EVI Data Research Articles

Exploration of the Spatiotemporal Characteristics and Driving Mechanisms of Vegetation Greenness Changes in Laos

In the context of climate change, vegetation changes in Laos have attracted widespread attention, especially the profound impact of its greenness changes on ecosystems, water cycles, and climate feedback. However, our understanding of the driving factors of vegetation greenness changes in different latitudes is still limited. This study utilized EVI and climate factor data from 2001 to 2023, employing trend analysis, correlation analysis, and machine learning methods to investigate the spatiotemporal patterns of vegetation greenness changes across Laos and their responses to climate factors. Results revealed an overall increasing trend in vegetation greenness, with 75% of the area exhibiting annual increases, primarily in northern, central, and parts of the southern regions. Conversely, 24.8% of the area experienced declines, concentrated near Vientiane and certain southern regions. Seasonal trends during the wet season largely aligned with annual patterns, although reduced rainfall negatively impacted some areas. The dry season exhibited the most pronounced changes, with 70% of the area showing increased greenness, especially in northern and central regions, despite localized rainfall constraints. Minimum temperature (TMMN) emerged as the most influential factor, with importance values of 0.42 for annual changes and 0.37 for dry season changes, while precipitation impacts varied across space and time. High temperatures affected vegetation more significantly in low-latitude regions, whereas high-latitude areas relied on changes in DSR. This significant finding underscores the differential impact of climate factors on vegetation greenness across latitudes, which is crucial for understanding the complex dynamics of tropical inland ecosystems under climate change and for developing targeted conservation and adaptation strategies.

Spatiotemporal Variations in MODIS EVI and MODIS LAI and the Responses to Meteorological Drought across Different Slope Conditions in Karst Mountain Regions

Based on monthly MODIS EVI and LAI data from 2001 to 2020, combined with the Standardized Precipitation Evapotranspiration Index (SPEI), this study employs Theil–Sen trend analysis, Mann–Kendall (MK) test, Hurst index analysis, and correlation analysis to comparatively analyze the overall vegetation trends, spatial distribution characteristics, and future trends of different vegetation types in Guizhou Province under varying slope conditions. The study also explores the response of vegetation to SPEI at different time scales across different slopes. The results indicate the following: (1) From 2001 to 2020, the average values of EVI (0.34%/a) and LAI (1.4%/a) during the growing season exhibited an increasing trend, with the improved vegetation areas primarily concentrated in the western region of Guizhou, while the degradation areas were mainly located in the central and eastern regions. (2) Under different slope conditions, EVI generally showed slight improvement, while LAI exhibited significant improvement, with dry-lands experiencing the largest changes. Future trends indicate continuous improvement, but the proportion of vegetation improvement area decreases with increasing slope. When the slope is less than 5°, the proportion of vegetation improvement area is the highest. (3) The positive correlation between EVI, LAI, and SPEI at different time scales is stronger than the negative correlation, with the strongest correlations observed when the slope is less than 5°. When the slope exceeds 35°, the relationship between vegetation and drought response is almost unaffected by the slope. These findings provide a scientific basis for vegetation growth monitoring and the study of climate change and vegetation interactions in Guizhou Province.

Machine learning and global vegetation: random forests for downscaling and gap filling

