Study on the Classification and Change Detection Methods of Drylands in Arid and Semi-Arid Regions

Zhu, Z. A.; Zhang, Zengxiang; Zuo, Lijun; Pan, Tao; Zhao, Xiaoli; Wang, Xiao; Sun, Fubao; Xu, Jilian; Liu, Ziyuan

doi:10.3390/rs14051256

Cited by 7 publications

(4 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…On the contrary, in dryland agriculture, frequent droughts cause precipitation to fall short of demand, coupled with the limitation of crop planting and growth period change, resulting in a smaller PUE in drylands compared with paddy fields. However, during the 13th Five‐Year Plan period, with the development of dryland agricultural science and technology, the PUE of drylands has been greatly improved (Xiao et al, 2023; Zhu et al, 2022). Woodlands is located in humid areas with abundant precipitation, and most of them are tree vegetation with large continuous area and high degree of depression, with strong photosynthesis, which dissipates a large amount of water, so the PUE shows a high level (Sancho‐Knapik et al, 2022).…”

Section: Discussionmentioning

confidence: 99%

Distribution, trends and drivers of precipitation use efficiency in the loess plateau

Shi,

Zhang,

Ding

et al. 2024

Hydrological Processes

View full text Add to dashboard Cite

Against the background of climate change, the global carbon and water cycle has undergone significant changes, and it is of great significance to explore the interrelationships of the carbon and water cycles in different regions to cope with future climate change. In this study, based on gross primary productivity (GPP) and precipitation (PRE) data, the precipitation use efficiency (PUE) of the Loess Plateau (LP) was calculated, and the Sen trend analysis and Mann‐Kendall test were used to analyse the temporal and spatial variation characteristics of PUE, as well as the first‐order difference method was used to derive the relative contributions to quantify the impact of vegetation growth and meteorological factors on PUE. The results show that (1) from 2001 to 2018, the spatial distribution pattern of vegetation PUE exhibited an increasing trend from northwest to southeast. On the time scale, the multi‐year average value of PUE was 1.17 gC m−2 mm−1, showing an overall upward trend. Among the different land cover types, paddy fields had the highest PUE level, and sparse grassland had the lowest; (2) LAI had the highest relative contribution to vegetation PUE, followed by temperature (Temp), net radiation of the ground surface (RN), specific humidity (Shum), and wind speed (WS). LAI contributes positively to 88.9% of the area. RN positively influences high‐altitude areas, while Shum had a larger area with a negative contribution. The contribution of Temp increases from northwest to southeast, and WS has balanced positive and negative contributions; (3) LAI is the dominant factor for the spatial and temporal variation of PUE in the LP, and the size of the areas where different meteorological factors dominate the changes of PUE are as follows: WS > Shum>RN > Temp. At high altitudes, the dominant meteorological driver is WS, while at low altitudes, the dominant climate driver is Shum. This study is of guiding significance for the ecological restoration and management of the LP, and it can also provide a scientific basis for the improvement of ecosystems and the sustainable management of water resources in the context of global climate change.

show abstract

Section: Discussionmentioning

confidence: 99%

Distribution, trends and drivers of precipitation use efficiency in the loess plateau

Shi,

Zhang,

Ding

et al. 2024

Hydrological Processes

View full text Add to dashboard Cite

show abstract

“…Training and validation data were randomly selected across each of the images over the areas of interest (AOIs). Random sampling is extensively used for landuse/landcover mapping, as reported in the literature [12,[62][63][64][65][66][67]. We obtained 282 labels for randomized pixels in the AOIs.…”

Section: Classification Scheme and Sampling For Ground Truthingmentioning

confidence: 99%

“…A change matrix depicting changes in mine areas and non-mine areas from the initial analysis year (2005) to the final year of analysis (2020) was computed from the classified maps. To quantify spatiotemporal changes [64,81,82], we generated maps of gains and losses of mine and non-mine cover. Further, change detection statistics were used to compile a detailed tabulation of changes between images acquired during the study period.…”

Section: Change Detectionmentioning

confidence: 99%

Unexpected Expansion of Rare-Earth Element Mining Activities in the Myanmar–China Border Region

