Rail survey plans to remote sensing: vegetation change in the Mulga Lands of eastern Australia and its implications for land use

There is a prevailing paradigm that woody vegetation is expanding at the expense of grassland with reduced burning under pastoralism in the Mulga Lands biogeographic region in eastern Australia. This raises the possibility that the region is acting as a carbon sink. Vegetation boundaries were precisely positioned from rail survey plans dating from 1895 to 1900. This baseline was compared with the position of boundaries on 1952 aerial photography and 2010 Google Earth imagery. The conversion of forest to non-forest by mechanical clearing was also mapped from satellite imagery. There was no consistent trend in the direction of boundary movement for mulga (Acacia aneura F.Muell. ex Benth.), gidgee (Acacia cambagei R.T. Baker) forest or miscellaneous other forest types. The stability of the boundaries, despite the transition from aboriginal management to rangeland pastoralism, contrasts with dramatic declines in tree cover resulting from mechanical clearing. Mapping of forest cover from satellite imagery reveals that conversion of forest to non-forest has reduced mulga forest to 74%, gidgee forest to 30% and miscellaneous forest types to 82% of their original area. Annual clearing rates for the period between 1997 and 2005 were 0.83, 0.95 and 0.43% for those forest types, respectively. Clearing has declined substantially in the period 2005–09 since the advent of recent regulations in Queensland. The area remains a source of carbon emissions but this situation may reverse if restoration of mulga dry forest becomes an attractive land use with an emerging carbon market.

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Investigating impact of land-use and land cover changes on hydro-ecological balance using GIS: insights from IIT Bombay, India

SN Applied Sciences ◽

10.1007/s42452-021-04328-7 ◽

2021 ◽

Vol 3 (3) ◽

Cited By ~ 1

Author(s):

Aman Srivastava ◽

Pennan Chinnasamy

Keyword(s):

Land Use ◽

Land Cover ◽

Surface Runoff ◽

Forest Cover ◽

Google Earth ◽

Negative Consequences ◽

Ecological Stability ◽

Barren Land ◽

Ecological Balance ◽

Lulc Changes

AbstractThe present study, for the first time, examined land-use land cover (LULC), changes using GIS, between 2000 and 2018 for the IIT Bombay campus, India. Objective was to evaluate hydro-ecological balance inside campus by determining spatio-temporal disparity between hydrological parameters (rainfall-runoff processes), ecological components (forest, vegetation, lake, barren land), and anthropogenic stressors (urbanization and encroachments). High-resolution satellite imageries were generated for the campus using Google Earth Pro, by manual supervised classification method. Rainfall patterns were studied using secondary data sources, and surface runoff was estimated using SCS-CN method. Additionally, reconnaissance surveys, ground-truthing, and qualitative investigations were conducted to validate LULC changes and hydro-ecological stability. LULC of 2018 showed forest, having an area cover of 52%, as the most dominating land use followed by built-up (43%). Results indicated that the area under built-up increased by 40% and playground by 7%. Despite rapid construction activities, forest cover and Powai lake remained unaffected. This anomaly was attributed to the drastically declining barren land area (up to ~ 98%) encompassing additional construction activities. Sustainability of the campus was demonstrated with appropriate measures undertaken to mitigate negative consequences of unwarranted floods owing to the rise of 6% in the forest cover and a decline of 21% in water hyacinth cover over Powai lake. Due to this, surface runoff (~ 61% of the rainfall) was observed approximately consistent and being managed appropriately despite major alterations in the LULC. Study concluded that systematic campus design with effective implementation of green initiatives can maintain a hydro-ecological balance without distressing the environmental services.

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Qualifying Land Use and Land Cover Dynamics and Their Impacts on Ecosystem Service in Central Himalaya Transboundary Landscape Based on Google Earth Engine

Land ◽

10.3390/land10020173 ◽

2021 ◽

Vol 10 (2) ◽

pp. 173

Author(s):

Changjun Gu ◽

Yili Zhang ◽

Linshan Liu ◽

Lanhui Li ◽

Shicheng Li ◽

...

