CARACTERÍSTICAS HIDROGEOMORFOMÉTRICAS E DINÂMICA DA COBERTURA DO SOLO NA MICROBACIA PROSPERIDADE, AMAZÔNIA OCIDENTAL, BRASIL

O planejamento e a gestão dos recursos naturais é essencial para alcançar o desenvolvimento sustentável na região amazônica, e tem como base as características da paisagem. Diante disso, o objetivo do presente estudo foi disponibilizar informações sobre as características hidrogeomorfométricas da paisagem e da dinâmica da cobertura do solo na microbacia Prosperidade, Amazônia Ocidental, Brasil. As informações foram obtidas com técnicas de sensoriamento remoto, utilizando softwares (QGIS 2.10.1, Google Earth e TrackMaker Free) e imagens de satélites (ALOS, Landsat 5 e Landsat 8), e equações. A microbacia tem área de 24 km2, perímetro de 27,21 km, forma alongada, altitudes de 241 a 331 m, relevos planos a forte ondulados, rede de drenagem de 35,52 km com padrão dendrítico, rios de 4ª ordem, 2,71 nascentes km-2, densidade de drenagem de 1,48 km km-2, coeficiente de manutenção de 675,7 m2 m-1, índice de sinuosidade de 20,97% e tempo de concentração de 3,01 h. No período de 1984 a 2021, ocorreu um aumento da área de agropecuária e uma redução da área de floresta nativa na microbacia e na zona ripária. A microbacia tem potencial para o desenvolvimento de atividades agropecuárias, contudo, o desmatamento na zona ripária está comprometendo a qualidade dos recursos hídricos. Recomenda-se a manutenção da vegetação nativa remanescente, recuperação da vegetação nativa na zona ripária, adoção de práticas conservacionistas nos sistemas agropecuários e a introdução do componente arbóreo como fonte econômica (ex: sistemas agroflorestais, silvipastoris e agrossilvipastoris).

Spatiotemporal change analysis of long time series inland water in Sri Lanka based on remote sensing cloud computing

Sri Lanka is an important hub connecting Asia-Africa-Europe maritime routes. It receives abundant but uneven spatiotemporal distribution of rainfall and has evident seasonal water shortages. Monitoring water area changes in inland lakes and reservoirs plays an important role in guiding the development and utilisation of water resources. In this study, a rapid surface water extraction model based on the Google Earth Engine remote sensing cloud computing platform was constructed. By evaluating the optimal spectral water index method, the spatiotemporal variations of reservoirs and inland lakes in Sri Lanka were analysed. The results showed that Automated Water Extraction Index (AWEIsh) could accurately identify the water boundary with an overall accuracy of 99.14%, which was suitable for surface water extraction in Sri Lanka. The area of the Maduru Oya Reservoir showed an overall increasing trend based on small fluctuations from 1988 to 2018, and the monthly area of the reservoir fluctuated significantly in 2017. Thus, water resource management in the dry zone should focus more on seasonal regulation and control. From 1995 to 2015, the number and area of lakes and reservoirs in Sri Lanka increased to different degrees, mainly concentrated in arid provinces including Northern, North Central, and Western Provinces. Overall, the amount of surface water resources have increased.

