Human population growth and the accompanying increase in anthropogenic activities pose a significant threat to forest ecosystems by reducing the natural services these ecosystems provide. Malam Jabba, located in the District Swat of Pakistan’s Hindukush-Himalayan temperate zone, is known for its ecotourism, skiing, timber-producing tree species, medicinal plants, and unique biodiversity. However, a large portion of Swat Valley’s population depends on the Malam Jabba forests for timber and fuelwood. This study investigates how deforestation rates have increased in response to the growing human population in Malam Jabba, District Swat. To monitor forest cover changes, we used remote sensing (RS) and geographic information systems (GIS) tools. Vegetation analysis was conducted using the Normalized Difference Vegetation Index (NDVI) based on multi-temporal satellite imagery from 1980, 2000, and 2020. Using a decay model, we calculated the deforestation rate from 1980 to 2020 and projected future rates using MATLAB, based on anticipated population growth. Our results show that over the last two decades, the average annual deforestation rate rose from 0.7% to 1.93%, coinciding with a population increase from 1.2 million to 2.3 million at a growth rate of 9% per year. Projections indicate that the deforestation rate will increase to 2.5% annually over the next 20 years, given the predicted 11.6% yearly population growth. Population growth in District Swat has severely endangered nearby forest ecosystems, and further increases in human activity, such as unsustainable tourism, fuel and timber collection, and urbanization, will likely exacerbate this trend. Based on our findings, we recommend: (i) the implementation of reforestation programs and sustainable forest resource use; (ii) the development of a long-term forest management plan that maintains equilibrium between forest density and population pressure; and (iii) prioritizing areas with extreme human impact for in-situ conservation efforts.

Changes taking place along the shoreline are attributed to several physical and dynamical processes. Sindh Coast of Pakistan particularly Karachi is continuously under the process of morphological changes due to anthropogenic activities and natural processes. This study examined spatiotemporal morphological changes over five decades (1972–2020) along Karachi Coast bordering Arabian Sea using SRS and GIS techniques. This study reveals major morphological changes along the shoreline of Karachi in the vicinity of Karachi Port, Clifton Beach, Bundal Island and Port Qasim, which have been calculated in the form of erosion and accretion using erase tool of ArcGIS software. Quantitative analysis shows highest accretion of >15.50 km2, at the Eastern End of Clifton Beach due to human intervention. Natural processes caused major erosion (10.95 km2) and accretion (19.50 km2) during 1972–2020 in the vicinity of Bundal Island. During 2011–2020, significant change (2.40 km2) is observed opposite to Port Qasim causing damage to mangrove vegetation of 1.40 km2 calculated using NDVI. Highest rate of erosion (0.31 km2year−1) and highest rate of accretion (0.70 km2year−1) are found in Bundal Island during 1972–1987 and 1987–1999, respectively. Maximum increase in the length of shoreline (6.78 km) is depicted at Eastern End of Clifton Beech between 1999–2010.

Net Primary Production (NPP) is a fundamental characteristic of an ecosystem, expressing the conversion of carbon dioxide into biomass driven by photosynthesis. The pixel value represents the mean daily NPP for that specific dekad.

Data publication: 2024-02-05

Supplemental Information:

No data value: -9999

Unit : gC/m²/day

Scale Factor : 0.001

Map code : L3-NPP-D.KWL

Scale factor: The pixel value in the downloaded data must be multiplied by

New dekadal data layers are released approximately 5 days after the end of a dekad. A higher quality version of the same data layer is uploaded after 6 dekads have passed. This final version of the dekadal dataset has a higher quality because gap filling and interpolation processes, where needed, have been based on more data observations.

Citation:

FAO WaPOR database, License: CC BY-NC-SA 4.0, [Date accessed: Day/Month/Year]

Contact points:

Resource Contact: WaPOR

Metadata Contact: WaPOR

Data lineage:

The calculation is based on the WaPOR-ETLook model described in the Wapor methodology document.

The core of the methodology for deriving NPP is detailed in Veroustraete et al. (2002), whilst the practical implementation, as developed for the MARS Crop Yield Forecasting System, is described in Eerens et al. (2004). These methodologies were improved within the framework of the Copernicus Global Land Component, the most important change being the incorporation of biome-specific light-use efficiencies (LUEs). The FRAME project applies this updated methodology, adding improvements which include the addition of a reduction factor to account for reduced water availability (i.e. soil moisture stress). The following data is used to calculate NPP:

Daily: Incoming solar radiation and temperature data (Tmin/Tmax);

Monthly: fAPAR and soil moisture stress;

Seasonal: Land Cover.

Data component are developed through collaboration with eLEAF. More information can be found on the WaPOR Website.

Resource constraints:

Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)

Online resources:

Download the data from Google Cloud Storage

Download the data from File-Browser

The actual EvapoTranspiration and Interception (ETIa) is the sum of the soil evaporation (E), canopy transpiration (T), and evaporation from rainfall intercepted by leaves (I). The value of each pixel represents the ETIa in a given year.

Data publication: 2024-02-05

Supplemental Information:

No data value: -9999

Unit : mm/year

Scale Factor : 0.1

Map code : L3-AETI-A.SNG

Scale factor: The pixel value in the downloaded data must be multiplied by

New dekadal data layers are released approximately 5 days after the end of a dekad. A higher quality version of the same data layer is uploaded after 6 dekads have passed. This final version of the dekadal dataset has a higher quality because gap filling and interpolation processes, where needed, have been based on more data observations. This implies that other temporal aggregations (monthly, seasonal, annual), and layers that depend on those, are updated as well. Practically this means that a final annual aggregation of the most recent full calendar year can only be produced after the end of February. Likewise, the final monthly aggregation of the most recent calendar months can only be produced 2 full months later.