Keyword(s):

Land Use ◽

Ecosystem Services ◽

Land Cover ◽

Ecosystem Service ◽

Forest Cover ◽

Google Earth ◽

Forest Cover Change ◽

Sacred Landscape ◽

Lulc Changes ◽

Google Earth Engine

Land use and land cover (LULC) changes are regarded as one of the key drivers of ecosystem services degradation, especially in mountain regions where they may provide various ecosystem services to local livelihoods and surrounding areas. Additionally, ecosystems and habitats extend across political boundaries, causing more difficulties for ecosystem conservation. LULC in the Kailash Sacred Landscape (KSL) has undergone obvious changes over the past four decades; however, the spatiotemporal changes of the LULC across the whole of the KSL are still unclear, as well as the effects of LULC changes on ecosystem service values (ESVs). Thus, in this study we analyzed LULC changes across the whole of the KSL between 2000 and 2015 using Google Earth Engine (GEE) and quantified their impacts on ESVs. The greatest loss in LULC was found in forest cover, which decreased from 5443.20 km2 in 2000 to 5003.37 km2 in 2015 and which mainly occurred in KSL-Nepal. Meanwhile, the largest growth was observed in grassland (increased by 548.46 km2), followed by cropland (increased by 346.90 km2), both of which mainly occurred in KSL-Nepal. Further analysis showed that the expansions of cropland were the major drivers of the forest cover change in the KSL. Furthermore, the conversion of cropland to shrub land indicated that farmland abandonment existed in the KSL during the study period. The observed forest degradation directly influenced the ESV changes in the KSL. The total ESVs in the KSL decreased from 36.53 × 108 USD y−1 in 2000 to 35.35 × 108 USD y−1 in 2015. Meanwhile, the ESVs of the forestry areas decreased by 1.34 × 108 USD y−1. This shows that the decrease of ESVs in forestry was the primary cause to the loss of total ESVs and also of the high elasticity. Our findings show that even small changes to the LULC, especially in forestry areas, are noteworthy as they could induce a strong ESV response.

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Mapping Regional Landscape by Using OpenstreetMap (OSM)

Environmental Information Systems ◽

10.4018/978-1-5225-7033-2.ch033 ◽

2019 ◽

pp. 771-790 ◽

Cited By ~ 1

Author(s):

Di Yang

Keyword(s):

Remote Sensing ◽

Land Use ◽

Forest Cover ◽

Regional Scale ◽

Google Earth ◽

Computing Power ◽

New Approach ◽

Computation Cost ◽

Regional Landscape ◽

Google Earth Engine

A forest patterns map over a large extent at high spatial resolution is a heavily computation task but is critical to most regions. There are two major difficulties in generating the classification maps at regional scale: large training points sets and expensive computation cost in classifier modelling. As one of the most well-known Volunteered Geographic Information (VGI) initiatives, OpenstreetMap contributes not only on road network distributions, but the potential of justify land cover and land use. Google Earth Engine is a platform designed for cloud-based mapping with a strong computing power. In this study, we proposed a new approach to generating forest cover map and quantifying road-caused forest fragmentations by using OpenstreetMap in conjunction with remote sensing dataset stored in Google Earth Engine. Additionally, the landscape metrics produced after incorporating OpenStreetMap (OSM) with the forest spatial pattern layers from our output indicated significant levels of forest fragmentation in Yucatan peninsula.

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Mapping Regional Landscape by Using OpenstreetMap (OSM)

Advances in Geospatial Technologies - Volunteered Geographic Information and the Future of Geospatial Data ◽

10.4018/978-1-5225-2446-5.ch008 ◽

2017 ◽

pp. 138-157

Author(s):

Di Yang

Keyword(s):

Remote Sensing ◽

Land Use ◽

Forest Cover ◽

Regional Scale ◽

Google Earth ◽

Computing Power ◽

New Approach ◽

Computation Cost ◽

Regional Landscape ◽

Google Earth Engine

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Tree Species Diversity and Socioeconomic Perspectives of the Urban (Food) Forest of Accra, Ghana

Sustainability ◽

10.3390/su10103417 ◽

2018 ◽

Vol 10 (10) ◽

pp. 3417 ◽

Cited By ~ 5

Author(s):

Bertrand Nero ◽

Nana Kwapong ◽

Raymond Jatta ◽

Oluwole Fatunbi

Keyword(s):

