2.0  Robust Design of L & D assessment Tools, Methodology, Guidelines, and Conducting the L & D Assessment works :

The loss and damage tools developed for the assessment of L & D aftermath of disaster, L & D caused by recurrent climate change impacts over the landscape elements( above 200 elements ) , livelihoods and livelihood assets, elements for food security, water security, human security over the climate frontlines being caused by the protracted climate change phenomena, recurrence events and spill over multi-dimensional impact.

The MHEWC considers ICT/GIS & Remote Sensing/Apps/UAV-based acquisition of climate risk and vulnerabilities from the landscape, impact attributes primary data collections from conducted field survey, participatory process, review secondary data sources and essentially to calibrate with ground truthing and spot verification for customized uses for rationing and presenting with a robust proposed scientific methodology ( illustrated below)  to validate the process. MHEWC intended to use the open-source GIS mapping tools with the proposed tools :

1. Primary Spatiotemporal data from ICT tools /GIS map and latest Remote Sensing image/GPS Apps/UAV(Drone/Lidar- Light Detection and Ranging  )  based  acquisition
2. Impact attribute Primary data collection: Conducting strategic Community risk and vulnerability report (CRVA), primary CRA data collection from household and community, elements captured by GPS apps, socio-economic data captured  by  Kobo-Toolbox
3. Secondary data: BBS HIES datasets, age-sex disaggregated data, Statistical department Census data, Socio-economic ( Poverty data, VGD/VGF datasets from Union Parishad, relevant assessment studies being undertaken
4. Geospatial data: GIS, Remote Sensing, GPS, Apps, Drone capture image, Google Earth Engine, Google Earth,

L & D assessment tools intended to facilitate the calculation of the L & D of local government sectors falling under NAP e,g.,  water resources, Disaster, social safety and security, Agriculture, Fisheries, aquaculture and livestock, Ecosystems, wetlands and biodiversity, Urban areas, Local Government Institutes, etc.

MHEWC  to develop Local Government Base maps with  Climate/multi-hazard exposure, risk and vulnerabilities (CRVA), impact-based forecasting and early warning and informed geospatial tools for facilitating  L & D assessments highest grid resolution, and supporting Post-disaster needs assessment (PDNA) and other risk-informed sectoral planning.

Developing CT tools (GIS maps, GPS apps based survey, Remote sensing image analysis, Kobo-toolbox based) driven Community-based climate exposure, risk and vulnerability (CVRA) assessment, Geospatial technology based multi-hazard risk assessment, risk mapping, repository database development, etc.

Develop ICT-based geospatial tools and a dashboard with the tailormade repository of CRVA   sufficiently to leverage designing the post-disaster recovery projects by systematically calculating loss and damage of different onset of impending multi-hazards, while multi-hazards take landfall and interacting with ground-level elements and post-disaster L & D by-and-large over the affected areas.

3.0 Proposed activities:

3.1 Prepare CRVA tools:

Development of GIS-based map on Local Government administrative boundary coverage to be used for CRVA purposes and all administrative layer base maps to be further customized by including necessary geospatial elements of all necessary elements over the landscape e.g.,  physical infrastructures, communication networks, land use layer, built-up infrastructures, basic service delivery services structures( infrastructures), waterbodies, socio-economic structures( infrastructure), commercial installations, market place, natural resources, agricultural elements( classified agricultural lands), environmental resources(elements), emergency shelters, service trigger points, etc., to be compiled from  Remote sensing google earth engine, drone-captured image etc., and ultimately to develop a comprehensive GIS maps for capturing climate and muti-hazards exposure, risks and vulnerabilities from the households and community level.

