scholarly journals Constraints and Opportunities for Detecting Land Surface Phenology in Drylands

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Shawn D. Taylor ◽  
Dawn M. Browning ◽  
Ruben A. Baca ◽  
Feng Gao
Keyword(s):  
High Resolution ◽  
Vegetation Cover ◽  
Land Surface ◽  
Scale Effects ◽  
Vegetation Indices ◽  
Simulated Data ◽  
Global Scale ◽  
Land Surface Phenology ◽  
Individual Level ◽  
The Individual

Land surface phenology (LSP) enables global-scale tracking of ecosystem processes, but its utility is limited in drylands due to low vegetation cover and resulting low annual amplitudes of vegetation indices (VIs). Due to the importance of drylands for biodiversity, food security, and the carbon cycle, it is necessary to understand the limitations in measuring dryland dynamics. Here, using simulated data and multitemporal unmanned aerial vehicle (UAV) imagery of a desert shrubland, we explore the feasibility of detecting LSP with respect to fractional vegetation cover, plant functional types, VI uncertainty, and two different detection algorithms. Using simulated data, we found that plants with distinct VI signals, such as deciduous shrubs, can require up to 60% fractional cover to consistently detect LSP. Evergreen plants, with lower seasonal VI amplitude, require considerably higher cover and can have undetectable phenology even with 100% vegetation cover. Our evaluation of two algorithms showed that neither performed the best in all cases. Even with adequate cover, biases in phenological metrics can still exceed 20 days and can never be 100% accurate due to VI uncertainty from shadows, sensor view angle, and atmospheric interference. We showed how high-resolution UAV imagery enables LSP studies in drylands and highlighted important scale effects driven by within-canopy VI variation. With high-resolution imagery, the open canopies of drylands are beneficial as they allow for straightforward identification of individual plants, enabling the tracking of phenology at the individual level. Drylands thus have the potential to become an exemplary environment for future LSP research.

scholarly journals Constraints and Opportunities for Detecting Land Surface Phenology in Drylands

2021 ◽  
Author(s):  
Shawn D Taylor ◽  
Dawn M Browning ◽  
Ruben A Baca ◽  
Feng Gao
Keyword(s):  
Remote Sensing ◽  
Vegetation Cover ◽  
Land Surface ◽  
Satellite Remote Sensing ◽  
Large Scale ◽  
Vegetation Index ◽  
Scale Effects ◽  
Simulated Data ◽  
Area Index ◽  
Land Surface Phenology

Land surface phenology, the tracking of seasonal productivity via satellite remote sensing, enables global scale tracking of ecosystem processes, but its utility is limited in some areas. In dryland ecosystems low vegetation cover can cause the growing season vegetation index (VI) to be indistinguishable from the dormant season VI, making phenology extraction impossible. Here, using simulated data and multi-temporal UAV imagery of a desert shrubland, we explore the feasibility of detecting LSP with respect to fractional vegetation cover, plant functional types, and VI uncertainty. We found that plants with distinct VI signals, such as deciduous shrubs with a high leaf area index, require at least 30-40\% fractional cover on the landscape to consistently detect pixel level phenology with satellite remote sensing. Evergreen plants, which have lower VI amplitude between dormant and growing seasons, require considerably higher cover and can have undetectable phenology even with 100\% vegetation cover. We also found that even with adequate cover, biases in phenological metrics can still exceed 20 days, and can never be 100\% accurate due to VI uncertainty from shadows, sensor view angle, and atmospheric interference. Many dryland areas do not have detectable LSP with the current suite of satellite based sensors. Our results showed the feasibility of dryland LSP studies using high-resolution UAV imagery, and highlighted important scale effects due to within canopy VI variation. Future sensors with sub-meter resolution will allow for identification of individual plants and are the best path forward for studying large scale phenological trends in drylands.

scholarly journals Estimating and Monitoring Land Surface Phenology in Rangelands: A Review of Progress and Challenges

2021 ◽  
Vol 13 (11) ◽  
pp. 2060
Author(s):  
Trylee Nyasha Matongera ◽  
Onisimo Mutanga ◽  
Mbulisi Sibanda ◽  
John Odindi
Keyword(s):  
Land Surface ◽  
Vegetation Index ◽  
Critical Role ◽  
Spectral Characteristics ◽  
Vegetation Indices ◽  
Machine Learning Algorithms ◽  
Special Focus ◽  
Land Surface Phenology ◽  
Spectral Vegetation Indices ◽  
Satellite Sensors

