Land suitability assessment methods for developing a European Land Information System for Agriculture and Environment (ELISA)

Abstract. Rahmawaty, Frastika S, Rauf A, Batubara R, Harahap FS. 2020. Land suitability assessment for Lansium domesticum cultivation on agroforestry land using matching method and geographic information system. Biodiversitas 21: 3683-3690. Lansium domesticum is one of the multipurpose tree species (MPTS) and is commonly found on agroforestry lands in Sumatra. This study aimed to evaluate the actual land suitability classes for L. domesticum and to map the potential land suitability for the species using matching method and geographic information system (GIS). The study was conducted in Sei Bingai Sub-district, Langkat District, North Sumatra, Indonesia. A survey was conducted to collect soil samples based on land units. Land unit information was obtained by overlaying soil map, land-use map, and slope map. Land suitability was evaluated based on the matching method and GIS was used to map the distribution of land suitability. The results showed that both the actual and potential land suitability classes based on matching approach for L. domesticum were moderately suitable (S2) which accounted for 88.95% of total land and marginally suitable (S3) which accounted for 11.05%. Availability of water (wa), erosion hazard (eh), root-zone medium (rc), oxygen availability (oa), and nutrient retention (nr) were the dominant limiting factors in this area. The most difficult constraints to manage were root-zone medium and water availability. The results of this study suggest that the development of L. domesticum in Sei Bingai is possible although it requires some land improvements to deal with the limiting factors.

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Study on the Assessment of Land Ecological Suitability in Urban Connecting Band Based on the Method of “Entropy Weight-Geographic Information System”

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.798-799.1178 ◽

2013 ◽

Vol 798-799 ◽

pp. 1178-1181

Author(s):

Zhi Hong Liu ◽

Ling Xu

Keyword(s):

Information System ◽

Geographic Information System ◽

Regional Scale ◽

Geographic Information ◽

Land Suitability ◽

Entropy Weight ◽

Irrigation Area ◽

Suitability Assessment ◽

Land Suitability Assessment ◽

Ecological Suitability

Taking the connecting band of urban integration as study area, based on an appropriate index system, the method of Entropy Weight-Geographic Information System (GIS) was employed to carry out the land suitability assessment at a regional scale. Shenyang Irrigation Area (SIA) was taken as the practical example. It was found that the method was reasonable to assess the land suitability and the assessment results can be used to support land planning in a flexible way.

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The Use of Artificial Neural Networks in a Geographical Information System for Agricultural Land-Suitability Assessment

Environment and Planning A Economy and Space ◽

10.1068/a260265 ◽

1994 ◽

Vol 26 (2) ◽

pp. 265-284 ◽

Cited By ~ 75

Author(s):

F Wang

Keyword(s):

Neural Network ◽

Neural Networks ◽

Pattern Classification ◽

Agricultural Land ◽

Assessment Methods ◽

Land Suitability ◽

Geographical Information ◽

Suitability Assessment ◽

Artificial Neural ◽

Land Suitability Assessment

Agricultural land-suitability assessment involves the analysis of a large variety and amount of physiographic data. Geographical information systems (GISs) may facilitate suitability assessment in data collection. To generate accurate results from the data, appropriate suitability-assessment methods are required. However, the assessment methods which can currently be used with GISs, such as that developed by the United Nations Food and Agriculture Organization and the statistical pattern—classification method, have limitations which may lead to inaccurate assessment. An artificial neural network is an effective tool for pattern analysis. A neural network allows decision rules of greater complexity to be applied in pattern classification. By formulating the land-suitability-assessment problem into a pattern—classification problem, neural networks can be used to achieve results of greater accuracy. In this paper, a neural-network-based method for land-suitability assessment is discussed, and a set of neural networks is described. The integration between the neural networks and a GIS is addressed, and some experimental results are presented and analyzed.

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

Land ◽

10.3390/land10020223 ◽

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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