3.2 Conduct climate and multi-hazard exposure, risk, and vulnerability (CRVA) survey: 

The updated CRVA baseline repository and tailormade informed tools are the primary requisites to categorizing and calculating element-specific exposure, risks, and vulnerability being induced by the impending nature of transboundary and residuals hydrometeorological, climatic, and non-climatic events with the likelihood of high-impacts.  MHEWC intended to conduct Union level CRVA survey e.g., a sample cluster survey considering the landscape vulnerability, homogeneous socio-economic conditions, livelihood assets, prevalence of income poverty, etc. Intended CRVA to capture elements-wise primary datasets of the elements plotted in the Local Government base map which essentially calculates and clusters the anticipatory loss and damages of impending multi-hazards and persistent residual risks lingering on the ground with persistent level and intensified levels. MHEWC Limited also intended to conduct a Kobo-Toolbox-based socio-economic sample survey based on homogenous socio-economic, poverty, and livelihood conditions at household and community levels.

4.3 Develop CRVA database:  

The CRVA repository database is to be developed with hybrid sources of information, e.g., primary data from the CRVA survey, and cluster household survey.  Secondary data from multiple sources e.g.  Statistical (HIES) datasets, age-sex disaggregated data, Statistical Census data, Socio-economic (Poverty data, VGD/VGF datasets from Local Governments ( entities), and other relevant assessment studies being undertaken. Other relevant datasets from the local level livelihood interventions. Exclusive PRA process (FGD, KII, transact walk) to be conducted at the household level (sample survey of homogeneous socio-economic group, vulnerable community, sector-specific CRVA survey). CRVA is intended to capture climate and multi-hazard risks and vulnerabilities with summarized historical hazards and disaster repositories of the last 30 years from secondary sources.

3.4 : Feeding local level  Impending multi-hazard exposure, risk, and vulnerability  by the Uapzila,  Union &  Ward Disaster Management Committee to geospatial platform :

 Mobile phone ( android ) based WhatsApp group and GPS apps to be installed with oriented to the UDMC/WDMC members, local volunteers, vulnerable groups, NGO workforce, NGO running IGA groups etc. to provide the geocooMHEWCnates to project actual areas of the extent of flooding, flash flooding, storm surge, coastal flooding,  salinity intrusion to livelihood assets, sudden onset tornadoes, heavy rainfall-induced loss and damages are likelihood and prevailing on the ground.

3.5 Develop GIS map-based online geospatial tools and evidence-based L & D Calculations:

Since intended L & D assessment tools and dashboard would be deployable to calculate L & D on the fly at the event of multi-hazard early warnings are being issued. MHEWC Limited intended to develop a Geospatial online platform using Open layer and leaflet tools. The Local Government Disaster Management Committee, National Civil Protection Committees, Village/Community/Ward level Disaster Management Committee, extension officers, NGO servicemen, lead farmers, and social volunteer groups to provide inputs of geocooMHEWCnates on the area of extents of prevailing hazards on the grounds and L & D figures to delineated and distribute on the geospatial online platform. Develop a High-resolution lowest level local government grid map (5km×5km) and show multi-hazard conditions impacting the elements to easily calculate the L & D of physical, socioeconomic, and financial assets at the local level.

4.6 Develop geospatial platform :

To facilitate the ICT driven Evidence-based geospatial services enabled facilities  MHEWC is intended to require to installation and operationalizing Open source Geospatial platform ( geonode, GeoServer, Postgis, PostgreSQL, Leaflet, Open Layer ) interfacing with ArcGIS Pro / QGIS software and uploading CRVA Union base maps with elements and attributes information. The technical aspects of the system to overlay the multi-hazard exposure, risks, and vulnerabilities (salinity intrusion areas, groundwater polluted areas, water looing areas, river flooding, flash flooding, riverbank erosion, persistent water logging to agricultural lands, char lands, standing crop loss. Seeding and sapling loss etc., damage of socioeconomic infrastructure, systemic L & D of the value chain) land over the highest grid resolution and the system.

4.0 Usability of evidence-based geospatial online/offline  System for L & D assessment  :

4.1  Facilitate dashboard-based government mechanism of  climate/multi-hazards exposure, risk, and vulnerability by engaging Stakeholders :

The intended geospatial-enabled evidence-based platform be able to govern local government Disaster management authorities to formulate the GIS-driven standing orders on disaster over the impending multi-hazards early warning being issued locally. The system is to be robustly designed as an integrated system one-stop solution architecture inclusively to facilitate risk governance. Facilitating digital standing orders on disaster ( SOD) critically advising the 5 W ( who will do, what when, where, and how) governance modality of the whole stakeholders at the local level e.g. disaster management committees, NGO-driven IGA groups, women groups, CSO, CBO, community volunteers, student brigade, local clubs, charities, and social groups ) to access to the geospatial platforms.

