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A contribution to the special feature 'Blue Carbon' organized by Catherine Lovelock.Electronic supplementary material is available online at https://doi.org/10.6084/m9.figshare. c.4444169.We assessed the carbon stocks (CS) in mangroves that developed after a magnitude 7.1 earthquake in Silonay, Oriental Mindoro, south Luzon, The Philippines in November 1994. The earthquake resulted in a 50 cm uplift of sediment that provided new habitat within the upper intertidal zone which mangroves colonized (from less than 2 ha pre-earthquake to the current 45 ha, 23 years post-earthquake). The site provided an opportunity for a novel assessment of the rate of carbon sequestration in recently established mangroves. The carbon stock was measured in above-ground, belowground and sediment compartments over a seaward to landward transect. Results showed a mean carbon stock of 549 + 30 Mg C ha 21 (of which 13% was from the above-ground biomass, 5% from the below-ground biomass and 82% from the sediments). There was high carbon sequestration at a 40 cm depth that can be inferred attributable to the developed mangroves. The calculated rate of C sequestration (over 23 years post-earthquake) was 10.2 + 0.7 Mg C ha 21 yr 21 and is comparable to rates reported from mangroves recovering from forest clearing. The rates we present here from newly developed mangroves contributes to calibrating estimates of total CS from restored mangroves (of different developmental stages) and in mangroves that are affected by disturbances.

Recent technological advances in remote sensing have led to significant increases in temporal, spatial, and spectral resolutions of these images. Improvements have also been made in data availability and accessibility, as well as advances in the processing methods required to effectively interpret these large amounts of data. These developments have led to an increase in the opportunities for the use of satellite imagery for more effective disaster risk management.One such application that has advanced because of these improvements is post-disaster damage assessment. Traditionally, in a post-disaster scenario, a team of experts is sent to the impact area to conduct a ground survey and assess damages (often connected to a Post-Disaster Needs Assessment report), which the Asian Development Bank (ADB) typically undertakes jointly with impacted country governments and other relevant agencies. While extremely useful, this process is time-consuming, costly, and problematic because often access to the affected area is difficult, dangerous, or restricted. Alternatively, governments, and especially public and private insurance companies, use risk models to quantify the damages. Risk models, however, can be inaccurate and are generally unable to account for compounding or cascading events, which are much more difficult to quantitatively model. Similarly, the standard approach to nowcast disaster impacts, which relies on risk models, does not typically account for the compounding impact of various hazard phenomena, such as wind and rainfall associated with tropical cyclones.The grant fund for the study was received from the Japan Fund for Prosperous and Resilient Asia and the Pacific financed by the Government of Japan through the Asian Development Bank.

ABSTRACT:The Agricultural Resources Extraction from LiDAR Surveys (PARMAP) project component of the Nationwide Detailed Resources Assessment using LiDAR (Phil-LiDAR 2) Program aims to produce detailed agricultural maps using LiDAR. Agricultural land cover at crop level was classified through object based image analysis using Support Vector Machine as classifier and LiDAR derivatives from point cloud (2 points per sq.m.) and orthophoto (0.5-meter resolution) as inputs. An accuracy of at least 90%, assessed using validation points from the field and through image interpretation, was required before proceeding to post-processing and map lay-out. Knowledge sharing and capacity development facilitated by the University of the Philippines Diliman (UPD) enabled partner universities across the Philippines to produce outputs for their assigned region. Considering output layers were generated by multiple teams working on different landscape complexities with some degree of data quality variability, quality checking is crucial to ensure accuracy standards were met. UPD PARMap devised a centralized and end-to-end scheme divided into four steps -land classification, GIS post-processing, schema application, and map lay-out. At each step, a block is reviewed and, subsequently, either approved or returned with documentation on required revisions. Turnaround time of review is at least one block (area ranging from 10 to 580 sq. km.) per day. For coastal municipalities, an additional integration process to incorporate mapped coastal features was applied. Common problems observed during quality checking include misclassifications, gaps between features, incomplete attributes and missing map elements. Some issues are particular to specific blocks such as problematic LiDAR derivatives. UPD addressed these problems through discussion and mentoring visits to partner universities. As of March 2017, a total of 336 municipal agricultural maps have been turned-over to various stakeholders. For the remaining months of the program, an additional 360 maps are expected to be distributed.

New technologies and global datasets enable transport projects to be assessed more effectively and efficiently. Geospatial data are available retrospectively and remotely, which is particularly useful for evaluators working in countries with constraints on their access to data, including those caused by COVID-19 pandemic. Another advantage is that data quality is the same and comparable across countries. This paper reports the results of geospatial portfolio analysis and economic impact analysis with geospatial data. It generated several interesting findings. For example, the contribution of projects carried out by the Asian Development Bank (ADB) became visible, as about 290 million people live along the road alignments of ADB projects and are therefore potential beneficiaries. Their presence and greater economic activity are indicated by increases in the radiance of nighttime light. The data also demonstrated which countries need to make additional efforts to reduce CO2 emissions from the project areas. Two levels of impact analysis using nighttime light were carried out, which measured local benefits of economic growth. The first assessed a national highway project in Armenia. The contribution of the project to economic growth was more than 2.5% per year. The second found that 33 transportation projects had made an average annual contribution to economic growth of 5%. The authors compared the impact assessed by nighttime light with conventional economic analysis using economic internal rates of return, measuring benefits enjoyed by road users and administrators, and observed a positive correlation between the two.