Abstract. Drought is a devastating natural disaster, during which water shortage often manifests itself in the health of vegetation. Unfortunately, it is difficult to obtain high-resolution vegetation drought impact information that is spatially and temporally consistent. While remotely sensed products can provide part of this information, they often suffer from data gaps and limitations with respect to their spatial or temporal resolution. A persistent feature among remote-sensing products is the trade-off between the spatial resolution and revisit time: high temporal resolution is met with coarse spatial resolution and vice versa. Machine learning methods have been successfully applied in a wide range of remote-sensing and hydrological studies. However, global applications to resolve drought impacts on vegetation dynamics still need to be made available, as there is significant potential for such a product to aid with improved drought impact monitoring. To this end, this study predicted global vegetation dynamics based on the enhanced vegetation index (evi) and the popular Random forest (RF) regressor algorithm at 0.1°. We assessed the applicability of RF as a gap-filling and downscaling tool to generate global evi estimates that are spatially and temporally consistent. To do this, we trained an RF regressor with 0.1° evi data, using a host of features indicative of the water and energy balances experienced by vegetation, and evaluated the performance of this new product. Next, to test whether the RF is robust in terms of spatial resolution, we downscale the global evi: the model trained on 0.1° data is used to predict evi at a 0.01° resolution. The results show that the RF can capture global evi dynamics at both a 0.1° resolution (RMSE: 0.02–0.4) and at a finer 0.01° resolution (RMSE: 0.04–0.6). Overall errors were higher in the downscaled 0.01° product compared with the 0.1° product. Nevertheless, relative increases remained small, demonstrating that RF can be used to create downscaled and temporally consistent evi products. Additional error analysis revealed that errors vary spatiotemporally, with underrepresented land cover types and periods of extreme vegetation conditions having the highest errors. Finally, this model is used to produce global, spatially continuous evi products at both a 0.1 and 0.01° spatial resolution for 2003–2013 at an 8 d frequency.

Spatial and temporal variations of vegetation phenology and its response to urbanization in central Yunnan urban agglomeration, Southwest China

Vegetation phenology is an important sensor that responds to environmental changes. Based on MOD13Q1 EVI data, we used the dynamic threshold method to extract vegetation phenological parameters of the central Yunnan urban agglomeration from 2001 to 2020, namely the start of growing season, the end of growing season, and the length of growing season, aiming to reveal the spatiotemporal variations in vegetation phenology and urban-rural differences. The results showed that vegetation phenology of the central Yunnan urban agglomeration from 2001 to 2020 generally showed a phenomenon of delayed start of growing season, delayed the end of growing season (0.66 days per year), and prolonged growing season. Compared with suburban and rural areas, growing season in urban areas in the past 20 years had started earlier (1.05 days per year), ended later (0.91 days per year), and thus growing season had been prolonged (1.79 days per year). Vegetation phenology showed significant difference on the gradient of urban, suburban, and rural areas. The start and the end of growing season of urban vegetation were the earliest, and the length of growing season was the longest, with the most significant changes in the urban areas and within the range of 0-2 km outward. The start of growing season in urban area was significantly earlier, the end of growing season was significantly delayed, and length of growing season was prolonged significantly with the increase of population density, per capita GDP, and the proportion of built-up area. The sensitivity of different phenological periods of vegetation and their duration to environmental changes varied on the gradient of urban, suburban and rural areas. Population density and proportion of built-up area in the study area played an important role in delaying the end of growing season of vegetation in the central Yunnan urban agglomeration.

Fostering deep learning approaches to evaluate the impact of urbanization on vegetation and future prospects

Vegetation is an essential component of our global ecosystem and an important indicator of the dynamics and productivity of land cover. Vegetation forecasting research has been accelerated using several deep learning (DL) algorithms through remote sensing (RS) data. In this context, we used artificial intelligence (AI) and the long-short-term memory recurrent neural network (LSTM-RNN) method to explore and forecast future urban–rural vegetation disparities (ΔEVI, where EVI is the enhanced vegetation index) in Pakistan’s six megacities using MODIS EVI data. The forecast results revealed that ΔEVI is decreasing in all cities. The Root Mean Square Error (RMSE) and the Mean Absolute Percentage Error (MAPE) were used to evaluate LSTM-RNN. RSME values were recorded as 0.03, 0.07, 0.02, 0.03, 0.05, and 0.06 for Faisalabad, Gujranwala, Rawalpindi, Lahore, Multan, and Sialkot, respectively. MAPE was estimated as 0.12, 0.55, 0.24, 0.18, 0.28, and 0.47 for Faisalabad, Gujranwala, Rawalpindi, Lahore, Multan, and Sialkot, respectively. This situation indicates that LSTM-RNN can be used as a new reliable AI technique for forecasting. The results suggested that the average of forecasted ΔEVI for the next 10 years is −0.23, −0.21, −0.09, −0.13, −0.22, and −0.11 for Faisalabad, Gujranwala, Rawalpindi, Lahore, Multan, and Sialkot, respectively. The findings of this study will help evaluate the impact of urbanization on EVI by leveraging DL techniques along with implementing an urbanization policy for urban development and environmental protection for long-term urban sustainability.