4.2   Calculating anticipatory  of L & D onset of  impending multi-hazards early  warning being issued :

The process encompasses to develop impact based forecasts with 5km/5km resolution ( or less) compiled from forecast CSV files of weather department-issued forecasts, BMD observation datasets of weather parameters, Department of Agriculture Extension ( DAE) local government-level agroclimatic data, and overlying weather datasets with Upazila Base map and prepare the impact based forecasts for the localized multi-hazards are likely to be impending.  Essentially the comprehensive CRVA repository database is to be linked with the Union GIS-based map and all the  GIS maps are to be uploaded to the geospatial platform. Given the circumstances that the flood or cyclone early warning being issued, the extent of areas to be delineated based on analyzing the intensity, tracking path of the cyclone, the extent of probable flooding areas with HFL, the extent of river bank areas likely to eroded and washed out by river flooding, delineating intensity and extent of areas being impacted by the intensity of flooding. Delineating the overall extent of areas classified by types of impending multi-hazards( salinity intrusion areas, groundwater polluted areas, water looing areas, river flooding, flash flooding, riverbank erosion, persistent water logging to agricultural lands, char lands, standing crop loss. Seeding and sapling loss etc., damage of socioeconomic infrastructure, systemic L & D of value chain) and intensities over the Upazila and Union GIS map. GIS spatial analytical tools to calculate types of elements falling under severity/intensity of hazards are likely to impend a summarized L & D datasets, maps, and infographic presentation to be made available for humanitarian action planning, PDNA, and recovery planning.

Evidence-based Identification and Spatiotemporal Assessment of the Loss and Damage in Satkhira and Kurigram districts of Bangladesh

1.0  Introduction:

1.1 Overview of Assignment :

The Satkhira and Kurgram districts being impacted by the persistent and perennial nature of climate risks and vulnerabilities as because of Satkhira positioned at the edge of bay of Bengal and impacted by multiple coastal hazards and Kurigram on the northern Bangladesh embrace the riverine floods in beginning of the monsoon being triggered by the giant Brammapurta and Tibetan Basin. This study intended to promote the geospatial technology based tools to assess the Loss and damages caused by multiple and seasonal impeding as well as persistent hydrometeorological exposure risk, vulnerability..

The assignment intended acquisition of climate risk and vulnerabilities from the primary data sources while secondary data sources to be calibrated with ground truthing and spot verification for customized uses for rationing and presenting with a robust proposed scientific methodology (illustrated below) to validate the process. RDI Limited intended to use the open-source GIS mapping tools with proposed tools.

2.0 Objective of the  assignment:

The main objective of the assignment to devise the appropriate assessment tools (data acquisition from primary and secondary sources, data collation techniques, Geospatial technology based spatial analytics of Disaster impacts at highest resolution over the sectoral elements being impacted (lost & damaged) of the selected study area.

Systematic steps and procedure being outlined on how to assess the L & D aftermath of disaster occurred. Considering  the study area geographical perspective two disaster events  ( Flood and storm structure) scientific method being proposed analyzing the L & D . Due to time-limitation the study area narrowed down to total two vulnerable Unions of Kurigram and Satkhira. Given the geographical settings , the Satkhira is the forefront of coastal vulnerability in any given parts of the world and over the northern Kurigram district embrace the severe riverine flooding of the beginning of the monsoon season.

2.0 Assessment Approach

2.1 Assessment Scoping Road map :

2.2  Assessment Preparation:

At the preliminary stage, the scoping the assessment L & D aftermath disaster normally depends on acquisition of overall impacts of disaster takes the landfall from the JNA to identify the high impact areas, impact level and other sectoral first-hand impression of impacts trailed by the disaster.

