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Albedo estimated from remote sensing correlates with ecosystem multifunctionality in global drylands
... Changes in vegetation affect the level of surface albedo (Myhre and Myhre, 2003), and different vegetation covers represent different albedo values (Kang and Hong, 2008). Albedo has the potential to monitor ecosystem performance changes in arid regions and provides warning of the beginning of desertification (Zhao et al., 2018). Meanwhile, surface albedo is one of the most important components causing surface radiation balance (BdaS et al., 2016). ...
... It is one of the most important factors controlling the energy available throughout the day with surface change processes (Houldcroft et al., 2009). Therefore, it can be said that the change in vegetation affects the surface albedo and desertification occurs with the destruction of vegetation, and the surface albedo increases in the degraded areas (Zhao et al., 2018). Zongyi et al. (2011) presented the albedo-NDVI model for monitoring desertification (Zongyi et al., 2011). ...
... Vegetation, the combination of water and heat and their changes, and desertified areas could be easily detected using multi-spectral remote sensing information. Remote sensingbased vegetation indices and land surface albedo are two preferable indicators for monitoring the degradation process (Zhao et al., 2018). Pan and Li (2013) selected three different groups, namely, vegetation, water, and bare soil, based on the spectral mixture analysis model (Pan and Li, 2013). ...
Monitoring degradation in arid and semi-arid areas is one of the main concerns for governments, given the growing degradation trend. Meanwhile, detecting the areas subjected to degradation requiring management in the shortest time and at the lowest cost is a necessity, especially in border areas such as Hamoun Wetland, located between Iran and Afghanistan. Albedo and normalized difference vegetation index (NDVI) were calculated using remote sensing technology to monitor the degradation intensity in different periods (August 1999, 2009, 2015, and 2020). Change vector analysis in brightness and greenness indices for 1999 and 2020 was used to determine the changes in intensity. Linear regression was run between albedo and NDVI. Finally, degradation intensity (DI) map was developed to monitor degradation intensity. A confusion matrix was created between the change vector analysis (CVA) and the albedo–NDVI model to evaluate the accuracy of the map obtained from this model for 1,476 pixels of different classes. The linear regression between NDVI and albedo showed a negative correlation between indices (R = −0.849). The results showed an increase for the regions with null, low, and medium degradation intensity, while an expansion was observed for the regions with severe and extreme degradation. The confusion matrix results indicated the high accuracy (0.705) of the degradation intensity model for the study area. These changes were about 52.01% from 1999 to 2009, 7.07% from 2009 to 2015, 56.26% from 1999 to 2015, and 55.15% from 2015 to 2020. Additionally, the average rate of changes in degradation intensity between 1999 and 2020 was 13.11%.
... Land degradation refers to loss of the biological or economic productivity of any land resulting in deterioration of physical, biological and/or economic properties of soil, and long-term loss of natural vegetation (United Nations, 1994;Bakr et al., 2012;Van den Elsen and Jetten, 2015). Since 1970, political and international interest over this phenomenon has increased, specially over arid, semi-arid and sub humid ecosystems, due to their significant role in food production and social development of communities (Li et al., 2016;Becerril-Piña et al., 2016;Liu et al., 2018;Zhao et al., 2018). ...
... This last definition has been formally and widely used, since then, for multiple studies on desertification around the world, thus providing multiple and variable scopes on how to measure, analyse, and model measure desertification. (Kassas, 1995;Li et al., 2006;Cui et al., 2011;Bakr et al., 2012;Lamchin et al., 2016;Liu et al., 2018;Xu et al., 2016;Becerril-Piña et al., 2016;Zhao et al., 2018). Taking into consideration these definitions, for this study, we adopt the definition of desertification as: "land degradation in arid, semi-arid, and dry sub-humid areas resulting from human activities and climate variation which can lead to desert-like conditions". ...
... The study and assessment of the desertification process and/or the advance or retreat of arid areas as a function of natural and anthropogenic causes is necessary for the prediction of future risk posed by climate change, and to rightly support the policymaking, action plans, and mitigation measures that can be taken at local and global scale (Kassas, 1995;Odjugo and Ikhijoria, 2003;Xu et al., 2016). The establishments of monitoring programs are the most effective way to assess desertification processes as it helps to understand the mechanisms and changes of this ecosystem before they become irreversible (Zhao et al., 2018;D'Odorico et al., 2013;Xiao et al., 2006). ...
The study and assessment of desertification and/or the advance or retreat of arid areas as a function of natural and anthropogenic causes is necessary for the prediction of future risks from climate change, and to support policymaking, action plans, and mitigation measures that can be taken at local and global scales. Remote sensing enables modelling, monitoring, and prediction of the behaviour of several elements of desertification. There have been numerous approaches to study desertification using remote sensing over the years. This research explored the timeline and global distribution of studies using remote sensing in studying desertification. Additionally, the review evaluated the key methods and variables that have been used to study desertification from remote sensing data.
The use of remote sensing for desertification studies can be trace back to 1991. From 2015 to 2020, more than 40 articles were published per year, showing that there has been a recent increase in the use of remote sensing techniques and its availability for monitoring desertification. Most regions of the world affected by desertification are being studied using remote sensing, however, there is a marked geographical variation between the number of studies in various regions, with Asia having disproportionately high number of studies compared to America or Africa. The country with most studies of desertification using remote sensing is China.
In terms of satellite data, Landsat images provide the bulk of data used to study desertification, especially the Thematic Mapper (TM) sensor. Classification and change detection are the most used methods to study desertification from remote sensing data. Additionally, land cover/land use change and vegetation and its attributes (e.g., Normalized Difference Vegetation Index - NDVI) are the most used variables to study desertification using remote sensing techniques. Finally, the review found major differences in terms of the ranges or thresholds applied to these variables when determining the presence or risk of desertification. Therefore, there is a need to develop thresholds and ranges of changes of key selected variables, which can be used to determine the presence of desertification.
... The detection of LCC in seasonally dry forests by using VIS-NIR, such as EVI and NDVI, is limited due to difficulties distinguishing deciduous vegetation from the underlying ground during the dry period (Daughtry, 2001;Jacques et al., 2014;Mayes et al., 2015;Nagler et al., 2000;Xu et al., 2014). Zhao et al. (2018) highlight that while vegetation indices are routinely used to monitor ecosystem attributes and functions such as vegetation cover and primary productivity, the remote sensingmeasured surface albedo (SA) can be used to assess ecosystem status in drylands. SA is more sensitive to changes in biomass (Rodríguez-Caballero et al., 2015); it has been used to monitor changes in dryland ecosystems because SA increases as soils become more exposed to direct sunlight (Yu et al., 2017), which is the first outcome of the LCC process on the terrestrial surface (Lamchin et al., 2016;Liu et al., 2016;Karnieli et al., 2014). ...
... where ρ and b are the surface bidirectional reflectance values and their corresponding conversion coefficients for the six non-thermal Landsat bands, i.e., blue, green, red, NIR and the two shortwave infrared bands (SWIR1 and SWIR2). Table 1 Zhao et al., 2018). As SA has an inverse behaviour to most VIS-NIR vegetation indices, we used its complement to one (1 -SA), thus ensuring a pattern of responses to LCC that corresponds to that of the vegetation indices EVI and NDVI. ...
... SA exhibited a greater sensitivity to changes involving characteristics other than the greenness of leaves because this index covers other bands (SWIR 1 and SWIR 2) of the electromagnetic spectrum Zhao et al., 2018), which are not used by indices that cover only the VIS-NIR. When a soil-plant-atmosphere system is altered by an action of deforestation, the leafless woody biomass -which represents ca. ...
Ongoing increase in human and climate pressures, in addition to the lack of monitoring initiatives, makes the Caatinga one of the most vulnerable forests in the world. The Caatinga is located in the semi-arid region of Brazil and its vegetation phenology is highly dependent on precipitation, which has a high spatial and temporal variability. Under these circumstances, satellite image-based methods are valued due to their ability to uncover human-induced changes from climate effects on land cover. In this study, a time series stack of 670 Landsat images over a period of 31 years (1985–2015) was used to investigate spatial and temporal patterns of land-cover clearing (LCC) due to vegetation removal in an area of the Caatinga. We compared the LCC detection accuracy of three spectral indices, i.e., the surface albedo (SA), the Enhanced Vegetation Index (EVI) and the Normalized Difference Vegetation Index (NDVI). We applied a residual trend analysis (TSS-RESTREND) to attenuate seasonal climate effects on the vegetation time series signal and to detect only significant structural changes (breakpoints) from monthly Landsat time series. Our results show that SA was able to identify the general occurrence of LCC and the year that it occurred with a higher accuracy (89 and 62%, respectively) compared to EVI (44 and 22%) and NDVI (46 and 22%). The overall outcome of the study shows the benefits of using Landsat time series and a spectral index that incorporates the short-wave infrared range, such as the SA, compared to visible and near-infrared vegetation indices for monitoring LCC in seasonally dry forests such as the Caatinga.
... The detection of LCC in seasonally dry forests by using VIS-NIR, such as EVI and NDVI, is limited due to difficulties distinguishing deciduous vegetation from the underlying ground during the dry period (Daughtry, 2001;Jacques et al., 2014;Mayes et al., 2015;Nagler et al., 2000;Xu et al., 2014). Zhao et al. (2018) highlight that while vegetation indices are routinely used to monitor ecosystem attributes and functions such as vegetation cover and primary productivity, the remote sensingmeasured surface albedo (SA) can be used to assess ecosystem status in drylands. SA is more sensitive to changes in biomass (Rodríguez-Caballero et al., 2015); it has been used to monitor changes in dryland ecosystems because SA increases as soils become more exposed to direct sunlight (Yu et al., 2017), which is the first outcome of the LCC process on the terrestrial surface (Lamchin et al., 2016;Liu et al., 2016;Karnieli et al., 2014). ...
... where ρ and b are the surface bidirectional reflectance values and their corresponding conversion coefficients for the six non-thermal Landsat bands, i.e., blue, green, red, NIR and the two shortwave infrared bands (SWIR1 and SWIR2). Table 1 Zhao et al., 2018). As SA has an inverse behaviour to most VIS-NIR vegetation indices, we used its complement to one (1 -SA), thus ensuring a pattern of responses to LCC that corresponds to that of the vegetation indices EVI and NDVI. ...
... SA exhibited a greater sensitivity to changes involving characteristics other than the greenness of leaves because this index covers other bands (SWIR 1 and SWIR 2) of the electromagnetic spectrum Zhao et al., 2018), which are not used by indices that cover only the VIS-NIR. When a soil-plant-atmosphere system is altered by an action of deforestation, the leafless woody biomasswhich represents ca. ...
Ongoing increases in human and climate pressures associated with the lack of monitoring initiatives make the Caatinga one of the most vulnerable biomes in the world. The Caatinga is located in the semi-arid region of Brazil, and its vegetation phenology is highly dependent on precipitation, which has a high spatial and temporal variability. Under these circumstances, satellite image-based methods are valued due to their ability to uncover human-induced changes from climate effects on land cover. In this study, 670 continuous Landsat images over a period of 31 years (1985–2015) were analysed to investigate spatial and temporal patterns of land-cover change (LCC) due to vegetation clearing in an area of the Caatinga biome. We compared the performance of surface albedo (SA), the Enhanced Vegetation Index (EVI) and the Normalized Difference Vegetation Index (NDVI) to evaluate their suitability for monitoring LCC driven by human actions in contrast to precipitation-related variations. We applied a residual trend analysis, with detection of significant breakpoints (TSS-RESTREND), to a monthly Landsat time series. Our results show that SA was able to identify the year of land-cover clearing with a higher accuracy (83%) than that of EVI (20%) and NDVI (34%). The overall outcome of the study shows the benefits of using different spectral bands instead of greenness indices of Landsat time series for the monitoring of LCC, as a result of environmental land surface processes in seasonal dry forests such as the Caatinga.
... To overcome this challenge, we need to move beyond remote sensing indicators (RSI) mainly related to plant cover and incorporate other indicators that can potentially analyse biophysical properties such as plant composition and functioning. For instance, Zhao et al. (2018) demonstrated a significant relationship between visible black-sky albedo and soil multifunctionality across global drylands. However, this study was limited to a selection of 61 homogeneous plots from the 224 dryland datasets compiled by Maestre et al. (2012) to avoid the mismatch between field data collected from 30 9 30 m plots and MODIS image resolution of 500 9 500 m (NASA LP DAAC, 2017). ...
... Furthermore, the accuracy obtained for predicting soil multifunctionality using the 1-RSI (r = 0.66, P < 0.01) and RSI-pca (r = 0.73, P < 0.01) models with Landsat data and EAM models represents a significant improvement compared to results from previous studies. For instance, Zhao et al. (2018) reported a correlation between soil multifunctionality and MODIS land surface albedo of only r = À0.314. These findings align with recent efforts to apply deep learning approaches to quantify soil organic carbon composition at the national level, as reported by Odebiri et al. (2022). ...
