Normalized Difference Vegetation Index Variables Research Articles

Assessment of vegetation dynamics in Upper East Region of Ghana based on wavelet multi-resolution analysis

Vegetation variation offers significant information for environmental planning, management, sustainability and prompts caution of ecosystem degradation, particularly for the semiarid regions. Normalized difference vegetation index (NDVI) discloses the coverage growth situation, biomass and photosynthesis strength of vegetation and land-cover alterations. Wavelet was used to decompose NDVI time series into subseries at various timescales. This study used a multi-resolution analysis in association with Mann–Kendall and Sen’s Slope at 95% confidence interval to determine the trends in vegetation dynamics at the Upper East Region (UER) of Ghana. GIMMS NDVI3g time series was used to evaluate the performance of the vegetation at seasonal, interannual and intraannual timescale from 1982 to 2015. The results showed that the variability in NDVI in the region is annually significant. At the seasonal level, the whole surface area showed negative vegetation trend. In terms of the intraannual changes, 11.76% of the surface area showed critical patterns. At the interannual scale, results revealed that 4.40% of the surface area demonstrated significant patterns, while 95.60% indicated nonsignificant pattern. Overall, there was negative performance in the vegetation growth from 1982 to 2015. The 16.6% decrease in vegetation dynamics can be attributed to anthropogenic activities. The results from this study would benefit and provide helpful assistance to water resources managers, agricultural and ecological development officers for sustainable planning of UER.

VARIATION OF NDVI VEGETATION INDEX IN LANDSCAPE CHANGE OF AMBON CITY, MALUKU PROVINCE

The development of Ambon City has always been linear with urbanization and land use, where an increasing number of people have urged forest lands to be converted to open lands, such as settlements, businesses, and other infrastructure in the form of built-up land. On a larger scale, the process of land-use change results in land cover changes that affect changes in the Ambon City landscape and microclimate conditions. The biogeophysical characteristic cs of Ambon City as a small island region have a large and small river basin and hilly topography makes it risky when city development pays little attention to the balance of ecological landscape aspects. Therefore this study aims to analyze the variation of the vegetation index NDVI (Normalized Difference Vegetation Index) on changes in the Ambon City, Maluku Province. The results showed that during the last 25 years (1993 to 2018) there was an increase in class area from Very Rare Forest to Rare Forest by 874.04 ha, an increase in the class area of ​​Medium Forest by 567.42 ha, an increase in class area from Dense Forest to Very Dense by 2,861.80 ha and a decrease in class area from Dense Forest to Very Dense Forest by 4,176.07 ha. If the reduction in the classes size of Dense Forest and Very Dense Forest is combined into groups of class increase of Very Rare Forest and Rare Forest, then the total increase in the land landscape area with a reduced or lost vegetation value was 5,050.11 ha.

Spatiotemporal vegetation response to extreme droughts in eastern Brazil

Prolonged drought periods over large regions can affect a wide range of biomes and vegetation primary productivity. Here, we evaluated the long-term response of Normalized Difference Vegetation Index (NDVI) from Moderate Resolution Imaging Spectroradiometer (MODIS) in two administrative regions in Brazil, Northeast (NE) and Southeast (SE). Comparisons were made with ancillary Global Land Data Assimilation System (GLDAS) hydrometeorological data, focusing on a recent prolonged drought period from 2012 to 2015, which affected both regions. We found that the drought significantly impacted vegetation cover in large areas in NE dominated by a semiarid climate, whereas in SE region drought effect in vegetation was less pronounced and just some patches of stressed vegetation was identified. Regarding individual biomes in both regions, Caatinga, a unique tropical dry biome located in the center of semi-arid in the NE region, and the Northern portion of Atlantic Forest, an evergreen rain forest in the Brazilian coast, were severely impacted. Based on a time series analysis using breaking point tests, we revealed that after four stable years, an intense greening drop occurred, followed by a strong successive recovery pulses during the mentioned drought. Using the singular value decomposition (SVD) technique, we identified, among the hydrological variables, that precipitation and soil moisture better explain NDVI spatiotemporal variability, whereas the evapotranspiration pattern is not directed associated to the index, mainly in the SE region. Our results indicate that the covariability between NDVI and hydrometeorological variables should be better explored to monitor and forecast future droughts over different biomes in Brazil.

