Assessing the Consistency of Five Remote Sensing-Based Land Cover Products for Monitoring Cropland Changes in China DOI Creative Commons

Fuliang Deng,

Xinqin Peng,

Jiale Cai

и другие.

Remote Sensing, Год журнала: 2024, Номер 16(23), С. 4498 - 4498

Опубликована: Ноя. 30, 2024

The accuracy assessment of cropland products is a critical prerequisite for agricultural planning and food security evaluations. Current assessments remote sensing-based focused on the consistency spatial patterns specific years, yet reliability these in time-series analysis remains unclear. Using area data from second third national land surveys China (referred to as NLSCD) benchmark, we evaluate area-based spatial-based changes five 30 m cover covering 2010 2020, including annual dataset (CACD), Land Cover Dataset (CLCD), China’s Land-use/cover (CLUD), Global Land-Cover product with Fine Classification System (GLC_FCS30), GlobeLand30. We also employed GeoDetector model explore relationships between change environmental factors (e.g., fragmentation, topographic features, frequency cloud cover, management practices). showed that all indicate declining trend areas over past decade, while amount loss ranges 5.59% 57.85% reported by NLSCD. At prefecture-level city scale, correlation coefficients detected NLSCD are low, GlobeLand30 having highest coefficient at 0.67. proportion cities where direction each inconsistent 13.27% 39.23%, CLCD showing CLUD lowest. pixel reveals 79.51% expansion pixels 77.79% completely across products, southern part exhibiting greater inconsistency compared Northwest China. Besides, practices irrigation) primary influencing loss, respectively. These results suggest low emphasizing need address inconsistencies when generating datasets via sensing.

Язык: Английский

Detecting the Phenological Threshold to Assess the Grassland Restoration in the Nanling Mountain Area of China DOI Creative Commons
Zhenhuan Liu, Sujuan Li,

Yueteng Chi

и другие.

Remote Sensing, Год журнала: 2025, Номер 17(3), С. 451 - 451

Опубликована: Янв. 28, 2025

The dynamics of vegetation changes and phenology serve as key indicators interannual in productivity. Monitoring the Nanling grassland ecosystem using remote sensing index is crucial for rational development, utilization, protection these resources. Grasslands hilly areas southern China’s middle low mountains have a high restoration efficiency due to favorable combination water temperature conditions. However, dynamic adaptation process under combined effects climate change human activities remains unclear. aim this study was conduct continuous phenological monitoring ecosystem, evaluate its seasonal characteristics, trends, thresholds changes. Normalized Difference Phenology Index (NDPI) values Mountains’ grasslands from 2000 2021 calculated MOD09A1 images Google Earth Engine (GEE) platform. Savitzky–Golay filter Mann–Kendall test were applied time series smoothing trend analysis, growing seasons extracted annually Seasonal Trend Decomposition LOESS. A segmented regression method then employed detect based on cover percentage. results showed that (1) NDPI increased significantly (p < 0.01) across all patches, particularly southeast, with notable rise 2010 2014, following an eastern western central mutation sequence. (2) annual lower upper 0.005~0.167 0.572~0.727, which mainly occurred January–March June–September, respectively. (3) Most same periods increasing season length varying 188 247 days. (4) overall potential productivity improved. (5) mountain associated coverage mean values, threshold identified at value 0.5 2.1% coverage. This indicates ensure sustainable development conservation ecosystems, targeted management strategies should be implemented, regions where factors influence fluctuations.

Язык: Английский

Процитировано

0
Understanding the process and mechanism of agricultural land transition in China: Based on the interactive conversion of cropland and natural ecological land DOI

Shilei Wang,

Xiaobin Jin, Bo Han

и другие.

Journal of Environmental Management, Год журнала: 2025, Номер 376, С. 124585 - 124585

Опубликована: Фев. 18, 2025

Язык: Английский

Процитировано

0
Automatic crop type mapping based on crop-wise indicative features DOI
Jieqing Yu, Longcai Zhao, Yanfu Liu

и другие.

