We have identified certain parameters which we believe (through literature analysis) to reflect the true state of a community’s health. These include agricultural data, water quality data, fire data, mobility data and soil fertility data.

We hypothesize that due to the COVID-19 restrictions and drop in employment in urban centers, migrant labourers belonging to tribal communities increased agricultural activity, resulting in higher yield or increased tendency of slash and burn agriculture. To identify the latter, we use fire incidence data correlated with tribal activity or regions of tribal habitation and agricultural data. 

As a result of increased habitation and population stress in tribal communities, along with heightened agricultural activity, we may hypothesize a depreciation in water quality due to mismanagement of waste. These can potentially identify tribal communities under population stress leading to water pollution as a result of improper water purification facilities. 

Due to the agricultural duress as well as loss of professional management of erosion due to COVID -19 restrictions, we hypothesize that there is a change in soil fertility. Heightened browsing activity may also result in loss of soil fertility. Therefore we wish to analyse the same to observe the impact of soil fertility on tribal communities using satellite data.

A direct effect of COVID -19 restrictions was the impact observed on aid mobility and resource allotment in far-flung tribal communities. These communities which due to micro-scale urbanization might have lost dependence on forests, are heavily dependent on external aid. Through mobility data analysis we might identify regions vulnerable to such outages and disruptions of aid activity. Such disruptions result in increased economic fluctuations and infrastructural disparity between urban centers and far-flung tribal economies.

One way to investigate the repercussions of covid-19 on tribal communities could be with the assistance of environmental data derived from satellite imagery. To infer the consequences of covid-19, we need specific indicators (agricultural, vegetation, atmospheric, soil fertility, water quality) from satellite imagery data that could be analysed to derive a comprehensive idea regarding changes in these communities due to COVID-19. Mobility and aid data could be associated with identifying isolated communities in dire need of relief and hence could be used in our study. In addition, the agricultural changes and their impact on vegetation can allow us to identify and bring forth customised plans to deal with the changes that these communities are undergoing. 

Therefore, we accessed various satellite imagery tools and databases to identify areas which contained data pertaining to our search parameters. We also identified several vulnerable communities through literature analysis, and found satellite data to be lacking in those areas. To continue with our project, we identified two areas, Brazil and Kenya, which had widespread data coverage. This was to act as proof of concept for our project goal. 

Once we identified the areas of interest, we performed sequential and correlational analysis of the various parameters. Each parameter underwent time-series analysis as well to identify any putative change between pre-COVID and COVId situations in these areas.

Below are the images for the various datasets used to design the parameters of our project .

1) Agricultural dataset

Source= https://earthdata.nasa.gov/covid19/explore/global?map=-55.0917%2C-16.1941%2C3.79&layers=agriculture&date=2021-01-01&lState=agriculture%7C0%7C0

Inference- We infered that before and during the covid-19 pandemic the crop harvesting decreased as indicated by the green patches in the image of Brazil.

Source= https://cropmonitor.org/index.php/eodatatools/cmet/

Inference- The above statistical data shows that soil moisture (surface) was decreased during covid ,evaporation stress index increased during covid which indicates drought conditions thereby decreasing soil productivity in Brazil .

2) Mobility dataset

Source- https://eodashboard.org/?country=IN&poi=IN01-N1&indicator=N1

Inference - As there was no official lockdown announced in the Brazil, it is clearly visible that the traffic didn’t come to a halt, and transportation was in continuity. But, the local public was taking all safety measures and precautions themselves, hence all recreation places like parks, travel transit stations, etc, didn’t have any records of mobility.There was no mobility data available for other tribal regions so we couldnt do the analysis.

3)Forest Fires

Source- https://worldview.earthdata.nasa.gov/?v=-112.62571707843246,-37.32249871173282,-6.573012501456063,12.527796017809475&l=MODIS_Terra_Thermal_Anomalies_Night,MODIS_Terra_Thermal_Anomalies_Day,VIIRS_NOAA20_Thermal_Anomalies_375m_Night(hidden),VIIRS_NOAA20_Thermal_Anomalies_375m_Day,MODIS_Aqua_Thermal_Anomalies_Night,MODIS_Aqua_Thermal_Anomalies_Day(hidden),VIIRS_SNPP_Thermal_Anomalies_375m_Night(hidden),VIIRS_SNPP_Thermal_Anomalies_375m_Day(hidden),Reference_Labels_15m(hidden),Reference_Features_15m(hidden),Coastlines_15m,VIIRS_SNPP_CorrectedReflectance_TrueColor(hidden),MODIS_Aqua_CorrectedReflectance_TrueColor(hidden),MODIS_Terra_CorrectedReflectance_TrueColor&lg=true&l1=MODIS_Terra_Thermal_Anomalies_Night,MODIS_Terra_Thermal_Anomalies_Day,VIIRS_NOAA20_Thermal_Anomalies_375m_Night(hidden),VIIRS_NOAA20_Thermal_Anomalies_375m_Day,MODIS_Aqua_Thermal_Anomalies_Night,MODIS_Aqua_Thermal_Anomalies_Day(hidden),VIIRS_SNPP_Thermal_Anomalies_375m_Night(hidden),VIIRS_SNPP_Thermal_Anomalies_375m_Day(hidden),Reference_Labels_15m(hidden),Reference_Features_15m(hidden),Coastlines_15m,VIIRS_SNPP_CorrectedReflectance_TrueColor(hidden),MODIS_Aqua_CorrectedReflectance_TrueColor(hidden),MODIS_Terra_CorrectedReflectance_TrueColor&lg1=true&ca=false&cv=57&t=2020-01-01-T00%3A00%3A00Z&t1=2021-01-01-T18%3A53%3A45Z