Towards a global understanding of vegetation–climate dynamics at multiple timescales

Abstract. Climate variables carry signatures of variability at multiple timescales. How these modes of variability are reflected in the state of the terrestrial biosphere is still not quantified or discussed at the global scale. Here, we set out to gain a global understanding of the relevance of different modes of variability in vegetation greenness and its covariability with climate. We used >30 years of remote sensing records of the normalized difference vegetation index (NDVI) to characterize biosphere variability across timescales from submonthly oscillations to decadal trends using discrete Fourier decomposition. Climate data of air temperature (Tair) and precipitation (Prec) were used to characterize atmosphere–biosphere covariability at each timescale. Our results show that short-term (intra-annual) and longer-term (interannual and longer) modes of variability make regionally highly important contributions to NDVI variability: short-term oscillations focus in the tropics where they shape 27 % of NDVI variability. Longer-term oscillations shape 9 % of NDVI variability, dominantly in semiarid shrublands. Assessing dominant timescales of vegetation–climate covariation, a natural surface classification emerges which captures patterns not represented by conventional classifications, especially in the tropics. Finally, we find that correlations between variables can differ and even invert signs across timescales. For southern Africa for example, correlation between NDVI and Tair is positive for the seasonal signal but negative for short-term and longer-term oscillations, indicating that both short- and long-term temperature anomalies can induce stress on vegetation dynamics. Such contrasting correlations between timescales exist for 15 % of vegetated areas for NDVI with Tair and 27 % with Prec, indicating global relevance of scale-specific climate sensitivities. Our analysis provides a detailed picture of vegetation–climate covariability globally, characterizing ecosystems by their intrinsic modes of temporal variability. We find that (i) correlations of NDVI with climate can differ between scales, (ii) nondominant subsignals in climate variables may dominate the biospheric response, and (iii) possible links may exist between short-term and longer-term scales. These heterogeneous ecosystem responses on different timescales may depend on climate zone and vegetation type, and they are to date not well understood and do not always correspond to transitions in dominant vegetation types. These scale dependencies can be a benchmark for vegetation model evaluation and for comparing remote sensing products.

Contributions of Climatic Factors to Interannual Variability of the Vegetation Index in Northern China Grasslands

AbstractUnderstanding the atmosphere–land surface interaction is crucial for clarifying the responses and feedbacks of terrestrial ecosystems to climate change. However, quantifying the effects of multiple climatic factors to vegetation activities is challenging. Using the geographical detector model (GDM), this study quantifies the relative contributions of climatic factors including precipitation, relative humidity, solar radiation, and air temperature to the interannual variation (IAV) of the normalized difference vegetation index (NDVI) in the northern grasslands of China during 2000 to 2016. The results show heterogeneous spatial patterns of determinant climatic factors on the IAV of NDVI. Precipitation and relative humidity jointly controlled the IAV of NDVI, illustrating more explanatory power than solar radiation and air temperature, and accounting for higher proportion of area as the determinant factor in the study region. It is noteworthy that relative humidity, a proxy of atmospheric aridity, is as important as precipitation for the IAV of NDVI. The contribution of climatic factors to the IAV of NDVI varied by vegetation type. Owing to the stronger explanatory power of climatic factors on NDVI variability in temperate grasslands, we conclude that climate variability may exert more influence on temperate grasslands than on alpine grasslands. Our study highlights the importance of the role of atmospheric aridity to vegetation activities in grasslands. We suggest focusing more on the differences between vegetation types when addressing the climate–vegetation relationships at a regional scale.

NDVI time series stochastic models for the forecast of vegetation dynamics over desertification hotspots

