scholarly journals Land Suitability Assessment for Olive Mill Wastewater Disposal by Integrating Multicriteria Decision Support Tools

2021 ◽  
Vol 26 (1) ◽  
pp. e947
Author(s):  
Dimitris Triantakonstantis ◽  
Spyridon Detsikas ◽  
Victor Kavvadias ◽  
Zoi Papadopoulou ◽  
Panagiotis Sparangis ◽  
...  
Keyword(s):  
Decision Support ◽  
Waste Disposal ◽  
Olive Mill Wastewater ◽  
Land Suitability ◽  
Multicriteria Decision ◽  
Suitability Assessment ◽  
The Mediterranean ◽  
Land Suitability Assessment ◽  
Multicriteria Decision Support ◽  
Olive Mill

Many on-site waste disposal systems fail regularly due to problems concerning suitable location and management. A potential environmental threat is inevitably propagated through on-site, off-site, downstream, soil surface and ground water pollution. Soil is a key component of land suitability for waste disposal. This paper presents a Geographic Information Systems (GIS) – based integrated multicriteria decision support system for evaluating the land suitability for olive mill wastewater (OMWW) disposal in the Mediterranean region. Two-scaled classification schemes are developed, the global scheme for Central and South Greece (scale: 1:30.000) and the local scheme for the study area in Xiromero, Aetolia-Acarnania Prefecture, Western Greece, scale 1:10.000. Constrains and factors are included into the spatial decision-making framework, where geostatistical and fuzzy set theory techniques, as well as Analytical Hierarchy Process (AHP) are appropriately integrated. Physical, chemical, and socioeconomic variables adapted to the Mediterranean soil conditions are incorporated as driving forces for the land suitability assessment and the produced maps reveal valuable results for final end-users, such as municipal authorities, agriculturalists, farmers and other national and local stakeholders.

scholarly journals Agricultural Land Suitability Assessment Using Satellite Remote Sensing-Derived Soil-Vegetation Indices

Land ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 223
Author(s):  
Rubaiya Binte Mostafiz ◽  
Ryozo Noguchi ◽  
Tofael Ahamed
Keyword(s):  
Remote Sensing ◽  
Satellite Remote Sensing ◽  
Agricultural Land ◽  
Expert Knowledge ◽  
Vegetation Indices ◽  
Land Suitability ◽  
Assessment System ◽  
Yield Estimation ◽  
Suitability Assessment ◽  
Land Suitability Assessment

Satellite remote sensing technologies have a high potential in applications for evaluating land conditions and can facilitate optimized planning for agricultural sectors. However, misinformed land selection decisions limit crop yields and increase production-related costs to farmers. Therefore, the purpose of this research was to develop a land suitability assessment system using satellite remote sensing-derived soil-vegetation indicators. A multicriteria decision analysis was conducted by integrating weighted linear combinations and fuzzy multicriteria analyses in a GIS platform for suitability assessment using the following eight criteria: elevation, slope, and LST vegetation indices (SAVI, ARVI, SARVI, MSAVI, and OSAVI). The relative priorities of the indicators were identified using a fuzzy expert system. Furthermore, the results of the land suitability assessment were evaluated by ground truthed yield data. In addition, a yield estimation method was developed using indices representing influential factors. The analysis utilizing equal weights showed that 43% of the land (1832 km2) was highly suitable, 41% of the land (1747 km2) was moderately suitable, and 10% of the land (426 km2) was marginally suitable for improved yield productions. Alternatively, expert knowledge was also considered, along with references, when using the fuzzy membership function; as a result, 48% of the land (2045 km2) was identified as being highly suitable; 39% of the land (2045 km2) was identified as being moderately suitable, and 7% of the land (298 km2) was identified as being marginally suitable. Additionally, 6% (256 km2) of the land was described as not suitable by both methods. Moreover, the yield estimation using SAVI (R2 = 77.3%), ARVI (R2 = 68.9%), SARVI (R2 = 71.1%), MSAVI (R2 = 74.5%) and OSAVI (R2 = 81.2%) showed a good predictive ability. Furthermore, the combined model using these five indices reported the highest accuracy (R2 = 0.839); this model was then applied to develop yield prediction maps for the corresponding years (2017–2020). This research suggests that satellite remote sensing methods in GIS platforms are an effective and convenient way for agricultural land-use planners and land policy makers to select suitable cultivable land areas with potential for increased agricultural production.

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