• Analyzing the datasets ( Geospatial and statistical )  scientific approach being proposed for the
• Procedure to L & D of selected Unions of  Kurigram and Satkhikhra considering the past severe disaster.

2.2   Desk review:

1. Review database on  secondary sources of age-sex disaggregated poverty, demographic information from the national statistics, local government institutions, sector departments, stakeholders, and actors at the local level.
2. Review the overall persistent and perineal risk and vulnerability assessment already conducted.
3.  Acquisition of pre-disaster satellite image(high-resolution), aerial photographs, drone Images, sector developed GIS maps distribution of spectral ,maps( Agricultural land use, cropping maps )
4. Review the detailed sectoral Information Management System (IMS), GIS maps on the sectoral elements, physical and infrastructure, socioeconomic, livelihood elements, built-environmental and natural resources, water resources, commercial, market infrastructure and value chain operating elements etc., at the local level.
5. Review local economical functionaries with cooperative firms, Integrated farms, small-holder farms, geolocation Value chain operators and elements, service trigger points, economic performance etc.

1. Preliminary tools preparations:

1. Preparation/Customize  GIS Maps:

• Synergy settings pre & post disaster impacts  :

• Preparation maps/tools from secondary data sources:  Secondary data analysis :

3.1.2 Data collection method:

1. Statistical and attribute data collection:

• Geospatial data collection :

1. Satellite  data: Latest satellite…………….._ of affected areas for impact analysis
2. Drone/UAV captured image ( aerial photo, multispectral and panchromatic  image ) for impact analysis

3.1.3   Conduct consultation[2] :

4.0    Methodology and Approaches  of   Loss and Damage Estimation

  Assessment Preparation:

The above methodology will be used to calculate loss and damage (L&D) for Storm Surge event in Satkhira area.  The process uses historical storm surge data to predict storm height by performing frequency analysis for different return period.  A storm surge decay model (for calculating how surge depth decreases after landfall with increasing distance from the coast) will be used to calculate surge decay coefficient. Then predicted storm surge height, surge decay coefficient and Digital Elevation are fed into surge model and by ArcGIS Spatial Analysis, storm surge depth and extent are calculated for different return period.

After getting surge depth and extent together with vector data (elements at risk), damaged elements will be identified using ArcGIS platform. As elements at risk or inundated features are identified, categorize the elements sector wise and apply ECLAC to calculate the loss and damage for storm surge.

The above methodology will be used to calculate loss and damage (L&D) for flood event in Kurigram area.  The process handles satellite based remote sensing data for L&D estimation.  To extract flood extent, Sentinel-1 SAR data will be used to avoid cloud cover during flood (monsoon) time. We could use SANP (Sentinel Application Platform) for classification of the image. Before classification necessary preprocessing (speckle filter/terrain correction etc.) need to be done. We can also use Google Earth Engine to identify flood extent.

On the other hand, to extract the Land cover, Landsat 8 or Sentinel-2 images of pre-monsoon time will be used. Before going for classification (supervised/unsupervised), necessary corrections (atmospheric, geometry, radiometric) need to be carried out. This operation can done in Google Earth Engine or Erdas Imagine.

After getting flood extent and land cover maps together with vector data (elements at risk), using ArcGIS Spatial Analysis and different commands, damaged elements will be identified. From time series data, flood duration map can also be developed to get inundated duration for some crops like paddy.

As elements at risk or inundated features are identified, categorize the elements sector wise and apply ECLAC (a damage and loss calculation methodology developed by Economic Commission of Latin America and the Caribbean). The sector wise detail calculations are shown in another section of this inception report.

Loss and Damage Estimation for Storm Surge

Loss and Damage Estimation for Salinity

The above methodology will be used to calculate loss and damage (L&D) for Salinity Intrusion in Satkhira area.  In this methodology, to identify salt affected area, Landsat 8 OLI (Operational Land Imager) satellite data will be used. After performing necessary correction in the image, different indices like NDSI (Normalized Difference Salinity Index), NDVI (Normalized Difference Vegetation Index) are calculated using Image processing software like Erdas Imagine or ArcGIS Pro. From indices values, intensity of Salinity can be assumed. If NDSI value is high, Salinity will be high. But for NDVI, this relationship will be reverse.