ABSTRACTLand degradation in semi-arid natural environments is usually associated with climate vulnerability and anthropic pressure, leading to devastating social, economic and environmental impacts. In this sense, remotely sensed vegetation parameters, such as the Normalized Difference Vegetation Index (NDVI), are widely used in the monitoring and forecasting of vegetation patterns in regions at risk of desertification. Therefore, the objective of this study was to model NDVI time series at six desertification hotspots in the Brazilian semi-arid region and to verify the applicability of such models in forecasting vegetation dynamics. We used NDVI data obtained from the MOD13A2 product of the Moderate Resolution Imaging Spectroradiometer sensor, comprising 16-day composites time series of mean NDVI and NDVI variance for each hotspot during the 2000–2018 period. We also used rainfall measured by weather stations as an explanatory variable in some of the tested models. Firstly, we compared Holt-Winters with Box-Jenkins and Box-Jenkins-Tiao (BJT) models. In all hotspots the Box-Jenkins and BJT models performed slightly better than Holt-Winters models. Overall, model performance did not improve with the inclusion of rainfall as an exogenous explanatory variable. Mean NDVI series were modelled with a correlation of up to 0.94 and a minimum mean absolute percentage error of 5.1%. NDVI variance models performed slightly worse, with a correlation of up to 0.82 and a minimum mean absolute percentage error of 22.0%. After the selection of the best models, we combined mean NDVI and NDVI variance models in order to forecast mean-variance plots that represent vegetation state dynamics. The combined models performed better in representing dry and degraded vegetation states if compared to robust and heterogeneous vegetation during wet periods. The forecasts for one seasonal period ahead were satisfactory, indicating that such models could be used as tools for the monitoring of short-term vegetation states.

Increasing interannual variability of global vegetation greenness

Despite the long-term greening trend in global vegetation identified in previous investigations, changes in the interannual variability (IAV) of vegetation greenness over time is still poorly understood. Using Global Inventory Modeling and Mapping Studies normalized difference vegetation index (NDVI) third generation data and corresponding meteorological data from 1982 to 2015, we studied the changes and drivers of the IAV of vegetation greenness as indicated by the coefficient of variation of vegetation greenness at a global scale. Dry and high-latitude areas exhibited high NDVI variability whereas humid areas exhibited relatively low NDVI variability. We detected an increase in the global IAV of vegetation greenness over time using a 15 year moving window. Spatially, we observed significant increases in the IAV of vegetation greenness in greater than 45% of vegetated areas globally and decreases in 21%. Our comparison of ecological models suggests good performance in terms of simulating spatial differences in vegetation variability, but relatively poor performance in terms of capturing changes in the IAV of vegetation greenness. Furthermore, the dominant climate variables controlling changes in the IAV of vegetation greenness were determined spatially using principal component regression and partial least squares regression. The two methods yielded similar patterns, revealing that temperature exerted the biggest influence on changes in the IAV of vegetation greenness, followed by solar radiation and precipitation. This study provides insights into global vegetation variability which should contribute to an understanding of vegetation dynamics in the context of climate change.

A spatio-temporal prediction of NDVI based on precipitation: an application for grazing management in the arid and semi-arid grasslands

ABSTRACTA method for predicting the dynamic spatio-temporal variations of the normalized difference vegetation index (NDVI) based on precipitation is proposed using combined nonlinear autoregressive with exogenous input (NARX) networks and artificial neural networks (ANNs). The proposed method is validated by applying to predict the spatio-temporal NDVI for the Hulunbuir grassland located in Inner Mongolia, China. The results show the good predictive ability for the spatio-temporal variations of NDVI with the mean absolute percentage error of 11.59%, mean absolute error of 7.11 × 10−2 and root mean square error of 8.06 × 10−2, respectively. The approach presented in the paper can be further used as the guidance to reduce the occurrence of overgrazing in the arid and semi-arid grasslands.

Peatland degradation: The relationship between raised bog hydrology and normalized difference vegetation index

AbstractPeatlands are recognized as important landscape elements and their disturbance, followed by the loss of their ecohydrological functions, leads to falling water tables and degradation. Sometimes, the shortage of hydrological monitoring data impedes the detection of degrading peatland areas. The application of cost‐effective remote sensing methods for assessment of humidity conditions permits monitoring of a large area and longer periods of time. Therefore, in this study, we assessed the links between hydrological parameters and the normalized difference vegetation index (NDVI) as well as identifying spatio‐temporal patterns in the condition of raised bog vegetation, according to NDVI, in the Čepkeliai raised bog, a semipristine peatland in south‐eastern Lithuania. Time series of NDVI (2000–2018) were obtained from the moderate resolution imaging spectroradiometer and compared with field measurements. A strong positive correlation between NDVI and water table depth was found. Moderate (for the whole raised bog) and strong (for control subbasin) positive correlations between NDVIavg and annual minimum flow over 30 and 7 days were found. This confirms that NDVI could be treated as an appropriate indicator, showing the degradation processes of the raised bog ecosystem and could be applied in its management. Analysis of the interannual NDVIavg variation confirmed the prevalence of stable favourable conditions during the growing season with a slight decrease in NDVImax over last 4 years. Meanwhile, analysis of spatial NDVI variation revealed obvious spatial patterns where possible degradation processes tend to appear.

