Quantifying spatial-temporal changes of aboveground carbon stocks using Landsat time series data: A case study of Miombo woodlands

ABSTRACT

There is a need for accurate reporting of the second-growth forest in accounting for the contribution of secondary forests to achieving global carbon sequestration strategies. This study develops a model relating aboveground live biomass (AGLB) to Landsat reflectance measurements, and this method is used on multi spatial-temporal Landsat data (1998, 2001, 2004, 2011 and 2016) to assess changes in Miombo woodlands in Zambia. Results showed that the enhanced vegetation index was a better predictor of AGLB (r² = 0.96; RMSE = 0.24 t ha^?1) than the other assessed vegetation indices. Forest carbon stocks increased by an average rate of 3.65 ± standard error (SE): 0.56 t ha^?1 yr^?1 from 1998 to 2001, but a continual decline was evident for the periods 2001–2004 (?0.60 t ha^?1 yr^?1), 2004–2011 (?0.88 t ha^?1 yr^?1) and 2011–2016 (?2.03 t ha^?1 yr^?1). The study provides an empirical model for evaluating carbon stock changes.     

An innovative method for determining hydrological calibration parameters for the WRF-Hydro model in arid regions

The techniques presented herein allow to directly determine certain crucial calibration parameters for the WRF-Hydro flood forecasting model. Typically, calibrations are chosen by an iterative, empirical, trial and error procedure. We suggest a more systematic methodology to arrive at a usable calibration. Our method is based on physical soil properties and does not depend on observed runoff from certain basins during specific storm events. Three specific calibration variables that most strongly affect the runoff predictions are addressed: topographic slope, saturated hydraulic conductivity, and infiltration. We outline a procedure for creating spatially distributed values for each of the three variables. Simulation runs are performed covering several storm events with calculated calibrations, with default values, and with an expert calibration. We show that our calibration, derived solely from soil physical properties, achieves forecast skill better than the default calibration and at least as good as an expert based calibration.     

The utility of multi-sensor data for mapping eroded lands

The availability of remote sensing data with improved spatial, spectral and radiometric resolution is now available to fully exploit their potential for a specific application subject to the relative merits and the limitations of each sensor's data. Presented here is a case study where Landsat MSS and TM; and SPOT MLA data for part of the Bijapur district, southern India, which were acquired on the same day, have been evaluated for mapping eroded lands. The approach involves the geometric registration of all three data to a common map grid using tie points and third order polynomial transform; and resampling the MSS and TM data to a 20m by 20 m pixel dimension and radiometric normalization. Thematic maps showing eroded lands were generated on a micro-VAXbased DIPIX system using a maximum likelihood classifier. Accuracy estimates were made for the thematic maps following stratified unaligned random sampling technique, and subsequently, computing overall accuracy and Kappa coefficient. Spectral separability and classification accuracy was maximum from SPOT-MLA data followed by a combination of Landsat MSS band 1, SPOT-MLA band 2 and Landsat TM band 4; Landsat TM, a combination of Landsat MSS, TM and SPOT MLA; and Landsat MSS data.     

Evaluating Land Surface Changes of Makassar City Using DInSAR and Landsat Thematic Mapper Images

Urban growth has been a major issue in environmental monitoring and changes occurred on land surfaces have been monitored by applying remote sensing as well as ground measurement. Most major cities in the world have experienced land subsidence phenomena on some parts of them due to the load of development and modernization. Excessive extraction of groundwater for the needs of industry has led to the condition where the water table drops, and this can possibly trigger subsidence, as observed in Indonesian cities. In this study the authors have shown that the application of DInSAR （differential interferometric synthetic aperture radar） technique using Japanese Earth Resources Satellite-I Synthetic Aperture Radar JERS-I SAR data can reveal subsidence conditions in the studied Makassar city area. Landsat TM （thematic mapper） images were used to evaluate the change of land cover during the observation period of 1994-1999. Makassar is fiat, covered mainly by alluvium deposit that is vulnerable to the load of constructions, and volcanic formations which is porous and will easily be degraded by groundwater extraction. It is found that mostly the subsidence has occurred in the western part of the city, including the industrial district, reclamation area, trading center area and the seaport area. The ground survey has indicated that high human activity exists in every point of subsidence. It is likely that various human activities such as ground water pumping and construction work should have affected the local subsidence phenomena in Makassar, as in the case of other large-scale cities in Indonesia.     