Citation:

FAO WaPOR database, License: CC BY-NC-SA 4.0, [Date accessed: Day/Month/Year]

Contact points:

Resource Contact: WaPOR

Metadata Contact: WaPOR

Data lineage:

The calculation is based on the WaPOR-ETLook model described in the Wapor methodology document.

The annual total is obtained by taking the ETIa in mm/day, multiplying by the number of days in a dekad, and summing the dekads of each year. See the methodology of the evapotranspiration data components (E, T and I) for further information.

Data component are developed through collaboration with eLEAF. More information can be found on the WaPOR Website.

Resource constraints:

Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)

Online resources:

Download the data from Google Cloud Storage

Download the data from File-Browser

Forest cover of study area & average deforestation.

The transpiration (T) data component (dekadal, in mm/day) is the actual transpiration of the vegetation canopy. The value of each pixel represents the average daily actual transpiration for that specific dekad.

Data publication: 2024-02-05

Supplemental Information:

No data value: 255

Unit : mm/day

Scale Factor : 0.1

Map code : L3-T-D.SNG

Scale factor: The pixel value in the downloaded data must be multiplied by

New dekadal data layers are released approximately 5 days after the end of a dekad. A higher quality version of the same data layer is uploaded after 6 dekads have passed. This final version of the dekadal dataset has a higher quality because gap filling and interpolation processes, where needed, have been based on more data observations.

Citation:

FAO WaPOR database, License: CC BY-NC-SA 4.0, [Date accessed: Day/Month/Year]

Contact points:

Resource Contact: WaPOR

Metadata Contact: WaPOR

Data lineage:

The calculation is based on the WaPOR-ETLook model described in the Wapor methodology document. See the methodology of the evapotranspiration data components (E, T and I) for further information.

Data component are developed through collaboration with eLEAF. More information can be found on the WaPOR Website

Resource constraints:

Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)

Online resources:

Download the data from Google Cloud Storage

Download the data from File-Browser

The Evaporation (E) data component is the actual evaporation of the soil surface. The value of each pixel represents the total annual evaporation for that specific year.

Data publication: 2024-02-05

Supplemental Information:

No data value: -9999

Unit : mm/year

Scale Factor : 0.1

Map code : L3-E-A.KWL

Scale factor: The pixel value in the downloaded data must be multiplied by

New dekadal data layers are released approximately 5 days after the end of a dekad. A higher quality version of the same data layer is uploaded after 6 dekads have passed. This final version of the dekadal dataset has a higher quality because gap filling and interpolation processes, where needed, have been based on more data observations. This implies that other temporal aggregations (monthly, seasonal, annual), and layers that depend on those, are updated as well. Practically this means that a final annual aggregation of the most recent full calendar year can only be produced after the end of February. Likewise, the final monthly aggregation of the most recent calendar months can only be produced 2 full months later.

Citation:

FAO WaPOR database, License: CC BY-NC-SA 4.0, [Date accessed: Day/Month/Year]

Contact points:

Resource Contact: WaPOR

Metadata Contact: WaPOR

Data lineage:

The calculation is based on the WaPOR-ETLook model described in the Wapor methodology document.

The annual total is obtained by taking the E in mm/day, multiplying by the number of days in a dekad, and summing the dekads of each year. See the methodology of the evapotranspiration data components (E, T and I) for further information.

Data component are developed through collaboration with eLEAF. More information can be found on the WaPOR Website.

Resource constraints:

Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)

Online resources:

Download the data from Google Cloud Storage

Download the data from File-Browser

The research on the vulnerability dataset of disaster bearing bodies in the China Pakistan Economic Corridor (domestic section) is based on multi-source data fusion, and a vulnerability evaluation system covering natural disasters and socio-economic systems has been constructed. This dataset integrates field survey data (infrastructure distribution, population density), satellite remote sensing data (surface deformation monitoring, vegetation coverage), and statistical yearbook data (GDP, disaster prevention investment), and forms a multidimensional vulnerability database through GIS spatial analysis, remote sensing interpretation, and data standardization processing. The research team has developed a three-dimensional evaluation index system that includes exposure, sensitivity, and adaptability. The exposure index covers physical elements such as the proportion of geological hazard prone areas and the density of transportation arteries; Sensitivity indicators involve socio-economic factors such as ecological vulnerability index and poverty incidence rate; The indicators of adaptability include emergency response capability, medical resource density, and other elements of disaster prevention and reduction capability. To improve the evaluation accuracy, the traditional vulnerability index model was improved by introducing the random forest algorithm for weight optimization, and the stability of the model was verified through Monte Carlo simulation. The analysis results show that there is significant spatial heterogeneity in the domestic section of the corridor: high vulnerability areas are concentrated in the Karakoram Pamir geologically active zone, driven by a combination of frequent extreme weather events, insufficient infrastructure disaster resistance standards, and weak regional economic resilience. The future research can be further extended to the high-altitude mountains along the "the Belt and Road". In combination with multi-scale remote sensing monitoring and socio-economic big data, we can deepen the research on the formation mechanism of cross-border disaster risk in the context of climate change, and provide scientific support for building a resilient Silk Road.