Monitoring the impact of climate change and human activities on grassland vegetation dynamics in the northeastern Qinghai-Tibet Plateau of China during 2000–2015

Climate change and human activities can influence vegetation net primary productivity (NPP), a key component of natural ecosystems. The Qinghai-Tibet Plateau of China, in spite of its significant natural and cultural values, is one of the most susceptible regions to climate change and human disturbances in the world. To assess the impact of climate change and human activities on vegetation dynamics in the grassland ecosystems of the northeastern Qinghai-Tibet Plateau, we applied a time-series trend analysis to normalized difference vegetation index (NDVI) datasets from 2000 to 2015 and compared these spatiotemporal variations with trends in climatic variables over the same time period. The constrained ordination approach (redundancy analysis) was used to determine which climatic variables or human-related factors mostly influenced the variation of NDVI. Furthermore, in order to determine whether current conservation measures and programs are effective in ecological protection and reconstruction, we divided the northeastern Qinghai-Tibet Plateau into two parts: the Three-River Headwater conservation area (TRH zone) in the south and the non-conservation area (NTRH zone) in the north. The results indicated an overall (73.32%) increasing trend of vegetation NPP in grasslands throughout the study area. During the period 2000–2015, NDVI in the TRH and NTRH zones increased at the rates of 0.0015/a and 0.0020/a, respectively. Specifically, precipitation accounted for 9.2% of the total variation in NDVI, while temperature accounted for 13.4%. In addition, variation in vegetation NPP of grasslands responded not only to long- and short-term changes in climate, as conceptualized in non-equilibrium theory, but also to the impact of human activities and their associated perturbations. The redundancy analysis successfully separated the relative contributions of climate change and human activities, of which village population and agricultural gross domestic product were the two most important contributors to the NDVI changes, explaining 17.8% and 17.1% of the total variation of NDVI (with the total contribution >30.0%), respectively. The total contribution percentages of climate change and human activities to the NDVI variation were 27.5% and 34.9%, respectively, in the northeastern Qinghai-Tibet Plateau. Finally, our study shows that the grassland restoration in the study area was enhanced by protection measures and programs in the TRH zone, which explained 7.6% of the total variation in NDVI.

Advanced Method to Capture the Time-Lag Effects between Annual NDVI and Precipitation Variation Using RNN in the Arid and Semi-Arid Grasslands

The latest research indicates that there are time-lag effects between the normalized difference vegetation index (NDVI) and the precipitation variation. It is well known that the time-lags are different from region to region, and there are time-lags for the NDVI itself correlated to the precipitation. In the arid and semi-arid grasslands, the annual NDVI has proved not only to be highly dependent on the precipitation of the concurrent year and previous years, but also the NDVI of previous years. This paper proposes a method using recurrent neural network (RNN) to capture both time-lags of the NDVI with respect to the NDVI itself, and of the NDVI with respect to precipitation. To quantitatively capture these time-lags, 16 years of the NDVI and precipitation data are used to construct the prediction model of the NDVI with respect to precipitation. This study focuses on the arid and semi-arid Hulunbuir grasslands dominated by perennials in northeast China. Using RNN, the time-lag effects are captured at a 1 year time-lag of precipitation and a 2 year time-lag of the NDVI. The successful capture of the time-lag effects provides significant value for the accurate prediction of vegetation variation for arid and semi-arid grasslands.

Using generalized additive models for interpolating microclimate in dry-site ponderosa pine forests

