Construction and application of high-resolution remote sensing ecological index
The Remote Sensing Ecological Index(RSEI)is the most widely used ecological environment quality assessment model.In general,the four indicators(greenness,wetness,heat,and dryness)of RSEI are calculated from Landsat images to construct an index that comprehensively reflects the ecological environment condition in pixel units.High-resolution remote sensing images generally lack the short-wave and thermal infrared bands involved in the calculation of RSEI;therefore,the application of RSEI to high-resolution ecological environment quality assessment is limited.The advantages of high-resolution remote sensing data cannot be fully utilized due to the limitation of spectral resolution,which is undoubtedly wasteful.To solve the problem of mismatch between high-resolution remote sensing image bands and the bands required for RSEI calculation,this study established a multi-resolution band fusion model with scale-invariant features.On the basis of Landsat 8 and Gaofen(GF)-2 remote sensing images,short-wave infrared bands and surface temperature with high resolution(4 m)were generated utilizing the statistical relationship between bands.The high-resolution RSEI(HRSEI)was constructed based on the principle of RSEI,filling the gap of RSEI research at the fine scale.This method was applied to Fan County in Henan Province.The results showed the following.(1)High-resolution short-wave infrared band and surface temperature can be generated by utilizing the multi-resolution band fusion technique.The correlation coefficients between the fitted and original images were higher than 0.7,indicating that the machine learning model based on the random forest algorithm was effective.The obtained high-resolution band/product can be used in the subsequent ecological environment quality evaluation work.This method can effectively compensate for the disadvantage posed by the band absence of high-resolution images,breaking through the limitation of RSEI application at the fine scale and expanding the application scenario of high-resolution remote sensing data.(2)The calculation results of the first principal component of HRSEI showed that the loadings of greenness and wetness were positive,while those of heat and dryness were negative,indicating that greenness and wetness promoted ecological environment quality,whereas heat and dryness impeded it.The above results are consistent with the objective actual pattern and coincided with the trend of the RSEI results.The Pearson correlation coefficient showed that HRSEI and RSEI were highly correlated(R=0.74).The contrast and information entropy of HRSEI for the three typical areas(built-up,village,and beach areas)were greater than those of RSEI.By maintaining high relevance and consistency,the information abundance presented by HRSEI generated from 4 m GF-2 data is significantly higher than that from 30 m Landsat data.(3)The results of HRSEI in 2016 and 2023 showed that the ecological environment quality of Fan County had been generally improved.However,some areas where ecological environment quality deteriorated remained.Two major factors contributed to the deterioration.First,urbanization led to the expansion of built-up land,with previously cultivated or forested land being changed to impervious surfaces.Second,villages near the Yellow River carried out demolition of old villages and construction of resettlement areas due to the policy of relocation and reclamation in the Yellow River beach area.In particular,the lack of timely reclamation after the demolition of old villages seriously expanded the scope of deterioration of ecological environment quality.
high resolutionremote sensing ecological indexecological environment qualityRSEIband fusion
万方数据
维普