Land surface phenology (LSP) has been extensively explored from global archives of satellite observations to track and monitor the seasonality of rangeland ecosystems in response to climate change. Long term monitoring of LSP provides large potential for the evaluation of interactions and feedbacks between climate and vegetation. With a special focus on the rangeland ecosystems, the paper reviews the progress, challenges and emerging opportunities in LSP while identifying possible gaps that could be explored in future. Specifically, the paper traces the evolution of satellite sensors and interrogates their properties as well as the associated indices and algorithms in estimating and monitoring LSP in productive rangelands. Findings from the literature revealed that the spectral characteristics of the early satellite sensors such as Landsat, AVHRR and MODIS played a critical role in the development of spectral vegetation indices that have been widely used in LSP applications. The normalized difference vegetation index (NDVI) pioneered LSP investigations, and most other spectral vegetation indices were primarily developed to address the weaknesses and shortcomings of the NDVI. New indices continue to be developed based on recent sensors such as Sentinel-2 that are characterized by unique spectral signatures and fine spatial resolutions, and their successful usage is catalyzed with the development of cutting-edge algorithms for modeling the LSP profiles. In this regard, the paper has documented several LSP algorithms that are designed to provide data smoothing, gap filling and LSP metrics retrieval methods in a single environment. In the future, the development of machine learning algorithms that can effectively model and characterize the phenological cycles of vegetation would help to unlock the value of LSP information in the rangeland monitoring and management process. Precisely, deep learning presents an opportunity to further develop robust software packages such as the decomposition and analysis of time series (DATimeS) with the abundance of data processing tools and techniques that can be used to better characterize the phenological cycles of vegetation in rangeland ecosystems.

scholarly journals A high-resolution global dataset of topographic index values for use in large-scale hydrological modelling

2014 ◽  
Vol 11 (6) ◽  
pp. 6139-6166 ◽  
Author(s):  
T. R. Marthews ◽  
S. J. Dadson ◽  
B. Lehner ◽  
S. Abele ◽  
N. Gedney
Keyword(s):  
High Resolution ◽  
Land Surface ◽  
Large Scale ◽  
Global Scale ◽  
Surface Fluxes ◽  
Data Sets ◽  
Topographic Index ◽  
Surface Water Flow ◽  
Land Surface Water ◽  
Compound Topographic Index

Abstract. Modelling land surface water flow is of critical importance for simulating land-surface fluxes, predicting runoff and water table dynamics and for many other applications of Land Surface Models. Many approaches are based on the popular hydrology model TOPMODEL, and the most important parameter of this model is the well-knowntopographic index. Here we present new, high-resolution parameter maps of the topographic index for all ice-free land pixels calculated from hydrologically-conditioned HydroSHEDS data sets using the GA2 algorithm. At 15 arcsec resolution, these layers are 4× finer than the resolution of the previously best-available topographic index layers, the Compound Topographic Index of HYDRO1k (CTI). In terms of the largest river catchments occurring on each continent, we found that in comparison to our revised values, CTI values were up to 20% higher in e.g. the Amazon. We found the highest catchment means were for the Murray-Darling and Nelson-Saskatchewan rather than for the Amazon and St. Lawrence as found from the CTI. We believe these new index layers represent the most robust existing global-scale topographic index values and hope that they will be widely used in land surface modelling applications in the future.

scholarly journals Understanding permaculturist motivations among residents of the “PermaKulturRaum” in Goettingen, Germany: a qualitative analysis

2020 ◽  
Vol 1 (1) ◽  
Author(s):  
Heinrich Petri ◽  
Heiko Faust
Keyword(s):  
Global Scale ◽  
Qualitative Approach ◽  
Childhood Experiences ◽  
Behavioral Determinants ◽  
Individual Level ◽  
Micro Scale ◽  
The Core ◽  
Behavioral Studies ◽  
The Individual