Microclimate in the understory of forests can be an important mediating influence for ecological attributes such a vegetation composition, wildlife habitat, fuel moisture, and downed wood and forest floor decomposition processes. Several tools exist for interpolating data from regional, broad-scale weather stations to derive geospatial climate surface maps representing mesoclimate variations at the earths’ surface. While useful, these tools typically do not provide interpolations that account for variations in forest canopy cover and therefore often fail to provide for higher resolution characterization of near-surface, understory microclimate conditions. In this paper we explore the development of understory microclimate surface maps from a dispersed set of microclimate sensors in a 991-ha managed landscape dominated by ponderosa pine forest patches of various densities in central Oregon, USA. We used generalized additive models (GAM) to interpolate microclimate at 50 m resolution from 2013 to 2016 using elevation, heat load, and normalized difference vegetation index (NDVI) as covariates. Models for all years revealed that air temperature trends were dependent on complex interactions of forest cover, site physiography and time, and produced interpolations closer to observed conditions than freely available coarse-scale models. We analyzed sub-canopy air temperature interpolation maps for trends in the context of differing canopy structures arising from five fuels mitigation treatments implemented between 2011–2014. Variations in NDVI within the treatments had statistically significant and visually discernible influence on sub-canopy temperature as evidenced by spatial temperature trends that were congruent with NDVI maps, and indicates the model accounted for the influence of canopy. The ability to develop higher resolution understory microclimate surface maps is of value to many aspects of ecosystem monitoring and the methods presented here may be of use to researchers and managers who work in forested environments.

Spatio-Temporal Patterns of Vegetation in the Yarlung Zangbo River, China during 1998–2014

Spatiotemporal vegetation patterns are of great importance for regional development. As one of the largest transnational rivers in China, the Yarlung Zangbo River in the Qinghai-Tibetan Plateau was selected as the study site, and the spatiotemporal patterns of vegetation during 1998–2014 were analyzed using the normalized difference vegetation index (NDVI). The results show that the NDVI increased with decreasing elevation, and the largest value was observed for the broadleaf forest. The lag time of NDVI to precipitation for most of the vegetation units was distinguished as approximately one month. In the region with an elevation of over 5000 m, the NDVI for the alpine vegetation was negatively correlated with the precipitation. Most NDVI variations were due to precipitation and temperature (approximately 75%). These results could provide a reference for ecological protection at a similar high elevation in the future.

Altitudinal forest-tundra ecotone categorization using texture-based classification

This study proposes a new technique involving texture-based image classification to categorize altitudinal FTEs by the degree of fragmentation of the interface. This allows a) universally adaptable altitudinal FTE categorization based on widely available satellite data products and b) assessment of sensitivity of altitudinal FTEs to shift with climate change at different locations based on the spatial distribution of the corresponding categories. The FTE categorization scheme used in this study corresponds partly to the globally occurring primary altitudinal FTE ‘forms.’ Specifically, ‘diffuse’ and ‘abrupt’ FTEs are recognized and separated. Normalized Difference Vegetation Index (NDVI) calculated from Sentinel-2 imagery is used for FTE delineation and categorization. A technique named FOurier-based Textural Ordination (FOTO) is implemented to extract textural information based on NDVI variations in image windows, and supervised classification is used to further separate these windows into FTE categories based on texture. The analysis is conducted on part of the Khibiny Mountains, Kola Peninsula, Russia, and further tested on six other study areas spread across the circumarctic region. The proposed method is able to adapt to different study areas with minimum changes in parameterization, and effectively extract altitudinal FTEs and categorize them into different FTE forms with satisfactory accuracies.

A Spatialization Method for Grain Yield Statistical Data: A Study on Winter Wheat of Shandong Province, China

Core Ideas Dividing the study area into subregions and classifying them improved winter wheat classification accuracy.Single‐phase Normalized Difference Vegetation Indices acquired on 6 March, 23 April, 25 May, and 29 June were the best variables for building the wheat yield spatialization model.The extraction accuracy of winter wheat area greatly impacted the spatialization of yield.The proposed model can provide a technical reference for producing high‐resolution crop yield distribution maps. Grain yield data based on administrative divisions (counties, cities, etc.) for statistics lack spatial information, which can be effectively solved by grain yield spatialization. This paper proposes a spatialization method for grain yield based on the Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) time series data. The method was tested by taking winter wheat (Triticum aestivum L.) in Shandong Province in China as an example. First, the classification and regression tree (CART) algorithm was trained to extract the winter wheat planting pixels in 2016. The average NDVIs of the different growing stages (returning green, jointing, heading, and milk ripening) were calculated from the MODIS NDVI time series data. The relationship between winter wheat yield and NDVI variables (including single‐phase NDVI and the average NDVI of different growing stages) was analyzed by univariate and multiple linear regressions. The NDVI variable with the highest correlation to winter wheat yield and the minimum root mean square error of the fitting equation were chosen as input to build the spatialization model. The results show that the classification accuracy of winter wheat estimated with the confusion matrix was 82.51% and that the average precision of planting acreage compared with county‐level statistical data was 87.64%. The average relative error of yield spatialization at the county level was 22.71%. The method developed in this paper is easy to operate and popularize, and it can provide a technical reference for producing high‐resolution crop yield distribution maps of long time series through spatialization.