Models derived from satellite image data are needed to monitor the status of terrestrial ecosystems across large spatial scales. However, a remote sensing‐based approach to quantify soil multifunctionality at the global scale is missing despite significant research efforts on this topic. A major constraint for doing so is the availability of suitable global‐scale field data to calibrate remote sensing indicators (RSI) and, to a lesser extent, the sensitivity of spectral data of available satellite sensors to soil background and atmospheric conditions. Here, we aimed to develop a soil multifunctionality model to monitor global drylands coupling ground data on 14 soil functions of 222 dryland areas from six continents to 18 RSI derived from a time series (2006–2013) Landsat dataset. Among the RSI evaluated, the chlorophyll absorption ratio index was the best predictor of soil multifunctionality in single‐variable‐based models ( r = 0.66, P < 0.01, NMRSE = 0.17). However, a multi‐variable RSI model combining the chlorophyll absorption ratio index, the global environment monitoring index and the canopy‐air temperature difference improved the accuracy of quantifying soil multifunctionality ( r = 0.73, P < 0.01, NMRSE = 0.15). Furthermore, the correlation between RSI and soil variables shows a wide range of accuracy with upper and lower values obtained for AMI ( r = 0.889, NMRSE = 0.05) and BGL ( r = 0.685, NMRSE = 0.18) respectively. Our results provide new insights on assessing soil multifunctionality using RSI that may help to monitor temporal changes in the functioning of global drylands effectively.
... For instance, the spatial variation of vegetation cover reflected on the value of surface albedo (Kang & Hong, 2008;Myhre & Myhre, 2003), so that a decrease in vegetation coverage is matched directly by an increase in albedo values, vice versa (Jiang et al., 2019;Ma et al., 2011;Oroud & Alghababsheh, 2023;Planque et al., 2017;Zolfaghari & Abdollahi, 2022). Thus, this metric can be applied as one of the indices of assessing desertification, especially in dryland, which can determine the initial alarming sings of desertification (Zhao et al., 2018). The shortwave broadband albedo for the study area was derived from MODIS data and Landsat-8 data using the following linear combination of narrowband-to-broadband conversion (Liang, 2001;Liang et al., 2003): ...
Resampling the same satellite image to conduct a multi-scale assessment of desertification can be accompanied by distortion of terrestrial objects and spectral information, which can lead to uncertainty in the generated information. To address this, this study assesses desertification severity in an area of arid and semi-arid climate in the Eastern Mediterranean (Jordan) that is characterised by cloudless scenes using multi-sensor data of the same scene at the same time. To this end, Sentinel-2 at 10 m and 60 m, Landsat-8 at 30 m and MODIS at 250 m and 500 m were collected to extract albedo and modified soil adjusted vegetation index (MSAVI), and subsequently to construct albedo-MSAVI feature space. Using the negative correlation between albedo and MSAVI, desertification degree index (DDI) was generated. The resulting multi-scale DDI maps bear a relative resemblance in terms of spatial distribution, patterns, and proportions. The DDI maps indicate that extremely serious and serious desertification are widespread, accounting for 50% of the study area, primarily in the eastern portions. However, finer DDI maps (10 m, 30 m and 60 m) are essential for detecting small-scale desertification characteristics due to their ability to capture local spatial variabilities, while coarser ones (250 m and 500 m) are better suited for capturing broad-scale desertification patterns driven by climatic factors, in which MODIS data exhibit a relatively higher positive correlation with seasonal average precipitation. Although finer DDI maps show higher accuracy compared to coarser ones, the accuracy of DDI maps of MODIS has shown an increase within a homogeneous landscape. Accordingly, synchronised multi-scale assessment of desertification severity is not only influenced by the spatial resolution but also by the landscape heterogeneity and the type of satellite sensor utilised. The multi-scale approach applied in this study can provide insights on scale-dependent desertification that help in devising overarching mitigation strategies.
... It is a scientific fact that the amount of reflected light from the Earth's surface in the range of 0.2 to 0.6 micrometers increases due to a decrease in vegetation cover [39]. ...
Wind erosion resulting from soil degradation is a significant problem in Iran’s Baluchistan region. This study evaluated the accuracy of remote sensing models in assessing degradation severity through field studies. Sentinel-2 Multispectral Imager’s (MSI) Level-1C satellite data was used to map Rutak’s degradation severity in Saravan. The relationship between surface albedo and spectral indices (NDVI, SAVI, MSAVI, BSI, TGSI) was assessed. Linear regression establishes correlations between the albedo and each index, producing a degradation severity map categorized into five classes based on albedo and spectral indices. Accuracy was tested with 100 ground control points and field observations. The Mann-Whitney U-Test compares remote sensing models with field data. Results showed no significant difference (P > 0.05) between NDVI, SAVI, and MSAVI models with field data, while BSI and TGSI models exhibited significant differences (P ≤ 0.001). The best model, BSI-NDVI, achieves a regression coefficient of 0.86. This study demonstrates the advantage of remote sensing technology for mapping and monitoring degraded areas, providing valuable insights into land degradation assessment in Baluchistan. By accurately identifying severity levels, informed interventions can be implemented to mitigate wind erosion and combat soil degradation in the region.
... As a low-cost option, RS data can provide continuous ground data such as LULC changes in Spatio-temporal terms over large areas (Kabisch et al., 2019;Zhao et al., 2018) and can be a cost-effective alternative to land survey in LULC mapping (Liu and Yang, 2015). Moreover, remote sensing is the only method for recording albedo globally (Sun et al., 2017). ...
Urbanization and urban population growth are increasing every day across the world. The replacement of natural surfaces with artificial surfaces such as bitumen, asphalt, and cement reduces albedo, increases the land surface temperature (LST), and contributes to creating urban heat islands (UHI). In this study, Landsat satellite time-series images were used to produce land use/land cover (LULC) and calculate albedo, LST, and normalized differential vegetation index (NDVI) in the 1985–2018 period of Tabriz. The results demonstrated an over 170% increase in the area of impervious surfaces. Monitoring of albedo and LST in the 1996–2018 period showed that reflective surfaces could reduce the mean temperature of impervious surfaces in the entire city by 4.42 °C and increase the mean albedo by 0.0647. Meanwhile, LST was decreased by 7.97 °C and albedo was increased by 0.1633 in densely populated urban areas. The Pearson correlation coefficient between LST and albedo in the impervious surface class was −0.6 for the entire city, which is higher than the correlation between NDVI and LST parameters in the vegetation class. Therefore, reflective surfaces can reduce the surface temperature more efficiently than vegetation. It is worth mentioning that this correlation reaches up to −0.8 in urban districts. The research findings showed that urban growth and the increasing area of impervious surfaces could create urban cold islands instead of urban heat islands if reflective surfaces are used in the rooftops.
... The list of ECVs is long, but spatiotemporal data are only available for a subset of these variables (see Table 1 in Giuliani et al. (2020) for details). Many studies have focused on individual, or the combination of a few, ecosystem state variables to investigate their past, present, and future states at different scales ranging from local to global (Bernardino et al., 2020;D'Adamo et al., 2021;Dang et al., 2022;de Jong et al., 2011;Fensholt et al., 2015;Liu et al., 2013;Piao et al., 2020;Qiu et al., 2016;Wild et al., 2022;Zhao et al., 2018). ...
Increasing aridity is one major consequence of ongoing global climate change and is expected to cause widespread changes in key ecosystem attributes, functions, and dynamics. This is especially the case in naturally vulnerable ecosystems, such as drylands. While we have an overall understanding of past aridity trends, the linkage between temporal dynamics in aridity and dryland ecosystem responses remain largely unknown. Here, we examined recent trends in aridity over the past two decades within global drylands as a basis for exploring the response of ecosystem state variables associated with land and atmosphere processes (e.g., vegetation cover, vegetation functioning, soil water availability, land cover, burned area, and vapor‐pressure deficit) to these trends. We identified five clusters, characterizing spatiotemporal patterns in aridity between 2000 and 2020. Overall, we observe that 44.5% of all areas are getting dryer, 31.6% getting wetter, and 23.8% have no trends in aridity. Our results show strongest correlations between trends in ecosystem state variables and aridity in clusters with increasing aridity, which matches expectations of systemic acclimatization of the ecosystem to a reduction in water availability/water stress. Trends in vegetation (expressed by leaf area index [LAI]) are affected differently by potential driving factors (e.g., environmental, and climatic factors, soil properties, and population density) in areas experiencing water‐related stress as compared to areas not exposed to water‐related stress. Canopy height for example, has a positive impact on trends in LAI when the system is stressed but does not impact the trends in non‐stressed systems. Conversely, opposite relationships were found for soil parameters such as root‐zone water storage capacity and organic carbon density. How potential driving factors impact dryland vegetation differently depending on water‐related stress (or no stress) is important, for example within management strategies to maintain and restore dryland vegetation.
... As a result, the spatial variation in the modelled responses and the degree of vulnerability of the ecosystems to the water conditions they have been suffering can be observed [88]. This provides a method that combines scientific rigor with the application of a series of proven technologies: the use of machine learning techniques for the processing of these images and modelling processes [89], multispectral image processing with spatial remote sensing techniques [90][91][92], and GIS for the management of the information obtained [93][94][95]. It is specified in a method for aiding decision-making in the management of natural areas [96]. ...
It has been scientifically proven that climate change is a reality. In subarid Mediterranean limates, this fact is observed in the irregular distribution of rainfall, resulting in alternating periods of more or less prolonged drought with episodes of torrential rains concentrated in short periods of time. We have selected 11 natural areas in southern Spain, where we will observe these circumstances and where a series of ecosystems composed of vegetation covers of a high ecological value are found. We start from the question of whether these climatic circumstances are really deteriorating them. For this study, we propose a method that combines three analysis techniques: the design of the time series, the application of vegetation indices, and the use of techniques analysis of changes in land use. From the combination of these techniques in the period from 1997 to 2021, we have observed that there have been a dynamic of changes in land use that has maintained its original characteristics by more than 70%, so it is possible to affirm that the adaptation of ecosystems to climatic conditions has occurred satisfactorily. However, this general statement shows some particularities which are those that we will show in this work.
... Os IVs derivados de imagens de satélite são utilizados no monitoramento das condições e dinâmicas da vegetação em escalas regionais ou globais (ZHANG et al., 2019;ZENG et al., 2020). Entretanto, Zhao et al. (2018) destacam que, embora tenha toda a capacidade de monitoramento vegetal e de cobertura do solo dos IVs, o albedo tem sido utilizado para avaliar o estado do ecossistema em terras áridas, com a presença de florestas a exemplo da Caatinga. Uma vez que é mais sensível a mudanças na biomassa (RODRÍGUEZ-CABALLERO et al., 2015) e a variações fenológicas sazonais (WANG et al., 2017), além de aumentar conforme os solos ficam mais expostos à luz solar (YU et al., 2017), sendo esta uma característica de áreas desmatadas . ...
Florestas sazonalmente secas como a Caatinga são influenciadas diretamente por mudanças pluviométricas. Nesse contexto, o objetivo deste estudo foi analisar o comportamento das mudanças sazonais na vegetação do bioma Caatinga, identificando possíveis alterações no cenário, por meio da sensibilidade espectral do NDVI e do Albedo, considerando uma análise espaço temporal (2015-2019), na Bacia Hidrográfica do Rio Pajeú – PE, Brasil. Para isso, foram utilizados os dados: MOD13Q1 (NDVI) e MOD09A1 (bandas espectrais) do sensor MODIS, a bordo dos satélites Aqua e Terra. O NDVI e o Albedo foram avaliados por meio de cartas-imagens nos períodos: chuvoso e seco. Os resultados obtidos apontaram um comportamento inversamente proporcional entre o albedo e vegetação integrados aos dados de precipitação em ambos os momentos climáticos analisados. No período seco os índices apresentaram os melhores relacionamentos com R² variando entre -0,5 a -0,6, correspondendo à dinâmica da precipitação na bacia, entretanto o NDVI se mostrou sensível à dinâmica do microclima da bacia e o albedo mais sensível à resposta de áreas não vegetadas. O uso de dados MODIS para a geração de produtos cartográficos em escala multitemporal mostrou-se um indicador da mudança de uso e ocupação do solo, em florestas sazonalmente secas como a Caatinga.
... We have used free products from spatial databases, and some derived from remote sensing. Nowadays, remote sensing products provide a realistic alternative to obtain land surface information across broad regions and over longer periods at a low cost [67]. This spatial approach to assess the risk by the vulnerability and the landslide susceptibility is the most proper way to challenge this type of disaster [10,11]. ...
