Spatiotemporal analysis of vegetation variability and its relationship with climate change in China

This paper investigated spatiotemporal dynamic pattern of vegetation, climate factor, and their complex relationships from seasonal to inter-annual scale in China during the period 1982–1998 through wavelet transform method based on GIMMS data-sets. First, most vegetation canopies demonstrated obvious seasonality, increasing with latitudinal gradient. Second, obvious dynamic trends were observed in both vegetation and climate change, especially the positive trends. Over 70% areas were observed with obvious vegetation greening up, with vegetation degradation principally in the Pearl River Delta, Yangtze River Delta, and desert. Overall warming trend was observed across the whole country (\u3e98% area), stronger in Northern China. Although over half of area (58.2%) obtained increasing rainfall trend, around a quarter of area (24.5%), especially the Central China and most northern portion of China, exhibited significantly negative rainfall trend. Third, significantly positive normalized difference vegetation index (NDVI)–climate relationship was generally observed on the de-noised time series in most vegetated regions, corresponding to their synchronous stronger seasonal pattern. Finally, at inter-annual level, the NDVI–climate relationship differed with climatic regions and their long-term trends: in humid regions, positive coefficients were observed except in regions with vegetation degradation; in arid, semiarid, and semihumid regions, positive relationships would be examined on the condition that increasing rainfall could compensate the increasing water requirement along with increasing temperature. This study provided valuable insights into the long-term vegetation–climate relationship in China with consideration of their spatiotemporal variability and overall trend in the global change process

A 33-year NPP monitoring study in southwest China by the fusion of multi-source remote sensing and station data

Knowledge of regional net primary productivity (NPP) is important for the systematic understanding of the global carbon cycle. In this study, multi-source data were employed to conduct a 33-year regional NPP study in southwest China, at a 1-km scale. A multi-sensor fusion framework was applied to obtain a new normalized difference vegetation index (NDVI) time series from 1982 to 2014, combining the respective advantages of the different remote sensing datasets. As another key parameter for NPP modeling, the total solar radiation was calculated by the improved Yang hybrid model (YHM), using meteorological station data. The verification described in this paper proved the feasibility of all the applied data processes, and a greatly improved accuracy was obtained for the NPP calculated with the final processed NDVI. The spatio-temporal analysis results indicated that 68.07% of the study area showed an increasing NPP trend over the past three decades. Significant heterogeneity was found in the correlation between NPP and precipitation at a monthly scale, specifically, the negative correlation in the growing season and the positive correlation in the dry season. The lagged positive correlation in the growing season and no lag in the dry season indicated the important impact of precipitation on NPP.Comment: 20 pages, 11 figure

Forest Phenology Dynamics and Its Responses to Meteorological Variations in Northeast China

Based on time series of Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) data (2000–2009), we extracted forest phenological variables in Northeast China using a threshold-based method, which included the start of the growing season (SOS), end of the growing season (EOS), and length of the growing season (LOS). The spatial variation of phenological trends was analyzed using the linear regression method. In Northeast China, SOS was delayed at the rate of <1.5 days per year. The delay trend of EOS was well distributed in the entire region with almost the same rates. LOS increased slightly. The analysis of the relationship between forest phenology and meteorological variations shows that SOS was mainly affected by spring temperature, whereas SOS had a negative relationship with precipitation in the warm-temperate deciduous broadleaf forest region. The EOS in temperate steppe region was affected by temperature and precipitation in August, whereas the others were significantly affected by temperature. Because of the increased temperature in spring, the LOS of the temperate steppe region and temperate mixed forest region increased, and the LOS was positively correlated with the mean temperature of summer in the cool-temperate needleleaf forest region

Spectral Characteristics and Mapping of Rice Fields using Multi-Temporal Landsat and MODIS Data: A Case of District Narowal

Availability of remote sensed data provides powerful access to the spatial and temporal information of the earth surface Real-time earth observation data acquired during a cropping season can assist in assessing crop growth and development performance As remote sensed data is generally available at large scale rather than at field-plot level use of this information would help to improve crop management at broad-scale Utilizing the Landsat TM ETM ISODATA clustering algorithm and MODIS Terra the normalized difference vegetation index NDVI and enhanced vegetation index EVI datasets allowed the capturing of relevant rice cropping differences In this study we tried to analyze the MODIS Terra EVI NDVI February 2000 to February 2013 datasets for rice fractional yield estimation in Narowal Punjab province of Pakistan For large scale applications time integrated series of EVI NDVI 250-m spatial resolution offer a practical approach to measure crop production as they relate to the overall plant vigor and photosynthetic activity during the growing season The required data preparation for the integration of MODIS data into GIS is described with a focus on the projection from the MODIS Sinusoidal to the national coordinate systems However its low spatial resolution has been an impediment to researchers pursuing more accurate classification results and will support environmental planning to develop sustainable land-use practices These results have important implications for parameterization of land surface process models using biophysical variables estimated from remotely sensed data and assist for forthcoming rice fractional yield assessmen