Spatiotemporal Variation and Influencing Factors of Vegetation Growth in Mining Areas: A Case Study in a Colliery in Northern China

Based on MODIS EVI data of August collected from 2010 to 2021, and taking the Yingpanhao coal mine as an example, the spatiotemporal variation features of vegetation are analyzed using time series analysis, trend analysis and correlation analysis methods in the eco-geo-environment of the phreatic water desert shallows oasis. A significant increase trend is found for vegetation variation, and its development has improved generally in most areas. There is an obvious positive correlation between precipitation and vegetation growth, and a negative correlation between coal mining intensity and vegetation growth, but the influence of atmospheric precipitation on vegetation growth is stronger than that of coal mining intensity in the eco-geo-environment. The research results effectively reflect that atmospheric precipitation is the primary factor advancing the vegetation growth status in the coal mining regions. Vegetation development response to coal mining would be degraded first, then improved, and finally restored in areas with a deeply buried phreatic water level; that would promote the transformation of vegetation species from hydrophilous plants to xerophyte plants in areas with a shallowly buried phreatic water level. Therefore, it is necessary to carry out reasonable mine field planning according to the phreatic water level and the vegetation type distribution and to adopt different coal mining methods or corresponding engineering and technical measures to realize water conservation to avoid damaging the original hydrogeological conditions as far as possible. This information is helpful for promoting the eco-geo-environmental protection and further establishing the need for the dynamic monitoring of the eco-environment in the coal mining regions in the arid and semi-arid ecologically vulnerable areas of Northern China, which play a significant role in the long-term protection and rehabilitation of the eco-geo-environment and in the promotion of sustainable development.

Mapping Crop Distribution Patterns and Changes in China from 2000 to 2015 by Fusing Remote-Sensing, Statistics, and Knowledge-Based Crop Phenology

Maps of different kinds of crops offer information about both crop distribution and crop mix, which support analyses on food security, environmental change, and climate change. Despite the growing capability for mapping specific crops, the majority of studies have focused on a few dominant crops, whereas maps with a greater diversity of crops lack research. Combining cropping seasons derived from MODIS EVI data, regional crop calendar data, and agricultural statistical surveys, we developed an allocation model to map 14 major crops at a 1 km resolution across China for the years 2000, 2010, and 2015. The model was verified based on the fitness between the area of the three typical combinations of region, crop/crop group derived from remote sensing data, and statistical data. The R2, indicating fitness, ranged from 0.51 to 0.75, with a higher value for the crops distributed in plain regions and a lower value in regions with topographically diverse landscapes. Within the same combination of region and crop/crop group, the larger harvest area a province has, the higher its fitness, suggesting an overall reliable result at the national level. A comparison of paddy rice between our results and the National Land Use/Cover Database of China showed a relatively high R2 and slope of fitness (0.67 and 0.71, respectively). Compared with the commonly used average allocation model, and without lending cropping season information, the diversity index of the results from our model is about 30% higher, indicating crop maps with greater spatial details. According to the spatial distribution analysis of the four main crops, the grids showing decreased trends accounted for 74.92%, 57.32%, and 59.00% of the total changed grid for wheat, rice, and soybean crops, respectively, while accounting for only 37.71% for maize. The resulting data sets can be used to improve assessments for nutrient security and sustainability of cropping systems, as well as their resilience in a changing climate.

Study on paddy phenomics ecosystem and yield estimation using space-borne multi sensor remote sensing data

In the present study three phenological stages of rice namely transplanting stage, heading stage and harvesting stages were derived from MODIS EVI data. SMAP L-band was used to identify the puddling field. The performance of the estimated phenological stages from MODIS EVI and SMAP were evaluated with field data and root mean square error (RMSE) was calculated. The rice yield estimation was also performed by application of second order polynomial method. The performance of the polynomial model showed good results with the coefficient of determination of 0.74.

