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.
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•Missing steps in the Sustainable Development Goal 15.3.1 productivity indicator.•Remotely-sensed vegetation indices alone are inadequate to monitor land degradation.•Physiological and ecological processes affect the significance of vegetation indices.•Identification of degradation requires explicit reference baselines.•Improvements in techniques are needed to attribute degradation to humans.
Semi-arid areas, defined as those areas of the world where water is an important limitation for plant growth, have become the subject of increased interest due to the impacts of current global ...changes and sustainability of human lifestyles. While many ground-based reports of declining vegetation productivity have been published over the last decades, a number of recent publications have shown a nuanced and, for some regions, positive picture. With this background, the paper provides an analysis of trends in vegetation greenness of semi-arid areas using AVHRR GIMMS from 1981 to 2007. The vegetation index dataset is used as a proxy for vegetation productivity and trends are analyzed for characterization of changes in semi-arid vegetation greenness. Calculated vegetation trends are analyzed with gridded data on potential climatic constraints to plant growth to explore possible causes of the observed changes. An analysis of changes in the seasonal variation of vegetation greenness and climatic drivers is conducted for selected regions to further understand the causes of observed inter-annual vegetation changes in semi-arid areas across the globe. It is concluded that semi-arid areas, across the globe, on average experience an increase in greenness (0.015 NDVI units over the period of analysis). Further it is observed that increases in greenness are found both in semi-arid areas where precipitation is the dominating limiting factor for plant production (0.019 NDVI units) and in semi-arid areas where air temperature is the primarily growth constraint (0.013 NDVI units). Finally, in the analysis of changes in the intra-annual variation of greenness it is found that seemingly similar increases in greenness over the study period may have widely different explanations. This implies that current generalizations, claiming that land degradation is ongoing in semi-arid areas worldwide, are not supported by the satellite based analysis of vegetation greenness.
► Trends in dryland vegetation greenness (NDVI) based on AVHRR data are analyzed. ► Climatic constraints to plant growth are anlysed to study causes of NDVI changes. ► Global drylands on average experience an increase in NDVI from 1981 to 2007. ► Trends have regional specific explanations and generalizations are not supported.
In water limited environments precipitation is often considered the key factor influencing vegetation growth and rates of development. However; other climate variables including temperature; ...humidity; the frequency and intensity of precipitation events are also known to affect productivity; either directly by changing photosynthesis and transpiration rates or indirectly by influencing water availability and plant physiology. The aim here is to quantify the spatiotemporal patterns of vegetation responses to precipitation and to additional; relevant; meteorological variables. First; an empirical; statistical analysis of the relationship between precipitation and the additional meteorological variables and a proxy of vegetation productivity (the Normalized Difference Vegetation Index; NDVI) is reported and; second; a process-oriented modeling approach to explore the hydrologic and biophysical mechanisms to which the significant empirical relationships might be attributed. The analysis was conducted in Sub-Saharan Africa; between 5 and 18 degree N; for a 25-year period 1982-2006; and used a new quasi-daily Advanced Very High Resolution Radiometer (AVHRR) dataset. The results suggest that vegetation; particularly in the wetter areas; does not always respond directly and proportionately to precipitation variation; either because of the non-linearity of soil moisture recharge in response to increases in precipitation; or because variations in temperature and humidity attenuate the vegetation responses to changes in water availability. We also find that productivity; independent of changes in total precipitation; is responsive to intra-annual precipitation variation. A significant consequence is that the degree of correlation of all the meteorological variables with productivity varies geographically; so no one formulation is adequate for the entire region. Put together; these results demonstrate that vegetation responses to meteorological variation are more complex than an equilibrium relationship between precipitation and productivity. In addition to their intrinsic interest; the findings have important implications for detection of anthropogenic dryland degradation (desertification); for which the effects of natural fluctuations in meteorological variables must be controlled in order to reveal non-meteorological; including anthropogenic; degradation.
Among policy institutions, researchers in other fields, and the public, there is an enduring misunderstanding of the nature of desertification. To a considerable extent, its meaning has been reduced ...to just two eye‐catching images: sand dunes encroaching on productive land and habitations and bare, cracked soil surfaces. Yet neither of these is an indicator of desertification—sometimes called dryland degradation. Rather desertification results in a wide range of changes, including erosion, loss of biodiversity, decline in soil fertility, and reduced carbon storage. Surprisingly, it is the pictures themselves, not the scientific literature, that have fixed this erroneous concept in the minds of even some who work in the field of land degradation. The results of this confusion include inappropriate management efforts, mistaken premises in research, and ill‐informed policies at local to global scales including misleading prognostications of institutions—even at the level of United Nations agencies.