Among the numerous natural hazards, landslides are one of the greatest, as they can cause enormous loss of life and property, and affect the natural ecosystem and their services. Landslides are disasters that cause damage to anthropic activities and innumerable loss of human life, globally. The landslide risk assessed by the integration of susceptibility and vulnerability maps has recently become a manner of studying sites prone to landslide events and managing these regions well. Developing countries, where the impact of landslides is frequent, need risk assessment tools that enable them to address these disasters, starting with their prevention, with free spatial data and appropriate models. Our study shows a heuristic risk model by integrating a susceptibility map made by AutoML and a vulnerability one that is made considering ecological vulnerability and socio-economic vulnerability. The input data used in the State of Guerrero (México) approach uses spatial data, such as remote sensing, or official Mexican databases. This aspect makes this work adaptable to other parts of the world because the cost is low, and the frequency adaptation is high. Our results show a great difference between the distribution of vulnerability and susceptibility zones in the study area, and even between the socioeconomic and ecological vulnerabilities. For instance, the highest ecological vulnerability is in the mountainous zone in Guerrero, and the highest socio-economic vulnerability values are found around settlements and roads. Therefore, the final risk assessment map is an integrated index that considers susceptibility and vulnerability and would be a good first attempt to challenge landslide disasters.
... The significant participation of the SWIR band in the mapping of the Caatinga vegetation, probably, is due to the high exposure of the soil to solar radiation. Spectral indices using the NIR and SWIR bands show better ability to detect phenology than NDVI and EVI (Jin et al., 2013), because of their higher sensitivity to the humidity in the vegetation and soil (Rodríguez-Caballero et al., 2015;Zhao et al., 2018). In the multi-date NDVI classification, the months of October 2015, September 2016 and August 2016 have the most contribution to the accuracy of the classification and the months of January 2016 and May 2016 have the worst (Fig. 7B). ...
Accurate information on the land cover is crucial for efficient monitoring and development of environmental studies in the Brazilian Caatinga forest. It is one of the largest and most biodiverse dry forests on the planet. Distinguishing different patterns of land cover through medium spatial-resolution remote sensing, such as the Landsat image series, is challenging to Caatinga due to heterogeneous land cover, complex climate-soil - vegetation interactions, and anthropogenic disturbance. Two remote sensing approaches have a high potential for accurate and efficient land-cover mapping in Caatinga: single and multi-date imagery. The heterogeneity of the land cover of this environment can contribute to a better performance of multispectral approaches, although it is usually applied for single-date images. In a land-cover mapping effort in Caatinga, the temporal factor gains relevance, and the use of time series can bring advantages, but, in general, this approach uses vegetation index, losing multispectral information. This manuscript assesses the accuracies and advantages of single-date multispectral and multi-date Normalized Difference Vegetation Index (NDVI) approaches in land-cover classification. Both approaches use the Random Forest method, and the results are evaluated based on samples collected during field surveys. Results indicate that land-cover classification obtained from multi-date NDVI performs better (overall accuracy of 88.8% and kappa of 0.86) than single-date multispectral data (overall accuracy of 81.4% and kappa coefficient of 0.78). The Z-test indicated that the difference in performance between the two approaches was statistically significant. The lower performance observed for single-date multispectral classification is due to similarities in spectral responses for targets of deciduous vegetation that lose their foliage and can be misread as non-vegetated areas. Meanwhile, an accurate classification by time series of plant clusters in seasonal forests allows incorporating seasonal variability of land-cover classes during the rainy and dry seasons, as well as transitions between seasons. The most important variables that contributed to the accuracy were the red, Near Infrared (NIR) and Short-Wave Infrared (SWIR) bands in single-date multispectral classification and the months in the dry season were the most relevant in multi-date NDVI classification.
... Nevertheless, broadband land surface albedo (bLSA) satellite products, accounting for the fraction of the reflected downwelling irradiance by earth surface (Tian et al., 2014) have not been yet considered in the literature as a proxy of pre-fire fuel structure to determine its influence on burn severity. Noteworthy, bLSA is more sensitive than spectral indices to subtle variations in biophysical parameters and structure of vegetation (Rodríguez-Caballero et al., 2015;Zhao et al., 2018). ...
The design and implementation of pre-fire management strategies in heterogeneous landscapes requires the identification of the ecological conditions contributing to the most adverse effects of wildfires. This study evaluates which features of pre-fire vegetation structure, estimated through broadband land surface albedo and Light Detection and Ranging (LiDAR) data fusion, promote high wildfire damage across several fire-prone ecosystems dominated by either shrub (gorse, heath and broom) or tree species (Pyrenean oak and Scots pine). Topography features were also considered since they can assist in the identification of priority areas where vegetation structure needs to be managed. The case study was conducted within the scar of a mixed-severity wildfire that occurred in the Western Mediterranean Basin. Burn severity was estimated using the differenced Normalized Burn Ratio index computed from Sentinel-2 multispectral instrument (MSI) Level 2 A at 10 m of spatial resolution and validated in the field using the Composite Burn Index (CBI). Ordinal regression models were implemented to evaluate high burn severity outcome based on three groups of predictors: topography, pre-fire broadband land surface albedo computed from Sentinel-2 and pre-fire LiDAR metrics. Models were validated both by 10-fold cross-validation and external validation. High burn severity was largely ecosystem-dependent. In oak and pine forest ecosystems, severe damage was promoted by a high canopy volume (model accuracy = 79%) and a low canopy base height (accuracy = 82%), respectively. Land surface albedo, which is directly related to aboveground biomass and vegetation cover, outperformed LiDAR metrics to predict high burn severity in ecosystems with sparse vegetation. This is the case of gorse and broom shrub ecosystems (accuracy of 80% and 77%, respectively). The effect of topography was overwhelmed by that of the vegetation structure portion of the fire triangle behavior, except for heathlands, in which warm and steep slopes played a key role in high burn severity outcome together with horizontal and vertical fuel continuity (accuracy = 71%). The findings of this study support the fusion of LiDAR and satellite albedo data to assist forest managers in the development of ecosystem-specific management actions aimed at reducing wildfire damage and promote ecosystem resilience.
... Temperatures (LST) ranged from −25 • C to 45 • C. (3) Albedo (ALB; MCD43A3.006). ALB is surrogate for surface properties such as the extent and nature of the vegetation cover, and it is affected by the change of the land-surface bio-physical factors such as vegetation, LST and soil moisture [35]. ALB values ranged from 0 to 1 (fresh snow and bare soil usually fall around 0.9). ...
Urgent action needs to be taken to halt global biodiversity crisis. To be effective in the implementation of such action, managers and policy-makers need updated information on the status and trends of biodiversity. Here, we test the ability of remotely sensed ecosystem functioning attributes (EFAs) to predict the distribution of 73 bird species with different life-history traits. We run ensemble species distribution models (SDMs) trained with bird atlas data and 12 EFAs describing different dimensions of carbon cycle and surface energy balance. Our ensemble SDMs—exclusively based on EFAs—hold a high predictive capacity across 71 target species (up to 0.94 and 0.79 of Area Under the ROC curve and true skill statistic (TSS)). Our results showed the life-history traits did not significantly affect SDM performance. Overall, minimum Enhanced Vegetation Index (EVI) and maximum Albedo values (descriptors of primary productivity and energy balance) were the most important predictors across our bird community. Our approach leverages the existing atlas data and provides an alternative method to monitor inter-annual bird habitat dynamics from space in the absence of long-term biodiversity monitoring schemes. This study illustrates the great potential that satellite remote sensing can contribute to the Aichi Biodiversity Targets and to the Essential Biodiversity Variables framework (EBV class “Species distribution”).
... The significant participation of the SWIR band in the mapping of the Caatinga vegetation, probably, is due to the high exposure of the soil to solar radiation. Spectral indices using the NIR and SWIR bands show better ability to detect phenology than NDVI and EVI (Jin et al., 2013), because of their higher sensitivity to the humidity in the vegetation and soil (Rodríguez-Caballero et al., 2015;Zhao et al., 2018). In the multi-date NDVI classification, the months of October 2015, September 2016 and August 2016 have the most contribution to the accuracy of the classification and the months of January 2016 and May 2016 have the worst (Fig. 7B). ...
Accurate information on the land cover is crucial for efficient monitoring and development of environmental studies in the Brazilian Caatinga forest. It is the largest tropical seasonal forest in South America, presenting high biodiversity and is under intense anthropogenic disturbance. Caatinga's land cover is heterogeneous, and rainfall is its primary phenological regulator, presenting mainly deciduous species. Different land-cover patterns show distinct spatial responses to climate and soils changes and modify their physical properties over time. Rainfall is highly variable over time and space, but seasonally concentrated between 2 to 4 months. Therefore, distinguishing the different patterns of land cover through medium spatial-resolution remote sensing, such as the Landsat image series, is challenging, due to the particularities of the climate-vegetation interaction. Two remote sensing approaches have a high potential for efficient land-cover mapping in Caatinga: single and multi-date imagery. The heterogeneity of the land cover of this environment can contribute to a better performance of multispectral approaches, although it is normally applied for single-date images. In a land-cover mapping effort in Caatinga, the temporal factor gains relevance, and the use of time series can bring advantages, but, in general, this approach uses vegetation index, losing multispectral information. This manuscript aims to assess the accuracies and advantages of single-date multispectral and multi-date Normalized Difference Vegetation Index (NDVI) approaches in land-cover classification. Both approaches use the Random Forest method, and the results are evaluated based on samples collected during field surveys. Results indicate that land-cover classification obtained from multi-date NDVI performs better than single-date multispectral data. The lower performance observed for single-date multispectral classification is due to similarities in spectral responses: targets of deciduous vegetation lose their foliage and can be misread as non-vegetated areas. Meanwhile, an accurate classification by time series of plant clusters in seasonal forests allows incorporating seasonal variability of land-cover classes during the rainy and dry seasons, as well as transitions between seasons.
... The data validation results showed that the method has certain applicability to monitoring of the agriculture drought conditions in semi-arid areas with moderately high spatial and time resolution images. Yanchuang et al. [15] found that the remotely sensed reflectivity is related to multifunctionality by studying the relationship between six albedo metrics and two VIs (normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI)), and multifunctionality has been related to the alternative states in global drylands, indicating that albedo may monitor changes in dryland ecosystem functioning. In addition, there are also many studies on island ecosystems, most of which were based on ecological vulnerability research. ...
Islands face increasingly prominent environmental problems with rapid urbanization. Hence, timely and objective monitoring and evaluation of island ecology is of great significance. This study took the Pingtan Comprehensive Experimental Zone (PZ) in the east sea of Fujian Province of China as the research object. Based on remote sensing technology, four Landsat images from 2007 to 2017 and the remote sensing ecological index (RSEI) were used to explore the ecological status and space–time change. The results showed that from 2007 to 2011, the average RSEI decreased from 0.519 to 0.506, indicating that the ecological quality generally showed a slight downward trend, mainly due to large-scale development brought by the construction; by 2014, although the ecology of the original area improved, the overall ecology was still declining with 0.502 mean RSEI mainly because of large-scale reclamation projects; by 2017, the average RSEI rebounded to 0.523, which was attributed to the fact that ecological construction and protection were emphasized in the construction of PZ, especially in reclamation areas. In conclusion, the increase of large area bare soil will lead to the decline of regional ecology, but the implementation of scientific ecological planning is conducive to ecological restoration and construction.
... Hunt et al., 2003). Better known methods include detection of bare ground using albedo (Zhao et al., 2018), increases in diurnal temperature ranges (Zhou et al., 2007), fire (Potter et al., 2003), and dust emissions (Ginoux et al., 2012). ...
Progress towards combatting land degradation as intended by Sustainable Development Goal 15.3.1 will be monitored using three sub-indicators, of which productivity of vegetation is one. This indicator is to be measured using trends in a remotely-sensed vegetation index. The use of vegetation indices is well-established and remotely-sensed data are readily available. However, their uses for monitoring production that is relevant to sustainable livelihoods have received little attention. This review identifies four areas in the currently-proposed monitoring methodology that are in need of further development. The first is the derivation of primary production from vegetation indices, which requires attention to physiological processes such as light-use efficiency and plant respiration. The second concerns the subsequent steps, in which ecological processes transform the net production into production of goods and services, such as crop products. The third is the need for explicit baselines or reference conditions that specify the productivity in the absence of anthropogenic degradation. The fourth, and most difficult, is to distinguish anthropogenic causes of degradation from potentially similar effects of natural environmental processes. Some of these issues are difficult to tackle with remote sensing alone, although several improvements are available, and others are in development. However, the current use of vegetation indices alone to remotely-sense degradation of ecosystem services does not provide an adequate SDG 15.3.1 productivity indicator.
