Estimation and mapping of vegetation biomass in forest-steppe and steppe zones of Mongolia using MODIS data
DOI:
https://doi.org/10.5564/mjgg.v60i44.2939Keywords:
Vegetation index, RF, SVM, PLSR, BiomassAbstract
In recent years, digital remote sensing optical datasets and various indices calculated by using them have been intensively applied for green vegetation biomass evaluation and other thematic studies. The main goals of this study were to evaluate the vegetation biomass in the forest-steppe and steppe zones of Mongolia using the indices calculated from medium-resolution satellite data and map the biomass distribution. Indices were calculated from different visible, near, and mid-infrared bands of MODIS data acquired on August 21, 2016, and then classified and compared using machine learning methods such as random forest (RF), support vector machine (SVM), and partial least square regression (PLSR). Among the selected methods for biomass mapping in the forest-steppe and steppe areas, the RF method demonstrated the highest accuracy with a coefficient of determination (R2) of 0.889, and a root mean square error (RMSE) of 0.713 c/ha. The PLSR method had an R2 of 0.296 and an RMSE of 1.854 c/ha, while the SVM method showed the lowest accuracy with an R2 of 0.273 and an RMSE of 1.889 c/ha. Our finding indicates that the RF is a more applicable approach for assessing and mapping the vegetation biomass in the forest-steppe and steppe zones of Mongolia.
MODIS дагуулын мэдээ ашиглан Монгол орны ойт хээр болон хээрийн бүсийн ургамлын биомассыг үнэлэн зураглах нь
ХУРААНГУЙ: Сүүлийн үед зайнаас тандсан оптикийн тоон өгөгдлүүд, тэдгээр дээр суурилан тооцоолсон төрөл бүрийн индексүүдийг ногоон ургамлын биомассын үнэлгээ болон бусад сэдэвчилсэн судалгаанд эрчимтэй ашиглаж байна. Судалгааны ажлын үндсэн зорилго нь Монгол орны ойт хээрийн ба хээрийн бүсийн ургамлын биомассыг дунд нарийвчлал бүхий хиймэл дагуулын мэдээг ашиглан тооцоолсон индексүүдийн тусламжтайгаар үнэлэх, улмаар биомассын тархалтыг зураглахад оршино. Энэ зорилгоор 2016 оны 8 дугаар сарын 21-ний өдрийн MODIS хиймэл дагуулын үзэгдэх гэрэл, ойрын болон дундын нэл улаан туяаны мужийн сувгуудын мэдээг ашиглан индексүүдийг тооцоолж, дараа нь санамсаргүй форестын арга (RF), тулах векторын арга (SVM), хэсэгчилсэн хамгийн бага квадратын регресс (PLSR)-ийн арга зэрэг машин сургалтын аргуудыг ашиглан уг индексүүдийг ангилж, харьцуулсан дүн шинжилгээг хийж гүйцэтгэлээ. Ойт хээрийн ба хээрийн бүсийн биомассыг зураглах аргуудаас RF аргын детерминацийн коэффициент (R2) 0.889, дундаж квадратын алдаа (RMSE) 0.713 ц/га буюу хамгийн өндөр нарийвчлалыг харуулсан бол PLSR аргын R2 нь 0.296, RMSE 1.854 ц/га, харин SVM аргын R2 0.273, RMSE 1.889 ц/га буюу хамгийн бага нарийвчлалтайгаар үнэлсэн байлаа. Энэхүү судалгааны үр дүнд RF арга нь Монгол орны ойт хээрийн ба хээрийн бүсийн ургамлын биомассыг үнэлэх, зураглахад илүү тохиромжтой болохыг харуулж байна.
Түлхүүр үгс: Ургамлын индекс, RF, SVM, PLSR, Биомасс
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