Geospatial technology applied to spatiotemporal assessment of Harike Wetland,Punjab

The Harike Wetland situated in Punjab is a Ramsar site and a wetland of national importance. The present study was undertaken to assess the spatiotemporal dynamics of the wetland on the basis of geospatial technology and ground‐based studies. Landsat images for the years 2002 and 2014 were acquired from the United States Geological Survey and classified digitally to generate landuse/land cover maps involving four classes (water, grassland (including water hyacinth), agriculture, built‐up (settlement), barren land). The total area of the Harike Wetland was found to be 8023.68 ha. Water sampling at eleven sites was carried out and evaluated for physicochemical parameters. The water quality at several sampling points was found to be severely degraded. Change detection analysis revealed the submerged area (area under water) and grassland (including water hyacinth) had decreased over the past 12 years, whereas that area under agriculture and built‐up land has increased, indicating a shrinkage in the total wetland area. The present study also indicated that the near‐infrared band is a good indicator of water quality parameters, as indicated by the significant positive correlation between the near‐infrared band and relevant water parameters. Because the wetland is important from both an ecological perspective and economic perspective, regular monitoring is recommended, for which geospatial technology has proven to be very useful.     

Mapping Chlorophyll-a Concentrations in West Lake,China using Landsat 7 ETM+

Eutrophication is a persistent problem that affects the ecological health of many shallow lakes in China. An indicator used to monitor trophic status is chlorophyll-a; however, collection and analysis can be spatially limiting and time consuming. In this study we utilized Landsat 7 ETM+ (Path/Row 119/39) imagery for West Lake, Hangzhou, to map chlorophyll-a concentrations. An optimal linear regression model with the band ratio ETM+3/ETM+1 (independent) and concurrent field-collected water quality measurements (dependant) was developed. The resulting model, lnChl.= 5.009 (ETM+3/ETM+1)-1.855, showed a strong (R² = 0.815) ability to accurately map the distribution of chlorophyll-a. The straightforward approach carried out to assess this fresh-water lake in a rapidly developing region increased the level of information required to combat aquatic ecosystem degradation.     

Time and Space Fusion Model Comparison of Temperature Vegetation Drought Index

Drought is the first disaster affecting agricultural production. The annual precipitation in Xinjiang of China is scarce and the climate is dry. This is one of the major obstacles to the agricultural transformation and rural revitalization in Xinjiang. Therefore, timely and accurate monitoring of agricultural drought in Xinjiang is of great significance for safeguarding agricultural production. Yanqi Basin in Xinjiang was took as an example. Landsat8 and MODIS data were used. The Spatio Temporal Adaptive Reflectivity Fusion Model (STARFM), the Enhanced STARFM (Enhanced STARFM, ESTARFM) Model and Flexible Spatio Temporal Data Fusion (FSDAF) model were used to construct the Temperature Vegetation Dryness Index (TVDI). At the same time, the Relative Soil Moisture (RSM) was used to verify the TVDI inversion results. The results show that coefficient of determination (R²) and root mean square error (RMSE) of the drought factors(NDVI and surface temperature) simulated by the ESTARFM model were better than that by the other two models. And the R² and RMSE of NDVI simulated by the ESTARFM model reached 0.924 and 0.076. In addition, the R² and RMSE of surface temperature simulated by the ESTARFM model reached 0.877 and 2.799. Comparing with TVDI of the real Landsat8 data inversion and RSM data, it was found that the TVDI simulated by the ESTARFM model is better than the other two models, with 0.873 of R² and 0.248 of RMSE. The ESTARFM model can more accurately simulate the TVDI distribution of the Landsat8 images in the same period, so as to monitor the drought degree of the farmland in Xinjiang.     