... However, the white-sky albedo (WSA) is related purely to the properties of the land surface and is not affected by atmospheric conditions (Strahler et al., 1999). Zhao et al. (2018) have reported that WSA correlates with ecosystem multifunctionality, which has been found to exhibit two states and abrupt and discontinuous changes along aridity gradients in global drylands . WSA (bihemispherical reflectance) is defined as albedo under the condition that the direct component is absent and the dif- Satellites (Lucht et al., 2000). ...
Empirical verification of multiple states in drylands is scarce, impeding the design of indicators to anticipate the onset of desertification. Remote sensing‐derived indicators of ecosystem states are gaining new ground due to the possibilities they bring to be applied inexpensively over large areas. Remotely sensed albedo has been often used to monitor drylands due to its close relationship with ecosystem status and climate. Here, we used a space‐for‐time‐substitution approach to evaluate whether albedo (averaged from 2000 to 2016) can identify multiple ecosystem states in African drylands spanning from the Saharan desert to tropical Africa. By using latent class analysis, we found that albedo showed two states (low and high; the cut‐off level was 0.22 at the shortwave band). Potential analysis revealed that albedo exhibited an abrupt and discontinuous increase with increased aridity (1 − [precipitation/potential evapotranspiration]). The two albedo states co‐occurred along aridity values ranging from 0.72 to 0.78, during which vegetation cover exhibited a rapid, continuous decrease from ~90% to ~50%. At aridity values of 0.75, the low albedo state started to exhibit less attraction than the high albedo state. Low albedo areas beyond this aridity value were considered as vulnerable regions where abrupt shifts in albedo may occur if aridity increases, as forecasted by current climate change models. Our findings indicate that remotely sensed albedo can identify two ecosystem states in African drylands. They support the suitability of albedo indices to inform us about discontinuous responses to aridity experienced by drylands, which can be linked to the onset of land degradation.
Assessment of ecological environment is an indispensable part of the eco-environment protection and restoration.
This study utilizes a remote sensing-based ecological index (RSEI) to better understand the environmental scenario in the Ganga basin. RSEI has been computed using five parameters: Wetness, Dryness, Greenness, Heat, and
a newly incorporated parameter Albedo representative of land degradation. Median-based RSEI maps have been
constructed using LANDSAT archives in Google Earth Engine (GEE) platform, covering three decades
(1990–2021). The Ganga basin has been divided into five agro-climatic zones. For each zone and time frame
(1990–99, 2000–09, 2010–19, and 2020–21), a median-based RSEI map has been generated. The analysis reveals
that RSEI becomes poorer for sub-basin 1 (SB1), sub-basin 2 (SB2), and sub-basin 5 (SB5) in the 2010–19 period
compared to the 1990–99 and 2000–09 periods. On the other hand, RSEI for sub-basin 3 (SB3) improved in the
2010–19 period compared to the previously mentioned periods. Sub-basin 4 (SB4) remained the least fluctuated
region compared to the other sub-basins. The Global Moran’s I value is highest for SB3 for the 1990–99 and
2000–09 periods, while for the 2010–19 and 2020–21 periods, SB2 has the highest Global Moran’s I. This study
incorporates big data analysis and can be indispensable in exploring the interactions between ecosystem services
and anthropogenic activities in river basin systems.
In Egypt, the phenomenon of desertification is a geographical phenomenon that is related to the decline or deterioration of the land's biological production capacity, which will eventually result in semi-desert conditions, or, in other words, the loss of fertility from productive lands. An understanding of the geographical distribution of environmentally sensitive areas (ESAs) is necessary for sustainable land use in the dry lands. The characteristics of the research region and the Mediterranean desertification and land use (MEDALUS) approach were used to evaluate the environmental sensitivity to desertification on the west-north coast of Egypt. Remote sensing images, topographic data, soils, and geological data are used to calculate desertification indicators. A hotspot of desertification risk exists on the north coast of Egypt due to soil degradation, climatic conditions, geomorphological and topographic features, soil quality and soil uses in each area. In each of these areas, these variables lead to varying levels and causes of soil degradation and desertification, as well as varying environmental, economic, and social effects. The obtained data reveal that (10.6%, 82.73%) of the west north coast are Sensitive and Very sensitive areas to desertification, About 1.22% of the research area is the moderately sensitive area, while the low sensitive and very low exhibit only (4.21,1.48) %. Remote sensing and GIS are recommended to monitor sensitivity. MEDALUS factors can be modified to obtain more reliable data at the local level.
Land desertification poses a severe global ecological threat. Loess Plateau, a typical region, was extensively studied. This study conducted a fitting analysis between the Vegetation Index: Enhanced Vegetation Index (EVI), kernel Normalized Difference Vegetation Index (kNDVI), Normalized Difference Vegetation Index (NDVI) and Albedo. A holistic analytical methodology was established, encompassing desertification evaluation, spatiotemporal changes, intensity, driving mechanisms, and management zoning. The results indicate that NDVI and albedo exhibit the best fit with an R² value of 0.72. Desertification primarily occurred in the northwest, displaying significant fluctuation. Climatic factors and human activities were the main drivers of Desertification Difference Index (DDI). Precipitation promoted DDI, while temperature inhibited it, and human activities primarily play a promoting role. Furthermore, management zones were delineated, encompassing ecological sensitive areas requiring urgent land protection, ecological restoration areas necessitating land management and restoration projects, ecological improvement areas and ecological stability areas aiming to maintain the existing ecological balance while concurrently strengthening monitoring.
Gelişen uzaktan algılama teknolojileri, kentsel alanlarda meydana gelen yüzey parametrelerindeki değişimlerin izlenmesinde etkin şekilde kullanılmaktadır. Yerel ve küresel ölçekte yüzeylerin spektral ve termal özelliklerinden yararlanarak kentsel ısı adaları hakkında bilgiler elde edilmektedir. Çalışmamızda Dünya Kültür Mirası listesinde bulunan Karabük ilinin Safranbolu ilçesi çalışma alanı olarak seçilmiştir. Albedo, Normalize Edilmiş Fark Bitki Örtüsü İndeksi (NDVI) ve Yer Yüzey Sıcaklığı (YYS) değişkenlerinin hesaplanması için 12/08/1999 tarihli Landsat-7 ve 11/08/2019 tarihli Landsat-8 uydu verileri kullanılmıştır. Değişkenler arasındaki ilişkiyi ortaya çıkarmak için korelasyon ve saçılım analizleri uygulanmıştır. Elde edilen sonuçlarda; YYS ve albedo arasında pozitif, YYS ve NDVI arasında negatif, albedo ve NDVI arasında negatif yönlü ilişkinin olduğu tespit edilmiştir. Bu ilişkiler hem korelasyon analizinde hem de saçılım grafiklerinde benzer şekilde çıkmıştır. YYS, albedo ve NDVI arasındaki ilişkiyi etkileyen başlıca etmenler; yüzeydeki malzemenin türü, yüzeydeki nem miktarı, bitki örtüsü ve yoğunluğu şeklinde sıralanabilir.
Grazing by domestic livestock is both the main land use across
drylands worldwide and a major desertification and global change driver. The
ecological consequences of this key human activity have been studied for
decades, and there is a wealth of information on its impacts on biodiversity
and ecosystem processes. However, most field assessments of the ecological
impacts of grazing on drylands conducted to date have been carried out at
local or regional scales and have focused on single ecosystem attributes
(e.g., plant productivity) or particular taxa (mainly aboveground, e.g.,
plants). Here we introduce the BIODESERT survey, the first systematic field
survey devoted to evaluating the joint impacts of grazing by domestic
livestock and climate on the structure and functioning of dryland ecosystems
worldwide. This collaborative global survey was carried out between 2016 and
2019 and has involved the collection of field data and plant, biocrust, and soil
samples from a total of 326 45 m × 45 m plots from 98 sites located in 25
countries from 6 continents. Here we describe the major characteristics
and the field protocols used in this survey. We also introduce the
organizational aspects followed, as these can be helpful to everyone wishing
to establish a global collaborative network of researchers. The BIODESERT
survey provides baseline data to assess the current status of dryland
rangelands worldwide and the impacts of grazing on these key ecosystems, and it
constitutes a good example of the power of collaborative research networks
to study the ecology of our planet using much-needed field data.
Rocky desertification is a serious ecological and environmental problem in Southwest China. Quickly and scientifically reveal the distribution and changes of ecological environment quality in rocky desertification areas, which is of great significance to rocky desertification restoration, ecological environment governance and sustainable development. Based on the remote sensing ecological index (RSEI), in addition to greenness, humidity, dryness, and heat, combined with the degree of rocky desertification, this study used the principal component analysis (PCA) method to construct a modified remote sensing ecological index (MRSEI). Then, the temporal and spatial variation characteristics and imaging factors of the ecological environment quality in the typical rocky desertification region of southeastern Chongqing from 2001 to 2021 were explored. The results revealed that the greenness and humidity indicators had a positive effect on the ecological quality, while the indicators of dryness, heat and rocky desertification had the opposite impact. From 2001 to 2021, the ecological environment quality in southeastern Chongqing showed a trend of gradual improvement, and the improvement area accounted for about 70% of the total area. The elevation, slope, monthly average precipitation, and land use pattern were the main factors influencing the quality of the ecological environment in the region.
Emerging evidence suggests that ecosystem responses to increases in atmospheric aridity, a hallmark of climate change, exhibit multiple thresholds across global drylands. However, it is not clear whether aridity thresholds exist in the relationships between ecosystem functions and remotely sensed indicators (RSIs). Assessing this is important because these empirical relationships underpin the statistical models commonly used to estimate ecosystem functioning across large spatial scales, which typically uses data from RSI. We evaluated how the relationships between nutrient cycling index (NCI; a proxy of ecosystem functioning) measured
in situ
and RSI [albedo and normalized difference vegetation index (NDVI)] change along with a wide aridity (1 – [precipitation/potential evapotranspiration]) gradient in Patagonia (Argentina). For doing so, we used field-based NCI data from 235 ecosystems that were surveyed twice (2008–2013 and 2014–2018). Three aridity thresholds were identified when evaluating the RSI–NCI relationships. The first threshold was found around aridity values ranging from 0.44 to 0.60, while the second and third were concentrated around aridity values of 0.69 and 0.82, respectively. These results indicate that RSI–NCI relationships changed drastically along aridity gradients, and these thresholds should be considered when evaluating ecosystem functions using RSI. In addition, we also found that the relationships between NCI and albedos were not significant around aridity values of 0.82. These results were consistent across sampling dates. Our findings imply that empirical models of the RSI–NCI relationship employing only albedos/reflectance as inputs are not reliable under the most arid conditions and can be used to improve the effectiveness of the use of RSI to monitor and predict changes in ecosystem functioning across large environmental gradients in drylands.
Global change is severely affecting ecosystem functioning and biodiversity globally. Remotely sensed ecosystem functional attributes (EFAs) are integrative descriptors of the environmental change —being closely related to the processes directly affecting food chains via trophic cascades. Here we tested if EFAs can explain the species fitness at upper trophic levels. We took advantage of a long-term time series database of the reproductive success of the Golden Eagle (Aquila chrysaetos) – an apex predator at the upper trophic level – over a 17-year period across a bioclimatic gradient (NW Spain; c. 29,575 km²). We computed a comprehensive database of EFAs from three MODIS satellite-products related to the carbon cycle, heat dynamics and radiative balance. We also assessed possible time-lag in the response of the Golden Eagle to fire, a critical disruptor of the surface energy budget in our region. We explored the role of EFAs on the fitness of the Golden Eagle with logistic-exposure nest survival models. Our models showed that the reproductive performance of the Golden Eagle is influenced by spatiotemporal variations in land surface temperature, albedo and vegetation productivity (AUC values from 0.71 to 0.8; ΣWi EFAs from 0.66 to 1). Fire disturbance also affected ecological fitness of this apex predator—with a limited effect at 3 years after fire (a time-lagged response to surface energy budget disruptions; ΣWi Fire= 0.62). Our study provides evidence for the influence of the matter and energy fluxes between land surface and atmosphere on the reproductive success of species at upper trophic levels.
Drought is a natural phenomenon that affects ecological processes, land productivity, and human life. The phenomenon is slow to settle down and its impacts are not instantaneous, requiring an early monitoring of their signs of appearance. In order to track the effect drought on the land surface dynamics by means of surface albedo changes, we intend to exploit the VIS-channel reference image from the METEOSAT satellite data, which is an associated product of the new version of the Infrared Difference Dust Index (IDDI). The surface albedo is retrieved from the calibrated VIS-channel reference radiance, atmospherically corrected by the radiative transfer code 6S (Second Simulation of a Satellite Signal in the Solar Spectrum). In this study, we inspect the sensitivity of monthly surface albedo to drought events over a semi-arid region (M’Sila, center of northern Algeria) during the period covered by three METEOSAT satellites (5, 6, and 7). Two drought indices, SPI (Standardized Precipitation Index) and SPEI (Standardized Precipitation Evapotranspiration Index), were calculated at multiple timescales and were correlated with surface albedo over the study region. The results show that the correlation of surface albedo with the two drought indices at the timescale of 1 month (current month) is not necessarily the best indicator of the installation of a drought event. Additionally, we find that the sensitivity of surface albedo to drought indices at a medium timescales class (average of correlation coefficient at 6, 9, and 12 months), is more sensitive to SPEI index (principally 6-month timescale), during the growing season (April to September). It is also more sensitive to SPI index (principally 12-month timescale), during the supplying and charging period of water resources (October to March).
