Flood Vulnerability Analysis using Weighted Overlay

Several areas in Bengkulu City are affected by floods every year; one of the worst is the Sungai Serut sub-district.  The existence of a river that stretches along Sungai Serut also plays a role in floods over the Bengkulu area. Furthermore, this study to see the priority area over this district is still limited.  Mapping flood areas is one way to provide information, especially for decision-makers in flood control using geographic information systems. Our research aims to map flood vulnerability areas based on land height, slope, and river buffer by weighting these criteria. This thematic map is the result of overlaying several maps according to the flood vulnerability criteria. The results of this study indicate that Sungai Serut is an area with "Very Vulnerable" and "Vulnerable" levels of flood vulnerability. The story of flood vulnerability is dominated by gently sloping contours, with land elevation levels mostly below 20 m above sea level. In addition, the thing that exacerbates the potential for flood vulnerability is the existence of the river that runs along the Sungai Serut sub-district.Several areas in Bengkulu City are affected by floods every year; one of the worst is the Sungai Serut sub-district.  The existence of a river that stretches along Sungai Serut also plays a role in floods over the Bengkulu area. Furthermore, this study to see the priority area over this district is still limited.  Mapping flood areas is one way to provide information, especially for decision-makers in flood control using geographic information systems. Our research aims to map flood vulnerability areas based on land height, slope, and river buffer by weighting these criteria. This thematic map is the result of overlaying several maps according to the flood vulnerability criteria. The results of this study indicate that Sungai Serut is an area with "Very Vulnerable" and "Vulnerable" levels of flood vulnerability. The story of flood vulnerability is dominated by gently sloping contours, with land elevation levels mostly below 20 m above sea level. In addition, the thing that exacerbates the potential for flood vulnerability is the existence of the river that runs along the Sungai Serut sub-district

Percent of building density (PBD) of urban environment: a multi-index approach based study in DKI Jakarta Province

This study developed a model to identify the percent of building density (PBD) of DKI Jakarta Province in each pixel of Landsat 8 imageries through a multi-index approach. DKI Jakarta province was selected as the location of the study because of its urban environment characteristics.  The model was constructed using several predictor variables i.e.  Normalized Difference Built-up Index (NDBI), Soil-adjusted Vegetation Index (SAVI), Normalized Difference Water Index (NDWI), and surface temperature from thermal infrared sensor (TIRS). The calculation of training sample data was generated from high-resolution imagery and was correlated to the predictor variables using multiple linear regression (MLR) analysis. The R values of predictor variables are significantly correlated. The result of MLR analysis shows that the predictor variables simultaneously have correlation and similar pattern to the PBD based on high-resolution imageries. The Adjusted R Square value is 0,734, indicates that all four variables influences predicting the PBD by 73%

IrrMapper: A Machine Learning Approach for High Resolution Mapping of Irrigated Agriculture Across the Western U.S.

High frequency and spatially explicit irrigated land maps are important for understanding the patterns and impacts of consumptive water use by agriculture. We built annual, 30 m resolution irrigation maps using Google Earth Engine for the years 1986–2018 for 11 western states within the conterminous U.S. Our map classifies lands into four classes: irrigated agriculture, dryland agriculture, uncultivated land, and wetlands. We built an extensive geospatial database of land cover from each class, including over 50,000 human-verified irrigated fields, 38,000 dryland fields, and over 500,000 km2 of uncultivated lands. We used 60,000 point samples from 28 years to extract Landsat satellite imagery, as well as climate, meteorology, and terrain data to train a Random Forest classifier. Using a spatially independent validation dataset of 40,000 points, we found our classifier has an overall binary classification (irrigated vs. unirrigated) accuracy of 97.8%, and a four-class overall accuracy of 90.8%. We compared our results to Census of Agriculture irrigation estimates over the seven years of available data and found good overall agreement between the 2832 county-level estimates (r2 = 0.90), and high agreement when estimates are aggregated to the state level (r2 = 0.94). We analyzed trends over the 33-year study period, finding an increase of 15% (15,000 km2) in irrigated area in our study region. We found notable decreases in irrigated area in developing urban areas and in the southern Central Valley of California and increases in the plains of eastern Colorado, the Columbia River Basin, the Snake River Plain, and northern California