There is a great deal of debate on the extent, causes, and even the reality of land degradation in the Sahel. Investigations carried out before approximately 2000 using remote sensing data suggest ...widespread reductions in biological productivity, while studies extending beyond 2000 consistently reveal a net increase in vegetation production, strongly related to the recovery of rainfall following the extreme droughts of the 1970s and 1980s, and thus challenging the notion of widespread, long-term, subcontinental-scale degradation. Yet, the spatial variations in the rates of vegetation recovery are not fully explained by rainfall trends. It is hypothesized that, in addition to rainfall, other meteorological variables and human land use have contributed to vegetation dynamics. Throughout most of the Sahel, the interannual variability in growing season capital sigma NDVIgs (measured from satellites, used as a proxy of vegetation productivity) was strongly related to rainfall, humidity, and temperature (mean r2 = 0.67), but with rainfall alone was weaker (mean r2 = 0.41). The mean and upper 95th quantile (UQ) rates of change in capital sigma NDVIgs in response to climate were used to predict potential capital sigma NDVIgs-that is, the capital sigma NDVIgs expected in response to climate variability alone, excluding any anthropogenic effects. The differences between predicted and observed capital sigma NDVIgs were regressed against time to detect any long-term (positive or negative) trends in vegetation productivity. Over most of the Sahel, the trends did not significantly depart from what is expected from the trends in meteorological variables. However, substantial and spatially contiguous areas (~8% of the total area of the Sahel) were characterized by negative, and, in some areas, positive trends. To explore whether the negative trends were human-induced, they were compared with the available data of population density, land use, and land biophysical properties that are known to affect the susceptibility of land to degradation. The spatial variations in the trends of the residuals were partly related to soils and tree cover, but also to several anthropogenic pressures.
Studies of the correlations of environmental factors with vegetation growth using remotely sensed measurements are necessarily made against a background of biophysical and anthropogenic factors, such ...as local fertility, microclimate, and the effects of human land use, in addition to the factors of interest. This is an inevitable outcome of a natural (unplanned) design where the effects of the factors of interest are confounded with other, often unknown factors, possibly rendering the results inaccurate or poorly-constrained. The problems associated with a natural design would be reduced if sites could be identified in which uncontrolled variables had no impact. However, rarely are such sites known a priori. Here, a component of the net primary production (NPP) local scaling (LNS) method was used to estimate the potential NPP in the absence of confounding factors. Subsequent analyses of the effects of the selected environmental variables were carried out using the potential NPP. The method was tested in relation to NPP along the transitional ecotone from desert to semiarid conditions in the northern Negev, Israel. The effects of four environmental factors were tested: precipitation, topography, land cover, and interannual variability. While precipitation is generally the only environmental variable that is considered in drylands, the other factors were found to be significant. The results provided unambiguous evidence of the value of the method.
Anthropogenic land degradation affects many biogeophysical processes, including reductions of net primary production (NPP). Degradation occurs at scales from small fields to continental and global. ...While measurement and monitoring of NPP in small areas is routine in some studies, for scales larger than 1 km2, and certainly global, there is no regular monitoring and certainly no attempt to measure degradation. Quantitative and repeatable techniques to assess the extent of deleterious effects and monitor changes are needed to evaluate its effects on, for example, economic yields of primary products such as crops, lumber, and forage, and as a measure of land surface properties which are currently missing from dynamic global vegetation models, assessments of carbon sequestration, and land surface models of heat, water, and carbon exchanges. This study employed the local NPP scaling (LNS) approach to identify patterns of anthropogenic degradation of NPP in the Burdekin Dry Tropics (BDT) region of Queensland, Australia, from 2000 to 2013. The method starts with land classification based on the environmental factors presumed to control (NPP) to group pixels having similar potential NPP. Then, satellite remotely sensing data were used to compare actual NPP with its potential. The difference in units of mass of carbon and percentage loss were the measure of degradation. The entire BDT (7.45 × 106 km2) was investigated at a spatial resolution of 250 × 250 m. The average annual reduction in NPP due to anthropogenic land degradation in the entire BDT was −2.14 MgC m−2 yr−1, or 17 % of the non-degraded potential, and the total reduction was −214 MgC yr−1. Extreme average annual losses of 524.8 gC m−2 yr−1 were detected. Approximately 20 % of the BDT was classified as “degraded”. Varying severities and rates of degradation were found among the river basins, of which the Belyando and Suttor were highest. Interannual, negative trends in reductions of NPP occurred in 7 % of the entire region, indicating ongoing degradation. There was evidence of areas that were in a permanently degraded condition. The findings provide strong evidence and quantitative data for reductions in NPP related to anthropogenic land degradation in the BDT.