Reconstructing landscapes of ungulate parturition and predation using vegetation phenology

Enhanced vegetation index (EVI) data can be used to identify and define the space in which ungulates practice parturition and encounter predation. This study explores the use of EVI data to identify landscapes linked to ungulate parturition and predation events across space, time, and environmental conditions. As a case study, we used the moose population (Alces alces) of northern Minnesota in the USA. Using remotely sensed EVI data rasters and global positioning system collar data, we quantified how vegetation phenology and moose movement shaped the births and predation of 52 moose calves from 2013 to 2020 on or adjacent to the Grand Portage Indian Reservation. The known sources of predation were American black bears (Ursus americanus, n = 22) and gray wolves (Canis lupus, n = 28). Satellite-derived data summarizing seasonal landscape features at the local level revealed that landscape heterogeneity use by moose can help to quantitatively identify landscapes of parturition and predation in space and time across large areas. Vegetation phenology proved to be differentiable between adult moose ranges, sites of cow parturition, and sites of calf predation. Landscape characteristics of each moose group were consistent and tractable based on environment, suggesting that sites of parturition and predation of moose are predictable in space and time. It is possible that moose selected specific landscapes for parturition despite risk of increased predation of their calves, which could be an example of an "ecological trap." This analytical framework can be employed to identify areas for future ungulate research on the impacts of landscape on parturition and predation dynamics.

Assessing the capability of MODIS to monitor mixed pastures with high-intensity grazing at a fine-scale

MODIS time series carries valuable long-term data essential to support several studies such as biogeochemical modelling. However, there is a lack of validation studies applying MODIS data at a fine-scale to monitor pasture management practices. In this study, we assessed the potential of MODIS sensor in monitoring at a fine-scale four intensively managed mixed-pastures fields located in São Paulo State, Brazil. The MODIS spectral response was compared with Sentinel-2, and the ability of the two sensors in predicting aboveground biomass (AGB) and canopy height (CH) was assessed using the Random Forest algorithm. EVI images from MODIS and Sentinel-2 were correlated with field measurements of AGB and CH. The prediction performance of AGB (R2: Sentinel-2 = 38%; MODIS =42%) and CH (R2: Sentinel-2 = 69%; MODIS =85%) models was superior using EVI data from MODIS than Sentinel-2, highlighting MODIS ability to monitor small and intensively managed pasture fields.

Green change in the core build-up areas of China: Information from MODIS data

Urban built-up areas are the core area of global population agglomeration, where the interactions between human beings and the surrounding ecological environment supporting their survival are particularly intense. With the acceleration of urbanization since the 21st century, the level of green vegetation in the core built-up area (CBA) and its response to human activities are of great interest. In this study, an innovative green index (GI) was developed for evaluating green level in CBA from three aspects of “quantity-stability-balance”. GI can be measured by integrating MODIS EVI data and metrics including green coverage index, nearest neighbor index, and coefficient of variance. Spatiotemporal pattern, regional differences and geographical variances of green level during 2001–2018 in China’s CBAs were explored using geographic analysis methods (i.e. mutation test, trend analysis, spatial autocorrelation, Theil index, and scale variance). Results showed that during 2001–2018, GI and its different aspects of green coverage (GC), green stability (GS), and the balance of the greenness distribution (BGD) were generally high in South China and low in North China. Average green level in China’s CBAs experienced a V-shaped change. Green level in 54.58% of CBAs indicated mutation phenomenon, and the green level in most of these CBAs improved after the turning point. Regional differences in green level were reduced, and gaps within group were far larger than gaps between groups. Scale variance in green level at the regional, provincial, and city levels fell by 96.91%, 53.27%, and 46%, respectively, and the contribution of the difference at the city level to total difference in China was far higher than that at the provincial and regional levels. More attention should be paid to green in CBA at the city level, and sustainable urban development and land management policies should be implemented in the future.

