Forest Land Types Precise Classification And Change Monitoring Based On Multi-source Remote Sensing Data

Forest is the main body of ecological construction,which mainly determined the ecological balance.In recent years,as the development of remote sensing,it is broadly used in forest,and it has become an accurate and high efficient method for monitoring the forest dynamics.However,the drawbacks of forest classification still exist due to the imperfect accuracy,low application frequency of new method,low automation and low reliability.In order to meet industry application needs of the national,this paper discussed the fine classification of forest land types based on high spatial resolution imagesin the largely complicated forest area,which locates in Genhe ecological station,Genhe City,Inner Mongolia Autonomous Region,with the support of the red-edge band satellite data to promote the deep application of the red-edge band satellite data in the investigation and detection of forest land type GF-6 satellite data with independent property rights and red side band lay a solid foundation in forestry applications.Firstly,the fine forest classification was conducted using Maximum Likelihood Classification(MLC),Support Vector Machine Classification(SVM),Image SVM based on IDL,and Random Forest(RF),and SVM with the participation of the normalized Red Edge Index,and object-oriented classification based on sample,based on multi-source data,including RapidEye,GF-1 PMS and Landsat 8 OLI remote sensing images obtained in July 2016;and other information spectral feature,texture and forest classification survey data.Finally,the results of field survey and the second forest resource survey data were used as the test samples to verify the classification results of different types of forest land types,and the classification results were evaluated by precision verification.The results showed that:ImageRF and ImageSVM have high precision for forest type information extraction.The high-resolution remote-sensing images of RapidEye and GF-1 PMS are more effective.The overall classification accuracy of ImageRF and ImageSVM is more than 6%higher than that of traditional SVM and MLC.But,the medium-resolution remote sensing image,Landsat 8 OLI,the overall classification accuracy increased by about 1%.Meanwhile,in the fine identification of forest land type,the RapidEye image carrying red-edge band information has better recognition precision and separability than Landsat 8 OLI image with no red edge band information.Under the same conditions,including the same image type,the same data range and the same operating environment,ImageRF and ImageSVM classification accuracy is basically the same,but ImageRF method is less time-consuming and efficient.For the RapidEye remote sensing image with red band information,the precision of the SVM classification including the NDRE and the traditional SVM classification increased from 84.08%to 91.69%.It can be seen that the forest type information is more sensitive to the red band,which can greatly improve the recognition accuracy of forest types.The sample-based object-oriented classification method can meet the fine classification requirements of forest land types,but its low degree of automation,time-consuming and labor-intensive and subject to human factors,it is not suitable for for large-scale forest land type fine-grained research.Secondly,this study investigated the changes of forest land types using EnMAP-Box model with the random forest(RF)based on Landsat Thematic Mapper 5(TM)and Landsat 8 OLI during 2008 and 2015 over Inner Mongolia Daxinganling area.The results showed that the classification based on EnMAP-Box model with the random forest(RF)was a more suitable method for the changing detection.Specially,the coverage of broad-leaved forest,coniferous forest and mixed forest increased,a slight decrease in sparse forests.The forest land increased from 9,619,700 hectares to 9,737,687 hectares,with an increase of 116,900 hectares.The cultivated land,water and construction land also increased,while the grassland,wetland and unutilized land decreased.Overall,during the period of study,the area of forest land in the Greater Xing'an Mountains in Inner Mongolia increased from 73.19%(2008)to 74.08%(2016),and the ecological situation continued to improve.From 2008 to 2015,the Sparse forest land converted 2.13%and 1.27%to broad-leaved forests and needles respectively;1.01%and 9.43%of grasslands were converted to broad-leaved forests and coniferous forests;there were 2.72%and 2.11%of unused land were converted to broad-leaved forests and coniferous leaves,respectively.The implementation of the natural forest resources protection project is one of the important reasons for the increase in the forest area in the Greater Hinggan Mountains in Inner Mongolia.Finally,this study applied EnMAP-Box model with the random forest(RF)based on Landsat Thematic Mapper 5(TM)and Chinese domestic high resolution satellite data,Gaofen-1,to investigate the changes of land cover types between 2008 and 2016 at Jishui county of Jiangxi province,the Central China,that is a validation study area.The results showed that the overall classifications of 2008 and 2016 are 81.43%and 83.89%,respectively,and the classification results were reliable.The total forest coverage rate increased 1.03 per cent from 2008(62.83%)to 2016(63.86%),which was in accordance with the actual forest increase.At the same time,the percentage of various types of objects also conforms to the actual situation in Jishui County.Therefore,this method also has a good applicability in the central region of China.