MAPPING SPATIAL ACCURACY OF FOREST TYPE CLASSIFICATION IN JAXA’s HIGH-RESOLUTION LAND USE AND LAND COVER MAP

Accuracy assessment of forest type maps is essential to evaluate the classification of forest ecosystems quantitatively. However, map users do not understand in which regions those forest types are well classified from conventional static accuracy measures. Hence, the objective of this study is to unveil spatial heterogeneities of accuracies of forest type classification in a map. Four forest types (deciduous broadleaf forest (DBF), deciduous needleleaf forest (DNF), evergreen broadleaf forest (EBF), and evergreen needleleaf forest (ENF)) found in the JAXA’s land use / cover map of Japan were assessed by a volunteered Site-based dataset for Assessment of Changing LAnd cover by JAXA (SACLAJ). A geographically weighted (GW) correspondence matrix was applied to them to calculate the degree of overall agreements of forest type classes (forest overall accuracy), and the degree of accuracy for each forest class (forest user’s and producer’s accuracies) in a spatially varying way. This study compared spatial surfaces of these measures with static ones of them. The results show that the forest overall accuracy of the forest map tends to be relatively more accurate in the central Japan, while less in the Kansai and Chubu regions and the northern edge of Hokkaido. Static forest user’s accuracy measures for DBF, DNF, and ENF are better than forest producer’s accuracy ones, while the GW approach tells us such characteristics vary spatially and some areas have opposite trends. This kind of spatial accuracy assessment provides a more informative description of the accuracy than the simple use of conventional accuracy measures

Mapping Complex Urban Land Cover from Spaceborne Imagery: The Influence of Spatial Resolution, Spectral Band Set and Classification Approach

Detailed land cover information is valuable for mapping complex urban environments. Recent enhancements to satellite sensor technology promise fit-for-purpose data, particularly when processed using contemporary classification approaches. We evaluate this promise by comparing the influence of spatial resolution, spectral band set and classification approach for mapping detailed urban land cover in Nottingham, UK. A WorldView-2 image provides the basis for a set of 12 images with varying spatial and spectral characteristics, and these are classified using three different approaches (maximum likelihood (ML), support vector machine (SVM) and object-based image analysis (OBIA)) to yield 36 output land cover maps. Classification accuracy is evaluated independently and McNemar tests are conducted between all paired outputs (630 pairs in total) to determine which classifications are significantly different. Overall accuracy varied between 35% for ML classification of 30 m spatial resolution, 4-band imagery and 91% for OBIA classification of 2 m spatial resolution, 8-band imagery. The results demonstrate that spatial resolution is clearly the most influential factor when mapping complex urban environments, and modern “very high resolution” or VHR sensors offer great advantage here. However, the advanced spectral capabilities provided by some recent sensors, coupled with contemporary classification approaches (especially SVMs and OBIA), can also lead to significant gains in mapping accuracy. Ongoing development in instrumentation and methodology offer huge potential here and imply that urban mapping opportunities will continue to grow

Sub-Pixel Classification of MODIS EVI for Annual Mappings of Impervious Surface Areas

Regular monitoring of expanding impervious surfaces areas (ISAs) in urban areas is highly desirable. MODIS data can meet this demand in terms of frequent observations but are lacking in spatial detail, leading to the mixed land cover problem when per-pixel classifications are applied. To overcome this issue, this research develops and applies a spatio-temporal sub-pixel model to estimate ISAs on an annual basis during 2001–2013 in the Jakarta Metropolitan Area, Indonesia. A Random Forest (RF) regression inferred the ISA proportion from annual 23 values of MODIS MOD13Q1 EVI and reference data in which such proportion was visually allocated from very high-resolution images in Google Earth over time at randomly selected locations. Annual maps of ISA proportion were generated and showed an average increase of 30.65 km2/year over 13 years. For comparison, a series of RF per-pixel classifications were also developed from the same reference data using a Boolean class constructed from different thresholds of ISA proportion. Results from per-pixel models varied when such thresholds change, suggesting difficulty of estimation of actual ISAs. This research demonstrated the advantages of spatio-temporal sub-pixel analysis for annual ISAs mapping and addresses the problem associated with definitions of thresholds in per-pixel approaches

A framework for scale-sensitive, spatially explicit accuracy assessment of binary built-up surface layers