Spatial distribution of GDP based on integrated NPS-VIIRS nighttime light and MODIS EVI data: a case study of Turkey

Satellite-derived nighttime light data have been increasingly used as a proxy measure for investigating economic activity. However, there are few studies focusing on the spatialised mapping of the GDP at pixel level and further analysis of the economic differences in agricultural and non-agricultural sectors for the different regions using the VIIRS-NPP data. This paper aims to fill this gap in the literature through developing a pixel-level agricultural and non-agricultural GDP map for Turkey in 2015 by combining the VIIRS-NPP nighttime imagery, Terra MODIS-Enhanced Vegetation Index, and land use/cover data from CORINE. The inclusion of vegetation indices and land cover data would significantly improve the estimates of sectorial GDP for Turkey where agriculture is one of the dominating sectors in the Country. GDP density map offers a significant database for both researchers and policy makers in the analysis of regional economic dynamics that will assist in formulating sustainable regional growth strategies.

Spatiotemporal Pattern Distribution of Drought Area using MODIS Vegetation Health Index. Case Study: Paddy Field in East Java, Indonesia

Drought is the main natural disasters in Indonesia. Study of spatial and temporal drought pattern distribution is great significance for disaster prevention and mitigation. Agricultural drought assessment was used to monitor agricultural sustainability, particularly in East Java as the national centre for agricultural production in Indonesia This study aimed to detect the spatial and temporal pattern of agricultural drought using Vegetation Health Index (VHI) and illustrated drought characteristics in East Java, Indonesia during 2017-2018. In this research, we use LST and EVI data from MODIS Terra satellite data. We used LST for calculating TCI, and EVI for calculating VCI. VHI were obtained by TCI and VCI score. The result showed that all drought-affected areas were experienced by moderate droughts. Bojonegoro, Magetan and Gresik district has the highest drought level compared to another district in East Java. Drought area increased significantly in September to October, which is dry season in Indonesia and cover 69.13% of agricultural area.

Estimation of Water-Use Efficiency Based on Satellite for the Typical Croplands

Water resources become scarce due to the melting of glaciers caused by climate change, and water pollution caused by human activities and overuse of freshwater resources are aggravating this phenomenon. Cropland is an essential part of the terrestrial ecosystem, and it is of great importance to make rational use of the limited water resources to have higher water use efficiency (WUE). The quantitative estimation of the ecosystem WUE and tracking its dynamics of croplands is crucial for addressing the impact of future climate change on crop production. Scaling up field observations of flux towers to a large scale remains a challenge and there are few reports on estimating WUE directly from remote sensing data. This study examined the main environmental factors that contribute to changes in WUE of typical crops, and found that the EVI showed strong correlation with the ecosystem WUE of typical croplands, which can be developed to track ecosystem WUE. This study proposed an alternative method exclusively based on MODIS EVI data analysis with an R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of nearly 0.90 and a small RMSE, which was much better than the calculations of the MODIS GPP and ET products, and provided an accessible method for modelling the ecosystem WUE to effectively manage water use in croplands.

Evaluation of the MODIS collections 5 and 6 for change analysis of vegetation and land surface temperature dynamics in North and South America