AbstractBy using an inductive qualitative approach, investigating the micro scale, that is, the individual level, we conducted a case study on the PermaKulturRaum in Goettingen, Germany—an experimental space for students to explore alternative lifedesigns. On the supposition that only a radical transition can achieve sustainability on a global scale, we identified permaculture as an appropriate method to achieve this. However, permaculture is not widely spread and largely ignored by scientific research. We started a first attempt to understand the underlying motivations of permaculturists. Using behavioral studies as our theoretical framework, we found out that behavioral determinants, like biospheric values, green-identity, and the intention to act green were extraordinarily high and that the core of their pro-environmental behavior is most likely their strong intrinsic motivation. Regarding the PermaKulturRaum, we could formulate following theses: (1) a comprehensive implementation of permacultural aspects requires an urge for an alternative lifedesign, (2) a radical lifedesign attracts primarily like-minded people, which creates isolated spaces, (3) early childhood experiences or single key moments are important to trigger a pro-environmental interest.

scholarly journals Influence of Varying Solar Zenith Angles on Land Surface Phenology Derived from Vegetation Indices: A Case Study in the Harvard Forest

2021 ◽  
Vol 13 (20) ◽  
pp. 4126
Author(s):  
Yang Li ◽  
Ziti Jiao ◽  
Kaiguang Zhao ◽  
Yadong Dong ◽  
Yuyu Zhou ◽  
...  
Keyword(s):  
Time Series ◽  
Land Surface ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Mean Difference ◽  
Land Surface Phenology ◽  
Harvard Forest ◽  
Ndvi Time Series ◽  
Bidirectional Reflectance Distribution

Vegetation indices are widely used to derive land surface phenology (LSP). However, due to inconsistent illumination geometries, reflectance varies with solar zenith angles (SZA), which in turn affects the vegetation indices, and thus the derived LSP. To examine the SZA effect on LSP, the MODIS bidirectional reflectance distribution function (BRDF) product and a BRDF model were employed to derive LSPs under several constant SZAs (i.e., 0°, 15°, 30°, 45°, and 60°) in the Harvard Forest, Massachusetts, USA. The LSPs derived under varying SZAs from the MODIS nadir BRDF-adjusted reflectance (NBAR) and MODIS vegetation index products were used as baselines. The results show that with increasing SZA, NDVI increases but EVI decreases. The magnitude of SZA-induced NDVI/EVI changes suggests that EVI is more sensitive to varying SZAs than NDVI. NDVI and EVI are comparable in deriving the start of season (SOS), but EVI is more accurate when deriving the end of season (EOS). Specifically, NDVI/EVI-derived SOSs are relatively close to those derived from ground measurements, with an absolute mean difference of 8.01 days for NDVI-derived SOSs and 9.07 days for EVI-derived SOSs over ten years. However, a considerable lag exists for EOSs derived from vegetation indices, especially from the NDVI time series, with an absolute mean difference of 14.67 days relative to that derived from ground measurements. The SOSs derived from NDVI time series are generally earlier, while those from EVI time series are delayed. In contrast, the EOSs derived from NDVI time series are delayed; those derived from the simulated EVI time series under a fixed illumination geometry are also delayed, but those derived from the products with varying illumination geometries (i.e., MODIS NBAR product and MODIS vegetation index product) are advanced. LSPs derived from varying illumination geometries could lead to a difference spanning from a few days to a month in this case study, which highlights the importance of normalizing the illumination geometry when deriving LSP from NDVI/EVI time series.

scholarly journals Object-Based Window Strategy in Thermal Sharpening

2019 ◽  
Vol 11 (6) ◽  
pp. 634 ◽  
Author(s):  
Haiping Xia ◽  
Yunhao Chen ◽  
Jinling Quan ◽  
Jing Li
Keyword(s):  
High Resolution ◽  
Land Surface ◽  
Urban Areas ◽  
Simulated Data ◽  
Window Size ◽  
Landsat 8 ◽  
Factors Affecting ◽  
Object Based ◽  
Land Surface Temperatures ◽  
The Mean

