Technical progress of China's national remote sensing mapping:from mapping western China to national dynamic mapping

Remote sensing mapping is an important research direction in the development of geographic surveying and mapping.In order to successfully implement the project of Mapping Western China(MWC),a technical mapping system has been established.In this project,many problems have been solved through technological innovation,such as block adjustment with scarce control points,large-scale aerial/satellite image mapping,and intelligent interpretation of multi-source images.Several softwares were developed,e.g.PixelGrid for aerial/satellite image mapping in a large area,FeatureStation for the integration of multi-source data in the complex terrain areas,and an airborne multi-band and multi-polarization interferometric data acquisition system for SAR mapping.For the first time,full coverage of 1∶50,000 topographic data of China's land territory has been produced,which means the geospatial framework of digital China is basically completed.With the implementation of other key national plans and projects(i.e.national geographic conditions monitoring and national remote sensing map-ping),the focus has changed from MWC to national dynamic mapping.Accordingly,a dynamic mapping system is established.The data acquisition capability has developed from a single source to multiple sources and multiple modalities.The mapping capability has developed into dynamic mapping,and the capability for database update shows the characteristics of colla-boration.The national geographic condition monitoring creates a multi-scale index system for statistical analysis for various needs.A multi-level and multi-dimensional technical system for statistical computing and decision-making service is developed for the transformation from dynamic monitoring to information service.In this paper,we give a brief introduction about the recent development of remote sensing mapping in China with respect to data acquisition,map production,and information service.The purpose of this paper is to motivate the establishment of theory and method for remote sensing mapping,technical and equipment in the smart mapping era,to improve the capability of perceiving,analyzing,mining,and applying geographic data,and to promote the intelligent development of geographic survey-ing and mapping.     

Response of glacier area variation to climate change in the Kaidu-Kongque river basin,Southern Tianshan Mountains during the last 20 years

Glaciers are crucial water resources for arid inland rivers in Northwest China. In recent decades, glaciers are largely experiencing shrinkage under the climate-warming scenario, thereby exerting tremendous influences on regional water resources. The primary role of understudying watershed scale glacier changes under changing climatic conditions is to ensure sustainable utilization of regional water resources, to prevent and mitigate glacier-related disasters. This study maps the current (2020) distribution of glacier boundaries across the Kaidu-Kongque river basin, south slope of Tianshan Mountains, and monitors the spatial evolution of glaciers over five time periods from 2000–2020 through thresholded band ratios approach, using 25 Landsat images at 30 m resolution. In addition, this study attempts to understand the role of climate characteristics for variable response of glacier area. The results show that the total area of glaciers was 398.21 km² in 2020. The glaciers retreated by about 1.17 km²/a (0.26%/a) from 2000 to 2020. The glaciers were reducing at a significantly rapid rate between 2000 and 2005, a slow rate from 2005 to 2015, and an accelerated rate during 2015–2020. The meteorological data shows slight increasing trends of mean annual temperature (0.02°C/a) and annual precipitation (2.07 mm/a). The correlation analysis demonstrates that the role of temperature presents more significant correlation with glacier recession than precipitation. There is a temporal hysteresis in the response of glacier change to climate change. Increasing trend of temperature in summer proves to be the driving force behind the Kaidu-Kongque basin glacier recession during the recent 20 years.© 2021 China Geology Editorial Office.     

World Heritage in danger: Big data and remote sensing can help protect sites in conflict zones

World Heritage sites provide a key mechanism for protecting areas of universal importance. However, fifty-four UNESCO sites are currently listed as “In Danger”, with 40% of these located in the Middle East. Since 2010 alone, thirty new sites were identified as under risk globally. We combined big-data and remote sensing to examine whether they can effectively be used to identify danger to World Heritage in near real-time. We found that armed-conflicts substantially threaten both natural- and cultural-heritage listed sites. Other major risks include poor management and development (globally), poaching (Africa mostly) and deforestation (tropics), yet conflict is the most prominent threat. We show that news-mining of big-data on conflicts and remote sensing of nights-lights enabled us to identify conflict afflicted areas in near real-time. These findings provide a crucial avenue for developing a global transparent early-warning system before irreversible damage to world heritage takes place.     

