Geo-Intelligence for Resilient Land-use Planning : A case study on Agriculture in Kenya’s Lower Eastern Region

Context and background:Geo-Intelligence integrates geospatial technologies such as satellite imagery, remote sensing, Geographic Information Systems (GIS), and Artificial Intelligence (AI) to gather, process, and analyze spatial data for decision-making. Goal and Objectives:This study explores the application of Geo-Intelligence for resilient land use planning in Kenya’s Lower Eastern Region, aiming to optimize land potential amidst climate change challenges. The study assesses how Geo-Intelligence can enhance resilient land use planning and augment return on adaptation investments. Methodology:The methodology leverages open-source satellite data and cloud computing to map land potential zones, utilizing 50 years of historical data (1990-2040) to forecast future scenarios. Data was processed using Google Earth Engine and evaluated with a Food Agricultural Organization (FAO)-adjusted methodology, adopting Representative Concentration Pathways (RCP) 4.5 and 8.5. Results:The study reveals significant shifts in the Length of Growing Period (LGP) under future climate scenarios. LGP, a key indicator of agricultural viability, shows both positive and negative impacts under RCP4.5 and RCP8.5. In the highlands, particularly Meru and Tharaka Nithi, LGP is expected to decrease by 20 to 80 days during the March-April-May (MAM) season under RCP4.5, threatening the viability of traditional crops like tea and coffee. Conversely, the lowland drylands are projected to remain relatively stable in LGP during MAM under RCP4.5. Notably, the October-November-December (OND) season shows an increase in LGP by approximately 20 days under both RCP scenarios, offering improved conditions for crop cultivation. This extended growing period could enhance food security and livelihoods in these regions, allowing for multiple cropping cycles and higher agricultural productivity. While highland regions may need to shift to more resilient crops, lowland drylands could benefit from improved growing conditions during the OND season. The land potential reveals an inverse change with an increasing potential in lowlands and decreasing potential in highlands. The study demonstrates the role of Geo-Intelligence in optimizing land use planning for agriculture. Further, the considerations of how climate change will influence land potential is critical in ensuring farmers plan their land according to potential for better gains.