Varying climate response across the tundra, forest-tundra and boreal forest biomes in northern West Siberia

Satellite studies using the normalized difference vegetation index (NDVI) have revealed changes in northern Eurasian vegetation productivity in recent decades, including greening in tundra and browning in the boreal forests. However, apparent NDVI changes and relationships to climate depend on the temporal and spatial sampling and the biome and forest-land cover type studied. Here we perform a consistent analysis of NDVI and climate across four bioclimatic zones (tundra, forest-tundra, northern and middle taiga) in northern West Siberia (NWS), further stratified into eight forest-land cover types. We utilize NDVI data from the Moderate Resolution Imaging Spectroradiometer and climate reanalysis data from 2000 to 2016, a period including the record warm anomaly in 2016 (+2 °C–5 °C June–July surface air temperature (SAT) across NWS). Statistically significant (α = 0.05) correlations were found for two bivariate relationships at the biome level: between NDVImax and June–July surface air temperature (SAT)(r ∼ +0.79), and between middle taiga NDVImax and July precipitation (r ∼ +0.48). No significant statistical relationships were found for the northern taiga and forest-tundra biomes. However, within these biomes we found that deciduous needle-leaf (larch) NDVImax is significantly correlated with July temperature (r ∼ +0.48). Qualitatively, spatial composites of NDVI and climate variables were effective for revealing insights and patterns of these relationships at the sub-regional scale. The spatial heterogeneity of NDVI patterns indicates divergent reactions of specific types of vegetation, as well as local effects that are clearly important on the background of a regional climate response.

On the relationship between normalized difference vegetation index and land surface temperature: MODIS-based analysis in a semi-arid to arid environment

This work focused on studying the relationships between Normalized Difference Vegetation Index (NDVI) and daytime and nighttime Land Surface Temperature (LST) in winter, spring, summer and fall and investigating the effects of land cover on these variables in Jordan, which represents a typical semi-arid to arid environment. Using MODIS-based data for the year 2017, multiple procedures were applied: one-way analysis of variance followed by comparison between means, Pearson correlation coefficient, global Moran’s index, simple linear regression, second-order polynomial regression, recursive-partitioning regression and geographically weighted regression. The results showed that land cover explained fair amount of the variability in NDVI but small amount of the variability in daytime and nighttime LST. In addition, an inverted surface urban heat island pattern was observed in daytime. Finally, applying different regression procedures produced different perspectives about the complex and variable relationships between daytime and nighttime LST and NDVI in different seasons.

Sensitivity of a tropical montane cloud forest to climate change, present, past and future: Mt. Marsabit, N. Kenya

During the Last Glacial Maximum (LGM) lowland forests contracted throughout the tropics but, by contrast, many montane forest taxa moved to lower elevations. These taxa are often found in cloud forests, which are globally important ecosystems that depend on the capture of atmospheric water from fog drifting through the canopy, here referred to as occult precipitation. Understanding the response of tropical montane taxa to climate variations is limited by a lack of modern data on fog capture; whereas palaeoecological data only provide indirect evidence for its importance. Hence, the response of vegetation to fog capture is not considered in palaeo-estimates of precipitation. We develop a method that uses satellite Normalized Difference Vegetation Index (NDVI) data to estimate the annual amount of occult precipitation and investigate the sensitivity of a cloud forest to past and future changes in both rainfall and occult precipitation. We apply this method using satellite and meteorological data from 1982 to 2015 collected at Mt Marsabit, which is located in northern Kenya (2.34∘ N, 37.97∘ E, summit 1707 m a.s.l.). Mt Marsabit has a sub-humid tropical montane cloud forest at its summit that is excessively green for the amount of rain it receives. We estimate the annual amount of occult precipitation for current conditions at about 900 mm y−1 which is more than the average annual rainfall of 700 mm y−1. This is consistent with the observation that, for the wider Marsabit area, interannual variations in NDVI are more closely linked to changes in cloud-base height (r2=0.87) than to changes in rainfall (r2=0.67). We investigate the sensitivity of forest extent to past and future changes; for the LGM we estimate that cloud-base height decreased by 500 m in response to a 4 ∘C cooling and that this caused a 20%–100% increase in forest area despite a 30% decrease in rainfall, a 22% decrease in atmospheric humidity and a substantial reduction of atmospheric CO2 levels (values representative for mountains in Kenya during the LGM). An expected increase of 250 m in the cloud-base height associated with a future 2 ∘C global warming is likely to reduce forest extent by 50%–100%. Our results indicate that the satellite vegetation record is useful to estimate modern hydrological inputs into drier cloud forests (up to 2000 mm y−1) and that this information can be used to estimate the contribution of occult precipitation to altitudinal displacements of tropical montane cloud-forest species during the Quaternary.