The latest collection (C6) of MODIS data provides several algorithmic improvements and calibration adjustments that correct for sensor degradation, theoretically making the C6 MODIS products more accurate compared to previous collections. C6 adjustments also introduce several improvements in the vegetation index (VI) retrieval algorithms. With these improvements, we expect only minor differences between data from Terra and Aqua, but significantly different results between C5 and C6. In this paper, we investigate three different MODIS products to determine the extent that improvements made to C6 influence the overall trend results for time series between 2001 and 2017. We focus on these three products specifically, both to allow for a comparison of vegetation index products—NDVI and EVI from MOD13C1, and NDVI and EVI calculated based on surface reflectance from MCD43C4—and also to gain an understanding of the improvements on an entirely different product from the same sensor, namely Land Surface Temperature (LST) from MOD11C2. For the MCD43C4 dataset, we find that 17.9% and 16.4% of EVI and NDVI pixels, respectively, display trend discordance between C5 and C6. For the MOD13C1 vegetation indices, we found comparable rates of trend discordance between C5 and C6: 18.5% and 17.4% for the EVI and NDVI pixels, respectively. For both products the greatest changes between C5 and C6 are an overall increase in pixels exhibiting a significant greening trend and an overall decline in pixels exhibiting a significant browning trend. Moreover, the largest differences between C5 and C6 for the NDVI and EVI data appear in cropland areas and in regions with relatively little human influence. In the Land Surface Temperature product (MOD11C2), the discordance between C5 and C6 is much lower: only 3.2% of day and 5.0% of night LST trends exhibited discordance between C5 and C6. We analyze the complementary results of vegetation index and land surface temperature trends and demonstrate that combining the results from different products observed at different portions of the electromagnetic spectrum—but linked through the biogeophysical processes of surface energy balance—allows us to portray change with more confidence than when relying on vegetation index data alone.

Fusing MODIS and OLI images: evaluating applications to winterwheat mapping

Moderate resolution imaging spectroradiometer (MODIS) series products are widely used to observe Earth’s surface, but the coarse resolution of MODIS images may negatively affect the monitoring accuracy. In this paper, MODIS vegetation index data (NDVI, normalized difference vegetation index; EVI, enhanced vegetation index) were fused with operational land imager (OLI) data (Landsat 8 OLI: pan band) using two image transform methods (WT, wavelet transform; GST, Gram-Schmidt transform) to improve the accuracy of winter wheat mapping. The findings showed that image fusion resulted in increased information entropy for image interpretation; however, the mapping accuracy was improved based on the fused OLI and MODIS images for only the OLI pan image acquired during the winter wheat growing season. Winter wheat was more often misclassified as urban green spaces in the summer than in the spring. For the entire study area, the overestimation of winter wheat based on the fused EVI and OLI data and the WT algorithm reached 5.98%. The time-series curves of the fused images were similar to those of the MODIS images, suggesting that time-series MODIS images might have expanded application in phenology-based wheat mapping.

Wavelet approach applied to EVI/MODIS time series and meteorological data

The aim of this study was to describe the phenology of different types of grasslands from the Pampa and Mata Atlântica biomes in the southern region of Brazil and its relation with meteorological variables, in a time series of EVI/MODIS data using the Wavelet approach (Transform – WT - and Coherence - WC). There is a lack of studies focusing on how climate variability influences the phenology of different Brazilian Pampa grassland typologies. This information is essential to describe the spatio-temporal dynamics of grasslands and contribute to actions of sustainable management and conservation strategies, threatened by crops, forestry, and expansion of overgrazed cattle farms. A series of EVI/MODIS acquired from February 2000 to December 2014, totaling 342 images, were sampled for each of the 10 grassland typologies at the study area. Mean EVI data and the WT indicated when and where changes in the grassland phenological dynamics occurred. The WC, applied to the EVI/MODIS time series with (i) rainfall and (ii) air temperature, helped to identify the correlation between the data. Two well - defined cycles were identified: annual, ranging from 1 to 23 observations, and interannual, from 92 to 184 observations. The different grassland typologies showed similar phenological patterns, although some spatial dependency was observed and related to the different soil and terrain morphometry at the study area. The influence of those abiotic factors on the grassland vegetation, phenological events and their expression on the EVI was also spatially dependent and strongly linked to weather conditions (e.g., the permanence of humidity after rainfall in shallow soils) and climate. The correlation between EVI and air temperature was stronger in the annual cycle for all grassland typologies. For the interannual cycles, El Niño and La Niña events caused higher correlation between EVI data and rainfall.