Land Use ◽

Tree Species ◽

Forest Cover ◽

Food System ◽

Food Policy ◽

Urban Forest ◽

Urban Forestry ◽

Tree Cover ◽

Land Use Types ◽

The City

Urban and peri-urban forestry has emerged as a complementary measure to contribute towards eliminating urban hunger and improved nutritional security. However, there is scanty knowledge about the composition, diversity, and socioeconomic contributions of urban food trees in African cities. This paper examines the diversity and composition of the urban forest and food trees of Accra and sheds light on perceptions of urbanites regarding food tree cultivation and availability in the city. Using a mixed methods approach, 105 respondents in six neighborhoods of Accra were interviewed while over 200 plots (100-m2 each) were surveyed across five land use types. Twenty-two out of the 70 woody species in Accra have edible parts (leaves, fruits, flowers, etc.). The food-tree abundance in the city is about half of the total number of trees enumerated. The species richness and abundance of the food trees and all trees in the city were significantly different among land use types (p < 0.0001) and neighborhood types (p < 0.0001). The diversity of food-bearing tree species was much higher in the poorer neighborhoods than in the wealthier neighborhoods. Respondents in wealthier neighborhoods indicated that tree and food-tree cover of the city was generally low and showed greater interest in cultivating food (fruit) trees and expanding urban forest cover than poorer neighborhoods. These findings demonstrate the need for urban food policy reforms that integrate urban-grown tree foods in the urban food system/culture.

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DETECÇÃO DE ÁREAS DE FLORESTAS INVARIANTES EM SÉRIES TEMPORAIS UTILIZANDO RANDOM FOREST

GEOgraphia ◽

10.22409/geographia2021.v23i50.a46996 ◽

2021 ◽

Vol 23 (50) ◽

Author(s):

Eduardo Ribeiro Lacerda ◽

Raúl Sanchéz Vicens

Keyword(s):

Land Use ◽

Time Series ◽

Random Forest ◽

Change Detection ◽

Vegetation Change ◽

Google Earth ◽

Detection Algorithms ◽

Palabras Clave ◽

Change Detection Algorithms ◽

Google Earth Engine

O surgimento de algoritmos de detecção de mudanças na vegetação na última década é impressionante. Mas os resultados gerados ainda possuem ruído que precisa ser tratado com a utilização de resultados de outros mapeamentos de cobertura vegetal. Além disso, a necessidade de gerar classes de uso do solo invariantes é importante para o melhor entendimento de processos que ocorrem em áreas florestais. Pensando nisso, este trabalho busca criar uma nova forma de mapear essas áreas invariáveis que possam ser utilizadas para mascarar ruídos e também como subsídio para outros estudos de conservação e restauração. A metodologia proposta aqui usa a plataforma Google Earth Engine e um algoritmo de aprendizado de máquina: o Random Forest, para classificar áreas de floresta invariáveis usando todo o acervo de imagens da série temporal Landsat, de uma só vez. Os resultados mostraram que a nova abordagem teve melhor desempenho do que o uso de técnicas mais tradicionais como a agregação de mapeamentos de uso e cobertura anuais, com uma acurácia global de 91,7%. O trabalho busca ainda contribuir com a comunidade de sensoriamento remoto ao apresentar, após exaustivos testes, as melhores opções de variáveis a serem utilizadas neste tipo de classificação. Palavras-chave: Séries Temporais, Detecção de Mudanças, Florestas, Google Earth Engine, Random Forest.DETECTION OF INVARIANT VEGETATION AREAS IN TIME SERIES USING RANDOM FOREST ALGORITHMAbstract: The emergence of vegetation change detection algorithms in the last decade is impressive. But the results still have a lot of noise that needs to be cleaned. And the data cleaning process still uses other landcover mapping results. Besides that, the necessity to generate invariant land use classes is important to know particularly to forest areas. Thinking about that, this paper seeks to create a new form of mapping these invariant areas that can be used to mask noise and as an input on other conservation and restoration studies. The methodology proposed here uses the Google Earth Engine platform and a Random Forest algorithm to classify invariant forest areas using all the image’s collection in the time series at once. The results showed that the new approach performed better than the use of more traditional techniques such as the aggregation of annual land-use and land-cover mappings, with an overall accuracy of 91.7%. Also, this paper seeks to contribute to the remote sensing community showing after exhaustive testing, good options of variables to use on this type of work. Keywords: Time Series, Change Detection, Forests, Google Earth Engine, Random Forest.DETECCIÓN DE ÁREAS DE VEGETACIÓN INVARIANTES EN SÉRIES TEMPORALES UTILIZANDO ALGORITMO RANDOM FORESTResumen: La aparición de algoritmos de detección de cambios en la vegetación en la última década es impresionante. Pero los resultados todavía tienen muchos ruidos que deben ser eliminados. Además, el proceso de limpieza de datos se basa en otros mapas de cobertura de la tierra. Además de eso, es importante conocer la necesidad de generar clases de uso de la tierra invariables, particularmente en las áreas forestales. Pensando en eso, este artículo busca crear una nueva forma de mapear estas áreas invariantes que se pueden utilizar para enmascarar el ruido y como un aporte para otros estudios de conservación y restauración. La metodología propuesta aquí utiliza la plataforma Google Earth Engine y un algoritmo de aprendizaje de máquina: o Random Forest para clasificar áreas invariantes de bosque, utilizando a la vez todas las imágenes de la serie temporal Landsat. Los resultados encontraron que el nuevo enfoque tuvo mejor desempeño que el uso de técnicas tradicionales, con una precisión global del 91,7%. Este trabajo busca además contribuir con la comunidad de la teledetección, mostrando mediante de exhaustivas pruebas, mejores opciones de variables para utilizar en este tipo de clasificación. Palabras clave: Series de Tiempo, Detección de Cambios, Bosques, Google Earth Engine, Random Forest.