基于Landsat 8的全球精选遥感数据集汇聚方法

空间对地观测技术为地球系统科学研究提供了多时相、宽覆盖、立体化的遥感数据资源，使对地球系统行为的观测、理解、模拟及预测变为可能。然而，如何从海量数据资源中及时有效地获取信息也成为日渐凸显的问题。现有遥感数据服务系统的检索结果冗余度高，既无法直接满足用户多样化需求，也增加了高质量数据清洗成本。为此，基于2017年全球Landsat 8卫星元数据检索信息，建立由空间完整性、时间邻近性及云覆盖量3个专有指标组成的质量评价体系，设计满足最简化和定制化需求的精选遥感数据集汇聚方法。实验结果表明:该方法在不改变现有遥感数据服务系统的同时提高数据的检索效率和精度。     

空间遥感现状及未来发展

从第三十五届国际宇航联合会的空同遥感专业小组会议上可以看出,目前空间遥感的现状及未来发展前景。今后空间遥感将从具有单一遥感能力向具有综合遥感能力方面发展,不仅能对陆地,而且对海     

Automatic Algorithm for Extracting Lake Boundaries in Qinghai-Tibet Plateau based on Cloudy Landsat TM/OLI Image and DEM

Lakes in the Qinghai-Tibet Plateau are numerous and widely distributed, accounting for 41% and 57% of the total number and area of lakes in China, which are very important for the study of lakes in the whole country and even in the whole world. Remote sensing has been used to monitor the lake distribution for a long time, but optical remote sensing images are often obscured by clouds, from which it’s impossible to automatically extract complete lake boundaries. An automatic interpolation algorithm for lake boundary generation based on cloudy Landsat TM/OLI image and Shuttle Radar Topography Mission (SRTM) 30 m resolution Digital Elevation Model (DEM) is proposed. Firstly, supported by the platform of Google Earth Engine, the tier1 data of Landsat TM/OLI images are used to eliminate the effects of cloud, cloud shadow, snow and mountain area, based on the Pixel Quality Assessment (pixel_qa) attribute and SRTM 30 m DEM. Then, the Modified Normalized Difference Water Index (MNDWI) is calculated, and the Canny edge detection algorithm are used to obtain the known part of the lake boundary (L) in cloud-free areas. The possible lake areas are obtained by range filtering of DEM locally. At the same time, DEM is used to generate contours with an isometric interval of 1 m, and a series of contours surrounding the possible lake area are automatically screened out. The tree structure is established according to the inclusion relationship between contours. The leaf nodes are the innermost contours, which are recorded as inner contours (C1). Because the acquisition time of Landsat and DEM is different, with the lake expanding or shrinking, the lake water surface will rise or fall relative to the inner contour. Different methods of determining the outer contour (C2) are adopted. Subsequently, the slope-aspect relationship between the inner contour C1 and the outer contour C2 and the known part of the lake boundary L is established, and the unknown lake boundary points are interpolated. Finally, the nearest neighbor method is used to connect the known lake boundary points with the interpolated Lake boundary points to form a complete lake boundary. The extracted lake boundaries were validated by visual digitized lake boundaries from ZiYuan-3 image or cloud-free Landsat image on the near date. It is found that they are basically coincided, and the percentage of differences in length and area are -6.81%~9.4% and -2.11%~2.7% respectively. It shows that this method is very effective for automatic extraction of Lake boundary from cloudy Landsat TM/OLI images, and provides a new method for automatic extraction of long time series Lake boundary and its temporal and spatial variation analysis in the Qinghai-Tibet Plateau on GEE and other big data platforms.