The Massive Remote Sensing Data Organization and Management Strategies

Modelling land degradation vulnerability (LDV) in the newly-reclaimed desert oases is a key factor for sustainable agricultural production. In the present work, a trial for usingremote sensing data, GIS tools, and Analytic Hierarchy Process (AHP) was conducted for modeling and evaluating LDV. The model was then applied within 144,566 ha in Farafra, an inland hyper-arid Western Desert Oases in Egypt. Data collected from climate conditions, geological maps, remote sensing imageries, field observations, and laboratory analyses were conducted and subjected to AHP to develop six indices. They included geology index (GI), topographic quality index (TQI), physical soil quality index (PSQI), chemical soil quality index (CSQI), wind erosion quality index (WEQI), and vegetation quality index (VQI). Weights derived from the AHP showed that the effective drivers of LDV in the studied area were as follows: CSQI (0.30) > PSQI (0.29) > VQI (0.17) > TQI (0.12) > GI (0.07) > WEQI (0.05). The LDV map indicated that nearly 85% of the total area was prone to moderate degradation risks, 11% was prone to high risks, while less than 1% was prone to low risks. The consistency ratio (CR) for all studied parameters and indices were less than 0.1, demonstrating the high accuracy of the AHP. The results of the cross-validation demonstrated that the performance of ordinary kriging models (spherical, exponential, and Gaussian) was suitable and reliable for predicting and mapping soil properties. Integrated use of remote sensing data, GIS, and AHP would provide an effective methodology for predicting LDV in desert oases, by which proper management strategies could be adopted to achieve sustainable food security.

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Utilization of Multi-Temporal Microwave Remote Sensing Data within a Geostatistical Regionalization Approach for the Derivation of Soil Texture

Remote Sensing ◽

10.3390/rs12162660 ◽

2020 ◽

Vol 12 (16) ◽

pp. 2660

Author(s):

Philip Marzahn ◽

Swen Meyer

Keyword(s):

Remote Sensing ◽

Soil Texture ◽

Land Surface ◽

Temporal Frequency ◽

Management Strategies ◽

Remote Sensing Data ◽

Microwave Remote Sensing ◽

Test Site ◽

Soil Parameters ◽

Sensing Data

Land Surface Models (LSM) have become indispensable tools to quantify water and nutrient fluxes in support of land management strategies or the prediction of climate change impacts. However, the utilization of LSM requires soil and vegetation parameters, which are seldom available in high spatial distribution or in an appropriate temporal frequency. As shown in recent studies, the quality of these model input parameters, especially the spatial heterogeneity and temporal variability of soil parameters, has a strong effect on LSM simulations. This paper assesses the potential of microwave remote sensing data for retrieving soil physical properties such as soil texture. Microwave remote sensing is able to penetrate in an imaged media (soil, vegetation), thus being capable of retrieving information beneath such a surface. In this study, airborne remote sensing data acquired at 1.3 GHz and in different polarization is utilized in conjunction with geostatistics to retrieve information about soil texture. The developed approach is validated with in-situ data from different field campaigns carried out over the TERENO test-site “North-Eastern German Lowland Observatorium”. With the proposed approach a high accuracy of the retrieved soil texture with a mean RMSE of 2.42 (Mass-%) could be achieved outperforming classical deterministic and geostatistical approaches.

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Collaborative Geospatial Web Services for Multi-Dimension

E-Strategies for Resource Management Systems ◽

10.4018/978-1-61692-016-6.ch013 ◽

2010 ◽

pp. 212-231

Author(s):

Chunyang Hu ◽

Yongwang Zhao ◽

Dianfu Ma

Keyword(s):

Remote Sensing ◽

Web Services ◽

Data Visualization ◽

Data Model ◽

Remote Sensing Data ◽

Geospatial Data ◽

Data Sets ◽

Visualization System ◽

Sensing Data ◽

Organization And Management

Satellite remote sensing imagery data is an important Geospatial data which is playing an increasingly important role in many applications such as crisis management, military activities and government decision-making. However, it will continue to be a great challenge to organize and manage these multi-dimension massive remote sensing data for collaborative visualization services in Internet environment. In this chapter the authors proposed a global hierarchical data model of massive multi-dimension remote sensing data based on tiling and pyramid technologies for the organization and management of multi-source and multi-scale remote sensing data. The authors implemented a collaborative Geospatial data visualization system based on their proposed storage structure of data model using Web Services, WSRF and Web2.0 technologies. Finally, the authors evaluated the prototype system with real data sets, which demonstrated the high performance data visualization in their system.