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Environmental Armed Conflict Assessment Using Satellite Imagery

Journal of Environmental Geography ◽

10.2478/jengeo-2020-0007 ◽

2020 ◽

Vol 13 (3-4) ◽

pp. 1-14

Author(s):

Fernando Arturo Mendez Garzón ◽

István Valánszki

Keyword(s):

Remote Sensing ◽

Satellite Imagery ◽

Forest Cover ◽

Geographical Location ◽

Forest Tree ◽

Armed Conflicts ◽

Dry Forest ◽

Human Populations ◽

Monsoon Forest ◽

Remote Sensing Methods

Abstract Armed conflicts not only affect human populations but can also cause considerable damage to the environment. Its consequences are as diverse as its causes, including; water pollution from oil spills, land degradation due to the destruction of infrastructure, poisoning of soils and fields, destruction of crops and forests, over-exploitation of natural resources and paradoxically and occasionally reforestation. In this way, the environment in the war can be approached as beneficiary, stage, victim or/and spoil of war. Although there are few papers that assess the use of remote sensing methods in areas affected by warfare, we found a gap in these studies, being both outdated and lacking the correlation of remote sensing analysis with the causes-consequences, biome features and scale. Thus, this paper presents a methodical approach focused on the assessment of the existing datasets and the analysis of the connection between geographical conditions (biomes), drivers and the assessment using remote sensing methods in areas affected by armed conflicts. We aimed to find; weaknesses, tendencies, patterns, points of convergence and divergence. Then we consider variables such as biome, forest cover affectation, scale, and satellite imagery sensors to determine the relationship between warfare drivers with geographical location assessed by remote sensing methods. We collected data from 44 studies from international peer-reviewed journals from 1998 to 2019 that are indexed using scientific search engines. We found that 62% of the studies were focused on the analysis of torrid biomes as; Tropical Rainforest, Monsoon Forest / Dry Forest, Tree Savanna and Grass Savanna, using the 64% Moderate-resolution satellite imagery sensors as; Landsat 4-5 TM and Landsat 7 ETM+. Quantitative analysis of the trends identified within these areas contributes to an understanding of the reasons behind these conflicts.

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Geospatial assessment of land use and land cover dynamics in the mid-zone of Ghana

Folia Forestalia Polonica ◽

10.2478/ffp-2020-0028 ◽

2020 ◽

Vol 62 (4) ◽

pp. 288-305

Author(s):

Addo Koranteng ◽

Isaac Adu-Poku ◽

Emmanuel Donkor ◽

Tomasz Zawiła-Niedźwiecki

Keyword(s):

