Remote sensing-based detection of tea land losses: The case of Lahijan, Iran

• Published in: Remote sensing applications 2021-08, Vol.23, p.100568, Article 100568
• Main Authors: Rahimi-Ajdadi, Fatemeh, Khani, Mahdi
• Format: Article
• Language: English
• Subjects: Classification; Land change modeler; Landsat; Neural network; Supervized classification
• ISSN: 2352-9385; 2352-9385
• DOI: 10.1016/j.rsase.2021.100568

Accurate change detection of cropland area and their spatial distributions are important for cropland monitoring, food security, and sustainable development. Tea as a strategic crop in northern Iran, has faced many challenges in the coming decades which has led to a decrease in its area under cultivation. The present study aims to identify changes in tea land area located in central district of Lahijan, Gilan, Iran by using Landsat 5 TM, Landsat 7 ETM+ and Landsat 8 OLI/TIRS images. First, pre-processing stage was performed on satellite images including atmospheric, radiometric and geometric corrections. The supervized neural network classification method was employed, resulting in three classification maps with overall accuracies of 94.82, 95.36 and 97.84% and kappa coefficients of 96.22, 97.91, and 98.42 for 1999, 2011, and 2019, respectively which these were satisfactory. The images were categorized into four different classes, namely tea land, agriculture, forest and urban area. The results of Land Change Modeler showed that during 1999–2019, tea and agricultural lands decreased by 23.46% (2142 ha) and 41.71% (3872 ha). The loss trend of tea area in 1999–2011 has been more remarkable with a decrease of 1943 ha (21.28%). This downward trend continued with a slower slope (2.77% equal to 199 ha). Urbanization with growth rate of 381.71% consumed 2096 ha of tea land. Further, forestland contributed 251 ha to tea land losses. Reversely, the contribution of agricultural land to net change of tea area were positive so that 403 ha in 1999–2011 and 74 ha in 2011–2019 from cropland were converted to tea land. The paper highlights the advantage of digital change detection techniques for policy makers to take appropriate decision to return the situation and to conserve the productive agricultural lands.