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1 World humidity classes (World Atlas of Desertification, United Nations Environment Programme 1997). Drylands are classified into four ranges of the Aridity Index (AI, the ratio between the mean annual precipitation and the mean annual potential evaporation): Hyperarid, AI < 0.05 (7.5 % of global land area); Arid, 0.05 < AI < 0.20 (12.1 % land); Semi-Arid, 0.20 < AI < 0.50 (17.7 % land); Dry subhumid, 0.50 < AI < 0.65 (9.9 % land)  

1 World humidity classes (World Atlas of Desertification, United Nations Environment Programme 1997). Drylands are classified into four ranges of the Aridity Index (AI, the ratio between the mean annual precipitation and the mean annual potential evaporation): Hyperarid, AI < 0.05 (7.5 % of global land area); Arid, 0.05 < AI < 0.20 (12.1 % land); Semi-Arid, 0.20 < AI < 0.50 (17.7 % land); Dry subhumid, 0.50 < AI < 0.65 (9.9 % land)  

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This chapter summarizes approaches to the detection of dryland vegetation change and methods for observing spatio-temporal trends from space. An overview of suitable long-term Earth Observation (EO) based datasets for assessment of global dryland vegetation trends is provided and a status map of contemporary greening and browning trends for global...

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... NDVI has been used extensively for analysis of dryland vegetation [50] as NDVI saturation that can occur in densely vegetated areas is rarely a concern in drylands [51]. NDVI was here used as a proxy for the vegetation condition during the period of analysis. ...
Article
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In Ethiopia land degradation through soil erosion is of major concern. Land degradation mainly results from heavy rainfall events and droughts and is associated with a loss of vegetation and a reduction in soil fertility. To counteract land degradation in Ethiopia, initiatives such as the Sustainable Land Management Programme (SLMP) have been implemented. As vegetation condition is a key indicator of land degradation, this study used satellite remote sensing spatiotemporal trend analysis to examine patterns of vegetation between 2002 and 2018 in degraded land areas and studied the associated climate-related and human-induced factors, potentially through interventions of the SLMP. Due to the heterogeneity of the landscapes of the highlands of the Ethiopian Plateau and the small spatial scale at which human-induced changes take place, this study explored the value of using 30 m resolution Landsat data as the basis for time series analysis. The analysis combined Landsat derived Normalised Difference Vegetation Index (NDVI) data with Climate Hazards group Infrared Precipitation with Stations (CHIRPS) derived rainfall estimates and used Theil-Sen regression, Mann-Kendall trend test and LandTrendr to detect changes in NDVI, rainfall and rain-use efficiency. Ordinary Least Squares (OLS) regression analysis was used to relate changes in vegetation directly to SLMP infrastructure. The key findings of the study are a general trend shift from browning between 2002 and 2010 to greening between 2011 and 2018 along with an overall greening trend between 2002 and 2018. Significant improvements in vegetation condition due to human interventions were found only at a small scale, mainly on degraded hillside locations, along streams or in areas affected by gully erosion. Visual inspections (based on Google Earth) and OLS regression results provide evidence that these can partly be attributed to SLMP interventions. Even from the use of detailed Landsat time series analysis, this study underlines the challenge and limitations to remotely sensed detection of changes in vegetation condition caused by land management interventions aiming at countering land degradation.
... Trend analyses are often performed using a pixelwise linear model. They provide the slope coefficient of an ordinary least squares regression (OLS) between the values over time and a linear time-series (Fensholt et al., 2015). However, the outcome of a regression analysis is influenced by several factors related to the data (data choice, data period and quality), the choice of vegetation metric and the nonlinearity of processes in relation to the predicting variable used as the trend indicator. ...