Assessing Spatiotemporal Drought Dynamics and Its Related Environmental Issues in the Mekong River Delta

Drought is a major natural disaster that creates a negative impact on socio-economic development and environment. Drought indices are typically applied to characterize drought events in a meaningful way. This study aims at examining variations in agricultural drought severity based on the relationship between standardized ratio of actual and potential evapotranspiration (ET and PET), enhanced vegetation index (EVI), and land surface temperature (LST) derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) platform. A new drought index, called the enhanced drought severity index (EDSI), was developed by applying spatiotemporal regression methods and time-series biophysical data derived from remote sensing. In addition, time-series trend analysis in the 2001–2018 period, along with the Mann–Kendal (MK) significance test and the Theil Sen (TS) slope, were used to examine the spatiotemporal dynamics of environmental parameters (i.e., LST, EVI, ET, and PET), and geographically weighted regression (GWR) was subsequently applied in order to analyze the local correlations among them. Results showed that a significant correlation was discovered among LST, EVI, ET, and PET, as well as their standardized ratios (|r| > 0.8, p 0.7 and a statistical significance p < 0.01. Besides, it was found that the temporal tendency of this phenomenon was the increase in intensity of drought, and that coastal areas in the study area were more vulnerable to this phenomenon. This study demonstrates the effectiveness of EDSI and the potential application of integrating spatial regression and time-series data for assessing regional drought conditions

Evaluating the relationship between drought and vegetation greenness in Chyulu-Amboseli Rangeland, Kenya

Remote sensing techniques have been widely used to monitor moisture-related vegetation conditions. Vegetation vigour response to drought however is complex and has not been adequately studied using satellite sensor data. This paper investigated the time lag response of vegetation to drought in Kenya’s Chyulu-Amboseli ecosystem based on Standardized Precipitation Index (SPI) derived from monthly precipitation data for the period January 2000-October 2016 downloaded from the Climate Hazards group InfraRed Precipitation with Stations and Normalized Difference Vegetation Index (NDVI) computed from Moderate Resolution Imaging Spectro-radiometer (MODIS) pre-processed images downloaded from the University of Natural Resources and Life Sciences (BOKU) database. Statistical analysis showed that drought severity increased over the study period while corresponding vegetation conditions degenerated. Results further revealed that the relationship between drought and vegetation greenness was significant (R2 = 0.6) with 2 months optimal lag. This calls for policy makers and programme managers to integrate the lag effect in measures to cope with drought in the rangelands.Keywords: Drought, Vegetation greenness, Chyulu-Amboseli, Statistical Analysis, Rangelan

Assessing water availability in Mediterranean regions affected by water conflicts through MODIS data time series analysis

Water scarcity is a widespread problem in arid and semi-arid regions such as the western Mediterranean coastal areas. The irregularity of the precipitation generates frequent droughts that exacerbate the conflicts among agriculture, water supply and water demands for ecosystems maintenance. Besides, global climate models predict that climate change will cause Mediterranean arid and semi-arid regions to shift towards lower rainfall scenarios that may exacerbate water conflicts. The purpose of this study is to find a feasible methodology to assess current and monitor future water demands in order to better allocate limited water resources. The interdependency between a vegetation index (NDVI), land surface temperature (LST), precipitation (current and future), and surface water resources availability in two watersheds in southeastern Spain with serious difficulties in meeting water demands was investigated. MODIS (Moderate Resolution Imaging Spectroradiometer) NDVI and LST products (as proxy of drought), precipitation maps (generated from climate station records) and reservoir storage gauging information were used to compute times series anomalies from 2001 to 2014 and generate regression images and spatial regression models. The temporal relationship between reservoir storage and time series of satellite images allowed the detection of different and contrasting water management practices in the two watersheds. In addition, a comparison of current precipitation rates and future precipitation conditions obtained from global climate models suggests high precipitation reductions, especially in areas that have the potential to contribute significantly to groundwater storage and surface runoff, and are thus critical to reservoir storage. Finally, spatial regression models minimized spatial autocorrelation effects, and their results suggested the great potential of our methodology combining NDVI and LST time series to predict future scenarios of water scarcity.Published versio