Dry land ecosystems extend over 40 % of the Earth, supporting an estimated 3 billion human population. Thus, quantifying LCLU changes in such ecosystems is essential for achieving sustainable development goals. In this context, this research aimed to examine the LCLU changes in the past three decades (1990 – 2020) in an arid ecosystem of Pakistan, i.e., the Cholisatn desert. Three remote sensing indices, the normalized difference vegetation index (NDVI), normalized difference barren index (NDBaI), and top grain soil index (TGSI) are taken as LCLU representatives to examine their temporal relationship associated with meteorological drought, e.g. the standardized precipitation index (SPI). Moreover, machine learning-based random forest (RF) classification followed by change detection techniques was implemented. Results from RF classifier revealed the applicability of RF in accurately predicting LULC with validation overall accuracy of 0.99. Output of the research revealed an interesting finding where the desert experienced significant LCLU change over the last three decades. The highest vegetation expansion (4.4 %) took place from 2014 to 2020 at the expense of the highest reduction of barren land (-6.3 %). Mann-Kendall trend (MK) and Sen’s slope (SS) analysis showed a significant (P
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