Chinkaka,

Klinger,

Davis

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

Mining for rare earth elements is rapidly increasing, driven by current and projected demands for information and energy technologies. Following China’s Central Government’s 2012 strategy to shift away from mining in favor of value-added processing, primary extraction has increased outside of China. Accordingly, changes in mineral exploitation in China and Myanmar have garnered considerable attention in the past decade. The prevailing assumption is that mining in China has decreased while mining in Myanmar has increased, but the dynamic in border regions is more complex. Our empirical study used Google Earth Engine (GEE) to characterize changes in mining surface footprints between 2005 and 2020 in two rare earth mines located on either side of the Myanmar–China border, within Kachin State in northern Myanmar and Nujiang Prefecture in Yunnan Province in China. Our results show that the extent of the mining activities increased by 130% on China’s side and 327% on Myanmar’s side during the study period. We extracted surface reflectance images from 2005 and 2010 from Landsat 5 TM and 2015 and 2020 images from Landsat 8 OLI. The Normalized Vegetation Index (NDVI) was applied to dense time-series imagery to enhance landcover categories. Random Forest was used to categorize landcover into mine and non-mine classes with an overall accuracy of 98% and a Kappa Coefficient of 0.98, revealing an increase in mining extent of 2.56 km2, covering the spatial mining footprint from 1.22 km2 to 3.78 km2 in 2005 and 2020, respectively, within the study area. We found a continuous decrease in non-mine cover, including vegetation. Both mines are located in areas important to ethnic minority groups, agrarian livelihoods, biodiversity conservation, and regional watersheds. The finding that mining surface areas increased on both sides of the border is significant because it shows that national-level generalizations do not align with local realities, particularly in socially and environmentally sensitive border regions. The quantification of such changes over time can help researchers and policymakers to better understand the shifting geographies and geopolitics of rare earth mining, the environmental dynamics in mining areas, and the particularities of mineral extraction in border regions.

show abstract

“…Existing work focused on the application of traditional shallow machine learning algorithms combined with medium-resolution data to identify dryland land cover [34,35]. For instance, Zhu et al [36] obtained satisfactory classification results by integrating meteorological data and vegetation indices derived from Landsat imagery to detect irrigated dryland distribution changes using a random forest classifier. Likewise, Weng et al [37] identified typical landscapes, such as deserts, oasis, Gobi, and water systems, at an acceptable level based on the spectral information of HJ-1A/1 B imagery and an improved gcForest algorithm.…”

Section: Introductionmentioning

confidence: 99%

Improving Dryland Urban Land Cover Classification Accuracy Using a Classical Convolution Neural Network

Luan

Zhang

et al. 2023

Land

View full text Add to dashboard Cite

Reliable information of land cover dynamics in dryland cities is crucial for understanding the anthropogenic impacts on fragile environments. However, reduced classification accuracy of dryland cities often occurs in global land cover data. Although many advanced classification techniques (i.e., convolutional neural networks (CNN)) have been intensively applied to classify urban land cover because of their excellent performance, specific classification models focusing on typical dryland cities are still scarce. This is mainly attributed to the similar features between urban and non-urban areas, as well as the insufficient training samples in this specific region. To fill this gap, this study trained a CNN model to improve the urban land classification accuracy for seven dryland cities based on rigorous training sample selection. The assessment showed that our proposed model performed with higher overall accuracy (92.63%) than several emerging land cover products, including Esri 2020 Land Cover (75.55%), GlobeLand30 (73.24%), GLC_FCS30-2020 (69.68%), ESA WorldCover2020 (64.38%), and FROM-GLC 2017v1 (61.13%). In addition, the classification accuracy of the dominant land types in the CNN-classified data exceeded the selected products. This encouraging finding demonstrates that our proposed architecture is a promising solution for improving dryland urban land classification accuracy and compensating the deficiency of large-scale land cover mapping.

show abstract

Study on the Classification and Change Detection Methods of Drylands in Arid and Semi-Arid Regions

Cited by 7 publications

References 38 publications

Distribution, trends and drivers of precipitation use efficiency in the loess plateau

Distribution, trends and drivers of precipitation use efficiency in the loess plateau

Unexpected Expansion of Rare-Earth Element Mining Activities in the Myanmar–China Border Region

Improving Dryland Urban Land Cover Classification Accuracy Using a Classical Convolution Neural Network

Contact Info

Product

Resources

About