Monitoring grassland biomass accurately and frequently is critical for ecological management, climate change assessment, and sustainable resource use. However, the use of single-satellite data faces challenges due to trade-offs between spatial resolution and temporal frequency, especially for large areas. High-resolution imagery, such as PlanetScope, provides detailed spatial data but presents significant challenges in data management and processing over large regions. Conversely, low-resolution sensors such as JPSS-VIIRS offer daily global coverage with low memory data but lack the spatial detail required for precise biomass estimation, making it difficult to retrieve or validate model parameters due to the mismatch with small ground reference data polygons. To overcome these limitations, this study introduces a robust methodology for accurate frequent biomass estimation based on JPSS-VIIRS data through spectral harmonization, adapting a high-resolution biomass estimation model originally developed from PlanetScope imagery. The core innovation is an optimized Spectral Band Adjustment Factor (SBAF) approach tailored specifically to grassland spectral characteristics. This method significantly enhances spectral alignment, reducing red-band reflectance discrepancies from 6.2% to 4.8% in grassy areas and from 6.9% to 4.0% in bare areas. NDVI discrepancies also improved substantially. Applied across Mongolia, the harmonized VIIRS data estimated a five-year average biomass of 71.4 g/m², clearly reflecting environmental variability. Specifically, the P375 dataset showed average biomass estimates of 54.8 g/m² for desert grasslands (10.5% higher than PlanetScope), 122.6 g/m² for dry grasslands (9.6% higher), and 134 g/m² for mountain grasslands (1.9% lower). The uncertainty analysis showed strong overall agreement with PlanetScope-derived biomass, with an RMSE of 11.6 g/m², a mean percentage difference of 10.74%, and an R² of 0.92. While mountain grasslands exhibited the lowest RMSE, a relatively lower R² indicated limited variability. Higher uncertainty in desert and dry grasslands highlighted the impact of ecological heterogeneity on biomass estimation accuracy. These detailed comparisons demonstrate the effectiveness and accuracy of the proposed methodology in bridging spatial and temporal gaps, providing a valuable tool for large-scale weekly grassland biomass monitoring with applicability beyond the Mongolian context.

In the present work we pay an attention to the molecular structural changes between raw coal and its enriched, dense productions. Bituminous Ukhaa-Khudag coal in the form of an enriched, dense coal product is currently used as an energy source by Mongolian consumers for everyday need. There are three types of samples from one source: it is a raw coal (sample 1) and two of industrial prod- ucts as an enriched, dopant (organic based) added coal (sample 2) and simply enriched coal (sample 3). The raw coal general characterization is given through the elemental analysis, thermogravimetry analysis, moreover functional groups were identified by Fourier Transform infrared spectroscopy. The differences in the FTIR spectra of the samples were done.Some details are as follows: hydroxyl group vibrations are existing, rather weak in agreement of TG result; vibrations of aliphatic group were rarely in the long wave near region, carbonate minerals in the region of 1300-900 cm−1 are visible but overlapped, and an isolated out-of-plane deformational vibration (around 800 cm−1) or two-adjacent (800-750 cm−1) or three-adjacent (around 700 cm−1) of sp2-hybrid bonded aromatic CHx-groups were still presenting.After the processing hydroxyl group bands were affected in enriched coals, sample 2 and 3. In the spectrum of the sample 2 the long wave bands were intensively blurred.Industry processing retains an amount of water in the productions. Water excess influenced absorbance bands were able to be removed via the drying processing, however, the affection to the spectral struc- ture in sample 2 was unrecoverable.

As the global space sector enters the NewSpace era, developing countries such as Mongolia face significant challenges in developing functional space programs without the institutional legacy of established space powers. This study examines the gap between Mongolia’s long-term development strategy, “Vision 2050”, its sectoral policy guidance for space science and technology, and the absence of a comprehensive national space governance system. Using Qualitative Systems Analysis (QSA), the study compares governance arrangements across advanced, developing, and emerging spacefaring nations. The method enables comparative evaluation of institutional interactions, regulatory adaptability, and governance structures in national space systems where quantitative indicators remain limited or difficult to standardize. The findings indicate that direct replication of traditional “Old Space” institutional models often produces structural misalignment in latecomer states with limited financial and technological capacity. In response, the study proposes an Agile Governance Model (AGM) based on the Governance–Resilience–Agility (GRA) Triad. The model combines strategic coordination, adaptive regulation, and operational implementation through a flexible governance structure emphasizing phased legal development, public–private partnerships, and national capacity-building. The study further argues that latecomer nations can avoid rigid legacy institutions by adopting modular governance structures and targeted specialization in areas such as Earth observation, geospatial services, and ground infrastructure. By leveraging geographic advantages and human capital, Mongolia and other emerging space nations can strengthen digital sovereignty and improve institutional resilience. Adaptive governance approaches can also support their long-term participation in the global space economy.