After getting the indices and soil salinity  extent together with vector data (elements at risk), damaged elements (mainly crops) will be identified using ArcGIS platform. As elements at risk are identified, categorize the elements sector wise and apply ECLAC to calculate the loss and damage for salinity intrusion.

5.0    L&D Repository development  

A proper professional-grade data visualization through maps, infographics, databases, etc.

would be required to present the L&D in both spatial and temporal manner. The specific objectives of the study would therefore be:

• Generating evidence to conclusively identify and declare L&D within the set geographic scale.
• Spatio-temporal comparison of the data and analysis of the trend of L&D
• Preparation of the L&D maps and infographics as part of the evidence generation
• Determining deterioration/ accumulation/ assimilation of geographical properties of the areas
• Using the study results as reference/evidence devising policy recommendations/ advocacy/ communications regarding mitigation/ adaptation/ L&D negotiations.

verification for customized uses for rationing and presenting with a robust proposed scientific methodology (illustrated below) to validate the process. RDI Limited intended to use the open-source GIS mapping tools with proposed tools:

1) Primary data: Conducting strategic Community risk and vulnerability report (CRA), primary CRA data collection from household and community, elements captured by GPS apps, socio-economic data captured by Kobo-Toolbox

2) Secondary data: BBS HIES datasets, age-sex disaggregated data, BBS Census data, Socio-economic (Poverty data, VGD/VGF datasets from Union Parishad, relevant assessment studies being undertaken

3) Geospatial data: GIS, Remote Sensing, GPS, Apps, Drone capture image, Google earth engine, Google earth,

L & D assessment tools intended to be fascinating to calculate the L & D of local government sectors

Unions of Kurigram district with Climate/multi hazard exposer, risk and vulnerabilities (CRVA), impact-based forecasting and early warning and informed geospatial tools for facilitating L & D assessments at 5kmX5km grid resolution, and supporting post-disaster needs assessment (PDNA) and other risk-informed sectoral planning. Considering the assessment time limit we would like to conduct a primary field survey of 25% of the most vulnerable Unions.

The FAO D&L methodology provides a set of procedural and computational steps for calculating

damage and loss from disasters in the agriculture sectors. It can be applied to a wide range of disaster

events, including climate-related events, from large-scale shocks to small-scale events. It can be used

in different national and regional contexts: and at various time scales.

The methodology’s five components cover direct damage and loss to crops, livestock, forestry,

aquaculture and fisheries. Together, they capture the total effect of disasters on agriculture:

Agriculture Damage Computation Procedures

( Source Copyright © 2025 USACE Hydrologic Engineering Center ,  Powered by Scroll Viewport and Atlassian Confluence )

When flooding occurs in agricultural areas, interruptions to the planting, growing, and harvesting of crops result in economic impacts. The computational procedures and data requirements for calculating economic losses to agriculture due to flooding is described below.

Geospatial Distribution of Crops

The NASS Cropland Data Layer is a product that represents the geographic distribution of the types of crops throughout the entire United States. NASS provides the Cropland Data Layer in GeoTiff format, where each cell represents a crop type for a 30 meter grid cell. This data can be leveraged directly from the NASS API to streamline the collection of the type and distribution of crops in the study area. For information about the NASS Cropland Data visit the following site https://www.nass.usda.gov/Research_and_Science/Cropland/SARS1a.php. 

• The image above is taken from an example NASS CDL tif. In this example each yellow pixel represents corn and each dark green pixel represents soybeans.
  The distribution of each agricultural category in the study area is used in combination with the planting, harvesting, and crop loss relationships in the calculation of agricultural flood damage.
  Hazard Information
  Damage to crops depends on the location, timing of the event and the duration of the event. The timing determines if the crops are planted, and how far the crop is in the production cycle. The duration defines how much damage the crop will sustain. Arrival time is expressed as time of the arrival of water above the soil (Time T1 in the image blow). Duration is defined in decimal days how long the water is above the ground surface elevation (GSE) (time T1 to T3 below) . The input must be geospatial to be intersected with the NASS CDL data.
  