The trade-off between spatial and temporal resolutions has led to the disaggregation of remotely sensed land surface temperatures (LSTs) for better applications. The window used for regression is one of the primary factors affecting the disaggregation accuracy. Global window strategies (GWSs) and local window strategies (LWSs) have been widely used and discussed, while object-based window strategies (OWSs) have rarely been considered. Therefore, this study presents an OWS based on a segmentation algorithm and provides a basis for selecting an optimal window size balancing both accuracy and efficiency. The OWS is tested with Landsat 8 data and simulated data via the “aggregation-then-disaggregation” strategy, and compared with the GWS and LWS. Results tested with the Landsat 8 data indicate that the proposed OWS can accurately and efficiently generate high-resolution LSTs. In comparison to the GWS, the OWS improves the mean accuracy by 0.19 K at different downscaling ratios, in particular by 0.30 K over urban areas; compared with the LWS, the OWS performs better in most cases but performs slightly worse due to the increasing downscaling ratio in some cases. Results tested with the simulated data indicate that the OWS is always superior to both GWS and LWS regardless of the downscaling ratios, and the OWS improves the mean accuracy by 0.44 K and 0.19 K in comparison to the GWS and LWS, respectively. These findings suggest the potential ability of the OWS to generate super-high-resolution LSTs over heterogeneous regions when the pixels within the object-based windows derived via segmentation algorithms are more homogenous.

scholarly journals ChinaCropPhen1km: a high-resolution crop phenological dataset for three staple crops in China during 2000–2015 based on leaf area index (LAI) products

2020 ◽  
Vol 12 (1) ◽  
pp. 197-214 ◽  
Author(s):  
Yuchuan Luo ◽  
Zhao Zhang ◽  
Yi Chen ◽  
Ziyue Li ◽  
Fulu Tao
Keyword(s):  
High Resolution ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Land Surface ◽  
Agricultural System ◽  
Crop Phenology ◽  
Area Index ◽  
Essential Information ◽  
Land Surface Phenology ◽  
Staple Crops

Abstract. Crop phenology provides essential information for monitoring and modeling land surface phenology dynamics and crop management and production. Most previous studies mainly investigated crop phenology at the site scale; however, monitoring and modeling land surface phenology dynamics at a large scale need high-resolution spatially explicit information on crop phenology dynamics. In this study, we produced a 1 km grid crop phenological dataset for three main crops from 2000 to 2015 based on Global Land Surface Satellite (GLASS) leaf area index (LAI) products, called ChinaCropPhen1km. First, we compared three common smoothing methods and chose the most suitable one for different crops and regions. Then, we developed an optimal filter-based phenology detection (OFP) approach which combined both the inflection- and threshold-based methods and detected the key phenological stages of three staple crops at 1 km spatial resolution across China. Finally, we established a high-resolution gridded-phenology product for three staple crops in China during 2000–2015. Compared with the intensive phenological observations from the agricultural meteorological stations (AMSs) of the China Meteorological Administration (CMA), the dataset had high accuracy, with errors of the retrieved phenological date being less than 10 d, and represented the spatiotemporal patterns of the observed phenological dynamics at the site scale fairly well. The well-validated dataset can be applied for many purposes, including improving agricultural-system or earth-system modeling over a large area (DOI of the referenced dataset: https://doi.org/10.6084/m9.figshare.8313530; Luo et al., 2019).

Investiganting Correlation LST and Vegetation Indices Using Landsat Images for the Warmest Month: A Case Study of Iasi County

2018 ◽  
Vol 18 (1) ◽  
pp. 33-40
Author(s):  
Paul Macarof ◽  
Stefan Groza ◽  
Florian Statescu
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Global Scale ◽  
Structural Variations ◽  
Landsat Images ◽  
Change Models

Abstract In this paper is investigating correlation between land surface temperature and vegetation indices (Normalized Difference Vegetation Index - NDVI, Enhanced Vegetation Index 2 - EVI2 and Modified Soil Adjusted Vegetation Index - MSAVI) using Landsat images for august, the warmest month, for study area. Iaşi county is considered as study area in this research. Study Area is geographically situated on latitude 46°48'N to 47°35'N and longitude 26°29'E to 28°07'E. Land surface temperature (LST) can be used to define the temperature distribution at local, regional and global scale. First use of LST was in climate change models. Also LST is use to define the problems associated with the environment. A Vegetation Indices (VI) is a spectral transformation what suppose spatial-temporal intercomparisons of terrestrial photosynthetic dynamics and canopy structural variations. Landsat5 TM, Landsat7 ETM+ and Landsat8 OLI, all data were used in this study for modeling. Landsat images was taken for august 1994, 2006 and 2016. Preprocessing of Landsat 5/7/8 data stage represent that process that prepare images for subsequent analysis that attempts to compensate/correct for systematic errors. It was observed that the “mean” parameter for LST increased from 1994 to 2016 at approximately 5°C. Analyzing the data from VI, it can be assumed that the built-up area increased for the Iasi county, while the area occupied by dense vegetation has decreased. Many researches indicated that between LST and VI is a linear relationship. It is noted that the R2 values for the LST-VI correlations decrease from 1994 (i.g.R2= 0.72 for LST-NDVI) in 2016 (i.g.R2= 0.23 for LST-NDVI). In conclusion, these correlation can be used to study vegetation health, drought damage, and areas where Urban Heat Island can occur.

