您当前的位置: > 详细浏览

Forecasting land use changes in crop classification and drought using remote sensing 后印本

请选择邀稿期刊:
摘要: Challenges in land use and land cover (LULC) include rapid urbanization encroaching on agricultural land, leading to fragmentation and loss of natural habitats. However, the effects of urbanization on LULC of different crop types are less concerned. The study assessed the impacts of LULC changes on agriculture and drought vulnerability in the Aguascalientes region, Mexico, from 1994 to 2024, and predicted the LULC in 2034 using remote sensing data, with the goals of sustainable land management and climate resilience strategies. Despite increasing urbanization and drought, the integration of satellite imagery and machine learning models in LULC analysis has been underutilized in this region. Using Landsat imagery, we assessed crop attributes through indices such as normalized difference vegetation index (NDVI), normalized difference water index (NDWI), normalized difference moisture index (NDMI), and vegetation condition index (VCI), alongside watershed delineation and spectral features. The random forest model was applied to classify LULC, providing insights into both historical and future trends. Results indicated a significant decline in vegetation cover (109.13 km2) from 1994 to 2024, accompanied by an increase in built-up land (75.11 km2) and bare land (67.13 km2). Projections suggested a further decline in vegetation cover (41.51 km2) and continued urban land expansion by 2034. The study found that paddy crops exhibited the highest values, while common bean and maize performed poorly. Drought analysis revealed that mildly dry areas in 2004 became severely dry in 2024, highlighting the increasing vulnerability of agriculture to climate change. The study concludes that sustainable land management, improved water resource practices, and advanced monitoring techniques are essential to mitigate the adverse effects of LULC changes on agricultural productivity and drought resilience in the area. These findings contribute to the understanding of how remote sensing can be effectively used for long-term agricultural planning and environmental sustainability.

版本历史

[V1] 2025-05-19 17:28:18 ChinaXiv:202505.00233V1 下载全文
点击下载全文
预览
许可声明
metrics指标
  •  点击量4292
  •  下载量365
评论
分享