Foto yang menggambarkan komparasi perubahan pada 26 April 2022-19 Februari 2024 tersebut sejatinya dirilis pada 19 Februari 2024 lalu Penyusutan tutupan hutan atau deforestasi di wilayah IKN ini juga dicatat oleh Forest Watch Indonesia (FWI). Dalam kurun waktu 3 tahun (2018-2021), deforestasi di wilayah IKN mencapai 18.000 hektar, dengan 14.010 hektar di antaranya berada di hutan produksi, 3.140 hektar di Area Penggunaan Lain, sisanya 807 hektar di Taman Hutan Rakyat (Tahura), 9 hektar di Hutan Lindung, dan 15 hektar di area lainnya. Catatan FWI (2023) menerangkan, sepanjang 2022 dan sampai Juni 2023 luas areal terdeforestasi mencapai 1.663 hektare. Terkait hal ini, Direktur Pengembangan Pemanfaatan Kehutanan dan Sumber Daya Air Otorita IKN Pungky Widiaryanto mengakui, isu perubahan tutupan hutan di Kalimantan, khususnya IKN, memang menjadi perhatian banyak pihak, baik yang mendukung maupun yang mengkritisi. Namun demikian, Pungky merasa perlu untuk memberikan klarifikasi, agar pemahaman masyarakat menjadi lebih baik. Bahwa, kondisi awal area IKN sebelum pembangunan dimulai pada 2022, didominasi oleh hutan tanaman industri, terutama pohon eucalyptus. Pertumbuhannya yang cepat dan siklus panen yang singkat, menjadikannya pilihan utama dalam hutan tanaman industri. "Oleh karena itu, perubahan yang terlihat dari citra satelit mungkin mencerminkan aktivitas pengelolaan hutan tanaman industri yang sudah ada sebelumnya," terang Pungky kepada Kompas.com, Selasa (28/1/2025). Sementara, IKN dirancang dengan prinsip keberlanjutan sebagai prioritas utama. Dari total area yang ada seluas 252.660 hektar, hanya 25 persen yang akan digunakan untuk bangunan, fasilitas, dan infrastruktur. Sebagian besar wilayah lainnya atau 75 persen, akan dihijaukan kembali dengan berbagai jenis pohon khas Kalimantan, bukan hanya eucalyptus. Strateginya adalah menggunakan pohon eucalyptus yang ada sebagai naungan bagi tanaman baru. Ketika eucalyptus mati, pohon-pohon khas Kalimantan akan siap tumbuh dengan baik. Sejak tahun 2022 hingga saat ini, reforestasi telah terlaksana di area seluas 8.420 hektar di wilayah delineasi IKN. Penanaman ini melibatkan berbagai pihak, termasuk instansi pemerintah, perusahaan swasta, yayasan, dan perguruan tinggi, dalam pengelolaan rimba kota. Pungky mengakui bahwa target mengubah 65 persen dari luas area IKN menjadi kawasan lindung dengan tutupan hutan hujan tropis merupakan target ambisius. "Ini adalah upaya besar yang memerlukan dukungan dari semua kalangan. Kami mengajak seluruh masyarakat untuk berpartisipasi dalam upaya reforestasi ini," imbuh Pungky. Untuk itu, Kedeputian Bidang Lingkungan Hidup dan Sumber Daya Air pun mengembangkan mekanisme pendanaan yang memiliki potensi besar untuk mendukung target reforestasi. sumber: Kompas

In war and conflict zones, the jamming of Global Navigation Satellite System (GNNS) signals by military forces disrupts the tracking of tagged animals, and has increased in frequency following the recent escalation of conflicts in Eastern Europe and the Middle East. Such disruption to data collection strongly hampers research into the protection and conservation of endangered animals. For decades, scientists have been uncovering the secrets of animal movements using various technological solutions1. Many of these technologies rely on the GNSS (including the Global Positioning System—GPS) to geolocate the tagged animals2. Although originally developed for military purposes3, GNSS has gained enormous popularity in a wide range of civilian applications, including those related to research on animal movement and conservation biology. By receiving signals from multiple satellites, a GNSS tracking device can gain a high-accuracy location of the carrying animal almost everywhere on the globe within milliseconds. Yet due to the common use of GNSS-enabled applications in military operations, the disruption of GNSS signals is becoming frequent, especially in conflict areas. This jamming or spoofing of GNSS signals is intended to disrupt navigation systems of enemy forces. However, the non-specific nature of this electronic warfare affects all GNSS based devices, including multiple civilian applications such as civilian aircraft4 and research involving animal tracking devices5. GNSS spoofing We report here the strong effects of