Mathematical modeling and use of orbital products in the environmental degradation of the Araripe Forest in the Brazilian Northeast

Vegetation cover is indispensable in the process of inhibiting environmental degradation. In the Northeast of Brazil, especially in the Araripe Nacional Forest (FLONA), this problem is related to the removal of vegetation for industrial and domestic use, in addition to the expansion of livestock. Thus, the objective of this work was to evaluate the environmental degradation in the area of FLONA from orbital products via remote sensing with the aid of mathematical modeling. For this, two orbital images of the orbit 65, point 217 were used for processing and obtaining the variables: (1st) July 7, 2003 from TM/Landsat-5 and (2nd) July 15, 2018 from OLI/Landsat-8. In mathematical modeling, the multiple linear regression (MRL) model was applied to the orbital products: land surface temperature (LST), normalized burn ratio (NBR), Normalized Difference Moisture Index, Normalized Difference Water Index (NDWI) to estimate the Soil Adjusted Vegetation Index (SAVI) and hence to predict the Normalized Difference Vegetation Index (NDVI). All the processing to obtain the results was carried out in the software R version 3.4-1. O NDVI pointed out a significant increase of 72.05% in dense vegetation, from 158.33 to 272.40 km2. However, vegetation is more likely to suffer from stress due to the increase in LST at 5 °C, which increased from 17.5 to 25.0 °C, reaching its highest value of 42 °C in July 2011. The MRL results indicated that the models have an excellent predictive capacity in the estimation of degradation, with R2 value greater than 92% of the explained variance. In addition, the MAE and root mean square error were less than 0.03 for both models. The models pointed out that SAVI, NBR and NDWI are responsible for the variability of NDVI in environmental degradation of FLONA. Highlight for the theoretical-conceptual model that can be applied to any semi-arid and highly-sensitive region to changes in the rainfall pattern.

Interannual and Seasonal Vegetation Changes and Influencing Factors in the Extra-High Mountainous Areas of Southern Tibet

The ecosystem of extra-high mountain areas is very fragile. Understanding local vegetation changes is crucial for projecting ecosystem dynamics. In this paper, we make a case for Himalayan mountain areas to explore vegetation dynamics and their influencing factors. Firstly, the interannual trends of the normalized difference vegetation index (NDVI) were extracted by the Ensemble Empirical Mode Decomposition (EEMD) algorithm and linear regression method. Moreover, the influence of environmental factors on interannual NDVI trends was assessed using the Random Forests algorithm and partial dependence plots. Subsequently, the time-lag effects of seasonal NDVI on different climatic factors were discussed and the effects of these factors on seasonal NDVI changes were determined by partial correlation analysis. The results show that (1) an overall weak upward trend was observed in NDVI variations from 1982 to 2015, and 1989 is considered to be the breakpoint of the NDVI time series; (2) interannual temperature trends and the shortest distance to large lakes were the most important factors in explaining interannual NDVI trends. Temperature trends were positively correlated with NDVI trends. The relationship between the shortest distance to large lakes and the NDVI trend is an inverted U-shaped; (3) the time-lags of NDVI responses to four climatic factors were shorter in Autumn than that in Summer. The NDVI responds quickly to precipitation and downward long-wave radiation; (4) downward long-wave radiation was the main climate factor that influenced NDVI changes in Autumn and the growing season because of the warming effect at night. This study is important to improve the understanding of vegetation changes in mountainous regions.