A rapid method for quantifying landscape-scale vegetation disturbances by surface coal mining in arid and semiarid regions

Quantifying landscape-scale vegetation disturbances by surface coal mining (SCM) is crucial for assessing and mitigating its negative impacts on the environment. Methods for detecting such disturbances in woody ecosystems exist, but these methods do not work well for deserts and grasslands in arid and semiarid regions because of their sensitive responses to precipitation variations. The objective of this study was to develop a new index to reliably detect the locations and spatial extents of SCM-induced vegetation disturbances in dryland regions in the face of fluctuating precipitation. We have developed a vegetation disturbance index (VDI) that combines MODIS EVI data with precipitation data to detect vegetation disturbances by SCM on the Mongolian Plateau during 2000–2015. The VDI is computed by comparing vegetation production per unit precipitation for a given year with a multi-year mean, and by considering distances from coal-mining areas. Our results show that the VDI was able to adequately distinguish vegetation disturbances by SCM from climate-driven vegetation changes in five selected sites across the Mongolian Plateau. The VDI provides an effective tool for quantifying the locations, spatial extents, and severity of vegetation disturbances by SCM in arid and semiarid regions.

Post-Fire Vegetation Succession and Surface Energy Fluxes Derived from Remote Sensing

The increasing frequency of fires inhibits the estimation of carbon reserves in boreal forest ecosystems because fires release significant amounts of carbon into the atmosphere through combustion. However, less is known regarding the effects of vegetation succession processes on ecosystem C-flux that follow fires. This paper describes intra- and inter-annual vegetation restoration trajectories via MODIS time-series and Landsat data. The temporal and spatial characteristics of the natural succession were analyzed from 2000 to 2016. Finally, we regressed post-fire MODIS EVI, LST and LSWI values onto GPP and NPP values to identify the main limiting factors during post-fire carbon exchange. The results show immediate variations after the fire event, with EVI and LSWI decreasing by 0.21 and 0.31, respectively, and the LST increasing to 6.89 °C. After this initial variation, subsequent fire-induced variations were significantly smaller; instead, seasonality began governing the change characteristics. The greatest differences in EVI, LST and LSWI were observed in August and September compared to those in other months (0.29, 6.9 and 0.35, respectively), including July, which was the second month after the fire. We estimated the mean EVI recovery periods under different fire intensities (approximately 10, 12 and 16 years): the LST recovery time is one year earlier than that of the EVI. GPP and NPP decreased after the fire by 22–45 g C·m−2·month−1 (30–80%) and 0.13–0.35 kg C·m−2·year−1 (20–60%), respectively. Excluding the winter period, when no photosynthesis occurred, the correlation between the EVI and GPP was the strongest, and the correlation coefficient varied with the burn intensity. When changes in EVI, LST and LSWI after the fire in the boreal forest were more significant, the severity of the fire determined the magnitude of the changes, and the seasonality aggravated these changes. On the other hand, the seasonality is another important factor that affects vegetation restoration and land-surface energy fluxes in boreal forests. The strong correlations between EVI and GPP/NPP reveal that the C-flux can be simply and directly estimated on a per-pixel basis from EVI data, which can be used to accurately estimate land-surface energy fluxes during vegetation restoration and reduce uncertainties in the estimation of forests’ carbon reserves.

Optimization of Parallel K-means for Java Paddy Mapping Using Time-series Satelite Imagery

Spatiotemporal analysis of MODIS Vegetation Index Imagery widely used for vegetation seasonal mapping both on forest and agricultural site. In order to provide a long-terms of vegetation characteristic maps, a wide time-series images analysis is needed which require high-performance computer and also consumes a lot of energy resources. Meanwhile, for agriculture monitoring purpose in Indonesia, that analysis has to be employed gradually and endlessly to provide the latest condition of paddy field vegetation information. This research is aimed to develop a method to produce the optimized solution in classifying vegetation of paddy fields that diverse both spatial and temporal characteristics. The time-series EVI data from MODIS have been filtered using wavelet transform to reduce noise that caused by cloud. Sequential K-means and Parallel K-means unsupervised classification method were used in both CPU and GPU to find the efficient and the robust result. The developed method has been tested and implemented using the sample case of paddy fields in Java Island. The best system which can accommodate of the extend-ability, affordability, redundancy, energy-saving, maintainability indicators are ARM-based processor (Raspberry Pi), with the highest speed up of 8 and the efficiency of 60%.