We consider high data rate free-space optical links between satellite and ground station, which are prone to strong variations of the received signal due to atmospheric turbulence. The high data rates towards hundreds of Gbit/s paired with channel coherence times of a few milliseconds pose a serious challenge for reliable transmission. On such non-ergodic channels, the use of appropriate diversity techniques combined with channel coding schemes is a must to ensure the required data rates. There are several ways to tackle the problem. A pragmatic approach is to rely on commercial transceivers from fiber optics, which are, however, not tailored to the free-space optical channel. Code rates are high and no suitable diversity schemes are foreseen. Such transceivers can be combined with a suitable retransmission scheme, which strongly reduces spectral efficiency. Another option is the addition of a complementary erasure coding scheme at a higher layer, which, due to its long codewords and additional redundancy, can correct longer sequences of errors. However, such a layered scheme yields a loss in achievable data rates. In this work, we rely on a theoretically optimal approach which is composed of a long physical layer interleaver as well as a long physical layer channel code. While the interleaver should provide the required time diversity, the selected strongly quantized Low-Density Parity-Check (LDPC) code should correct the errors in the data. To support the required data rates, highly optimized hardware implementation becomes mandatory for both interleaver and decoder. To achieve high error correction performance and data rates towards hundreds of Gbit/s, a cross-layer design methodology is mandatory in which interleaver design, code, and decoding algorithms are jointly considered with its hardware implementation. We show that an Application-Specific Integrated Circuit (ASIC) implementation can reach the target data rates with a moderate backoff from theoretical limits.
Forest change detection is crucial for sustainable forest management. The changes in the forest area due to deforestation (such as wild fires or logging due to development activities) or afforestation alter the total forest area. Additionally, it impacts the available stock for commercial purposes, climate change due to carbon emissions, and biodiversity of the forest habitat estimations, which are essential for disaster management and policy making. In recent years, foresters have relied on hand-crafted features or bi-temporal change detection methods to detect change in the remote sensing imagery to estimate the forest area. Due to manual processing steps, these methods are fragile and prone to errors and can generate inaccurate (i.e., under or over) segmentation results. In contrast to traditional methods, we present AI-ForestWatch, an end to end framework for forest estimation and change analysis. The proposed approach uses deep convolution neural network-based semantic segmentation to process multi-spectral space-borne images to quantitatively monitor the forest cover change patterns by automatically extracting features from the dataset. Our analysis is completely data driven and has been performed using extended (with vegetation indices) Landsat-8 multi-spectral imagery from 2014 to 2020. As a case study, we estimated the forest area in 15 districts of Pakistan and generated forest change maps from 2014 to 2020, where major afforestation activity is carried out during this period. Our critical analysis shows an improvement of forest cover in 14 out of 15 districts. The AI-ForestWatch framework along with the associated dataset will be made public upon publication so that it can be adapted by other countries or regions.
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