Hyperspectral imaging, a rapidly evolving technique, furnishes exhaustive information on Earth's surface reflectance over an extensive spectrum of wavelengths. We have developed a more precise method for analyzing spectral data by meticulously selecting plant leaf pixels and employing a relative reference approach. This enhanced technique correlates camera pixel values with light intensity, bypassing the need for complex equations and thereby providing clearer insights into vegetation vitality. The findings of this study not only enhance the precision of hyperspectral imaging but also bolster our capability for ecological monitoring and understanding environmental health. A novel algorithm based on the red-edge slope was established to classify images, exploiting the spectral reflectance data gleaned from the hyperspectral Liquid Crystal Tunable Filter (LCTF) camera. Compared with conventional SVM and K-means algorithms, our novel algorithm demonstrated commendable performance, achieving an overall accuracy of 90% with a Kappa Coefficient of 0.86. This comparison highlights the novel algorithm's relative strength and potential for large-scale satellite and drone imagery analysis applications. The advancements facilitated by this research are instrumental in ecological assessments, conservation efforts, and resource management strategies, signaling a leap forward in the era of environmental monitoring and analysis.

Энэ судалгааны хүрээнд хэт олон сувагт LCTF (Liquid Crystal Tunable Filter) буюу шингэн кристалл шүүлтүүртэй камерын тусламжтай ургамлын зүйлийг түүний спектр шинж чанарт тулгуурлан ялгаж, навчны талбайн индекс LAI (Leaf Area Index) тооцож гаргах шинэ алгоритмыг туршин үзэв. 2022 оны 7-р сарын эхний долоо хоногт Хар Ямаат байгалийн нөөц газарт хэд хэдэн бодит талбарт LCTF камераар хээрийн ургамлуудын зургийг авч, цуглуулсан зурган мэдээллээс ургаж буй олон зүйлийн ургамлуудын гэрлийн ойлтын спектрийг дүрс боловсруулах аргаар ялгаж, түүгээр спектр сан үүсгэв. Үүсгэсэн сангийн мэдээллээс бид улаан хилийн зурваст тулгуурлан өвслөг ургамлын зүйлийн бүрдлийг тодорхойлох алгоритм туршиж үзсэн. Тухайн алгоритмаар навчны талбайн индексийг мөн тооцож гаргахад эрүүл ургамлыг хагдарсан өвс болон ургамал бус зүйлээс 95% дээш нийлэмжтэй ялгаж байна. Энэ ажлын үр дүнд ургамлын төрөл зүйлийг машинаар олон сувагт камерын тусламжтай өндөр нарийвчлалтай ялгах боломжтойг харуулж байна. Цаашид орчин үеийн хиймэл дагуул, дроноор авсан зургаас уг бидний хөгжүүлсэн алгоритмыг ашиглан хаана ямар төрөл зүйлийн ургамал хэр хэмжээгээр ургаж буйг ялгах боломж бүрдэх юм. [ENGLISH]Within the framework of this research, a new algorithm was designed to determine the plant’s red edge slope and the Leaf Area Index (LAI) based on their spectral characteristics using a hyperspectral LCTF (Liquid Crystal Tunable Filter) camera. In the first week of July 2022, we took pictures of field vegetation in several fields in the Khar Yamaat Nature Reserve with the LCTF camera. From the collected data, the light reflectance spectrum of many species of growing plants was distinguished with the help of image processing, and with that, we created a spectrum dataset of 21 different species. From the generated dataset, we designed an algorithm to determine plant species or dry grass based on the red edge slope, which works with approximately 95% accuracy. The algorithm also calculates the leaf area index, and healthy plants are distinguished from damaged grass and non-plant species. The results of this work demonstrate that vegetation can be classified by vegetation’s red edge slope using hyperspectral cameras. In the future, it will be possible to determine the location and number of growing plants using the algorithm we developed on the images taken by modern satellites and drones.