Thresholds of aridity
Increasing aridity due to climate change is expected to affect multiple ecosystem structural and functional attributes in global drylands, which cover ∼45% of the terrestrial globe. Berdugo et al. show that increasing aridity promotes thresholds on the structure and functioning of drylands (see the Perspective by Hirota and Oliveira). Their database includes 20 variables summarizing multiple aspects and levels of ecological organization. They found evidence for a series of abrupt ecological events occurring sequentially in three phases, culminating with a shift to low-cover ecosystems that are nutrient- and species-poor at high aridity values. They estimate that more than 20% of land surface will cross at least one of the thresholds by 2100, which can potentially lead to widespread land degradation and desertification worldwide.
Science , this issue p. 787 ; see also p. 739
Classic, model-based theory of land-atmosphere interactions across the Sahel promote positive vegetation-rainfall feedbacks dominated by surface albedo mechanism. However, neither the proposed positive vegetation-rainfall feedback nor its underlying albedo mechanism has been convincingly demonstrated using observational data. Here, we present observational evidence for the region’s proposed positive vegetation-rainfall feedback on the seasonal to interannual time scale, and find that it is associated with a moisture recycling mechanism, rather than the classic albedo-based mechanism. Positive anomalies of remotely sensed vegetation greenness across the Sahel during the late and post-monsoon periods favor enhanced evapotranspiration, precipitable water, convective activity and rainfall, indicative of amplified moisture recycling. The identified modest low-level cooling and anomalous atmospheric subsidence in response to positive vegetation greenness anomalies are counter to the responses expected through the classic vegetation-albedo feedback mechanism. The observational analysis further reveals enhanced dust emissions in response to diminished Sahel vegetation growth, potentially contributing to the positive vegetation-rainfall feedback.
The terrestrial biosphere and atmosphere interact through a series of feedback loops. Variability in terrestrial vegetation growth and phenology can modulate fluxes of water and energy to the atmosphere, and thus affect the climatic conditions that in turn regulate vegetation dynamics. Here we analyse satellite observations of solar-induced fluorescence, precipitation, and radiation using a multivariate statistical technique. We find that biosphere–atmosphere feedbacks are globally widespread and regionally strong: they explain up to 30% of precipitation and surface radiation variance in regions where feedbacks occur. Substantial biosphere–precipitation feedbacks are often found in regions that are transitional between energy and water limitation, such as semi-arid or monsoonal regions. Substantial biosphere–radiation feedbacks are often present in several moderately wet regions and in the Mediterranean, where precipitation and radiation increase vegetation growth. Enhancement of latent and sensible heat transfer from vegetation accompanies this growth, which increases boundary layer height and convection, affecting cloudiness, and consequently incident surface radiation. Enhanced evapotranspiration can increase moist convection, leading to increased precipitation. Earth system models underestimate these precipitation and radiation feedbacks mainly because they underestimate the biosphere response to radiation and water availability. We conclude that biosphere–atmosphere feedbacks cluster in specific climatic regions that help determine the net CO2 balance of the biosphere.
Over 65% of drylands are used for grazing of managed livestock. Understanding what drives grazing effects on the structure and functioning of rangelands is critical for achieving their sustainability. We studied a network of 239 sites across Patagonian rangelands (Argentina), which constitute one of the world's largest rangeland area. We aimed to (i) evaluate how aridity and grazing affect ecosystem structure and functioning and (ii) test the usefulness of the landscape function analysis (LFA) indices (stability, infiltration and nutrient cycling) as surrogates of soil functioning. Aridity decreased species richness and the cover of palatable grasses but increased the cover of palatable shrubs. Grazing pressure negatively impacted the cover of palatable grasses and species richness but did not affect the cover of shrubs. Aridity had direct and indirect negative relationships with the LFA indices. Grazing pressure had no direct effects on the LFA indices but had an indirect negative effect on them by affecting vegetation structure. The LFA indices were positively and negatively correlated with soil organic carbon and sand contents, respectively, suggesting that these indices are useful proxies of soil functional processes in Patagonian rangelands. Our findings indicate that aridity and overgrazing have convergent effects on the structure and functioning of ecosystems, as both promoted reductions in species richness, the cover of palatable grasses and soil functioning. Rangeland management activities should aim to enhance species richness and the cover of palatable grasses, as these actions could contribute to offset adverse effects of ongoing increases in aridity on drylands.
The Paris Agreement aims to limit global mean surface warming to less than 2 °C relative to pre-industrial levels1, 2, 3. However, we show this target is acceptable only for humid lands, whereas drylands will bear greater warming risks. Over the past century, surface warming over global drylands (1.2–1.3 °C) has been 20–40% higher than that over humid lands (0.8–1.0 °C), while anthropogenic CO2 emissions generated from drylands (~230 Gt) have been only ~30% of those generated from humid lands (~750 Gt). For the twenty-first century, warming of 3.2–4.0 °C (2.4–2.6 °C) over drylands (humid lands) could occur when global warming reaches 2.0 °C, indicating ~44% more warming over drylands than humid lands. Decreased maize yields and runoff, increased long-lasting drought and more favourable conditions for malaria transmission are greatest over drylands if global warming were to rise from 1.5 °C to 2.0 °C. Our analyses indicate that ~38% of the world’s population living in drylands would suffer the effects of climate change due to emissions primarily from humid lands. If the 1.5 °C warming limit were attained, the mean warming over drylands could be within 3.0 °C; therefore it is necessary to keep global warming within 1.5 °C to prevent disastrous effects over drylands.
Climatic conditions shift gradually over millennia, altering the rates at which carbon (C) is fixed from the atmosphere and stored in the soil. However, legacy impacts of past climates on current soil C stocks are poorly understood. We used data from more than 5000 terrestrial sites from three global and regional data sets to identify the relative importance of current and past (Last Glacial Maximum and mid-Holocene) climatic conditions in regulating soil C stocks in natural and agricultural areas. Paleoclimate always explained a greater amount of the variance in soil C stocks than current climate at regional and global scales. Our results indicate that climatic legacies help determine global soil C stocks in terrestrial ecosystems where agriculture is highly dependent on current climatic conditions. Our findings emphasize the importance of considering how climate legacies influence soil C content, allowing us to improve quantitative predictions of global C stocks under different climatic scenarios.
Seasonal vegetation phenology can significantly alter surface albedo which in turn affects the global energy balance and the albedo warming/cooling feedbacks that impact climate change. To monitor and quantify the surface dynamics of heterogeneous landscapes, high temporal and spatial resolution synthetic time series of albedo and the enhanced vegetation index (EVI) were generated from the 500 m Moderate Resolution Imaging Spectroradiometer (MODIS) operational Collection V006 daily BRDF/NBAR/albedo products and 30 m Landsat 5 albedo and near-nadir reflectance data through the use of the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM). The traditional Landsat Albedo (Shuai et al., 2011) makes use of the MODIS BRDF/Albedo products (MCD43) by assigning appropriate BRDFs from coincident MODIS products to each Landsat image to generate a 30 m Landsat albedo product for that acquisition date. The available cloud free Landsat 5 albedos (due to clouds, generated every 16 days at best) were used in conjunction with the daily MODIS albedos to determine the appropriate 30 m albedos for the intervening daily time steps in this study. These enhanced daily 30 m spatial resolution synthetic time series were then used to track albedo and vegetation phenology dynamics over three Ameriflux tower sites (Harvard Forest in 2007, Santa Rita in 2011 and Walker Branch in 2005). These Ameriflux sites were chosen as they are all quite nearby new towers coming on line for the National Ecological Observatory Network (NEON), and thus represent locations which will be served by spatially paired albedo measures in the near future. The availability of data from the NEON towers will greatly expand the sources of tower albedometer data available for evaluation of satellite products. At these three Ameriflux tower sites the synthetic time series of broadband shortwave albedos were evaluated using the tower albedo measurements with a Root Mean Square Error (RMSE) less than 0.013 and a bias within the range of ±0.006. These synthetic time series provide much greater spatial detail than the 500 m gridded MODIS data, especially over more heterogeneous surfaces, which improves the efforts to characterize and monitor the spatial variation across species and communities. The mean of the difference between maximum and minimum synthetic time series of albedo within the MODIS pixels over a subset of satellite data of Harvard Forest (16 km by 14 km) was as high as 0.2 during the snow-covered period and reduced to around 0.1 during the snow-free period. Similarly, we have used STARFM to also couple MODIS Nadir BRDF Adjusted Reflectances (NBAR) values with Landsat 5 reflectances to generate daily synthetic times series of NBAR and thus Enhanced Vegetation Index (NBAR-EVI) at a 30 m resolution. While normally STARFM is used with directional reflectances, the use of the view angle corrected daily MODIS NBAR values will provide more consistent time series. These synthetic times series of EVI are shown to capture seasonal vegetation dynamics with finer spatial and temporal details, especially over heterogeneous land surfaces.
The response of drylands to environmental gradients can be abrupt rather than gradual. These shifts largely occur unannounced and are difficult to reverse once they happen; their prompt detection is of crucial importance. The distribution of vegetation patch sizes may indicate the proximity to these shifts, but the use of this metric is hampered by a lack of large-scale studies relating these distributions to the provision of multiple ecosystem functions (multifunctionality) and comparing them to other ecosystem attributes, such as total plant cover. Here we sampled 115 dryland ecosystems across the globe and related their vegetation attributes (cover and patch size distributions) to multifunctionality. Multifunctionality followed a bimodal distribution across our sites, suggesting alternative states in the functioning of drylands. Although plant cover was the strongest predictor of multifunctionality when linear analyses were used, only patch size distributions reflected the bimodal distribution of multifunctionality observed. Differences in the coupling between nutrient cycles and in the importance of self-organizing biotic processes characterized the two multifunctionality states observed. Our findings support the use of vegetation patterns as indicators of ecosystem functioning in drylands and pave the way for developing effective strategies to monitor desertification processes.
Understanding how drylands respond to ongoing environmental change is extremely important for global sustainability. In this review, we discuss how biotic attributes, climate, grazing pressure, land cover change, and nitrogen deposition affect the functioning of drylands at multiple spatial scales. Our synthesis highlights the importance of biotic attributes (e.g., species richness) in maintaining fundamental ecosystem processes such as primary productivity, illustrates how nitrogen deposition and grazing pressure are impacting ecosystem functioning in drylands worldwide, and highlights the importance of the traits of woody species as drivers of their expansion in former grasslands. We also emphasize the role of attributes such as species richness and abundance in controlling the responses of ecosystem functioning to climate change. This knowledge is essential to guide conservation and restoration efforts in drylands, as biotic attributes can be actively managed at the local scale to increase ecosystem resilience to global change. Expected final online publication date for the Annual Review of Ecology, Evolution, and Systematics Volume 47 is November 01, 2016. Please see http://www.annualreviews.org/catalog/pubdates.aspx for revised estimates.
Due to the different degrees of controls exerted by biological and geochemical processes, climate changes are suggested to uncouple biogeochemical C, N and P cycles, influencing biomass accumulation, decomposition and storage in terrestrial ecosystems. However, the possible extent of such disruption in grassland ecosystems remains unclear, especially in China’s steppes which have undergone rapid climate changes with increasing drought and warming predicted moving forward in these dryland ecosystems. Here, we assess how soil C-N-P stoichiometry is affected by climatic change along a 3500-km temperate climate transect in Inner Mongolia, China. Our results reveal that the soil from more arid and warmer sites are associated with lower soil organic C, total N and P. The ratios of both soil C:P and N:P decrease, but soil C:N increases with increasing aridity and temperature, indicating the predicted decreases in precipitation and warming for most of the temperate grassland region could lead to a soil C-N-P decoupling that may reduce plant growth and production in arid ecosystems. Soil pH, mainly reflecting long-term climate change in our sites, also contributes to the changing soil C-N-P stoichiometry, indicating the collective influences of climate and soil type on the shape of soil C-N-P balance.