Chronology and backtracking of oil slick trajectory to source in offshore environments using ultraspectral to multispectral remotely sensed data

Offshore natural seepage confirms the occurrence of an active petroleum system with thermal maturation and migration, regardless its economic viability for petroleum production. Ocean dynamics, however, impose a challenge for correlation between oil seeps detected on the water surface and its source at the ocean floor. This hinders the potential use of seeps in petroleum exploration. The present study aims to estimate oil exposure time on the water surface via remote sensing in order to help locating ocean floor seepage sources. Spectral reflectance properties of a variety of fresh crude oils, oil films on water and oil–water emulsions were determined. Their spectral identity was used to estimate the duration of exposure of oil–water emulsions based on their temporal spectral responses. Laboratory models efficiently predicted oil status using ultraspectral (>2000 bands), hyperspectral (>300 bands), and multispectral (<10 bands) sensors covering near infrared and shortwave infrared wavelengths. An oil seepage recorded by the ASTER sensor on the Brazilian coast was used to test the designed predictive model. Results indicate that the model can successfully forecast the timeframe of crude oil exposure in the ocean (i.e., the relative “age” of the seepage). The limited spectral resolution of the ASTER sensor, though, implies less accurate estimates compared to higher resolution sensors. The spectral libraries and the method proposed here can be reproduced for other oceanic areas in order to approximate the duration of exposure of noticeable natural oil seepages. This type of information is optimal for seepage tracing and, therefore, for oceanic petroleum exploration and environmental monitoring.     

The Earth Observation Data for Habitat Monitoring (EODHaM) system

To support decisions relating to the use and conservation of protected areas and surrounds, the EU-funded BIOdiversity multi-SOurce monitoring System: from Space TO Species (BIO_SOS) project has developed the Earth Observation Data for HAbitat Monitoring (EODHaM) system for consistent mapping and monitoring of biodiversity. The EODHaM approach has adopted the Food and Agriculture Organization Land Cover Classification System (LCCS) taxonomy and translates mapped classes to General Habitat Categories (GHCs) from which Annex I habitats (EU Habitats Directive) can be defined. The EODHaM system uses a combination of pixel and object-based procedures. The 1st and 2nd stages use earth observation (EO) data alone with expert knowledge to generate classes according to the LCCS taxonomy (Levels 1 to 3 and beyond). The 3rd stage translates the final LCCS classes into GHCs from which Annex I habitat type maps are derived. An additional module quantifies changes in the LCCS classes and their components, indices derived from earth observation, object sizes and dimensions and the translated habitat maps (i.e., GHCs or Annex I). Examples are provided of the application of EODHaM system elements to protected sites and their surrounds in Italy, Wales (UK), the Netherlands, Greece, Portugal and India.     

A total variation model based on edge adaptive guiding function for remote sensing image de-noising

The unexpected noise generated during the process of remote sensing images formation and transmission process is a main factor undermining the images’ quality and usage. In recent years, thanks to its local self-adapting characteristics, formal normalization, and modeling flexibility, PDE has received wide attention for its image de-noising functions, thus pushing the realization of maintaining image details while successfully de-noising a new goal for remote sensing images filtering. Having firstly analyzed and discussed the TV model and M model, a modified variation-model (S model for short) based on edge adaptive guiding function is proposed in this paper. The model introduces edge adaptive guiding function based on the standard gradient into the non-linear diffusion term and re-constructed approaching term, which adaptively adjust the smooth intensity around edge and texture information-rich regions of remote sensing images. S-model does not only overcome staircase effect that is easily produced in the TV model, but also avoids losing details and texture information which is often seen in M model, it can efficiently eliminate noises, maintain a good image edge and keep texture details perfectly. The experimental results validate the effectiveness and stability of the proposed model.     

Plant phenolics and absorption features in vegetation reflectance spectra near 1.66 μm