ANTROPOGEOMORFOLOGIA DA MICROBACIA DO RIO TERRA NOVA, AMAZÔNIA, BRASIL

O planejamento de ações para a boa gestão dos recursos naturais é essencial para o desenvolvimento sustentável na região amazônica, e informações associadas às características da paisagem são a base para tal. Nessa perspectiva, o trabalho tem como objetivo compreender as características geométricas, topográficas, hidrográficas e as alterações na cobertura da paisagem provocadas pela ação humana na microbacia do rio Terra Nova. As informações foram obtidas de equações e sensoriamento remoto, utilizando softwares (QGIS 2.10.1, TrackMaker e Google Earth) e imagens de satélite. A microbacia tem área de 27,32 km2 de área, perímetro de 28,5 km, forma alongada, baixa suscetibilidade a enchentes, altitudes de 210 a 446 m, predominância de relevo suave ondulado (48,76%), 89,28% da área apta a extremamente apta a mecanização agrícola e com baixa influência na propagação de incêndios, rede de drenagem com padrão dendrítico, 5ª ordem de drenagem, médias densidade de nascentes e de drenagem, coeficiente de manutenção de 561,9 m2 m-1, canal principal divagante e baixo tempo de concentração. A área de floresta nativa na microbacia e em sua zona ripária foi reduzida constantemente de 1984 a 2021, restando apenas 26,13% e 50,95%, respectivamente, desta cobertura no último ano analisado. Na microbacia do rio Terra Nova comprovou-se a necessidade de considerar o componente arbóreo como parte do sistema produtivo nas propriedades rurais, portanto, recomenda-se o monitoramento da cobertura florestal e recuperação das áreas degradadas e áreas protegidas por lei, para mitigar os impactos ambientais.

Comparison of Winter Wheat Extraction Methods Based on Different Time Series of Vegetation Indices in the Northeastern Margin of the Qinghai–Tibet Plateau: A Case Study of Minhe, China

The northeastern margin of the Qinghai–Tibet Plateau (QTP) is an agricultural protection area in China’s new development plan, and the primary region of winter wheat growth within QTP. Winter wheat monitoring is critical for understanding grain self-sufficiency, climate change, and sustainable socioeconomic and ecological development in the region. However, due to the complex terrain and high altitude of the region, with discontinuous arable land and the relatively low level of agricultural development, there are no effective localization methodologies for extracting and monitoring the detailed planting distribution information of winter wheat. In this study, Sentinel-2A/B data from 2019 to 2020, obtained through the Google Earth Engine platform, were used to build time series reference curves of vegetation indices in Minhe. Planting distribution information of winter wheat was extracted based on the phenology time-weighted dynamic time warping (PT-DTW) method, and the effects of different vegetation indices’ time series and their corresponding threshold parameters were compared. The results showed that: (1) the three vegetation indices—normalized difference vegetation index (NDVI), normalized differential phenology index (NDPI), and normalized difference greenness index (NDGI)—maintained high mapping potential; (2) under the optimal threshold, >88% accuracy of index identification for winter wheat extraction was achieved; (3) due to improved extraction accuracy and resulting boundary range, NDPI and its corresponding optimal parameter (T = 0.05) performed the best. The process and results of this study have certain reference value for the study of winter wheat planting information change and the formulation of dynamic monitoring schemes in agricultural areas of QTP.

Monitoring Urban Expansion and Loss of Agriculture on the North Coast of West Java Province, Indonesia, Using Google Earth Engine and Intensity Analysis

Uncontrolled urban expansion resulting from urbanization has a disastrous impact on agricultural land. This situation is being experienced by the densely populated and fertile island Java in Indonesia. Remote sensing technologies have developed rapidly in recent years, including the creation of Google Earth Engine (GEE). Intensity analysis (IA) is increasingly being used to systematically and substantially analyze land-use/land-cover (LULC) change. As yet, however, no study of land conversion from agriculture to urban areas in Indonesia has adopted GEE and IA approaches simultaneously. Therefore, this study aims to monitor urban penetration to agricultural land in the north coastal region of West Java Province by applying both methods to two time intervals: 2003–2013 and 2013–2020. Landsat data and a robust random forest (RF) classifier available in GEE were chosen for producing LULC maps. Monitoring LULC change using GEE and IA has demonstrated reliable findings. The overall accuracy of Landsat image classification results for 2003, 2013, and 2020 were 88%, 87%, and 88%, respectively. IA outputs at interval levels for all categories showed that the annual change-of-area rate was higher during 2013–2020 than during 2003–2013. At the category level, IA results showed that the area of agricultural land experienced net losses in both periods, with net loss in 2013–2020 being 2.3 times greater than that in 2003–2013 (∼1,850 ha per year). In contrast, the built-up area made net gains in both periods, reaching almost twice as much in the second period as in the first (∼2,030 ha per year). The transition-level IA performed proved that agricultural land had been the primary target for the expansion of built-up areas. The most extensive spatial distribution of land conversion from agriculture to built-up area was concentrated in the regencies of Bekasi, Karawang, and Cirebon. These findings are intended to provide stakeholders with enrichment in terms of available literature and with valuable inputs useful for identifying better urban and regional planning policies in Indonesia and similar regions.