Desertification is a serious ecological, environmental, and socio-economic threat to the world, and there is a pressing need to develop a reasonable and reproducible method to assess it at different scales. In this paper, the Hogno Khaan protected area in Mongolia was selected as the study area, and a quantitative method for assessing land cover change and desertification assessment was developed using Landsat TM/ETM+ data on a local scale. In this method, NDVI (Normalized Difference Vegetation Index), TGSI (Topsoil Grain Size Index), and land surface albedo were selected as indicators for representing land surface conditions from vegetation biomass, landscape pattern, and micrometeorology. A Decision Tree (DT) approach was used to assess the land cover change and desertification of the Hogno Khaan protected area in 1990, 2002, and 2011. Our analysis showed no correlation between NDVI and albedo or TGSI but high correlation between TGSI and albedo. Strong correlations (0.77-0.92) between TGSI and albedo were found in the non-desertification areas. The TGSI was less strongly correlated with albedo in the low and non desertification areas, at 0.70 and 0.92; respectively. The desertification of the study area is increasing each year; in the desertification map for 1990-2002, there is a decrease in areas of zero and low desertification, and an increase in areas of high and severe desertification. From 2002 to 2011, areas of non desertification increased significantly, with areas of severe desertification also exhibiting increase, while areas of medium and high desertification demonstrated little change.
The spatial extent of desertified vs. rehabilitated areas in the Mu Us Sandy Land, China, was explored. The area is characterized by complex landscape changes that were caused by different drivers, either natural or anthropogenic, interacting with each other, and resulting in multiple consequences. Two biophysical variables, NDVI, positively correlated with vegetation cover, and albedo, positively correlated with cover of exposed sands, were computed from a time series of merged NOAA-AVHRR and MODIS images (1981 to 2010). Generally, throughout the study period, NDVI increased and albedo decreased. Improved understanding of spatial and temporal dynamics of these environmental processes was achieved by using the Change Vector Analysis (CVA) technique applied to NDVI and albedo data extracted from four sets of consecutive Landsat images, several years apart. Changes were detected for each time step, as well as over the entire period (1978 to 2007). Four categories of land cover were created-vegetation, exposed sands, water bodies and wetlands. The CVA's direction and magnitude enable detecting and quantifying finer changes compared to separate NDVI or albedo difference/ratio images and result in pixel-based maps of the change. Each of the four categories has a biophysical meaning that was validated in selected hot-spots, employing very high spatial resolution images (e. g., Ikonos). Selection of images, taking into account inter and intra annual variability of rainfall, enables differentiating between short-term conservancies (e. g., drought) and long-term alterations. NDVI and albedo, although comparable to tasseled cap's brightness and greenness indices, have the advantage of being computed using reflectance values extracted from various Landsat platforms since the early 1970s. It is shown that, over the entire study period, the majority of the Mu Us Sandy Land area remained unchanged. Part of the area (6%), mainly in the east, was under human-induced rehabilitation processes, in terms of increasing vegetation cover. In other areas (5.1%), bare sands were found to expand to the central-north and the southwest of the area.
The importance of biodiversity for the integrated functioning of ecosystems remains unclear because most evidence comes from analyses of biodiversity's effect on individual functions. Here we show that the effects of biodiversity on ecosystem function become more important as more functions are considered. We present the first systematic investigation of biodiversity's effect on ecosystem multifunctionality across multiple taxa, trophic levels and habitats using a comprehensive database of 94 manipulations of species richness. We show that species-rich communities maintained multiple functions at higher levels than depauperate ones. These effects were stronger for herbivore biodiversity than for plant biodiversity, and were remarkably consistent across aquatic and terrestrial habitats. Despite observed tradeoffs, the overall effect of biodiversity on multifunctionality grew stronger as more functions were considered. These results indicate that prior research has underestimated the importance of biodiversity for ecosystem functioning by focusing on individual functions and taxonomic groups.
Soil organic carbon (SOC) is extremely important in the global carbon (C) cycle as C sequestration in non-disturbed soil ecosystems can be a C sink and mitigate greenhouse-gas-driven climate change. Soil organic carbon changes in space and time are relevant to understand the soil system and its role in the C cycle. This is why the influence of topographic position on SOC should be studied. Seven topographic positions from a toposequence between 607 and 1168 m were analyzed in the Despeñaperros Natural Park (Jaén, SW Spain). Depending on soil depth, one to three control sections (0–25, 25–50 and 75 cm) were sampled at each site. The SOC content in studied soils was below 30 g kg−1 and strongly decreases with depth. These results were related to the gravel content and to the bulk density. The SOC content from the topsoil (0–25 cm) varied largely through the altitudinal gradient ranging between 27.3 and 39.9 g kg−1. The SOC stock (SOCS) varied between 53.8 and 158.0 Mg ha−1 in the studied area, which had been clearly conditioned by the topographic position. Therefore, results suggest that elevation should be included in SOCS models and estimations at local and regional scales.
The biochemistry of the weathering, landscape movements, and chemical transformations of phosphorus and its availability of plants were examined in a chronosequence of soils developed from quartz monzonite alluvium in southern New Mexico. Total P in the soil profile decreased with increasing soil age and was removed from the ecosystem as readily as the most leachable base cations. Although Ca-bound forms of P decreased with increasing soil age, Ca-P remained the singlee largest fraction of total P in all soils. In contrast, Fe- and Al-bound P was a very small percent of total P in all soils. There was little evidence for the stabilization of P by soil organic matter within this ecosystem; both soil organic P and microbial P represented very small pools of total soil P. Phosphorus availability, measured by in situ resin bags, was not well correlated with soil age or total soil P, and P concentrations in shrub tissues did not reflect changes in forms or total amounts of soil P. The biogeochemical cycle of P in this system differs sharply from that in a more mesic, forested system, where fixation by iron and aluminium oxides and biologic activity play more dominant roles in the conservation of P within the ecosystem.
We used a functional trait‐based approach to assess the impacts of aridity and shrub encroachment on the functional structure of Mediterranean dryland communities (functional diversity ( FD ) and community‐weighted mean trait values ( CWM )), and to evaluate how these functional attributes ultimately affect multifunctionality (i.e. the provision of several ecosystem functions simultaneously).
Shrub encroachment (the increase in the abundance/cover of shrubs) is a major land cover change that is taking place in grasslands worldwide. Studies conducted on drylands have reported positive or negative impacts of shrub encroachment depending on the functions and the traits of the sprouting or nonsprouting shrub species considered.
FD and CWM were equally important as drivers of multifunctionality responses to both aridity and shrub encroachment. Size traits (e.g. vegetative height or lateral spread) and leaf traits (e.g. specific leaf area and leaf dry matter content) captured the effect of shrub encroachment on multifunctionality with a relative high accuracy ( r ² = 0.63). FD also improved the resistance of multifunctionality along the aridity gradient studied.
Maintaining and enhancing FD in plant communities may help to buffer negative effects of ongoing global environmental change on dryland multifunctionality.
For several decades, long-term time series datasets of multiple global land surface albedo products have been generated from satellite observations. These datasets have been used as one of the key variables in climate change studies. This study aims to assess the surface albedo climatology and to analyze long-term albedo changes, from nine satellite-based datasets for the period 1981–2010, on a global basis. Results show that climatological surface albedo datasets derived from satellite observations can be used to validate, calibrate, and further improve surface albedo simulations and parameterizations in current climate models. However, the albedo products derived from the International Satellite Cloud Climatology Project (ISCCP) and the Global Energy and Water Exchanges Project (GEWEX) have large seasonal biases. At latitudes higher than 50°, the maximal difference in winter zonal albedo ranges from 0.1 to 0.4 among the nine satellite datasets. Satellite-based albedo datasets agree relatively well during the summer at high latitudes, with a standard deviation of 0.04 for the 70°–80° zone in both hemispheres. The fine-resolution (0.05°) datasets agree well with each other for all the land cover types in mid- to low latitudes; however, large spread was identified for their albedos at mid- to high latitudes over land covers with mixed snow and sparse vegetation. By analyzing the time series of satellite-based albedo products over the past three decades, albedo of the Northern Hemisphere was found to be decreasing in July, likely due to the shrinking snow cover. Meanwhile, albedo in January was found to be increasing, likely because of the expansion of snow cover in northern winter. However, to improve the albedo estimation at high latitudes, and ultimately the climate models used for long-term climate change studies, a still better understanding of differences between satellite-based albedo datasets is required.
This paper quantitatively explores, in terms of roughness indices, the effect of soil surface irregularities on the diurnal variation of the broadband blue-sky albedo of a large range of soil properties. Field studies were carried out on cultivated and uncultivated soil surfaces in Poland and Israel that vary in roughness and brightness. It was found that these irregularities, formed by different agricultural equipment and modified by rain or sprinkler irrigation, can be quantified by two roughness indices. Soil roughness not only affects the overall level of the diurnal variation of the albedo, but also affects the intensity of the diurnal increase from the solar zenith angle ( ) at the local noon to about . The roughness indices are variables that precisely determine only the albedo at the local solar noon of soils with the same color value. If the contents of soil organic carbon (SOC) and calcium carbonate are treated as the dominant variables, combined with one of the indices, these three variables together would significantly describe the albedo at the local solar noon of all soil surfaces. The soils, with their high irregularities, showed almost no rising values of albedo at a lower than 75 , while the smooth soil surfaces exhibited a gradual increase of the albedo at these angles. It is concluded that the roughness indices provide sufficient means to accurately describe the diurnal variation of the albedo of a wide range of surfaces, disregarding other soil properties.
Significance
Biological diversity is the foundation for the maintenance of ecosystems. Consequently it is thought that anthropogenic activities that reduce the diversity in ecosystems threaten ecosystem performance. A large proportion of the biodiversity within terrestrial ecosystems is hidden below ground in soils, and the impact of altering its diversity and composition on the performance of ecosystems is still poorly understood. Using a novel experimental system to alter levels of soil biodiversity and community composition, we found that reductions in the abundance and presence of soil organisms results in the decline of multiple ecosystem functions, including plant diversity and nutrient cycling and retention. This suggests that below-ground biodiversity is a key resource for maintaining the functioning of ecosystems.
Key Points
Two metrics are proposed to evaluate vegetation cover simulated by ESMs
On a global scale, tree cover is satisfactory simulated by MPI‐ESM
Land‐surface albedois evaluated using the net surface radiation
The biogeochemical cycles of carbon (C), nitrogen (N) and phosphorus (P) are interlinked by primary production, respiration and decomposition in terrestrial ecosystems. It has been suggested that the C, N and P cycles could become uncoupled under rapid climate change because of the different degrees of control exerted on the supply of these elements by biological and geochemical processes. Climatic controls on biogeochemical cycles are particularly relevant in arid, semi-arid and dry sub-humid ecosystems (drylands) because their biological activity is mainly driven by water availability. The increase in aridity predicted for the twenty-first century in many drylands worldwide may therefore threaten the balance between these cycles, differentially affecting the availability of essential nutrients. Here we evaluate how aridity affects the balance between C, N and P in soils collected from 224 dryland sites from all continents except Antarctica. We find a negative effect of aridity on the concentration of soil organic C and total N, but a positive effect on the concentration of inorganic P. Aridity is negatively related to plant cover, which may favour the dominance of physical processes such as rock weathering, a major source of P to ecosystems, over biological processes that provide more C and N, such as litter decomposition. Our findings suggest that any predicted increase in aridity with climate change will probably reduce the concentrations of N and C in global drylands, but increase that of P. These changes would uncouple the C, N and P cycles in drylands and could negatively affect the provision of key services provided by these ecosystems.
The biogeochemical cycles of carbon (C), nitrogen (N) and phosphorus
(P) are interlinked by primary production, respiration and
decomposition in terrestrial ecosystems1. It has been suggested that
the C, N and P cycles could become uncoupled under rapid climate
change because of the different degrees of control exerted on the
supply of these elements by biological and geochemical processes1–5.
Climatic controls on biogeochemical cycles are particularly relevant
in arid, semi-arid and dry sub-humid ecosystems (drylands) because
their biological activity is mainly driven by water availability6–8.
The increase in aridity predicted for the twenty-first century in
many drylands worldwide9–11 may therefore threaten the balance
between these cycles, differentially affecting the availability of essential
nutrients12–14. Here we evaluate how aridity affects the balance
between C, N and P in soils collected from 224 dryland sites from
all continents except Antarctica. Wefind a negative effect of aridity
on the concentration of soil organic C and total N, but a positive
effect on the concentration of inorganic P. Aridity is negatively
related to plant cover, which may favour the dominance of physical
processes such as rock weathering, a major source of P to ecosystems,
over biological processes that provide more C and N, such as
litter decomposition12–14. Our findings suggest that any predicted
increase in aridity with climate change will probably reduce the
concentrations of N and C in global drylands, but increase that of
P. These changes would uncouple the C, N and P cycles in drylands
and could negatively affect the provision of key services provided by
these ecosystems.