Past laboratory and field studies have quantified phenolic substances in vegetative matter from reflectance measurements for understanding plant response to herbivores and insect predation. Past remote sensing studies on phenolics have evaluated crop quality and vegetation patterns caused by bedrock geology and associated variations in soil geochemistry. We examined spectra of pure phenolic compounds, common plant biochemical constituents, dry leaves, fresh leaves, and plant canopies for direct evidence of absorption features attributable to plant phenolics. Using spectral feature analysis with continuum removal, we observed that a narrow feature at 1.66 μm is persistent in spectra of manzanita, sumac, red maple, sugar maple, tea, and other species. This feature was consistent with absorption caused by aromatic CH bonds in the chemical structure of phenolic compounds and non-hydroxylated aromatics. Because of overlapping absorption by water, the feature was weaker in fresh leaf and canopy spectra compared to dry leaf measurements. Simple linear regressions of feature depth and feature area with polyphenol concentration in tea resulted in high correlations and low errors (% phenol by dry weight) at the dry leaf (r² = 0.95, RMSE = 1.0%, n = 56), fresh leaf (r² = 0.79, RMSE = 2.1%, n = 56), and canopy (r² = 0.78, RMSE = 1.0%, n = 13) levels of measurement. Spectra of leaves, needles, and canopies of big sagebrush and evergreens exhibited a weak absorption feature centered near 1.63 μm, short ward of the phenolic compounds, possibly consistent with terpenes. This study demonstrates that subtle variation in vegetation spectra in the shortwave infrared can directly indicate biochemical constituents and be used to quantify them. Phenolics are of lesser abundance compared to the major plant constituents but, nonetheless, have important plant functions and ecological significance. Additional research is needed to advance our understanding of the spectral influences of plant phenolics and terpenes relative to dominant leaf biochemistry (water, chlorophyll, protein/nitrogen, cellulose, and lignin).     

Indicators of vegetation productivity under a changing climate in British Columbia,Canada

Understanding the relationship between vegetation and climate is essential for predicting the impact of climate change on broad-scale landscape processes. Utilizing vegetation indicators derived from remotely sensed imagery, we present an approach to forecast shifts in the future distribution of vegetation. Remotely sensed metrics representing cumulative greenness, seasonality, and minimum cover have successfully been linked to species distributions over broad spatial scales. In this paper we developed models between a historical time series of Advanced Very High Resolution Radiometer (AVHRR) satellite imagery from 1987 to 2007 at 1 km spatial resolution with corresponding climate data using regression tree modeling approaches. We then applied these models to three climate change scenarios produced by the Canadian Centre for Climate Modeling and Analysis (CCCma) to predict and map productivity indices in 2065. Our results indicated that warming may lead to increased cumulative greenness in northern British Columbia and seasonality in vegetation is expected to decrease for higher elevations, while levels of minimum cover increase. The Coast Mountains of the Pacific Maritime region and high elevation edge habitats across British Columbia were forecasted to experience the greatest amount of change. Our approach provides resource managers with information to mitigate and adapt to future habitat dynamics. Forecasting vegetation productivity levels presents a novel approach for understanding the future implications of climate change on broad scale spatial patterns of vegetation.     

Land cover changes in the Brazilian Cerrado and Caatinga biomes from 1990 to 2010 based on a systematic remote sensing sampling approach

The main objective of our study was to provide consistent information on land cover changes between the years 1990 and 2010 for the Cerrado and Caatinga Brazilian seasonal biomes. These areas have been overlooked in terms of land cover change assessment if compared with efforts in monitoring the Amazon rain forest. For each of the target years (1990, 2000 and 2010) land cover information was obtained through an object-based classification approach for 243 sample units (10  km × 10  km size), using (E)TM Landsat images systematically located at each full degree confluence of latitude and longitude. The images were automatically pre-processed, segmented and labelled according to the following legend: Tree Cover (TC), Tree Cover Mosaic (TCM), Other Wooded Land (OWL), Other Land Cover (OLC) and Water (W). Our results indicate the Cerrado and Caatinga biomes lost (gross loss) respectively 265,595 km² and 89,656 km² of natural vegetation (TC + OWL) between 1990 and 2010. In the same period, these areas also experienced gain of TC and OWL. By 2010, the percentage of natural vegetation cover remaining in the Cerrado was 47% and in the Caatinga 63%. The annual (net) rate of natural vegetation cover loss in the Cerrado slowed down from −0.79% yr⁻¹ to −0.44% yr⁻¹ from the 1990s to the 2000s, while in the Caatinga for the same periods the rate increased from −0.19% yr⁻¹ to −0.44% yr⁻¹. In summary, these Brazilian biomes experienced both loss and gains of Tree Cover and Other Wooded Land; however a continued net loss of natural vegetation was observed for both biomes between 1990 and 2010. The average annual rate of change in this period was higher in the Cerrado (−0.6% yr⁻¹) than in the Caatinga (−0.3% yr⁻¹).