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Throughout the world’s progressive history and exponential population growth, agriculture has been associated with the cultivation of land to satisfy humans’ ample needs for food, fabrics, energy, and a source of income. It plays a fundamental role to sustain livelihood and economic system of any country. For instance, it composes approximately 27.2% of employment rate and contributed to ~ 68% of the global added value reaching USD $3.4 trillion in 2018 (World Bank, 2020; FAO, 2020). Furthermore, in 2018 agriculture contributed to 4% of global gross domestic product (GDP) as well as ~25% for developing countries (World Bank, 2021).
Globally, in 2018, ~ 4.8 billion hectares (ha) was accounted as arable land and ~1.6 billion ha was used for cropland, with irrigated area of 340 million ha (FAO, 2020). Croplands render major shares of the global food supply with approximately 99.7% of all human food calories as well as 80% of all food proteins and fats for human sustainability (Pimentel & Burgess, 2013). However, there is still a global concern on agricultural land and food security due to population growth, the declination in the cropland area per capita, and agricultural land degradation (Alfiky et al., 2012; FAO, 2020).
In these circumstances, about 10 billion ha is needed to sustain global food demands by 2050 (Tilman et al., 2011; Gomiero, 2016). Due to the above situation, there is a pressing need to augment the agriculture production by reclaiming the arable lands and developing new technologies for food production. In this context, several studies (Table 1) analyzed the agricultural land areas and its expansion particularly in developing countries using geospatial technologies based on different approaches.
Table 1. Examples of different methods appearing in the literature used for monitoring and mapping agricultural land areas and its expansion in some developing countries
| Ref | Approaches |
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Basnet & Vodacek, 2015
| Utilized Landsat TM/ETM+ over Lake Kivu region in central Africa using RF algorithm to monitor land cover change and found agricultural land had expanded from 28,730 km2 in 1988 to 34,630 km2 in 2011, with overall accuracies between 90.91- 94.52%. |
|
Butt et al., 2015
| Used TM and SPOT-5 HRG to detect LULC changes over Simly watershed, Pakistan. They applied MLC-based classification and found that the agricultural lands were increased from 1775 ha in 1992 to 4681 ha in 2012. |
|
Singh et al., 2016
| Employed Landsat TM and ETM+ to assess the change in LULC in lower Assam, India. MLC and NDVI-based classification have been applied. The results showed that the area of the agricultural field has increased from 3065.2 km2 in 1990 to 3290.4 km2 in 2014. |
|
Knauer et al., 2017
| Used Landsat (TM, ETM+, OLI) and MODIS data over Burkina Faso, West Africa. They developed an automated framework for delineating the agricultural areas using RF-based classification and found an expansion of agricultural area of 61,100 km2 in 2001 to 116,900 km2 in 2014, with overall accuracies between 91- 92%. |
|
She et al., 2017
| Used TM and OLI for monitoring LULC change over Dongtai County, China. They applied MLC-based classification. The results demonstrated that the agricultural fields increased from 95.7 km2 in 1985 to 198.5 km2 in 2010. |
|
Youssef et al., 2019
| Employed TM, ETM+ and OLI to assess the agriculture activities over Al-Jouf region, Saudi Arabia. They applied NDVI and MLC-based classification. The results showed that the agriculture land expanded from ~37.9 km2 in 1988 to 2734.6 km2 in 2017. |
|
Alawamy et al., 2020
| Used TM, ETM+ and OLI to detect LULC changes over Al-Jabal Al-Akhdar, Libya by applying MLC-based classification. They found that the Orchards and rain-fed agriculture lands gained 4095 ha, and the land under irrigated crops increased by 2266 ha with overall accuracy in between 81- 83%. |
Note: TM—Thematic Mapper; ETM+— Enhanced Thematic Mapper Plus; OLI—Operational Land Imager; SPOT-5 HRG— Satellite Pour l’Observation de la Terre 5 High Resolution Geometric; MODIS—Moderate Resolution Imaging Spectroradiometer; NDVI—Normalized Difference Vegetation Index. RF—Random Forest; LULC—Land use/Land cover; MLC—Maximum Likelihood Classifier.