Global land surface albedo data sets derived from the Terra Moderate-Resolution Imaging Spectroradiometer (MODIS) from March 2000 to present have been completed for ready use by the global modeling community. This paper describes these albedo and bidirectional reflectance distribution function Climate Modeling Grid products and their variability within major global vegetation types. Preliminary results based on collection 4 data reveal that these coarse resolution (0.05°), geographic (latitude/longitude), global albedos have spatial and temporal patterns appropriate for the underlying land cover classes, further encouraging modelers to introduce albedos as functions of ground cover, geographic location, temporal season, and spatial resolution in the various climate-modeling schemes.
New satellite instruments have been delivering a wealth of information regarding land surface albedo. This basic quantity describes what fraction of solar radiation is reflected from the earth's surface. However, its concept and measurements have some ambiguity resulting from its dependence on the incidence angles of both the direct and diffuse solar radiation. At any time of day, a surface receives direct radiation in the direction of the sun, and diffuse radiation from the various other directions in which it may have been scattered by air molecules, aerosols, and cloud droplets. This contribution proposes a complete description of the distribution of incident radiation with angles, and the implications in terms of surface albedo are given in a mathematical form, which is suitable for climate models that require evaluating surface albedo many times. The different definitions of observed albedos are explained in terms of the coupling between surface and atmospheric scattering properties. The analytical development in this paper relates the various quantities that are retrieved from orbiting platforms to what is needed by an atmospheric model. It provides a physically simple and practical approach to evaluation of land surface albedo values at any condition of sun illumination irrespective of the current range of surface anisotropic conditions and atmospheric aerosol load. The numerical differences between the various definitions of albedo for a set of typical atmospheric and surface scattering conditions are illustrated through numerical computation.
One of the most important changes taking place in drylands worldwide is the increase of the cover and dominance of shrubs in areas formerly devoid of them (shrub encroachment). A large body of research has evaluated the causes and consequences of shrub encroachment for both ecosystem structure and functioning. However, there are virtually no studies evaluating how shrub encroachment affects the ability of ecosystems to maintain multiple functions and services simultaneously (multifunctionality). We aimed to do so by gathering data from ten ecosystem functions linked to the maintenance of primary production and nutrient cycling and storage (organic C, activity of β-glucosidase, pentoses, hexoses, total N, total available N, amino acids, proteins, available inorganic P, and phosphatase activity), and summarizing them in a multifunctionality index (M). We assessed how climate, species richness, anthropic factors (distance to the nearest town, sandy and asphalted road, and human population in the nearest town at several historical periods) and encroachment by sprouting shrubs impacted both the functions in isolation and M along a regional (ca. 350 km) gradient in Mediterranean grasslands and shrublands dominated by a non-sprouting shrub. Values of M were higher in those grasslands and shrublands containing sprouting shrubs (43 and 62%, respectively). A similar response was found when analyzing the different functions in isolation, as encroachment by sprouting shrubs increased functions by 2–80% compared to unencroached areas. Encroachment was the main driver of changes in M along the regional gradient evaluated, followed by anthropic factors and species richness. Climate had little effects on M in comparison to the other factors studied. Similar responses were observed when evaluating the functions in isolation. Overall, our results showed that M was higher at sites with higher sprouting shrub cover, longer distance to roads and higher perennial plant species richness. Our study is the first documenting that ecosystem multifunctionality in shrublands is enhanced by encroaching shrubs differing in size and leaf attributes. Our findings reinforce the idea that encroachment effects on ecosystem functioning cannot be generalized, and that are largely dependent on the traits of the encroaching shrub relative to those of the species being replaced.
Extensive research shows that more species-rich assemblages are generally
more productive and efficient in resource use than comparable assemblages with
fewer species. But the question of how diversity simultaneously affects the
wide variety of ecological functions that ecosystems perform remains relatively
understudied, and it presents several analytical and empirical challenges that
remain unresolved. In particular, researchers have developed several disparate
metrics to quantify multifunctionality, each characterizing different aspects
of the concept, and each with pros and cons. We compare four approaches to
characterizing multifunctionality and its dependence on biodiversity,
quantifying 1) magnitudes of multiple individual functions separately, 2) the
extent to which different species promote different functions, 3) the average
level of a suite of functions, and 4) the number of functions that
simultaneously exceed a critical threshold. We illustrate each approach using
data from the pan-European BIODEPTH experiment and the R multifunc package
developed for this purpose, evaluate the strengths and weaknesses of each
approach, and implement several methodological improvements. We conclude that a
extension of the fourth approach that systematically explores all possible
threshold values provides the most comprehensive description of
multifunctionality to date. We outline this method and recommend its use in
future research.
Grassland salinization causes considerable changes to soil and vegetation, which can lead to changes in soil organic carbon (C) and total nitrogen (N). These changes have complex causal relationships. A significant correlation between soil organic C and total N and any soil or vegetation property does not necessarily imply a significant direct effect of the property on soil organic C and total N. In this study, a field survey was conducted to investigate the changes in soil organic C and total N in grassland along a salinity gradient in Hexi corridor, China, and the direct and indirect effects of soil and vegetation properties on both stocks were quantified using a path analysis approach. Significant decrease in soil organic C and total N contents were observed with increasing salinity. Both had significant positive correlations with the Normalized Difference Vegetation Index (NDVI), soil water, and fine particles (silt+clay) content (p<0.01) and significant negative correlations with soil EC, and sand content (p<0.01). NDVI, fine particles content and soil water content had positive direct effects on soil organic C and total N stocks. Soil EC affected soil organic C and total N stocks mainly through its indirect negative effect on NDVI, soil texture, and water content. NDVI, soil texture, and moisture also indirectly affected soil organic C and total N stocks via changes in each other. These indirect effects augmented each other, although in some cases indirect effects worked in opposing directions.
With the launch of NASA's Terra satellite and the MODerate Resolution Imaging Spectroradiometer (MODIS), operational Bidirectional Reflectance Distribution Function (BRDF) and albedo products are now being made available to the scientific community. The MODIS BRDF/Albedo algorithm makes use of a semiempirical kernel-driven bidirectional reflectance model and multidate, multispectral data to provide global 1-km gridded and tiled products of the land surface every 16 days. These products include directional hemispherical albedo (black-sky albedo), bihemispherical albedo (white-sky albedo), Nadir BRDF-Adjusted surface Reflectances (NBAR), model parameters describing the BRDF, and extensive quality assurance information. The algorithm has been consistently producing albedo and NBAR for the public since July 2000. Initial evaluations indicate a stable BRDF/Albedo Product, where, for example, the spatial and temporal progression of phenological characteristics is easily detected in the NBAR and albedo results. These early beta and provisional products auger well for the routine production of stable MODIS-derived BRDF parameters, nadir reflectances, and albedos for use by the global observation and modeling communities.
Drylands, a critical terrestrial system of the Earth due to low water availability, are known for their extensive global reach, estimated by most scientific sources at approximately 41% of the world's land area, or ~ 60 mil km². However, the analysis of the global dryland areas, using new climate data, suggests a total of ~ 45% of the Earth's terrestrial area, almost 7 mil km² more than initially estimated. This new spatial dimension involves a wide range of environmental issues, some of which have yet to be associated with these critical global systems. This paper primarily aims to accurately quantify the global, continental and national extent of drylands by using a high-resolution climate database presently available at global level. Also, based on relevant scientific literature, this approach attempts to briefly highlight the main environmental issues (natural and anthropogenic) of the major continental and regional dryland areas. In this respect, special attention was given to the land degradation processes (water and wind erosion, vegetation degradation, salinization, soil compaction and nutrient loss), as it is known to be the main environmental perturbation in almost all dryland systems. Research shows that, given the fact that Africa and Asia have the most extensive dryland systems on Earth (each of them has almost 23 mil km², or ~ 15% of the global land area), these continents are especially threatened by major environmental perturbations (desertification, in addition to other ecological and climatic disturbances such as drought, dust storms, heat waves, water stress, extreme rainfall events, wildfire, dzud, or disease emergence), which are currently affecting 46 African states (37% of the 126 states affected by aridity worldwide) and 38 Asian states (30%). Given this context, anthropogenic systems are indirectly severely threatened by the crisis generated by soaring poverty, food insecurity, population migration, and escalating conflicts and regional political instability. Moreover, in the current context of large-scale aridity identified at high latitudes, another critical threat reviewed was the cryosphere's destabilization, which can potentially accelerate climate warming by means of positive feedback mechanisms that can be triggered in the global climate system. In this respect, a major concern is attributed to permafrost melting that, against the background of a significant expansion in the terrestrial northern hemisphere (in Russia, Alaska and Canada), can generate a massive acceleration of climate warming due to the potential release of large quantities of carbon dioxide (CO2) and methane (CH4), which are currently stored in these frozen soils in the Arctic and sub-Arctic regions.
Surface albedo is an easy access parameter in reflecting the status of both human disturbed soil and indirectly influenced area, whose characteristic is an important indicator in sustainable development under the background of global climate change. In this study, we employed meteorological data, MODIS 8-day BRDF/Albedo and LAI products from 2000 to 2014 to show the amelioration and mechanism around the Badain Jaran Desert. Results showed that the human-dominated afforestation activities significantly increased the leaf area index (LAI) in summer and autumn. Lower reflectance at visible band was sensed inside the desert compared with the ecozone and the lowest albedo at forested area. The contribution of soil and vegetation reflectance to surface albedo determined the linear sensitivity of albedo to LAI variation. Decreased albedo dominated the spatial-temporal pattern of the Badain Jaran Desert. This study suggested that surface albedo can be regarded as a useful index in indicating the change process and evaluating the sustainable development of biological management around the Badain Jaran Desert.
The identification of properties that contribute to the persistence and resilience of ecosystems despite climate change constitutes a research priority of global relevance. Here we present a novel, empirical approach to assess the relative sensitivity of ecosystems to climate variability, one property of resilience that builds on theoretical modelling work recognizing that systems closer to critical thresholds respond more sensitively to external perturbations. We develop a new metric, the vegetation sensitivity index, that identifies areas sensitive to climate variability over the past 14 years. The metric uses time series data derived from the moderate-resolution imaging spectroradiometer (MODIS) enhanced vegetation index, and three climatic variables that drive vegetation productivity (air temperature, water availability and cloud cover). Underlying the analysis is an autoregressive modelling approach used to identify climate drivers of vegetation productivity on monthly timescales, in addition to regions with memory effects and reduced response rates to external forcing. We find ecologically sensitive regions with amplified responses to climate variability in the Arctic tundra, parts of the boreal forest belt, the tropical rainforest, alpine regions worldwide, steppe and prairie regions of central Asia and North and South America, the Caatinga deciduous forest in eastern South America, and eastern areas of Australia. Our study provides a quantitative methodology for assessing the relative response rate of ecosystems—be they natural or with a strong anthropogenic signature—to environmental variability, which is the first step towards addressing why some regions appear to be more sensitive than others, and what impact this has on the resilience of ecosystem service provision and human well-being.
Biogeochemistry-winner of a 2014 Textbook Excellence Award (Texty) from the Text and Academic Authors Association-considers how the basic chemical conditions of the Earth, from atmosphere to soil to seawater, have been and are being affected by the existence of life. Human activities in particular, from the rapid consumption of resources to the destruction of the rainforests and the expansion of smog-covered cities, are leading to rapid changes in the basic chemistry of the Earth. This expansive text pulls together the numerous fields of study encompassed by biogeochemistry to analyze the increasing demands of the growing human population on limited resources and the resulting changes in the planet's chemical makeup. The book helps students extrapolate small-scale examples to the global level, and also discusses the instrumentation being used by NASA and its role in studies of global change. With extensive cross-referencing of chapters, figures and tables, and an interdisciplinary coverage of the topic at hand, this updated edition provides an excellent framework for courses examining global change and environmental chemistry, and is also a useful self-study guide.
The biogeochemical cycles of carbon (C), nitrogen (N), and phosphorus (P) are fundamental to life on Earth. Because organisms require these elements in strict proportions, the cycles of C, N, and P are coupled at molecular to global scales through their effects on the biochemical reactions controlling primary production, respiration, and decomposition. The coupling of the C, N, and P cycles constrains organismal responses to climatic and atmospheric change, suggesting that present-day estimates of climate warming through the year 2100 are conservative. N and P supplies constrain C uptake in the terrestrial biosphere, yet these constraints are often not incorporated into global-scale analyses of Earth's climate. The inclusion of coupled biogeochemical cycles is critical to the development of next-generation, global-scale climate models.
http://www.lrrd.org/lrrd26/9/kurg26162.html
Climate Change affect various sectors in Kenya, with the most vulnerable being agriculture, livestock, water, health, fisheries and tourism. Accurate estimates of soil organic carbon stocks (SOCS) in the rangelands are critical in developing strategies to help mitigate impacts of climate change. The study therefore, sought to establish the relationship between vegetation cover types and SOCS in northern rangelands of Kenya as an indirect method of estimating SOCS in the field. Landsat 5 Thematic Mapper satellite image was used to differentiate vegetation cover types and soil samples taken along the transect line laid at intervals of 50 m across each vegetation cover type. Colourimetric and core sampling methods were used to determine SOC concentrations and soil bulk densities, respectively. Analysis of variance and simple linear regression were used in the statistical analysis.