GNSS spoofing on bird tracking following the recent escalation of conflicts in Eastern Europe and the Middle East. By remotely monitoring the daily movements of birds across these regions during autumn 2023 to summer 2024, we repeatedly recorded erroneous positioning for many individuals across multiple species. This resulted in a significant loss of invaluable data, which in turn may have a severe impact on the ability to monitor and interpret animal movements and draw relevant conclusions for understanding their biology and developing conservation strategies. We observed such cases for multiple species (including eagles, falcons, shorebirds, and bustards), involving both resident and migratory birds. The erroneously recorded positions occurred over multiple countries and were often—but not exclusively—translocated to international airports, for example in Russia, Ukraine, Lebanon, Jordan, Syria, and Egypt. We illustrate the issue with the tracks of black-tailed godwits (Limosa limosa) migrating from Finland to Romania while flying in or near Russia, Belarus, and Ukraine, and of Bonelli’s eagles (Aquila fasciata) dispersing from their natal sites in Israel. Black-tailed godwits Fifteen black-tailed godwits were tagged in Finland in May 2024, as part of the Habitrack EU-funded research program (https://habitrack.eu). Breeding near Oulu or in Karelia, the godwits migrated southwards later in June or July. Most godwits followed a migratory pathway over Russia, Belarus and Ukraine leading to the Danube River delta in Romania. Eight displayed spoofed geolocations during their migrations, many birds being localized at the exact same place despite not migrating simultaneously (Fig. 1). We identified three obvious locations: west of St Petersburg (4 individuals), in Smolensk oblast (for 7 individuals migrating over Belarus), and in Crimea (5 individuals). For the latter, the tags mostly pointed to Simferopol airport in Crimea (45.037°N, 33.966°E) when the birds actually reached the Odessa region of Ukraine. As examples, one female godwit (ring ST340089, in pink) had her location moved to Simferopol airport on 22 June 2024 while she was flying just north of Mykolaiv. The total distance added to this migration track was estimated at 550 km, though covered in 16 min only. For another female godwit (ringed ST320391, in black), we recorded 9 locations at Simferopol airport, while the interleaved positions recorded by the tag indicated that the bird was in the Danube River delta in Romania, close to the Ukrainian border: 4 on 28 June, 1 on 29 June, 2 on 4 July, 1 on 14 July and 1 on 21 July. The distance between the actual locations and the airport ranged between 380 and 420 km, so that the nine erroneously-recorded round trips represented a total distance of ~7200 km. This however represents less than 0.1% of all locations (9 out of 9 053) obtained while the bird lingered in the Danube River delta between 26 June and 10 August 2024. Seven other godwits took more western migratory routes and their tracking devices did not record spoofed geolocations. Bonelli’s eagles Forty-eight Bonelli’s eagles were tracked with GPS tags across Israel between October 2023 and September 2024, as part of a national conservation program led by the Israel Nature and Parks Authority. Electrocution has been identified as the primary threat to this population6. Most of the tagged eagles typically exhibit local dispersal movements within Israel and adjacent Jordan, Egypt, Lebanon, and Syria. Coinciding with escalating regional conflict from October 2023 onwards, an increase in GPS interference was observed. The majority of spoofed locations were diverted to international airports in the region, while some outliers appeared in a specific area in the Mediterranean Sea (Fig. 2). By early April 2024, interference levels reached 100% for some individuals, with the entire monitored population experiencing 20–50% spoofed locations (Fig. 2 insert). This disruption hampers efforts to identify risk factors such as hazardous power pylons, poisoning events, and direct persecution, thereby significantly weakening long-term conservation and mitigation efforts. Handling spoofed positions For scientists studying animal movements, these virtual translocations can be detected if they are truly outlying, or