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Journal of Arid Land
Journal of Arid Land  2025, Vol. 17 Issue (5): 575-589    DOI: 10.1007/s40333-025-0013-y    
Research article     
Forecasting land use changes in crop classification and drought using remote sensing
Mashael MAASHI1, Nada ALZABEN2, Noha NEGM3,*(), Venkatesan VEERAMANI4, Sabarunisha Sheik BEGUM5, Geetha PALANIAPPAN6
1Department of Software Engineering, College of Computer and Information Sciences, King Saud University, Riyadh 11451, Saudi Arabia
2Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh 11671, Saudi Arabia
3Department of Computer Science, Applied College at Mahayil, King Khalid University, Abha 61421, Saudi Arabia
4Department of Civil Engineering, University College of Engineering, Anna University, Ariyalur 621731, India
5Department of Biotechnology, P.S.R. Engineering College, Sivakasi 626140, India
6Department of Electronics and Communication Engineering, School of Engineering and Technology, Dhanalakshmi Srinivasan University, Samayapuram 621112, India
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Abstract  

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.

Key words： land use and land cover (LULC)      crop attributes      drought vulnerability      machine learning models      remote sensing     
Received: 04 September 2024      Published: 31 May 2025
Corresponding Authors: *Noha NEGM (E-mail: negmnoha9@gmail.com)
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Mashael MAASHI
Nada ALZABEN
Noha NEGM
Venkatesan VEERAMANI
Sabarunisha Sheik BEGUM
Geetha PALANIAPPAN
Cite this article:

Mashael MAASHI, Nada ALZABEN, Noha NEGM, Venkatesan VEERAMANI, Sabarunisha Sheik BEGUM, Geetha PALANIAPPAN. Forecasting land use changes in crop classification and drought using remote sensing. Journal of Arid Land, 2025, 17(5): 575-589.

URL:

http://jal.xjegi.com/10.1007/s40333-025-0013-y     OR     http://jal.xjegi.com/Y2025/V17/I5/575

Fig. 1 Flowchart of method in this study. USGS, U.S. Geological Survey; TM, thematic mapper; OLI/TIRS, operational land imager/thermal infrared sensor; SRTM, shuttle radar topography mission; DEM, digital elevation model; NDVI, normalized difference vegetation index; NDWI, normalized difference water index; NDMI, normalized difference moisture index; VCI, vegetation condition index; RF, random forest; LULC, land use and land change. The abbreviations are the same in the following figures.
Data Source Date (yyyy-mm-dd) Resolution (m) Projection
Landsat 4 TM USGS Earth Explorer 1994-06-02 30.0 UTM 13N
Landsat 4 TM USGS Earth Explorer 2004-05-12 30.0 UTM 13N
Landsat 8 OLI/TIRS USGS Earth Explorer 2024-02-23 30.0 UTM 13N
Landsat 8 OLI/TIRS USGS Earth Explorer 2014-01-16 30.0 UTM 13N
SRTM DEM Data USGS Earth Explorer 2000-02-11 30.0 UTM 13N
Road data Google Earth Pro 2024-02-25 2.5 UTM 13N
Table 1 Data used in this study
Fig. 2 Elevation (a), slope (b), proximity to road (c), and proximity to drainage (d) in the Aguascalientes region, central Mexico
Fig. 3 Spatiotemporal changes of NDVI (a), NDWI (b), NDMI (c), and VCI (d) in 2024 in the Aguascalientes region, central Mexico
Fig. 4 Spatiotemporal changes of NDVI (a and b) and VCI (c and d) in 1994 and 2024 in the Aguascalientes region, central Mexico
Fig. 5 Random forest (RF) model result based on LULC classification in 1994 (a), 2004 (b), 2014 (c) and 2024 (d) in the Aguascalientes region, central Mexico
LULC 1994 2004 2014 2024 Trend from 2024 to 1994
(km2)
Water body 3.12 2.96 2.45 2.23 -0.89
Agricultural land 808.78 796.25 758.45 699.65 -109.13
Barren land 306.99 325.14 365.45 374.15 67.16
Grassland 415.10 370.76 349.47 380.818 -34.28
Built-up land 145.74 182.58 201.87 220.85 75.11
Table 2 Land use and land cover (LULC) changes from 1994 to 2024 in the Aguascalientes region, central Mexico
Fig. 6 Spatial distribution of predicted LULC in 2034 in the Aguascalientes region, central Mexico
LULC 2024 2034 Trend from 2034 to 2024
(km2)
Water body 2.23 1.63 -0.60
Agricultural land 699.65 658.14 -41.51
Barren land 374.15 390.19 16.04
Grassland 380.82 390.80 9.98
Built-up land 220.85 236.94 16.09
Table 3 Area changes of predicted LULC from 2024 to 2034 in the Aguascalientes region, central Mexico
Crop type Area (km2) Mean NDVI Mean NDWI Mean NDMI
Common bean 120.35 0.35 0.26 0.37
Corn plant 150.72 0.42 0.27 0.29
Golden pothos 85.43 0.36 0.32 0.34
Maize 145.67 0.39 0.35 0.33
Mixed cropping 88.98 0.49 0.37 0.36
Paddy 67.99 0.52 0.45 0.48
Table 4 Crop types, areas, and normalized difference indices in the Aguascalientes region, central Mexico
Crop type VCI in 1994 VCI in 2024 Drought in 1994 Drought in 2024
Common bean 21.00 28.00 No drought Light drought
Corn plant 29.00 32.00 Light drought Moderate drought
Golden pothos 30.00 39.00 Moderate drought Moderate drought
Maize 25.00 35.00 Light drought Moderate drought
Mixed cropping 30.00 41.00 Moderate drought Severe drought
Paddy 32.00 43.00 Moderate drought Severe drought
Table 5 Vegetation condition index (VCI) of different crop types in 1994 and 2024 in the Aguascalientes region, central Mexico
LULC 1994 2004 2014 2024 2034
(%)
Water body 0.19 0.18 0.15 0.13 0.10
Agricultural land 48.21 47.46 45.21 41.70 39.23
Barren land 18.30 19.38 21.78 22.30 23.26
Grassland 24.74 22.10 20.83 22.70 23.29
Built-up land 8.69 10.88 12.03 13.16 14.12
Table 6 Percentage change of LULC from 1994 to 2024 and predicted LULC in 2034 in the Aguascalientes region, central Mexico
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