Significant Greening Trend Research Articles

The application of polynomial analyses to detect global vegetation dynamics during 1982–2012

ABSTRACTUse of the normalized difference vegetation index (NDVI) to build long-term vegetation trends is one of the most effective techniques for identifying global environmental change. Trend identification can be achieved by ordinary least squares (OLS) analysis or the Theil–Sen (TS) procedure with a Mann–Kendall (MK) significance test, and these linear regression approaches have been widely used. However, vegetation changes are not linear, and thus the response of vegetation to global climate change may follow non-linear trends. In this article, a polynomial trend-fitting method, which uses stepwise regression and expands on previous research, is presented. With an improved fitting ability, this procedure may reveal trends that were concealed by linear fitting methods. Globally, the traditional TS-MK method reveals significant greening trends for 37.27% of vegetated land, and significant browning trends for 7.98%. Using the polynomial analysis, 34.62% of pixels were fitted by high-order trends. The significant greening trends covered up to 30% of cultivated land, thus indicating that cultivated vegetation may be increasing faster than natural vegetation. Significant vegetation browning mostly occurred in sparse vegetation areas, which suggests that vegetation growth may be more sensitive to climate change in arid regions. Our results show that use of polynomial analysis can help further elucidate global NDVI trends.

Environmental change in time series – An interdisciplinary study in the Sahel of Mali and Senegal

Climatic changes and human activities have caused major environmental change in the Sahel. Remote sensing studies detect various vegetation trends; however, explanations are rarely studied in detail. We present a methodology using time series, high-resolution imagery and fieldwork to validate trend analyses for two regions in the Sahel of Mali and Senegal. Both study areas show significant greening trends from 1982 to 2010. Reasons can be very site-specific, but several factors are valid for both research areas: (1) farmer-managed agro-forestry, (2) planting programs and protection laws, (3) widespread dispersion of robust species, which replace the former diverse woody vegetation and simulate a greening which conceals a shift in biodiversity and (4) an increase of annual rainfall. However, the situation is still far from the pre-drought conditions, which are reconstructed by Corona imagery (1965) and interviews with the local population. Rather a transformation is observed: a decrease in natural vegetation, tree density and diversity. Reasons are climatic and anthropogenic: (1) drought events, less rain and higher temperatures, (2) increased demand for cropping areas and wood, especially in times of droughts. Our example validates that climatic factors are important drivers of change, but much of today's environment and vegetation composition is controlled by humans.

Local Vegetation Trends in the Sahel of Mali and Senegal Using Long Time Series FAPAR Satellite Products and Field Measurement (1982–2010)

Local vegetation trends in the Sahel of Mali and Senegal from Geoland Version 1 (GEOV1) (5 km) and the third generation Global Inventory Modeling and Mapping Studies (GIMMS3g) (8 km) Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) time series are studied over 29 years. For validation and interpretation of observed greenness trends, two methods are applied: (1) a qualitative approach using in-depth knowledge of the study areas and (2) a quantitative approach by time series of biomass observations and rainfall data. Significant greening trends from 1982 to 2010 are consistently observed in both GEOV1 and GIMMS3g FAPAR datasets. Annual rainfall increased significantly during the observed time period, explaining large parts of FAPAR variations at a regional scale. Locally, GEOV1 data reveals a heterogeneous pattern of vegetation change, which is confirmed by long-term ground data and site visits. The spatial variability in the observed vegetation trends in the Sahel area are mainly caused by varying tree- and land-cover, which are controlled by human impact, soil and drought resilience. A large proportion of the positive trends are caused by the increment in leaf biomass of woody species that has almost doubled since the 1980s due to a tree cover regeneration after a dry-period. This confirms the re-greening of the Sahel, however, degradation is also present and sometimes obscured by greening. GEOV1 as compared to GIMMS3g made it possible to better characterize the spatial pattern of trends and identify the degraded areas in the study region.

SPATIAL PATTERN OF TEMPORAL TREND OF CROP PHENOLOGY MATRICES OVER INDIA USING TIMESERIES GIMMS NDVI DATA (19826ndash;2006)

Abstract. NOAA-AVHRR bi-monthly NDVI data of 8×8 km for the period of 1982–2006 were used to analyze the trend of crop phenology matrices over Indian region. Time series principal component analysis of NDVI was performed to produce six calibration zones for fitting equations of temporal NDVI profile. Savitzky-Golay filter with different seasonality parameters, adaptation strengths and window sizes for different calibration zones were use to smoothen the NDVI profile. Three crop phenology matrices i.e. start of the growing season (SGS), Seasonal NDVI amplitude (AMP), Seasonally Integrated NDVI (SiNDVI) were extracted using TIMESAT software. Direction and magnitude of trends of these crop phenology matrices were analyzed at pixel level using Mann-Kendall test. Further the trends was assessed at meteorological subdivisional level using "Field significance test". Significant advancement of SGS was observed over Punjab, Haryana, Marathwada, Vidarbha and Madhya Maharashtra where as delay was found over Rayalaseema, Coastal Andhra Pradesh, Bihar, Gangetic West Bengal and Sub-Himalayan West Bengal. North, West and central India covering Punjab, Haryana, West & East Uttar Pradesh, West & East Rajasthan, West & East Madhya Pradesh, Sourastra & Kutch, Rayalaseema, Marathwada, Vidarbha, Bihar and Sub-Himalayan West Bengal showed significant greening trend of kharif season. Most of the southern and eastern part of India covering Tamilnadu, South Interior Karnataka, Coastal Andhra Pradesh, Madhya Maharashtra, Gujarat region, Chhattisgarh, Jharkhand and Gangetic West Bengal showed significant browning trend during kharif season.