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Complementary Differences in Primary Production and Phenology among Vegetation Types Increase Ecosystem Resilience to Climate Change and Grazing Pressure in an Iconic Mediterranean Ecosystem

Remote Sensing ◽

10.3390/rs13193920 ◽

2021 ◽

Vol 13 (19) ◽

pp. 3920

Author(s):

Juan Miguel Giralt-Rueda ◽

Luis Santamaria

Keyword(s):

Climate Change ◽

Remote Sensing ◽

Primary Production ◽

Management Strategies ◽

Remote Sensing Data ◽

Grazing Pressure ◽

Plant Production ◽

Vegetation Types ◽

Ecosystem Resilience ◽

Sensing Data

Plant primary production is a key factor in ecosystem dynamics. In environments with high climatic variability such as the Mediterranean region, plant primary production shows strong seasonal and inter-annual fluctuations, which both drive and interplay with herbivore grazing. Knowledge on the responses of different vegetation types to the variability in both rainfall and grazing pressure by wild and domestic ungulates is a necessary starting point for the sustainable management of these ecosystems. In this work we combine a 15 year series of remote sensing data on plant production (NDVI) with meteorological (daily precipitation data) and ungulate abundance (annual counts of four species of wild and domestic ungulates: red deer, fallow deer, cattle, and horses) in an iconic protected area (the Doñana National Park, SW Spain) to (i) estimate the impact of intra- and inter-annual variation in rainfall and herbivore pressure on primary production, for each of four main vegetation types; and (ii) evaluate the potential impact of different policy (i.e., herbivore management) strategies under expected climate change scenarios. Our results show that the production of different vegetation types differed strongly in their responses to phenology (a surrogate of the effect of climatology on vegetation development), water availability (rainfall accumulated until the phenological peak), and grazing pressure. Although the density of domestic ungulates shows a linear, negative effect on the primary production of three of the four vegetation types, differences in primary production and phenology among vegetation types increase ecosystem resilience to both climatological variability and grazing pressure. Such resilience may, however, be reduced under the conditions predicted by climate change models, if the moderate predicted reduction in rainfall levels combines with moderate to high densities of domestic ungulates, resulting in important reductions in primary production that may compromise plant regeneration, leading to irreversible degradation. New management strategies taking advantage of habitat heterogeneity and phenological alternation, more flexible stocking rates, and the redistribution of management units should be considered to mitigate these effects. The use of available remote sensing data and techniques in combination with statistical models represents a valuable tool for developing, monitoring, and refining such strategies.

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Estimating Crop Coefficients using multitemporal Sentinel-2 remote sensing data to estimate actual evapotranspiration of a citrus orchard

10.5194/egusphere-egu21-4650 ◽

2021 ◽

Author(s):

Matteo Ippolito ◽

Dario De Caro ◽

Mario Minacapilli ◽

Giuseppe Ciraolo ◽

Giuseppe Provenzano

Keyword(s):

Remote Sensing ◽

Water Stress ◽

Management Strategies ◽

Remote Sensing Data ◽

Crop Coefficient ◽

Actual Evapotranspiration ◽

Irrigation Systems ◽

Citrus Orchard ◽

Sensing Data ◽

Sentinel 2

Estimation of evapotranspiration using the crop coefficient method is one of the most common approaches for irrigation water management. The crop coefficient, Kc, can be estimated as the ratio between maximum crop evapotranspiration, ETmax, and reference evapotranspiration, ET0. However, in the last few decades, many correction factors have been proposed to split Kc into separate coefficients to account for water stress conditions, as well as to estimate separately crop transpiration and soil evaporation. Furthermore, the remote sensing data collected from various satellite platforms have shown their full potential in mapping various vegetation indices (VI), which can be directly related to the spatio-temporal variability of Kc values. Despite various VI-Kc relationships have been proposed in the past years, only recently, thanks to the availability of new sensors with higher temporal and spatial resolutions, it is possible to retrieve new relationships able to follow the variability of the crop coefficient during the different crop phenological stages.This study aimed at identifying a VI-Kc relationship suitable to map actual evapotranspiration of a citrus orchard based on an extended time-series of NDVI images retrieved by Sentinel-2 platform and combined with a set of field micro-meteorological measurements.The experiments were carried out during 2019 and 2020 in a commercial citrus orchard (C. reticulata cv. Tardivo di Ciaculli) with tree spacing of 5 x 5 m, located near the city of Palermo, Italy, in which different irrigation systems and management strategies were applied in three different portions of the orchard. In particular, the first portion was irrigated with a traditional micro-sprinkler system (TI) whereas the other two with a subsurface drip system maintained under full irrigation (FI) and deficit irrigation (DI) applied during the phase II of fruit growth (from 1-July to 20-August). The orchard was equipped with a standard weather station (WS) and an Eddy Covariance (EC) tower to acquire, every half-an-hour, precipitation, air temperature and relative humidity, wind speed and direction, global and net solar radiation and, finally, sensible and latent heat fluxes. During the entire period, a weekly dataset of Sentinel-2 images characterized by a spatial resolution of 10 m was acquired and processed in a GIS environment to obtain the spatial and temporal distribution of NDVI. Using the data acquired in 2019, a functional relationship between Kc and NDVI was calibrated accounting only for those periods in which the crop was maintained in the absence of water stress. The values &#8203;&#8203;of Kc were determined as the ratio between actual daily ET measured by the EC tower and reference Penman-Monteith ET0 obtained as indicated by the Food and Agriculture Organization of the United Nations. The procedure was then validated with the data recorded in 2020, by comparing estimated crop ET &#8203;and the corresponding measured by the EC tower. The comparative analysis indicated root-mean-square-error and mean-bias-error associated with estimated ET of about 0.5 mm/d and 0.2 mm/d, respectively. Finally, based on the NDVI maps it was possible to derive the spatial variability of Kc and actual ET, under the different irrigation systems and management strategies.

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