Inference - The data showed that in Brazil forests the forest fires increased during covid-19 as compared to the pre covid-era.This implies that the tribal community used burn and slash method for farming thereby changing the soil fertility.

We analysed satellite images for four main categories

1) Agricultural - We used Crop harvesting, vegetative reproduction, soil fertility data was from EO dashboard, crop monitor website using LANDSAT-8 ,SENTINEL-2 satellite datasets.

2)Mobility - We used EO dashboard for analysing mobility trends in Brazil , however we found various data gaps for other regions like kenya,Nepal, Greenland and Philippines which host majority of the worlds tribal population. Hence these regions need to be taken into consideration .

3)Forest Fire - We used the Forest fire data from NASA Worldview earthdata website using MODIS satellite images.

https://www.canva.com/design/DAEisHV1rnc/_zmkGzO8v4YRszypp2WcnQ/view?utm_content=DAEisHV1rnc&utm_campaign=designshare&utm_medium=link&utm_source=publishsharelink

We can develop a separate covid-19 dashboard for tribal communities so they can access the nearby food markets, learn better harvesting techniques to improve their soil fertility and transportation services for improving their conditions and also understand the importance of incorporation of technology for safeguarding theirs as well as other local tribes cultural heritage.

https://worldview.earthdata.nasa.gov/?v=-112.62571707843246,-37.32249871173282,-6.573012501456063,12.527796017809475&l=MODIS_Terra_Thermal_Anomalies_Night,MODIS_Terra_Thermal_Anomalies_Day,VIIRS_NOAA20_Thermal_Anomalies_375m_Night(hidden),VIIRS_NOAA20_Thermal_Anomalies_375m_Day,MODIS_Aqua_Thermal_Anomalies_Night,MODIS_Aqua_Thermal_Anomalies_Day(hidden),VIIRS_SNPP_Thermal_Anomalies_375m_Night(hidden),VIIRS_SNPP_Thermal_Anomalies_375m_Day(hidden),Reference_Labels_15m(hidden),Reference_Features_15m(hidden),Coastlines_15m,VIIRS_SNPP_CorrectedReflectance_TrueColor(hidden),MODIS_Aqua_CorrectedReflectance_TrueColor(hidden),MODIS_Terra_CorrectedReflectance_TrueColor&lg=true&l1=MODIS_Terra_Thermal_Anomalies_Night,MODIS_Terra_Thermal_Anomalies_Day,VIIRS_NOAA20_Thermal_Anomalies_375m_Night(hidden),VIIRS_NOAA20_Thermal_Anomalies_375m_Day,MODIS_Aqua_Thermal_Anomalies_Night,MODIS_Aqua_Thermal_Anomalies_Day(hidden),VIIRS_SNPP_Thermal_Anomalies_375m_Night(hidden),VIIRS_SNPP_Thermal_Anomalies_375m_Day(hidden),Reference_Labels_15m(hidden),Reference_Features_15m(hidden),Coastlines_15m,VIIRS_SNPP_CorrectedReflectance_TrueColor(hidden),MODIS_Aqua_CorrectedReflectance_TrueColor(hidden),MODIS_Terra_CorrectedReflectance_TrueColor&lg1=true&ca=false&cv=57&t=2020-01-01-T00%3A00%3A00Z&t1=2021-01-01-T18%3A53%3A45Z

https://earthdata.nasa.gov/covid19/explore/global?map=-55.0917%2C-16.1941%2C3.79&layers=agriculture&date=2021-01-01&lState=agriculture%7C0%7C0

https://eodashboard.org/?poi=W6-N6&indicator=N6

https://www.scielo.br/j/rsbmt/a/SMg7WdLGxT4gG9R9hpr3tkL/?lang=en

https://t.co/QRXpsaVFsP?amp=1

#Science #TribalCommunities #UNPFII #COVID-19Relief

This project has been submitted for consideration during the Judging process.