repeated. For example, an animal commuting back and forth daily to an international airport, or several birds that receive the same geolocation despite their routes diverge so they are clearly not migrating in the same flock. However, some cases might be less obvious and may require a refined knowledge of the species’ typical movement patterns in order  to be detected. Whether obvious or not, researchers must consider the risk of location errors caused by GNSS spoofing when analyzing movement trajectories or habitat use. As we show with these data samples, such errors are widespread, and might appear in many new places in the future. GNSS spoofing in conflict zones poses a significant challenge to wildlife tracking and conservation efforts. This phenomenon compromises not only the accuracy of migration studies but also critical conservation activities such as mortality detection and epidemic monitoring. The implications extend beyond scientific research, potentially affecting endangered species management and human-wildlife conflict mitigation7,8,9. In response to these challenges, researchers may start exploring potential solutions to mitigate the effects of GNSS spoofing. While advanced anti-spoofing algorithms and encrypted signals are being developed in other fields like civil aviation and military applications, such technologies have not yet been widely applied to wildlife tracking due to cost and complexity. Given the gravity of environmental crises worldwide and the ubiquity with which wildlife research relies on GNSS technologies, such solutions are no less imperative and should be developed and shared among practitioners.

1.  Perkenalan Geodatabase (.gdb), Geodatabase adalah format penyimpanan data spasial yang digunakan dalam Sistem Informasi Geografis (SIG). Dikembangkan oleh Esri, geodatabase berfungsi sebagai wadah untuk menyimpan, mengelola, dan menganalisis data geografis secara efisien dalam bentuk yang terorganisir. Geodatabase memungkinkan pengguna untuk mengelola data spasial dan atributnya secara terintegrasi dalam satu basis data.   2. Geodatabase VS Shapefile. Geodatabase dan Shapefile adalah dua format data yang sering digunakan dalam Sistem Informasi Geografis (SIG) untuk menyimpan data spasial. Namun, keduanya memiliki perbedaan yang signifikan dalam hal kemampuan, efisiensi, dan fungsionalitas. Perbandingan antara keduanya meliputi struktur penyimpanan, kapasitas penyimpanan, dukungan data dan fungsi, skalabilitas dan kolaborasi, kinerja dan kompabilitas   Pilih Geodatabase jika: - Anda bekerja dengan dataset besar dan kompleks. - Membutuhkan pengelolaan data terintegrasi (multi-layer, relasi, aturan topologi). - Menggunakan SIG pada skala organisasi besar. Pilih Shapefile jika: -Anda memerlukan format sederhana untuk berbagi data dengan banyak platform. - Dataset Anda kecil, dengan kebutuhan analisis yang sederhana. Meskipun shapefile masih banyak digunakan karena kesederhanaannya, geodatabase menawarkan kemampuan yang jauh lebih unggul untuk kebutuhan modern dalam SIG.   3. Ekspor SHP ke GDB GDB mampu membuat feature baru namun pada kesempatan ini kita akan mengekspor data SHP yang sudah ada ke GDB, selain menghemat waktu, kita juga dapat berlatih. selain SHP, format data yang populer lainnya adalah KML dan geoJSON.   4. Mengolah data survey lapangan dalam bentuk XLS, mengedit dan membersihkan data Sebelum di olah di ArcMAP, data lapangan dalam format XLS terlebih dahulu dibersihkan/cleaning seperti nama kolom yang tidak boleh ada spasi.   5. Ekspor XLS ke CSV Setelah dibersihkan, data XLS di ekspor ke CSV.   6. Plotting data sebaran titik survey CSV ke ArcMAP, data XY dalam Geographic Coordinate System (GCS) berformat decimal degree (DD) Data CSV kemudian ditambahkan dan plotting ke ArcMAP. Plotting atau menampilkan sebaran titik survey diatas kanvas ArcMAP dilakukan dengan menggunakan 2 (dua) kolom/field kombinasi X/Longitude/Bujur dan field Y/Latitude/Lintang sebagai titik koordinat bumi lokasi responden survey. Koordinat system yang digunakan dalam kursus kali ini adalah Geographic Coordinate System (GCS) dengan satuan derajat (Degree) dan berformat Decimal Degree/DD   7. Ekspor data plotting ke GDB Sebaran titik survey yang telah di tambahkan di kanvas ArcMAP tersimpan sementara di memori (temporary layer), untuk membuatnya permanen maka kita akan ekspor data sebaran titik ini ke GDB   8. Membuat model spasial kita akan membuat model spasial dari sebaran titik survey yang telah tersimpan di GDB. Model spasial ini dapat berbentuk thematik dan khoropleth. Model spasial akan memberikan gambaran lebih jelas bagaimana data ini tersebar berdasarkan data atribut yang diperoleh seperti model spasial usia, model spasial omset perbulan, model spasial omset pertahun dan lainnya.   