Integrating Remote Sensing, Landscape Metrics, and Random Forest Algorithm to Analyze Crop Patterns, Factors, Diversity, and Fragmentation in a Kharif Agricultural Landscape

Surajit Banerjee, Tuhina Nandi, Vishwambhar Prasad Sati, Wiem Mezlini, Wafa Saleh Alkhuraiji, Djamil Al-Halbouni () and Mohamed Zhran ()
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Surajit Banerjee: Department of Geography and Resources Management, Mizoram University, Aizawl 796004, India
Tuhina Nandi: Agricultural Insurance Company of India Limited, New Delhi 110023, India
Vishwambhar Prasad Sati: Department of Geography and Resources Management, Mizoram University, Aizawl 796004, India
Wiem Mezlini: Department of Geology, Faculty of Sciences, University of Tunis El Manar, Tunis 2092, Tunisia
Wafa Saleh Alkhuraiji: Department of Geography and Environmental Sustainability, College of Humanities and Social Sciences, Princess Nourah bint Abdulrahman University, P.O. BOX 84428, Riyadh 11671, Saudi Arabia
Djamil Al-Halbouni: Institute for Earth System Science and Remote Sensing, University of Leipzig, 04103 Leipzig, Germany
Mohamed Zhran: Public Works Engineering Department, Faculty of Engineering, Mansoura University, Mansoura 35516, Egypt

Land, 2025, vol. 14, issue 6, 1-23

Abstract: Despite growing importance, agricultural landscapes face threats, like fragmentation, shrinkage, and degradation, due to climate change. Although remote sensing and GIS are widely used in monitoring croplands, integrating machine learning, remote sensing, GIS, and landscape metrics for the holistic management of this landscape remains underexplored. Thus, this study monitored crop patterns using random forest (94% accuracy), the role of geographical factors (such as elevation, aspect, slope, maximum and minimum temperature, rainfall, cation exchange capacity, NPK, soil pH, soil organic carbon, soil type, soil water content, proximity to drainage, proximity to market, proximity to road, population density, and profit per hectare production), diversity, combinations, and fragmentation using landscape metrics and a fragmentation index. Findings revealed that slope, rainfall, temperature, and profit per hectare production emerged as significant drivers in shaping crop patterns. However, anthropogenic drivers became deciding factors during spatial overlaps between crop suitability zones. Rice belts were the least fragmented and highly productive with a risk of monoculture. Croplands with a combination of soybean, black grams, and maize were highly fragmented, despite having high diversity with comparatively less production per field. These diverse fields were providing higher profits and low risks of crop failure due to the crop combinations. Equally, intercropping balanced the nutrient uptakes, making the practice sustainable. Thus, it can be suggested that productivity and diversity should be prioritized equally to achieve sustainable land use. The development of the PCA-weighted fragmentation index offers an efficient tool to measure fragmentation across similar agricultural regions, and the integrated approach provides a scalable framework for holistic management, sustainable land use planning, and precision agriculture.

Keywords: crop classification; crop monitoring; machine learning; fragmentation index; monocropping; climate resilient agriculture (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2025
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