Sensitivity of the ECMWF Land surface model to vegetation and LU/LC maps

2020 ◽  
Author(s):  
Souhail Boussetta ◽  
Gianpaolo Balsamo ◽  
Emanuel Arduini ◽  
Miguel Nogueira ◽  
Gabriele Arduini ◽  
...  
Keyword(s):  
Land Cover ◽  
Vegetation Cover ◽  
Land Surface ◽  
Land Surface Model ◽  
Global Scale ◽  
Surface Model ◽  
Land Use Land Cover ◽  
Area Index ◽  
Land Cover Map ◽  
Land Cover Maps

<p><span><span>The effects of vegetation and land use/land cover maps on surface energy and carbon fluxes predictions from land surface model are investigated. The model is applied at global scale and a comparison between two configurations using different land cover maps is performed. In the first configuration, the land cover is based on the operational GLCCv1.2 map, in the second the ESA-CCI land cover map is used.</span></span></p><p><span><span>Based on these two configurations, the observation operator that disaggregates the satellite-based leaf area index into high and low vegetation components is also modified to ensure optimal conservation of the observed LAI. The Seasonal variability of the vegetation cover is also investigated by introducing a modified lamber-beer formulation that allows varying the vegetation cover as a function of the LAI. </span></span></p>

scholarly journals Spatiotemporal Comparison and Validation of Three Global-Scale Fractional Vegetation Cover Products

2019 ◽  
Vol 11 (21) ◽  
pp. 2524 ◽  
Author(s):  
Duanyang Liu ◽  
Kun Jia ◽  
Xiangqin Wei ◽  
Mu Xia ◽  
Xiwang Zhang ◽  
...  
Keyword(s):  
Vegetation Cover ◽  
Land Surface ◽  
Large Scale ◽  
High Spatial Resolution ◽  
Remote Sensing Data ◽  
Global Scale ◽  
Glass Product ◽  
Fractional Vegetation Cover ◽  
Temporal Profiles

Fractional vegetation cover (FVC) is an important parameter for many environmental and ecological models. Large-scale and long-term FVC products are critical for various applications. Currently, several global-scale FVC products have been generated with remote sensing data, such as VGT bioGEOphysical product Version 2 (GEOV2), PROBA-V bioGEOphysical product Version 3 (GEOV3) and Global LAnd Surface Satellite (GLASS) FVC products. However, studies comparing and validating these global-scale FVC products are rare. Therefore, in this study, the performances of three global-scale time series FVC products, including the GEOV2, GEOV3, and GLASS FVC products, are investigated to assess their spatial and temporal consistencies. Furthermore, reference FVC data generated from high-spatial-resolution data are used to directly evaluate the accuracy of these FVC products. The results show that these three FVC products achieve general agreements in terms of spatiotemporal consistencies over most regions. In addition, the GLASS and GEOV2 FVC products have reliable spatial and temporal completeness, whereas the GEOV3 FVC product contains much missing data over high-latitude regions, especially during wintertime. Furthermore, the GEOV3 FVC product presents higher FVC values than GEOV2 and GLASS FVC products over the equator. The main differences between the GEOV2 and GLASS FVC products occur over deciduous forests, for which the GLASS product presents slightly higher FVC values than the GEOV2 product during wintertime. Finally, temporal profiles of the GEOV2 and GLASS FVC products show better consistency than the GEOV3 FVC product, and the GLASS FVC product presents more reliable accuracy (R2 = 0.7878, RMSE = 0.1212) compared with the GEOV2 (R2 = 0.5798, RMSE = 0.1921) and GEOV3 (R2 = 0.7744, RMSE = 0.2224) FVC products over these reference FVC data.

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