The Impact Analysis of Land Features to JL1-3B Nighttime Light Data at Parcel Level: Illustrated by the Case of Changchun, China

Nighttime lights (NTL) create a unique footprint left by human activities, which can reflect the economic index and demographic characteristics of a country or region to some extent. It is of great significance to explore the impact of land features related to social–economic indexes to NTL intensity in urban areas. At present, there are few studies on the impact factors of high-resolution NTL remote sensing data to analyze the influence of NTL intensity variation at a fine scale. In this paper, taking Changchun, China as a case study, we selected the new generation of high spatial resolution (0.92 m) and multispectral bands NTL image JL1-3B data to evaluate the relationship between NTL intensity and related land features such as the normalized difference vegetation index (NDVI), land use types and point of information (POI) at the parcel level, and combined Luojia 1-01 images for comparative analysis. After screening features by the Gini index, 17 variables were selected to establish the best random forest (RF) regression model for the Luojia 1-01 and JL1-3B data, corresponding to out-of-bag (oob) scores of 0.8304 and 0.9054, respectively. The impact of features on NTL was determined by calculating the features contribution. It was found that JL1-3B data perform better on a finer scale and provide more information. In addition, JL1-3B data are less affected by light overflow effect and saturation, and they could provide more accurate information at smaller parcels. Through the impact analysis of land features on the two kinds of NTL data, it is proven that JL1-3B images can be used to study effectively the relationship between NTL and human activities information. This paper aims to establish a regression model between the radiance of two types of NTL data and land features by RF algorithm, to further excavate the main land features that impact radiance according to the feature contribution, and compare the performance of two types of NTL data in regression. The study is expected to provide a reference to the further application of NTL data such as land feature inversion, artificial surface monitoring and evaluation, geographic information point estimation, information mining, etc., and a more comprehensive cognition of land feature impact to urban social–economic indexes from a unique perspective, which can be used to assist urban planning and related decision-making.