Landcover Mapping

Despite the growing understanding of the global climate change, great uncertainties exist in the prediction of responses of arid regions to global and regional, natural and human-induced climate change. Meteorological data series show a steady increase of annual and winter temperatures in Central Asia since the beginning of the 20th century that might have a strong potential impact on the region's natural ecosystems, agricultural crops, and human health. Analyses of the NOAA AVHRR temporal series since the 1980s showed a decrease in aridity from 1991-2000 compared to [1982][1983][1984][1985][1986][1987][1988][1989][1990]. While most climate models agree that the temperature in arid Central Asia will increase by 1-2 1C by 2030-2050, precipitation projections vary from one model to another and projected changes in the aridity index for different model runs show no consistent trend for this region. Local and regional human impacts in arid zones can significantly modify surface albedo, as well as water exchange and nutrient cycles that could have impacts on the climatic system both at the regional and global scales. r

LAPAN-A3/LAPAN-IPB is the third generation of micro-satellite developed by Indonesian National Institute of Aeronautics and Space (LAPAN). The satellite carries a multispectral push-broom sensor that can record the earth’s surface at the visible and near-infrared spectrum. Being launched in June 2016, there has no been many publications related to the use of LAPAN-A3 multispectral data for landuse/landcover (LULC) mapping. This paper aims to provide information regarding the use of LAPAN-A3 data for the LULC extraction using a maximum likelihood algorithm as well as the artificial neural network and then evaluate the results. The LAPAN-A3 image was geometrically corrected by using Landsat-8 OLI image as reference data. Three test areas with a size of 1200x945 pixels are then selected for pixel-based classification with the two algorithms mentioned above. For comparison, both LAPAN-A3 and Landsat-8 data were classified for 3 test areas. Accuracy assessment was performed on both datasets using manually interpreted SPOT-6 Pansharpened image as reference data. Preliminary results showed that LAPAN-A3 were able to extract ten- different LULC classes, comprises of built-up area, forest, rivers, fishponds, shrubs, wetland forests, rice fields, sea, ag- ricultural land, and bare soil. The overall accuracy of LAPAN-A3 data is generally lower than Landsat-8, which ranges from 49.76% to 71.74%. These results illustrate the potential of LAPAN-A3 data to derive LULC information. The lack of necessary parameters to perform radiometric correction and blurring effect are several issues that need to be solved to improve the accuracy LULC.

The validation process for a moderate resolution leaf area index (LAI) product (i.e., MODIS) involves the creation of a high spatial resolution LAI reference map (Lai-RM), which when scaled to the moderate LAI resolution (i.e., > 1 km) allows for comparison and analysis with this LAI product. This research addresses two major sources of uncertainty in the creation of the LAI-RM: (1) the uncertainty associated with the indirect in situ optical measurements of southeastern United States needle-leaf LAI and (2) the uncertainty in the process of classifying land cover (LC). Uncertainty within the loblolly pine (Pinus taeda) in situ data collection was highest for the assessment of the plant area index (PAI), Le (27.2%), and
the woody-to-total ratio, α, (30.6%). The needle-to-shoot ratio, λE, and the element clumping index, ΩE, contributed 14.9% and 9.3%, respectively, to the uncertainty in the calculation of
LAI. Combining LC differences (3.4%) with the uncertainty within the loblolly pine component resulted in doubling the LAI-RM variability (σ = 0.50 to σ = 0.97) at the 1 km*km validation site located in Appomattox, Virginia, USA.

This study presents an operational case of advancements in urban land cover classification and change detection by using very high resolution spatial and multispectral information from 4-band QuickBird (QB) and 8-band WorldView-2 (WV-2) image sequence. Our study accentuates quantitative, pixel based, image difference approach for operational change detection using very high resolution pansharpened QB and WV-2 images captured over San Francisco city, California, USA (37° 44" 30N', 122° 31" 30' W and 37° 41" 30'N ,122° 20" 30' W). In addition to standard QB image, we compiled three multiband images from eight pansharpened WV-2 bands: (1) multiband image from four traditional spectral bands, i.e., Blue, Green, Red and near-infrared 1 (NIR1) (henceforth referred as "QB equivalent WV-2"), (2) multiband image from four new spectral bands, i.e., Coastal, Yellow, Red Edge and NIR2 (henceforth referred as "new band WV-2"), and (3) multiband image consisting of four traditional and four new bands (henceforth referred as "standard WV-2"). All the four multiband images were classified using support vector machine (SVM) classifier into four most abundant land cover classes, viz, hard surface, vegetation, water and shadow. The assessment of classification accuracy was performed using random selection of 356 test points. Land cover classifications on "standard QB" image (kappa coeffiecient, κ = 0.93), "QB equivalent WV-2" image (κ = 0.97), and "new band WV-2" image (κ = 0.97) yielded overall accuracies of 96.31%, 98.03% and 98.31%, respectively, while "standard WV-2" image (κ = 0.99) yielded an improved overall accuracy of 99.18%. It is concluded that the addition of four new spectral bands to the existing four traditional bands improved the discrimination of land cover targets, due to increase in the spectral characteristics of WV-2 satellite. Consequently, to test the validity of improvement in classification process for implementation in operational change detection application, comparative assessment of transition of various landcover classes in three WV-2 images with respect to "standard QB" image was carried out using image difference method. As far as waterbody class is concerned, there was no significant transition observed in all the three WorldView-2 Images, whereas, hard surface class showed lowest transition in "standard WV-2" image and highest in case of "new band WV-2". The most significant transition was occurred in vegetation class in all of the three images, showing positive change (increase) in standard WV-2 image (0.31 Sq. Km) and negative change (decrease) in other two images (-0.12 Sq. Km for "QB equivalent WV-2" image and -31.15 Sq. Km in "new band WV-2" image) with considerable amount. Similar case was observed with the shadow class, but the difference is, transition from shadow to other classes was negative in all the three WV-2 images which can be attributed to the fact that, "standard QB" image had more shadow area (based on acquisition time and sun position) than WV-2, that means all the band combinations of WV-2 succeeded in extracting the features hidden below the shadow in "standard QB" image. These trends indicate that the overall bandwise transition in landcover classes in case of "standard WV-2" is more precise than other two images. We note that "QB equivalent WV-2" image had narrower band widths than those of "standard QB" image but the observed vegetation change is not prominent as in case of other two images, but at the same time, transition in hard surface and waterbody was discerned more efficiently than "new band WV-2" image. The addition of new bands in WV-2 enabled more effective vegetation analysis, so the vegetation transition results shown by "new band WV-2" image were at par with the "standard WV-2" image, showing the importance of these newly added bands in the WV-2 imagery, with comparatively lower transitions in other classes. In a nutshell, it can be claimed that incorporation of new bands along with even narrower Red, Green, Blue and Near Infrared-1 bands in WV-2 image holds remarkable importance which leads to enhancement in the potential of WV-2 imagery in change detection and other feature extraction studies.