Examining the potential of damage in threat zones around LPG storage sphere in Hassi R’Mel city, Algeria

Purpose This study aims to determine the maximum extent of damage in the threat zones, the result of a catastrophic failure in one liquefied petroleum gas storage sphere, located in storage and transfer center in Hassi R’Mel city, Algeria. Design/methodology/approach To reach the desired results, we relied on ALOHA® v. 5.4.7 software (Areal Locations of Hazardous Atmospheres) for accidents simulation, and on Google Earth as an output tool to show results on the city map. Findings The results prove that the city of Hassi R’Mel is almost completely threatened by thermal effects resulting from a boiling liquid expanding vapor explosion phenomenon, which can reach a distance of 3.9 km. Originality/value Determining the extent to which the damages resulting from an industrial accident may reach is of great importance in preventing industrial hazards, as well as in decision-making in the field of urbanization.

Natural Afforestation on Abandoned Agricultural Lands during Post-Soviet Period: A Comparative Landsat Data Analysis of Bordering Regions in Russia and Belarus

Remote monitoring of natural afforestation processes on abandoned agricultural lands is crucial for assessments and predictions of forest cover dynamics, biodiversity, ecosystem functions and services. In this work, we built on the general approach of combining satellite and field data for forest mapping and developed a simple and robust method for afforestation dynamics assessment. This method is based on Landsat imagery and index-based thresholding and specifically targets suitability for limited field data. We demonstrated method’s details and performance by conducting a case study for two bordering districts of Rudnya (Smolensk region, Russia) and Liozno (Vitebsk region, Belarus). This study area was selected because of the striking differences in the development of the agrarian sectors of these countries during the post-Soviet period (1991-present day). We used Landsat data to generate a consistent time series of five-year cloud-free multispectral composite images for the 1985–2020 period via the Google Earth Engine. Three spectral indices, each specifically designed for either forest, water or bare soil identification, were used for forest cover and arable land mapping. Threshold values for indices classification were both determined and verified based on field data and additional samples obtained by visual interpretation of very high-resolution satellite imagery. The developed approach was applied over the full Landsat time series to quantify 35-year afforestation dynamics over the study area. About 32% of initial arable lands and grasslands in the Russian district were afforested by the end of considered period, while the agricultural lands in Belarus’ district decreased only by around 5%. Obtained results are in the good agreement with the previous studies dedicated to the agricultural lands abandonment in the Eastern Europe region. The proposed method could be further developed into a general universally applicable technique for forest cover mapping in different growing conditions at local and regional spatial levels.

Estimation of the Energy Consumption of an All-Terrain Mobile Manipulator for Operations in Steep Vineyards

A heterogeneous robotic system that can perform various tasks in the steep vineyards of the Mediterranean region was developed and tested as part of the HEKTOR—Heterogeneous Autonomous Robotic System in Viticulture and Mariculture—project. This article describes the design of hardware and an easy-to-use method for evaluating the energy consumption of the system, as well as, indirectly, its deployment readiness level. The heterogeneous robotic system itself consisted of a flying robot—a light autonomous aerial robot (LAAR)—and a ground robot—an all-terrain mobile manipulator (ATMM), composed of an all-terrain mobile robot (ATMR) platform and a seven-degree-of-freedom (DoF) torque-controlled robotic arm. A formal approach to describe the topology and parameters of selected vineyards is presented. It is shown how Google Earth data can be used to make an initial estimation of energy consumption for a selected vineyard. On this basis, estimates of energy consumption were made for the tasks of protective spraying and bud rubbing. The experiments were conducted in two different vineyards, one with a moderate slope and the other with a much steeper slope, to evaluate the proposed estimation method.