International Journal of Applied Earth Observation and Geoinformation, Год журнала: 2025, Номер 139, С. 104554 - 104554

Опубликована: Апрель 27, 2025

Язык: Английский

Процитировано

0
Assessing the Accuracy and Consistency of Cropland Datasets and Their Influencing Factors on the Tibetan Plateau DOI Creative Commons

Fuyao Zhang,

Xue Wang, Liangjie Xin

и другие.

Remote Sensing, Год журнала: 2025, Номер 17(11), С. 1866 - 1866

Опубликована: Май 27, 2025

With advancements in cloud computing and machine learning algorithms, an increasing number of cropland datasets have been developed, including the China land-cover dataset (CLCD) GlobeLand30 (GLC). The unique climatic conditions Tibetan Plateau (TP) introduce significant differences uncertainties to these datasets. Here, we used a quantitative visual integrated assessment approach assess accuracy spatial consistency five around 2020 TP, namely CLCD, GLC30, land-use remote sensing monitoring (CNLUCC), Global Land Analysis Discovery (GLAD), global product with fine classification system (GLC_FCS). We analyzed impact terrain, climate, population, vegetation indices on using structural equation modeling (SEM). In this study, GLAD area had highest fit national land survey (R2 = 0.88). County-level analysis revealed that CLCD GLC_FCS underestimated areas high-elevation counties, whereas GLC CNLUCC tended overestimate TP. Considering overall accuracy, performed best scores 0.76 0.75, respectively. contrast, (0.640), (0.620) exhibited poor accuracy. This study highlights significantly low croplands only 10.60% high complete agreement. results showed substantial among zones, relatively higher observed low-altitude zones notably poorer sparse or fragmented cropland. SEM indicated elevation slope directly influenced consistency, temperature precipitation indirectly affected by influencing indices. provides valuable reference for implementing future mapping studies TP region.

Язык: Английский

Процитировано

0
Morphology's importance for farmland landscape pattern assessment and optimization: A case study of Jiangsu, China DOI

Suchen Ying,

Xiaobin Jin, Xinyuan Liang

и другие.

Applied Geography, Год журнала: 2024, Номер 171, С. 103364 - 103364

Опубликована: Авг. 13, 2024

Язык: Английский

Процитировано

1
Assessing the Consistency of Five Remote Sensing-Based Land Cover Products for Monitoring Cropland Changes in China DOI Creative Commons

Fuliang Deng,

Xinqin Peng,

Jiale Cai

и другие.

Remote Sensing, Год журнала: 2024, Номер 16(23), С. 4498 - 4498

Опубликована: Ноя. 30, 2024

The accuracy assessment of cropland products is a critical prerequisite for agricultural planning and food security evaluations. Current assessments remote sensing-based focused on the consistency spatial patterns specific years, yet reliability these in time-series analysis remains unclear. Using area data from second third national land surveys China (referred to as NLSCD) benchmark, we evaluate area-based spatial-based changes five 30 m cover covering 2010 2020, including annual dataset (CACD), Land Cover Dataset (CLCD), China’s Land-use/cover (CLUD), Global Land-Cover product with Fine Classification System (GLC_FCS30), GlobeLand30. We also employed GeoDetector model explore relationships between change environmental factors (e.g., fragmentation, topographic features, frequency cloud cover, management practices). showed that all indicate declining trend areas over past decade, while amount loss ranges 5.59% 57.85% reported by NLSCD. At prefecture-level city scale, correlation coefficients detected NLSCD are low, GlobeLand30 having highest coefficient at 0.67. proportion cities where direction each inconsistent 13.27% 39.23%, CLCD showing CLUD lowest. pixel reveals 79.51% expansion pixels 77.79% completely across products, southern part exhibiting greater inconsistency compared Northwest China. Besides, practices irrigation) primary influencing loss, respectively. These results suggest low emphasizing need address inconsistencies when generating datasets via sensing.

Язык: Английский

Процитировано

0