• 
  
• 
  Crop Schedule
  The Crop Schedule defines a window of crop planting time and the time required for the maturity of the crop. A crop schedule has three parameters, the start time for the planting window, the end time for the planting window, and the duration in days for the crop to reach maturity. A crop schedule can be used to compute the crop damage case which helps determine how to apply the crop loss methodology.
  
  
  The arrival time of the flood is compared to the start planting window and the days to maturity to determine if the flood started outside of the flood season. If the event started outside of the flood season, then the duration of the flood event is used to determine if the planting window was impacted. If the duration of the event covers the entire planting window either the crop is not planted at all or a substitute crop is planted. If the duration of the event delays planting, then the crop damages are computed in terms of the delayed planting losses.
  If the arrival time of the event happens after the start of the planting window but before the days to maturity the crop is determined to be impacted, and the duration is used to determine the significance of the impact. 
  Crop schedules can have planting windows in one calendar year and mature in the next calendar year, but the base implementation of the crop schedule requires that the days to maturity be less than 365.
  Crop Loss Function
  The crop loss function describes the loss of expenses that will not be recouped by the farmer. It is based on the loss of crop or the reduction in the harvest for the crop based upon the arrival time and duration of the event.
  Damage to crops is dependent upon the value added by the farmer to the field at the time of flooding, and the vulnerability of the crop at that time to flooding. The driving damage parameters are duration and the timing of the event.
  Crop Budget Data
  In order to determine the exposed value across time the user inputs a Production Function. The Production Function (P) is expressed as the sum of the fixed costs (FC) such as rent or land taxes and monthly variable costs (MVC) and is time dependent. The Production Function, specifically the monthly variable costs, are crop dependent. The crop production function information typically comes from local Cooperative Extension System Offices, which can be found on the USDA’s National Institute of Food and Agriculture (NIFA)’s website. The final components of the crop planting data required are the dates of the first possible and last possible plantings. The crop planting data is used to create a curve see the Seasonally Based Value image below, that represents the cumulative cost of the inputs in the field at any point during the year from the first plant date.
  The cumulative cost is converted to a percentage of the maximum total cost of the inputs in the field to calculate the potential losses at any point during the year. The maximum total cost input into the field is not necessarily equivalent to the total cost experienced by the farmer for the crops pulled from the field. First, the harvest cost and shipping costs need to be added to all of the inputs to get the full cost to produce the crops. The computation uses a proration of the total cost input less the harvest costs as a proxy for an exposed value.
  
  
  
  
  Variable
  Name
  Description
  
  Pt(c)
  
  Production Function
  The cumulative fixed and variable costs.
  
  c
  
  Crop
  example: Corn, Soybeans, etc…
  
  MVCt(c)
  
  Monthly Variable Costs
  Costs of growing the unique crops in the field. example: detasseling corn.  
  
  FCt(c)
  
  Fixed Costs
  Rent and land taxes. 
  
  EV(Pt(c),at)
  
  Exposed Value
  The cumulative fixed and variable costs at the arrival time of the event
  
  at
  
  Arrival Time
  The time inundation begins
  
  Crop Value
  Crop characteristics are necessary to compute the appropriate reduction in crop value due to the costs associated with harvesting. The required input characteristics are harvest date, harvest cost, yield, unit price, and percentage of total crop value lost due to late planting. Crop values should reflect prices with subsidies removed for federal benefit to cost analysis.
   
  
  Pt(c)=MVCt(c)+FCt(c)
  
  The Exposed Value (EV) is dependent upon the arrival time of the event and the production Function
  
  EV(Pt(c),at)=∑t=0atPt(c)
  
  To illustrate how the seasonally based value changes with time an example plot below is provided. In the figure below the seasonally based value is expressed as a ratio of seasonal value to total value.
  