To better understand the dynamics of human settlements, thorough knowledge of the uncertainty in geospatial built-up surface datasets is critical. While frameworks for localized accuracy assessments of categorical gridded data have been proposed to account for the spatial non-stationarity of classification accuracy, such approaches have not been applied to (binary) built-up land data. Such data differs from other data such as land cover data, due to considerable variations of built-up surface density across the rural-urban continuum resulting in switches of class imbalance, causing sparsely populated confusion matrices based on small underlying sample sizes. In this paper, we aim to fill this gap by testing common agreement measures for their suitability and plausibility to measure the localized accuracy of built-up surface data. We examine the sensitivity of localized accuracy to the assessment support, as well as to the unit of analysis, and analyze the relationships between local accuracy and density / structure-related properties of built-up areas, across rural-urban trajectories and over time. Our experiments are based on the multi-temporal Global Human Settlement Layer (GHSL) and a reference database for the state of Massachusetts (USA). We find strong variation of suitability among commonly used agreement measures, and varying levels of sensitivity to the assessment support. We then apply our framework to assess localized GHSL data accuracy over time from 1975 to 2014. Besides increasing accuracy along the rural-urban gradient, we find that accuracy generally increases over time, mainly driven by peri-urban densification processes in our study area. Moreover, we find that localized densification measures derived from the GHSL tend to overestimate peri-urban densification processes that occurred between 1975 and 2014, due to higher levels of omission errors in the GHSL epoch 1975.Comment: 28 pages, 17 figure

Application of Geospatial Technologies for Land Use Analysis and Soil Science Education

This research is composed of three parts: 1) Adaptation of Soil Judging to Libya, 2) Predicting the classes and distribution of salt-affected soils in Northwest Libya, and 3) Geospatial analysis of land use change in the Savannah River Basin using Google Earth. Soil judging (Evaluation) plays an important role in soil science education. Libya has six soil orders according to the U.S. Soil Taxonomy (Entisols, Aridisols, Alfisols, Inceptisols, Vertisols, and Mollisols) and the most common soil orders are Entisols and Aridisols. A Soil judging (Evaluation) scorecard was tested at two different universities in Libya: The University of Tripoli and University of Zawia. Eighty-two percent of Libyan users were not aware of Soil Judging prior to this study. After completing Soil Judging trials in various locations in Libya, ninety-five percent of those surveyed indicated that Soil Judging is useful to the natural science education. Libya is mostly a dry and arid country, where sodicity and salinity problems are often accelerated by the prevailing climatic condition and geographical setting of the area. A framework was identified for classifying and mapping salt-affected soils in northwest Libya using field measurements (ECe, soil pH, and SAR) and Geographic Information Systems (GIS). The majority of soils in this region of Libya are normal (slight degree of limitation). Twenty percent of the topsoil is saline-sodic (extreme degree of limitation). Land use change and the loss of wildlife habitats are serious issues facing the Southeastern United States. Across the Savannah River basin, the major change of land use was deforestation and reforestation during the entire study period with most of the changes located near lakes and water tributaries

Key issues in rigorous accuracy assessment of land cover products

© 2019 Accuracy assessment and land cover mapping have been inexorably linked throughout the first 50 years of publication of Remote Sensing of Environment. The earliest developers of land-cover maps recognized the importance of evaluating the quality of their maps, and the methods and reporting format of these early accuracy assessments included features that would be familiar to practitioners today. Specifically, practitioners have consistently recognized the importance of obtaining high quality reference data to which the map is compared, the need for sampling to collect these reference data, and the role of an error matrix and accuracy measures derived from the error matrix to summarize the accuracy information. Over the past half century these techniques have undergone refinements to place accuracy assessment on a more scientifically credible footing. We describe the current status of accuracy assessment that has emerged from nearly 50 years of practice and identify opportunities for future advances. The article is organized by the three major components of accuracy assessment, the sampling design, response design, and analysis, focusing on good practice methodology that contributes to a rigorous, informative, and honest assessment. The long history of research and applications underlying the current practice of accuracy assessment has advanced the field to a mature state. However, documentation of accuracy assessment methods needs to be improved to enhance reproducibility and transparency, and improved methods are required to address new challenges created by advanced technology that has expanded the capacity to map land cover extensively in space and intensively in time