Coniferous Forest ◽  
Northwestern Part ◽  
Drought Effect ◽  
Vegetation Season ◽  
Vegetation Condition ◽  
Baltic Sea Region ◽  
The Impact ◽  
Eastern Baltic

Abstract. Droughts are phenomena that affect large areas. Remote sensing data covering large territories can be used to assess the impact and extent of droughts. Drought effect on vegetation was determined using the normalized difference vegetation index (NDVI) and Vegetation Condition Index (VCI) in the eastern Baltic Sea region located between 53–60° N and 20–30° E. The effect of precipitation deficit on vegetation in arable land and broadleaved and coniferous forest was analysed using the Standardized Precipitation Index (SPI) calculated for 1- to 9-month timescales. Vegetation has strong seasonality in the analysed area. The beginning and the end of the vegetation season depends on the distance from the Baltic Sea, which affects temperature and precipitation patterns. The vegetation season in the southeastern part of the region is 5–6 weeks longer than in the northwestern part. The early spring air temperature, snowmelt water storage in the soil and precipitation have the largest influence on the NDVI values in the first half of the active growing season. Precipitation deficit in the first part of the vegetation season only has a significant impact on the vegetation on arable land. The vegetation in the forests is less sensitive to the moisture deficit. Correlation between VCI and the same month SPI1 is usually negative in the study area. It means that wetter conditions lead to lower VCI values, while the correlation is usually positive between the VCI and the SPI of the previous month. With a longer SPI scale the correlation gradually shifts towards the positive coefficients. The positive correlation between 3- and 6-month SPI and VCI was observed on the arable land and in both types of forests in the second half of vegetation season. The precipitation deficit is only one of the vegetation condition drivers and NDVI cannot be used universally to identify droughts, but it may be applied to better assess the effect of droughts on vegetation in the eastern Baltic Sea region.

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