Land degradation in drylands is the process in which undesirable conditions emerge due to human and natural causes. Despite the particularly deleterious effects of degradation, and it’s potentially ...irreversible nature, regional assessments have provided conflicting extents, rates, and severities of degradation, both globally and regionally. Current monitoring of degradation relies upon the detection of green, photosynthetically active parts of vegetation (e.g., leaves). Less is known, however, about the effect of degradation on the non-photosynthetic components of vegetation (e.g., wood, stems, leaf litter) and the relationship between photosynthetic vegetation (PV), non-photosynthetic vegetation (NPV), and bare soil under degraded conditions (BS). The major objective of the study was to evaluate regional patterns of fractional cover (i.e., PV, NPV, BS) under degraded and non-degraded NPP conditions in a managed rangeland in north Queensland, Australia. Homogenous environmental conditions were identified and each of NPP, PV, NPV, and BS were scaled according to their potential, reference values. We found a strong spatial and temporal correlation between scaled NPP with both scaled PV and scaled BS. Drastic differences were also found for PV and BS between degraded and non-degraded conditions. NPV displayed similarity to both PV and BS, however no clear relationship was found for NPV in all areas, irrespective of degradation conditions.
Phenotypic diversity in urinary metabolomes of different geographical populations has been recognized recently. In this study, urinary metabolic signatures from Western (United Kingdom) and ...South-East Asian (Thai) cholangiocarcinoma patients were characterized to understand spectral variability due to host carcinogenic processes and/or exogenous differences (nutritional, environmental and pharmaceutical). Urinary liquid chromatography mass spectroscopy (LC-MS) spectral profiles from Thai (healthy = 20 and cholangiocarcinoma = 14) and UK cohorts (healthy = 22 and cholangiocarcinoma = 10) were obtained and modelled using chemometric data analysis. Healthy metabolome disparities between the two distinct populations were primarily related to differences in dietary practices and body composition. Metabolites excreted due to drug treatment were dominant in urine specimens from cholangiocarcinoma patients, particularly in Western individuals. Urine from participants with sporadic (UK) cholangiocarcinoma contained greater levels of a nucleotide metabolite (uridine/pseudouridine). Higher relative concentrations of 7-methylguanine were observed in urine specimens from Thai cholangiocarcinoma patients. The urinary excretion of hippurate and methyladenine (gut microbial-host co-metabolites) showed a similar pattern of lower levels in patients with malignant biliary tumours from both countries. Intrinsic (body weight and body composition) and extrinsic (xenobiotic metabolism) factors were the main causes of disparities between the two populations. Regardless of the underlying aetiology, biological perturbations associated with cholangiocarcinoma urine metabolome signatures appeared to be influenced by gut microbial community metabolism. Dysregulation in nucleotide metabolism was associated with sporadic cholangiocarcinoma, possibly indicating differences in mitochondrial energy production pathways between cholangiocarcinoma tumour subtypes. Mapping population-specific metabolic disparities may aid in interpretation of disease processes and identification of candidate biomarkers.
The AVHRR (Advanced Very High Resolution Radiometer) series of instruments has frequently been used for vegetation studies. The 25+ year record has enabled important time-series studies. Many ...applications use NDVI (Normalized Difference Vegetation Index), or derivatives of it, as their operational variable. However, most AVHRR datasets have incomplete atmospheric correction, because of which there is considerable, but largely unknown, uncertainty in the significance of differences in NDVI and other short wave observations from AVHRR instruments.
The purpose of this study was to gain better understanding of the impact of incomplete or lack of atmospheric correction in widely-used, publicly available processed AVHRR-NDVI long-term datasets. This was accomplished by comparison with atmospherically corrected AVHRR data at AERONET (AErosol RObotic NETwork) sunphotometer sites in 1999. The datasets included in this study are: TOA (Top Of Atmosphere) that is with no atmospheric correction; PAL (Pathfinder AVHRR Land); and an early version of the new LTDR (Long Term Data Record) NDVI. The other publicly available datasets like GIMMS (Global Inventory Modeling and Mapping studies) and GVI (Global Vegetation Index) have atmospheric error budget similar to that of TOA, because no atmospheric correction is used in either processing stream. Of the three datasets, LTDR was found to have least errors (accuracy
=
0.0064 to −
0.024, precision
=
0.02 to 0.037 for clear and average atmospheric conditions) followed by PAL (accuracy
=
−
0.145 to −
0.035, precision
=
0.0606 to 0.0418), and TOA (accuracy
=
−
0.0791 to −
0.112, precision
=
0.0613 to 0.0684). It was also observed that temporal maximum value compositing technique does not cause significant improvement of precision in regions experiencing persistently high AOT (Aerosol Optical Thickness).
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