   
  As shown in the figure above, if a crop is planted later in the season, the crop has a different value curve (e.g., red curve in the figure above). For seasonally based value changes, it is assumed that if the flood does not interrupt the farmer, the crops will be planted by the first plant date. Thus, late planting will only occur if the flood start plus the duration ends before the late plant date but after the first plant date. This seasonally based value is intended to reflect that farmers may adopt different schedules for application of watering, fertilizer, and other processes in raising the crop; or, the farmer may choose to grow another crop altogether.
  The Loss (L) is a function of the crop type the arrival time and the duration. The loss is computed by taking the exposed value and multiplying it by the damage percentage computed by the crop type, arrival time of the flood and the duration of the event. The duration defines which seasonal loss function to choose and the arrival time defines which percentage loss value to pick off of the duration loss function family.
  
  L(c,at,d)=EV(Pt(c),at)∗D(c,at,d)
  
  Damage fluctuates based on duration and the maturity of the crop as shown in the example relationships in the figure below. Some crops can better withstand short duration floods if the crops are out of critical development stages. 
  
  As shown in the figure above, longer duration flooding would yield larger losses, but more mature plants would be more robust to damage. This input (planting, harvesting, and crop loss relationship inputs) is provided by the user, and must reflect their expectation of how plants will react to different durations of flooding throughout the growing season.
  Computational Results
  Results from the agricultural damage computations include: a point based shapefile specifying the crop type; arrival time; duration; location; and total damage for each damaged grid cell. These results can be aggregated by crop type, or by any polygon loaded as a boundary shapefile.
• .

Crop-Agricultural Damage and Loss estimation/calculation  

Analysis impacts over standing crops  :

Technical Approach of Methodology :

Consultation :

Data Collection :

a)            Geospatial Datasets:

• LANDSAT-1f SAR
• image,
• Sentinel-1
• Google earth, Google Earth Engine
• GIS base Map (Secondary Sources)

b)            Statistical datasets:

• Conducted CRA reports from UP/ NGOs
• Crop extent area and cropping’s data from DAE
• Statistical data on fisheries pond from Fisheries department
• Other features data from Upazila Government Departments
• HIES data from BBS
• Other relevant datasets ( e.g. Oxfam etc.)

Calculation of L & D

1)            Defining baseline risk and vulnerability conditions:

• Baseline Scenario – Detailed spatial distribution of multi-hazards over the GIS Base maps (Most vulnerable, Moderate Vulnerable, Vulnerable ) based on frequency and impact intensity .
• Delineated impact areas from historical multi-hazards with impact categories(Most vulnerable, Moderate Vulnerable, Vulnerable )

2)            Estimation/Statistics of elements (food sector/livelihood) are likely to be damaged( forecasted)  :

• Anticipatory loss and damage calculations based on Impending Hazards (forecasted Flood, water logging, flash floods, tidal surge, storm surge, high winds, hailstorm, nor ’wester, tornadoes etc.)  = Extent of areas of hazards are likely to be impacting over the number/volume of elements (food/livelihood sector) to be impacted
• Statistics of elements (farmland, standing crops, Seedling/Sapling, homestead gardens, fishing ponds) likely to be damaged
• Calculation of economic loss of damaged farmlands, standing crops, Seedling/Sapling, homestead gardens, fishing ponds ( Current market price)

3)            Calculation/Estimation/Statistics of elements( food sector/livelihood) are being damaged by the hazards/disaster occurred:

• Statistics of elements (farmland, standing crops, Seedling/Sapling, homestead gardens, fishing ponds )  being impacted by disastger  ( Flood, storm surge )  
• Calculation of element specific economic loss of damaged farmlands, standing crops, Seedling/Sapling, homestead gardens, fishing ponds ( Current market price)

[1]&2  Due to time-limit the process may not be followed

For complete tools/ documents – Please email at : zmsajjad@gmail.com

This research is strictly being copyrighted by the researcher: Z M Sajjadul Islam , Multi-hazard early warning Expert

For system design, ICT Technologies, Geospatial & IT Programmatic  coding, and other issues related to scientific-based L & D assessments, please email: zmsajjad@gmail.com

Call me at WhatsApp: +88 01711 979179 , Skype: zmsajjad