Abstract

Biomass production (BIO) and its anomalies were modeled using MODIS satellite images and gridded weather data to test an environmental monitoring system in the biomes Atlantic Forest (AF) and Caatinga (CT) within SEALBA, an agricultural growing region bordered by the states of Sergipe (SE), Alagoas (AL), and Bahia (BA), Northeast Brazil. Spatial and temporal variations on BIO between these biomes were strongly identified, with the annual long-term averages (2007-2023) for AF and CT of 78 ± 22 and 58 ± 17 kg ha^-1 d^-1, respectively. BIO anomalies were detected through its standardized indexes - STD (BIO_STD), comparing the results for each of the years from 2020 to 2023 with the long-term rates from 2007 to each of these years. The highest negative BIO_STD values were in 2023, but concentrated in CT, indicating periods with the lowest vegetation growth, regarding the long-term conditions from 2007 to 2023. The largest positive BIO_STD values were for the AF biome in 2022, evidencing the highest vegetative vigor in comparison with the long-term period 2007-2022.  The proposed BIO monitoring system is important for environmental policies as they picture suitable periods and places for agricultural and forestry explorations, allowing sustainable managements under climate and land-use changes conditions, with possibilities for replication of the methods in other environmental conditions.

Abstract

The persistence of coastal ecosystems is jeopardized by deforestation, conversion, and climate change, despite their capacity to store more carbon than terrestrial vegetation. The study’s objectives were to investigate how spatiotemporal changes impacted blue carbon storage and sequestration in the Satkhira coastal region of Bangladesh over the past three decades and, additionally to assess the monetary consequences of changing blue carbon sequestration. For analyzing the landscape change (LSC) patterns of the last three decades, considering 1992, 2007, and 2022, the LSC transformations were evaluated in the research area. Landsat 5 of 1992 and 2007, and Landsat 8 OLI-TIRS multitemporal satellite images of 2022 were acquired and the Geographical Information System (GIS), Remote Sensing (RS) techniques were applied for spatiotemporal analysis, interpreting and mapping the output. The spatiotemporal dynamics of carbon storage and sequestration of 1992, 2007, and 2022 were evaluated by the InVEST carbon model based on the present research years. The significant finding demonstrated that anthropogenic activity diminished vegetation cover, vegetation land decreased by 7.73% over the last three decades, and agriculture land converted to mariculture. 21.74% of mariculture land increased over the last 30 years, and agriculture land decreased by 12.71%. From 1992 to 2022, this constant LSC transformation significantly changed carbon storage, which went from 11,706.12 Mega gram (Mg) to 9,168.03 Mg. In the past 30 years, 2,538.09 Mg of carbon has been emitted into the atmosphere, with a combined market worth of almost 0.86 million USD. The findings may guide policymakers in establishing a coastal management strategy that will be beneficial for carbon storage and sequestration to balance socioeconomic growth and preserve numerous environmental services.

Abstract The 40 m fall of the Dead Sea since 1980 has relieved roughly 0.48 MPa of hydrostatic load on the Lisan Peninsula and accelerated halite dissolution and sinkhole formation beneath the Arab Potash Company’s rehabilitated pond SP-0B. To quantify and anticipate this hazard, we fuse two complementary data streams. First, six decades of aerial photographs, CORONA frames, multispectral scenes and sub-meter imagery were orthorectified and stacked to create morpho-tectonic maps that pinpoint transform-parallel splays, diapiric joints and an active sinkhole belt—structural corridors that concentrate groundwater flow. Second, 2015–2025 Sentinel-1 interferograms were processed with a “sibling-coherence” filter that clusters pixels with similar amplitude statistics, preserving sharp deformation gradients and boosting reliable-pixel density by 20–30 % over conventional box car averaging. The result is a 6- to 12-day displacement cube with centimeter precision. Integrating the static map with these ground-motion data partitions the pond into watch cells: trial thresholds of |dLOS| > 6 mm in 12 days or any positive acceleration flag cells where intensified field inspections, denser sensor sampling and temporary throttling of brine inflow are advised; three simultaneous breaches warrant halting impoundment. In the planned early warning system, InSAR-derived motions will be cross-validated with extensometers, piezometers and episodic GNSS to confirm magnitudes. The twin-layer workflow therefore provides the quantitative backbone for a basin-scale warning scheme and a transferable template for evaporite basins undergoing rapid base-level decline.

Abstract Disaster Risk Management benefits from innovative techniques including AI and Multi Sensor Fusion. The Firefguard Approach uses such technologies to improve the Wildfire Management works in Saxony, Eastern Germany by supporting standing efforts in Early Warning, Disaster Response and Monitoring. Unmanned Aerial Systems (UAS) play a vital role in providing real-time information via a 5G network to a central information management system that delivers geospatial information to response teams. This study highlights the potential of combining UAS, AI, geospatial solutions and existing data for real-time wildfire monitoring and risk assessment systems.