Matches in SemOpenAlex for { <https://semopenalex.org/work/W4385737918> ?p ?o ?g. }
- W4385737918 endingPage "3968" @default.
- W4385737918 startingPage "3968" @default.
- W4385737918 abstract "Accurate and timely estimation of grass yield is crucial for understanding the ecological conditions of grasslands in the Mongolian Plateau (MP). In this study, a new artificial neural network (ANN) model was selected for grassland yield inversion after comparison with multiple linear regression, K-nearest neighbor, and random forest models. The ANN performed better than the other machine learning models. Simultaneously, we conducted an analysis to examine the spatial and temporal characteristics and trends of grass yield in the MP from 2000 to 2020. Grassland productivity decreased from north to south. Additionally, 92.64% of the grasslands exhibited an increasing trend, whereas 7.35% exhibited a decreasing trend. Grassland degradation areas were primarily located in Inner Mongolia and the central Gobi region of Mongolia. Grassland productivity was positively correlated with land surface temperature and precipitation, although the latter was less sensitive than the former in certain areas. These findings indicate that ANN model-based grass yield estimation is an effective method for grassland productivity evaluation in the MP and can be used in a larger area, such as the Eurasian Steppe." @default.
- W4385737918 created "2023-08-11" @default.
- W4385737918 creator A5006954207 @default.
- W4385737918 creator A5023656131 @default.
- W4385737918 creator A5032595741 @default.
- W4385737918 creator A5034148893 @default.
- W4385737918 creator A5037249071 @default.
- W4385737918 creator A5080328818 @default.
- W4385737918 date "2023-08-10" @default.
- W4385737918 modified "2023-10-16" @default.
- W4385737918 title "Spatial-Temporal Pattern Analysis of Grassland Yield in Mongolian Plateau Based on Artificial Neural Network" @default.
- W4385737918 cites W189066446 @default.
- W4385737918 cites W1972280525 @default.
- W4385737918 cites W1975727482 @default.
- W4385737918 cites W2032804723 @default.
- W4385737918 cites W2053027480 @default.
- W4385737918 cites W2055906016 @default.
- W4385737918 cites W2084229106 @default.
- W4385737918 cites W2116337177 @default.
- W4385737918 cites W2159162331 @default.
- W4385737918 cites W2261059368 @default.
- W4385737918 cites W2466134222 @default.
- W4385737918 cites W2553987695 @default.
- W4385737918 cites W2587579504 @default.
- W4385737918 cites W2594878624 @default.
- W4385737918 cites W2738401756 @default.
- W4385737918 cites W2765453548 @default.
- W4385737918 cites W2767349927 @default.
- W4385737918 cites W2800536838 @default.
- W4385737918 cites W2801958376 @default.
- W4385737918 cites W2804871356 @default.
- W4385737918 cites W2901262054 @default.
- W4385737918 cites W2913434368 @default.
- W4385737918 cites W2942586472 @default.
- W4385737918 cites W2952754273 @default.
- W4385737918 cites W2964781623 @default.
- W4385737918 cites W2970860546 @default.
- W4385737918 cites W2981876183 @default.
- W4385737918 cites W2987549625 @default.
- W4385737918 cites W3000516103 @default.
- W4385737918 cites W3001493036 @default.
- W4385737918 cites W3003061015 @default.
- W4385737918 cites W3012176968 @default.
- W4385737918 cites W3086299013 @default.
- W4385737918 cites W3137925769 @default.
- W4385737918 cites W3151207257 @default.
- W4385737918 cites W3152432465 @default.
- W4385737918 cites W3196489693 @default.
- W4385737918 cites W4200424363 @default.
- W4385737918 cites W4220839205 @default.
- W4385737918 cites W4280516963 @default.
- W4385737918 cites W4285597229 @default.
- W4385737918 cites W4288871131 @default.
- W4385737918 cites W4295339437 @default.
- W4385737918 cites W4298003768 @default.
- W4385737918 cites W4306320285 @default.
- W4385737918 cites W4308127287 @default.
- W4385737918 cites W4313705952 @default.
- W4385737918 cites W4319750964 @default.
- W4385737918 cites W4323047299 @default.
- W4385737918 cites W4323665917 @default.
- W4385737918 doi "https://doi.org/10.3390/rs15163968" @default.
- W4385737918 hasPublicationYear "2023" @default.
- W4385737918 type Work @default.
- W4385737918 citedByCount "0" @default.
- W4385737918 crossrefType "journal-article" @default.
- W4385737918 hasAuthorship W4385737918A5006954207 @default.
- W4385737918 hasAuthorship W4385737918A5023656131 @default.
- W4385737918 hasAuthorship W4385737918A5032595741 @default.
- W4385737918 hasAuthorship W4385737918A5034148893 @default.
- W4385737918 hasAuthorship W4385737918A5037249071 @default.
- W4385737918 hasAuthorship W4385737918A5080328818 @default.
- W4385737918 hasBestOaLocation W43857379181 @default.
- W4385737918 hasConcept C100970517 @default.
- W4385737918 hasConcept C105795698 @default.
- W4385737918 hasConcept C105895522 @default.
- W4385737918 hasConcept C107054158 @default.
- W4385737918 hasConcept C119857082 @default.
- W4385737918 hasConcept C134306372 @default.
- W4385737918 hasConcept C139719470 @default.
- W4385737918 hasConcept C153294291 @default.
- W4385737918 hasConcept C162324750 @default.
- W4385737918 hasConcept C166957645 @default.
- W4385737918 hasConcept C173727882 @default.
- W4385737918 hasConcept C18903297 @default.
- W4385737918 hasConcept C191935318 @default.
- W4385737918 hasConcept C204983608 @default.
- W4385737918 hasConcept C205649164 @default.
- W4385737918 hasConcept C2775835988 @default.
- W4385737918 hasConcept C2779179000 @default.
- W4385737918 hasConcept C2780030769 @default.
- W4385737918 hasConcept C3017660400 @default.
- W4385737918 hasConcept C33923547 @default.
- W4385737918 hasConcept C39432304 @default.
- W4385737918 hasConcept C41008148 @default.
- W4385737918 hasConcept C50644808 @default.
- W4385737918 hasConcept C6557445 @default.
- W4385737918 hasConcept C86803240 @default.