9. Mendesain Layout dalam ArcMAP document (MXD). Kita akan membuat layout di ArcMAP. Kita membuat layout untuk masing-masing model spasial di atas.   download: https://rapidgator.net/file/2f100a5bfa0ca590da1b0572a8d22163/SANET.STProcessingSurveyDatainGCSWithArcGISDesktop10.8.part1.rar.html https://rapidgator.net/file/c81950916039d65e0ecfd400014cbaa3/SANET.STProcessingSurveyDatainGCSWithArcGISDesktop10.8.part2.rar.html  

LORAN — an acronym for Long Range Navigation — was a US byproduct of World War II and was similar in many ways to Britain’s Gee system. However, LORAN operated at lower frequencies to improve its range. It was instrumental in helping convoys cross the Atlantic and also found use in the Pacific theater.   How it Worked The video shows a Loran-A receiver, which, in its day, would have been known as LORAN. The A was added after versions B and C appeared. Back in the 1940s, something like this with a CRT and precision electronics would have been very expensive. Unlike GPS, keeping a highly synchronized clock over many stations was impractical at the time. So, LORAN stations operated in pairs on different frequencies and with a known distance between the two. The main station sends a blip. When the secondary station hears the blip, it sends its own blip. Sometimes there were multiple secondaries, too. If you receive both blips, you can measure the time between them and use it to get an idea of where you are. Suppose the stations were 372 miles apart. That means the secondary will hear the blip roughly 2 milliseconds after the primary sends it (the speed of light is about 186 miles per millisecond). You can characterize how much the secondary delays, so let’s just say that’s another millisecond.   Reception Now both transmitted blips have to make it to your receiver. Let’s take a sill example. Suppose you are on top of station B. You’ll hear station A at the same time station B hears it. Then, when you subtract out the delay for station B, you’ll hear its blip immediately. You could easily guess you were 372 miles from station A.   It is more likely, though, that you will be somewhere else, which complicates things. If you find there is a 372-mile difference in your distance from station A to station B, that could mean you were 186 miles away from each station. Or, you could be 202 miles from station A and 170 miles from station B. If you plot all the possibilities, you’ll get a hyperbolic curve. You are somewhere on the curve. How do you know where? You take a reading on a different pair of transmitters, and the curves should touch on two points. You are on one of those points. This is similar to stellar navigation, and you usually have enough of an idea where you are to get rid of one of the points as ridiculous. You do, however, have to take into account the motion of your vehicle between readings. If there are multiple secondary stations, that can help since you can get multiple readings without switching to an entirely new pair. The Coast Guard video below explains it graphically, if that helps. Receiver Tech The receiver was able to inject a rectangular pulse on both channels to use as a reference, which is what the video talks about being the “pedestal” (although the British typically called it a cursor). LORAN could operate up to 700 nautical miles in the day, but nighttime propagation would allow measurements up to 1,400 nautical miles away. Of course, the further away you are, the less accurate the system is. During the day, things were simple because you typically just got one pulse from each station. But at night, you could get multiple bounces, and it was much more difficult to interpret. If you want to dive really deep into how you’d take a practical fix, [The Radar Room] has a very detailed video. It shows multiple pulses and uses a period-appropriate APN-4 receiver.