... Photonic structures which are both highly reflective in the solar spectrum (below 2.5 µm) and highly emissive in the infrared atmospheric transmission window (8-14 µm) can suppress solar heating and remove heat through infrared (IR) radiation to cool throughout the diurnal cycle. Radiative cooling structures are particularly applicable in regions with low humidity, where the atmosphere is most transparent, such as Mexico, northern and southern Africa, the Middle East, Australia, India, parts of North and South America, and areas of northern Asia [2,7]. The primary requirement of a radiative cooler is to provide enough cooling power at a specified temperature to more than offset its own parasitic heating, thus providing net cooling, and is constrained by the limited bandwidth of the infrared atmospheric transmission window and stringent reflectivity requirements in the solar spectrum. ...
Article
Research on radiative cooling has attracted recent widespread interest owing to the potential for low-cost passive structures to enable large-scale thermal energy management. Using a generalized effective medium theory, we theoretically show that two-layer films comprised of SiO₂ and Si₃N₄ nanoparticle layers on an Ag back reflector exhibit superior radiative cooling compared to single-layer or two-layer dense solid films, and can outperform other reported designs. The performance enhancement is a result of the ability to tune the nanoparticle fill fraction, which improves the spectral match between emissivity of this structure and the atmospheric transmission window. We also propose a standardized method for comparing the performance of radiative cooling structures reported by the research community.
... (NIR) wavelengths (0.8-1.0 µm), where chlorophyll in green vegetation reflects more NIR compared to red wavelength (Fensholt et al. 2015). Other than chlorophyll content, leaf angle distribution, dead plant material, sun-target-sensor geometric configurations, canopy and soil surface also influence red-NIR reflectance (Eklundh and Jönsson 2015). ...
... Not only there is an upward trend in mean and seasonal series, but trends in peak EVI are also positive (Figure 9(a)). Globally, Fensholt et al. (2012Fensholt et al. ( , 2015 have reported greening trend in semi-arid zones between 1982 and 2007. Qamer et al. (2016) also observed reverse in deforestation patterns especially in upper Dir (northwest of KP). ...
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In this study, we analyzed time series of a vegetation index to identify land-cover/land-use changes in HHK mountain regions administered by Pakistan. Monthly MODIS (Moderate Resolution Imaging Spectroradiometer) EVI (Enhanced Vegetation Index) series were decomposed to retrieve changes in long-term mean, peaking magnitude and seasonal characteristics. Resulting linear trend patterns were used as a baseline to map syndromes over the study area. To address non-linear changes, inter-annual variation (IAV) and short-term variation (STV) was also computed. Distributed lag-models (DLM) were used to determine significant correlation between GIMMS (Global Inventory Monitoring and Mapping) NDVI (Normalized Difference Vegetation Index) and rainfall as an additional syndrome to highlight climate sensitive regions. Our results indicate that land use is mainly controlled by two factors: elevation and river network. Based on that, there is a particular spatial distribution of agricultural intensification because of deforestation, forest degradation from unsustainable use, forest regeneration and human settlements near rivers. Most of the trends observed showed a persistent greening pattern compared to small groups of pixels with negative trends. Outcomes of DLM do not provide plausible links between rain and forest biomass. It does however suggest a positive response of crops to rainfall events in the arid zones of the study area. High short-term fluctuation in EVI residuals occurred in areas that experienced considerable land modification and where land-use patterns are not stable. IAV was high in regions around rivers and water works and its impact on forest dynamics could not be substantiated. Land change patterns described here can be used by decision makers for forest restoration programmes.
... The MODIS 8-day composite land surface reflectance product (MOD09Q1, collection 6, spatial resolution 250 m) was used to calculate the NDVI during 2017 (Vermote et al., 2002). MOD09Q1 provides adequate observations for extracting Sahelian vegetation phenology, as the product minimizes the impacts from viewing geometry, cloud cover and aerosol loading and retains at the same time a suitable temporal resolution (Fensholt et al., 2015). ...