Four vegetation cover types indentified were: Acacia bush land (ABL), bare land (BRL), sparsely distributed acacia with bare ground (SAB) and sparsely distributed acacia with forbs (SAF) and. The means of SOC for each vegetation cover were different. However, soil bulk densities under BRL and SAB were similar but different from that of ABL and SAF that were alike. Further, overall mean of SOCS was 6.76±2.85 t C ha-1 for all the vegetation cover types. A positive relationship was established between the average mean values of both Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI) when regressed with the average mean values of SOCS. The findings suggest that vegetation indices measured with GIS are good predictors of SOCS for the study region, with the potential for extrapolation to the arid and semi-arid areas to which this ecosystem belongs.
Drylands cover about 41% of E arth's land surface, and 65% of their area supports domestic livestock that depends on the above‐ground net primary productivity ( ANPP ) of natural vegetation. Thus, understanding how biotic and abiotic factors control ANPP and related ecosystem functions can largely help to create more sustainable land‐use practices in rangelands, particularly in the context of ongoing global environmental change.
We used 311 sites across a broad natural gradient in Patagonian rangelands to evaluate the relative importance of climate (temperature and precipitation) and vegetation structure (grass and shrub cover, species richness) as drivers of ANPP , precipitation‐use efficiency ( PUE ) and precipitation marginal response ( PMR ).
Climatic variables explained 60%, 52% and 12% of the variation in grass cover, shrub cover and species richness, respectively. Shrub cover increased in areas with warmer, drier and winter rainfall climates, while the response observed for both grass cover and species richness was the opposite. Climate and vegetation structure explained 70%, 60% and 29% of the variation in ANPP , PUE and PMR , respectively. These three variables increased with increasing vegetation cover, particularly grass cover. Species richness also increased with ANPP , PUE and PMR . ANPP increased, and PUE decreased with increasing mean annual precipitation, whereas PMR increased with the proportion of precipitation falling in spring–summer. Temperature had a strong negative effect on ANPP and PUE , and a positive direct effect on PMR . Standardized total effects from structural equation modelling showed that vegetation structure and climate had similar strengths as drivers of ecosystem functioning. Grass cover had the highest total effect on ANPP (0.58), PUE (0.55) and PMR (0.41). Among the climatic variables, mean annual precipitation had the strongest total effect on ANPP (0.51) and PUE (−0.41), and the proportion of the precipitation falling in spring–summer was the most influential on PMR (0.36).
Synthesis . Vegetation structure is as important as climate in shaping ecosystem functioning Patagonian rangelands. Maintaining and enhancing vegetation cover and species richness, particularly in grasses, could reduce the adverse effects of climate change on ecosystem functioning in these ecosystems.
In Europe, the most susceptible areas to land degradation and desertification (LDD) are found in the Mediterranean region. The present study focuses on the island of Lesvos (Greece) and maps the environmental sensitivity of the island to LDD between the years 1990 and 2000. Sensitivity is estimated with a modification of the MEDALUS Environmentally Sensitive Area Index (ESAI) approach, employing 21 quantitative parameters divided in five main quality indices: climate, vegetation, soils, groundwater and socio-economic quality. Parameterisation of these indices is achieved via remote sensing and ancillary data in a GIS. Results show that ~85% of the island is fragile or critically sensitive in both epochs. Fragile areas are on the increase, covering an estimated 72% of the island in 1990 and 77% in 2000, while critically sensitive areas decrease from 214km2 to 113km2. By modifying the ESAI to include 10 additional parameters related to soil erosion, groundwater quality, demographic as well as grazing pressure, and by applying the modified ESAI in two -rather than one- periods this study was able to identify that, contrary to previous belief, critically sensitive areas are also found in the eastern side of the island mainly due to human-related factors. It is concluded that the proposed methodology is a useful tool for regional scale trend analyses of environmental sensitivity and the identification of LDD hot-spots in Mediterranean environments. This article is protected by copyright. All rights reserved.
Global trends in a new multi-satellite surface soil moisture dataset
were analyzed for the period 1988-2010. 27% of the area covered by
the dataset showed significant trends (p = 0.05). Of these,
73% were negative and 27% positive. Subtle drying trends were found in
the Southern US, central South America, central Eurasia, northern Africa
and the Middle East, Mongolia and northeast China, northern Siberia, and
Western Australia. The strongest wetting trends were found in southern
Africa and the subarctic region. Intra-annual analysis revealed that
most trends are not uniform among seasons. The most prominent trend
patterns in remotely sensed surface soil moisture were also found in
GLDAS-Noah and ERA Interim modeled surface soil moisture and GPCP
precipitation, lending confidence to the obtained results. The
relationship with trends in GIMMS-NDVI appeared more complex. In areas
of mutual disagreement more research is needed to identify potential
deficiencies in models and/or remotely sensed products.
Two Landsat images, acquired in 1987 and 2008, were analyzed to evaluate desertification processes in central North Kurdufan State (Sudan). Spectral Mixture Analysis (SMA) and multitemporal comparison techniques (change vector analysis) were applied to estimate the long-term desertification/re-growing of vegetation cover over time and in space.Site-specific interactions between natural processes and human activity played a pivotal role in desertification. Over the last 21 years, desertification significantly prevailed over vegetation re-growth, particularly in areas around rural villages. Changes in land use and mismanagement of natural resources were the main driving factors affecting degradation. More than 120,000 km2 were estimated as being subjected to a medium-high desertification rate. Conversely, the reforestation measures, adopted by the Government in the last decade and sustained by higher rainfall, resulted in low-medium re-growth conditions over an area of about 20,000 km2.Site-specific strategies which take into account the interactions of the driving factors at local scale are thus necessary to combat desertification, avoiding any implementation of untargeted measures. In order to identify the soundest strategies, high-resolution tools must be applied. In this study the application of spectral mixture analysis to Landsat data appeared to be a consistent, accurate and low-cost technique to identify risk areas.
Assessing the spatial variability of ecosystem structure and functioning is an important step towards developing monitoring systems to detect changes in ecosystem attributes that could be linked to desertification processes in drylands. Methods based on ground-collected soil and plant indicators are being increasingly used for this aim, but they have limitations regarding the extent of the area that can be measured using them. Approaches based on remote sensing data can successfully assess large areas, but it is largely unknown how the different indices that can be derived from such data relate to ground-based indicators of ecosystem health. We tested whether we can predict ecosystem structure and functioning, as measured with a field methodology based on indicators of ecosystem functioning (the landscape function analysis, LFA), over a large area using spectral vegetation indices (VIs), and evaluated which VIs are the best predictors of these ecosystem attributes. For doing this, we assessed the relationship between vegetation attributes (cover and species richness), LFA indices (stability, infiltration and nutrient cycling) and nine VIs obtained from satellite images of the MODIS sensor in 194 sites located across the Patagonian steppe. We found that NDVI was the VI best predictor of ecosystem attributes. This VI showed a significant positive linear relationship with both vegetation basal cover (R2 = 0.39) and plant species richness (R2 = 0.31). NDVI was also significantly and linearly related to the infiltration and nutrient cycling indices (R2 = 0.36 and 0.49, respectively), but the relationship with the stability index was weak (R2 = 0.13). Our results indicate that VIs obtained from MODIS, and NDVI in particular, are a suitable tool for estimate the spatial variability of functional and structural ecosystem attributes in the Patagonian steppe at the regional scale.
We investigated soil carbon (C) and nitrogen (N) distribution and developed a model, using readily available geospatial data, to predict that distribution across a mountainous, semi-arid, watershed in southwestern Idaho (USA). Soil core samples were collected and analyzed from 133 locations at 6 depths (n=798), revealing that aspect dramatically influences the distribution of C and N, with north-facing slopes exhibiting up to 5 times more C and N than adjacent south-facing aspects. These differences are superimposed upon an elevation (precipitation) gradient, with soil C and N contents increasing by nearly a factor of 10 from the bottom (1100m elevation) to the top (1900m elevation) of the watershed. Among the variables evaluated, vegetation cover, as represented by a Normalized Difference Vegetation Index (NDVI), is the strongest, positively correlated, predictor of C; potential insolation (incoming solar radiation) is a strong, negatively correlated, secondary predictor. Approximately 62% (as R2) of the variance in the C data is explained using NDVI and potential insolation, compared with an R2 of 0.54 for a model using NDVI alone. Soil N is similarly correlated to NDVI and insolation. We hypothesize that the correlations between soil C and N and slope, aspect and elevation reflect, in part, the inhibiting influence of insolation on semi-arid ecosystem productivity via water limitation. Based on these identified relationships, two modeling techniques (multiple linear regression and cokriging) were applied to predict the spatial distribution of soil C and N across the watershed. Both methods produce similar distributions, successfully capturing observed trends with aspect and elevation. This easily applied approach may be applicable to other semi-arid systems at larger scales.
A substantial part of current research efforts on desertification are devoted to establish monitoring systems to evaluate the status of natural resources and the onset of desertification processes. Methodologies based on ground-collected soil and plant indicators are being increasingly used for this aim because they are affordable yet do not compromise accuracy. Despite their inherent value, these methods have limitations regarding the extent of the area that can be monitored using them. Such limitations can be overcome combining field-based approaches with remote sensing data, which allow the establishment of monitoring programs over large areas. In this article we tested the relationship between a field methodology based on indicators of ecosystem functioning, the landscape function analysis (LFA), and a vegetation index (NDVI) obtained from satellite images of the ASTER sensor using data gathered in Stipa tenacissima steppes from central Spain. LFA uses soil surface indicators to assess the condition of a given ecosystem by producing three numerical indices (stability, infiltration and nutrient cycling) reflecting the status of basic soil functions. We found a significant positive linear relationship between the NDVI, the three LFA indices and some key structural attributes of vegetation related to the cover of perennial plants. Our results indicate that NDVI can be used as a surrogate of ecosystem functioning in semi-arid Mediterranean steppes, and thus can be a helpful index to monitor the functional status of large areas in these ecosystems, and the possible onset of desertification processes.
This study evaluates in detail the indicators of desertification process in semi-arid lands by making use of temporal satellite information along with the surface and statistical data with the aid of a GIS. The indicators were correlated to the surface information to establish the severity of desertification and factors helping the desertification process to continue. The results show that the process is a natural phenomenon but is aggravated by human activity.
To identify the functioning of the soil-landscape system and its effects on plant growth for native rangeland the relationships between soil properties and landscape function analysis (LFA) indices and between plant growth characteristics and LFA indices were investigated. The results interpreted based on statistical analysis and expert knowledge. This research was carried out for a semi-arid rangeland in the Lar aquifer in Iran. Land stratification allowed the study area to be subdivided into Land Units, according to specified criteria including landform attributes (slope, aspect, and altitude), and vegetation type. A factorial model on the basis of a completely randomized design was used to analyse the data collected from 236 land units. The landscape function indices including nutrient cycling index, infiltration index, stability index, and landscape organization index were derived by various integrations of soil surface attributes. Landscape attributes differed from one another in their effects on the different landscape function indices. Increasing slope gradient significantly reduced all landscape function indices as well as soil organic carbon and total nitrogen percentages. Slope class exhibited highly significant interaction effects with vegetation type factors for stability, nutrient cycling, and landscape organization indices. Aspect did not significantly affect stability, infiltration, and landscape organization indices, but significantly affected the nutrient cycling index. The Duncan test indicated that north aspect (shady side) had the highest mean value (28.42) and south aspect the lowest mean value (25.57) for nutrient cycling index. These results are consistent with the effects of aspect on total soil nitrogen and soil organic carbon percentage for which the north aspect had the highest values. The values declined in the sequence east, west, and south aspects, respectively. This research indicates that the nature of native rangeland plant communities and their measures of production are closely related to nutrient cycling index.
The paper presents results on the use of NOAA AVHRR data for desertification monitoring on a regional–global level. It is based on processing of the GIMMS 8 km global NDVI data set. Time series of annually integrated and standardized annual NDVI anomalies were generated and compared with a corresponding rainfall data set (1981–2003).The regions studied include the Mediterranean basin, the Sahel from the Atlantic to the Red Sea, major parts of the drylands of Southern Africa, China–Mongolia and the drylands of South America, i.e. important parts of the desertification prone drylands of the world.It is concluded that the suggested methodology is a robust and reliable way to assess and monitor vegetation trends and related desertification on a regional–global scale. A strong general relationship between NDVI and rainfall over time is demonstrated for considerable parts of the drylands. The results of performed trend analysis cannot be used to verify any systematic generic land degradation/desertification trend at the regional–global level. On the contrary, a “greening-up” seems to be evident over large regions.