Inicio  /  Agronomy  /  Vol: 14 Par: 2 (2024)  /  Artículo
ARTÍCULO
TITULO

Analysis and Prediction of Land Use/Land Cover Changes in Korgalzhyn District, Kazakhstan

Onggarbek Alipbeki    
Chaimgul Alipbekova    
Gauhar Mussaif    
Pavel Grossul    
Darima Zhenshan    
Olesya Muzyka    
Rimma Turekeldiyeva    
Dastan Yelubayev    
Daniyar Rakhimov    
Przemyslaw Kupidura and Eerassyl Aliken    

Resumen

Changes occurring because of human activity in protected natural places require constant monitoring of land use (LU) structures. Therefore, Korgalzhyn District, which occupies part of the Korgalzhyn State Natural Reserve territory, is of considerable interest. The aim of these studies was to analyze changes in the composition of the land use/land cover (LULC) of Korgalzhyn District from 2010 to 2021 and predict LU transformation by 2030 and 2050. Landsat image classification was performed using Random Forest on the Google Earth Engine. The combined CA-ANN model was used to predict LULC changes by 2030 and 2050, and studies were carried out using the MOLUSCE plugin. The results of these studies showed that from 2010 to 2021, there was a steady increase in the share of ploughable land and an adequate reduction in grassland. It is established that, in 2030 and 2050, this trend will continue. At the same time, there will be no drastic changes in the composition of other land classes. The obtained results can be helpful for the development of land management plans and development policies for the Korgalzhyn District.

 Artículos similares

       
 
Shuaibo Shao, Yuanping Li, Zhongwei Li, Xiaoxiao Ma, Yanqi Zhu, Yuqing Luo, Pumo Cai, Xiaoli Jia, Christopher Rensing and Qisong Li    
This study focused on examining the early stages of tea cultivation (1, 3, and 5 years) in mountainous tea plantations. It specifically aimed to investigate the changes in soil micro-ecology at different locations (inter-row, terrace surfaces, and terrac... ver más
Revista: Agronomy

 
Mykhailo Lohachov, Ryoji Korei, Kazuo Oki, Koshi Yoshida, Issaku Azechi, Salem Ibrahim Salem and Nobuyuki Utsumi    
This article investigates approaches for broccoli harvest time prediction through the application of various machine learning models. This study?s experiment is conducted on a commercial farm in Ecuador, and it integrates in situ weather and broccoli gro... ver más
Revista: Agronomy

 
Meiling Sheng, A-Xing Zhu, Tianwu Ma, Xufeng Fei, Zhouqiao Ren and Xunfei Deng    
Global climate change is a serious threat to food and energy security. Crop growth modelling is an important tool for simulating crop food production and assisting in decision making. Planting date is one of the important model parameters. Larger-scale s... ver más
Revista: Agronomy

 
Xiaobin Mou, Fangxin Wan, Jinfeng Wu, Qi Luo, Shanglong Xin, Guojun Ma, Xiaoliang Zhou, Xiaopeng Huang and Lizeng Peng    
To enhance the utilization of seed-used watermelon peel and mitigate environmental pollution, a hammer-blade seed-used watermelon peel crusher was designed and manufactured, and its structure and working parameters were optimized. Initially, the seed-use... ver más
Revista: Agriculture

 
Haipeng Lin, Xuefeng Song, Fei Dai, Fengwei Zhang, Qiang Xie and Huhu Chen    
Hardness is a critical mechanical property of grains. Accurate predictions of grain hardness play a crucial role in improving grain milling efficiency, reducing grain breakage during transportation, and selecting high-quality crops. In this study, we dev... ver más
Revista: Agriculture