Article
Remote sensing-derived cropland products have depicted the location and extent of agricultural lands with an ever increasing accuracy. However, limited attention has been devoted to distinguishing between actively cropped fields and fallowed fields within agricultural lands, and in particular so in grass fallow systems of semi-arid areas. In the Sahel, one of the largest dryland regions worldwide, crop-fallow rotation practices are widely used for soil fertility regeneration. Yet, little is known about the extent of fallow fields since fallow is not explicitly differentiated within the cropland class in any existing remote sensing-based land use/cover maps, regardless of the spatial scale. With a 10 m spatial resolution and a 5-day revisit frequency, Sentinel-2 satellite imagery made it possible to disentangle agricultural land into cropped and fallow fields, facilitated by Google Earth Engine (GEE) for big data handling. Here we produce the first Sahelian fallow field map at a 10 m resolution for the baseline year 2017, accomplished by designing a remote sensing driven protocol for generating reference data for mapping over large areas. Based on the 2015 Copernicus Dynamic Land Cover map at 100 m resolution, the extent of fallow fields in the cropland class is estimated to be 63% (403,617 km²) for the Sahel in 2017. Similar results are obtained for five contemporary cropland products, with fallow fields occupying 57–62% of the cropland area. Yet, it is noted that the total estimated area coverage depends on the quality of the different cropland products. The share of cropped fields within the Copernicus cropland area is found to be higher in the arid regions (200–300 mm rainfall) as compared to the semi-arid regions (300–600 mm rainfall). The woody cover fraction within cropped and fallow fields is found to have a reversed pattern between arid (higher woody cover in cropped fields) and semi-arid (higher woody cover in fallow fields) regions. The method developed, using cloud-based Earth Observation (EO) data and computation on the GEE platform, is expected to be reproducible for mapping the extent of fallow fields across global croplands. Future applications based on multi-year time series is expected to improve our understanding of crop-fallow rotation dynamics in grass fallow systems being key in teasing apart how cropland intensification and expansion affect environmental variables, such as soil fertility, crop yields and local livelihoods in low-income regions such as the Sahel. The mapping result can be visualized via a web viewer (https://buwuyou.users.earthengine.app/view/fallowinsahel).
... Analysis of remote imagery is becoming the method of choice to understand how human and environmental factors influence LULC and vegetative productivity in areas such as the IBB (Fensholt et al 2012, Sohl and Sleeter 2012, Wulder et al 2018. Although to our knowledge, this is the first such direct comparison of the spatial and temporal dynamics of LULC and NPP in the upper basin in China and the lower basin in Kazakhstan, portions of the IBB have been subjected to similar analysis in the past. ...
Article
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Using remotely sensed data, we documented changes in land use/land cover (LULC) from 1995 to 2015 and net primary productivity (NPP) from 2000 to 2014 in Central Asia's 415,048 km2 Ili-Balkhash Basin (IBB). This basin, which is shared by China and Kazakhstan, is in the midst of significant socioeconomic transformation due to the collapse of the Soviet Union, the emergence of Kazakhstan, and the economic rise of China. Grazing land covered 82.4% of the IBB in 1995; water bodies and bare land were the only other LULC categories occupying more than 3% of the basin's area. Changes in LULC were most evident on the Chinese side of the border, where crop production areas increased and grazing areas decreased between 1995 and 2015. The area of irrigated cropland in China grew by nearly 30%, primarily in the upper Tekes River valley and along the Ili River near the border with Kazakhstan. In contrast, the irrigated lands in Kazakhstan shifted geographically during this period, but the extent did not change. Expansion of wetlands and permanent water bodies, which occupied 2.1 to 2.9% and 4.6 to 4.7%, respectively, of the IBB in 1995 and 2015, was associated with accretion of Lake Balkhash and Kapchagai Reservoir in Kazakhstan and the construction of new reservoirs in China. NPP of the basin approached 700 g C m−2/year in a few areas but was generally less than half this level and characterized by a declining trend except in highly productive irrigated areas of dense, stable vegetation. NPP decreases of more than −10 g C m−2/year were apparent in mountainous and upland areas, as well as broad band of grassland and cropland in Kazakhstan. Areas surrounding Lake Balkhash were characterized by unstable to moderately stable, often sparse vegetation.
... A growing community in remote sensing is using the strengths of the temporal component, e.g. [8,9,10]. In the future, further phenological pattern profiles for different types of measures and its temporal changes need to be found, which could be used in the system of environmental monitoring and measure control. ...
Conference Paper
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On-site controlling of subsidy measures in rural areas are cost-and time-intensive for authorities and can only be done for a small subset of subsidized areas within short time frames. The use of high resolution satellite image time series covers many applications in the field of agricultural monitoring. We present a method for an automated control system based on vegetation indices from Sentinel-2 and Sentinel-1 image time series. We calculated a catch crop phenological reference profile (TS_ref) from a subset of sampling sites in the German federal state Rhineland-Palatinate and compared it with observed Sentinel time series (TS_obs) via Pearson's correlation coefficient. The utilization of the classification results reduces on-site controls noticeably and helps to define areas with higher and lower control demands.
... Satellite data provides information at the resolution of the sensor, which can be relatively coarse (up to 25 km), and interpretations of the data at sub-pixel levels are challenging. The most widely used remotely sensed vegetation index is the NDVI, providing a measure of canopy greenness that is related to the quantity of standing biomass (Bai et al. 2008;de Jong et al. 2011;Fensholt et al. 2012;Andela et al. 2013;Fensholt et al. 2015;Le et al. 2016) (Figure 3.5). A main challenge associated with NDVI is that although biomass and productivity are closely related in some systems, they can differ widely when looking across land uses and ecosystem types, giving a false positive in some instances (Pattison et al. 2015;Aynekulu et al. 2017). ...
... Most common methods are based on the interpretation of vegetation productivity indicators (Fensholt et al., 2015a) such as the Normalized Difference Vegetation Index (NDVI) (Tucker, 1979). The NDVI integrated over the growing season (sometimes referred to as cyclic fraction (Ivits et al., 2013) or small integral (Jönsson and Eklundh, 2004)) has been shown to be closely related to vegetation productivity or net primary production (NPP) in drylands Fensholt et al., 2015b;Olsen et al., 2015;Prince, 1991;Tagesson et al., 2015). Thus, the linear trend in vegetation indices has been used to broadly study changes in vegetation productivity over time de Jong et al., 2011;Eklundh and Olsson, 2003;Herrmann et al., 2005;Heumann et al., 2007;Higginbottom and Symeonakis, 2014;Olsson et al., 2005), though with limited capacity to separate and attribute drivers to human or climate induced influence on the state of dryland vegetation. ...
... Long-term, consistent satellite data can be used to monitor and quantify intra-and inter-annual trends in vegetation cycles (e.g. Villa et al., 2012;Fensholt et al., 2015). A large corpus of scientific literature on remote sensing applied to land surface phenology has been accumulated in the last decade, focusing particularly on terrestrial biomes (e.g. ...
Article
The improved spatial and temporal resolution of latest-generation Earth Observation missions, such as Landsat 8 and Sentinel-2, has increased the potential of remote sensing for mapping land surface phenology in inland water systems. The ability of a time series of medium-resolution satellite data to generate quantitative information on macrophyte phenology was examined, focusing on three temperate shallow lakes with connected wetlands in Italy, France, and Romania. Leaf area index (LAI) maps for floating and emergent macrophyte growth forms were derived from a semi-empirical regression model based on the best-performing spectral index, with an error level of 0.11 m2 m−2. Phenology metrics were computed from LAI time series using TIMESAT to analyze the seasonal dynamics of macrophyte spatial distribution patterns and species-dependent variability. Particular seasonal patterns seen in the autochthonous and allochthonous species across the three study areas related to local ecological and hydrological conditions. How characteristics of the satellite dataset (cloud cover threshold, temporal resolution, and missing acquisitions) influenced the phenology metrics obtained was also assessed. Our results indicate that, with a full-resolution time series (5-day revisit time), cloud cover introduced a bias in the phenology metrics of less than 2 days. Even when the temporal resolution was reduced to 15 days (like the Landsat revisit time) the timing of the start and the peak of macrophyte growth could still be mapped with an error of no more than 2–3 days.