Remote Sensing ◽

Land Use ◽

Land Cover ◽

Satellite Imagery ◽

Forest Cover ◽

Anthropogenic Activities ◽

Arable Land ◽

Rapid Urbanization ◽

The Past ◽

Open Forest

AbstractLand use and land cover (LULC) terrain in Ghana has undergone profound changes over the past years emanating mainly from anthropogenic activities, which have impacted countrywide and sub-regional environment. This study is a comprehensive analysis via integrated approach of geospatial procedures such as Remote Sensing (RS) and Geographic Information System (GIS) of past, present and future LULC from satellite imagery covering Ghana’s Ashanti regional capital (Kumasi) and surrounding districts. Multi-temporal satellite imagery data sets of four different years, 1990 (Landsat TM), 2000 (Landsat ETM+), 2010 (Alos and Disaster Monitoring Constellation-DMC) and 2020 (SENTINEL), spanning over a 30-year period were mapped. Five major LULC categories – Closed Forest, Open Forest, Agriculture, Built-up and Water – were delineated premised on the prevailing geographical settings, field study and remote sensing data. Markov Cellular Automata modelling was applied to predict the probable LULC change consequence for the next 20 years (2040). The study revealed that both Open Forest and Agriculture class categories decreased 51.98 to 38.82 and 27.48 to 20.11, respectively. Meanwhile, Built-up class increased from 4.8% to 24.8% (over 500% increment from 1990 to 2020). Rapid urbanization caused the depletion of forest cover and conversion of farmlands into human settlements. The 2040 forecast map showed an upward increment in the Built-up area up to 35.2% at the expense of other LULC class categories. This trend from the past to the forecasted future would demand that judicious LULC resolutions have to be made to keep Ghana’s forest cover, provide arable land for farming activities and alleviate the effects of climate change.

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Participatory Mapping of Village Potential with Geotagging Data (Case Study: Wedomartani Village, Sleman, Yogyakarta)

BHUMI Jurnal Agraria dan Pertanahan ◽

10.31292/jb.v5i3.394 ◽

2020 ◽

Vol 5 (3) ◽

Author(s):

Dandi Arianto Pelly ◽

Maryadi Budi Wiyono

Keyword(s):

Land Use ◽

Satellite Imagery ◽

Spatial Data ◽

Google Earth ◽

Wide Range ◽

Real Objects ◽

Village Communities ◽

Medium Businesses ◽

The Village ◽

Imagery Data

Digital spatial data has the highest demand, especially for the needs of analysis in terms of mapping. Mapping is currently the focus of attention of many institutions because real objects in the field in a wide range can be visualized in a precision field with a specif ic scale. Many villages do not have digital spatial data; one ofthem is Wedomartani village. Therefore, an inventory of digital spatial data of important village objects needs to be done. This study aims to map the potential of village using satellite imagery data from Google Earth and Geotagging photographs and determine the zoning potential of the Land Use of Wedomartani village. The method used to map the potential of villages using satellite imagery data is the method of interpretation, then geotagging photo data obtained through surveys utilizing GPS tag technology from smartphones and the participatory role of village communities. The determination of village land-use zoning used the matching method of the potential map with validation of geotagging photo data. The results interpretation of satellite images shows that the potential in the village of Wedomartani in the form of important objects as the potential of the village is public facilities, tourism objects, theme parks, sports facilities, buildings, roads, rivers, and agriculture. The zoning results of the potential land use of the Wedomartani village consist of Trade and Service Zones in the form of micro, small and medium businesses spread along the main road as a sector of economicpotential (212.73 Ha); The Recreation Zone is in the form of Maguwoharjo Football Stadium, Jogja Bay Pirates Adventure Park Family Park, Tambak Boyo Reservoir and Gebang Temple Cultural Heritage Site as a potential tourism sector (23.48 Ha); Agricultural and Plantation Zones in the form of irrigated rice, maize and chili as potential for sustainable agriculture (661.19 Ha).

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Urbanization and Long-Term Forest Dynamics in a Metropolitan Region of Southern Europe (1936–2018)

Sustainability ◽

10.3390/su132112164 ◽

2021 ◽

Vol 13 (21) ◽

pp. 12164

Author(s):

Leonardo Bianchini ◽

Alvaro Marucci ◽

Adele Sateriano ◽

Valerio Di Stefano ◽

Riccardo Alemanno ◽

...

Keyword(s):

Land Use ◽

Forest Dynamics ◽

Forest Cover ◽

Urban Expansion ◽

Environmental Conservation ◽

Southern Europe ◽

Forest Types ◽

Environmental Implications ◽

Settlement Density

Although peri-urban landscapes in Southern Europe still preserve a relatively high level of biodiversity in relict natural places, urban expansion is progressively consuming agricultural land and, in some cases, forest cover. This phenomenon has (direct and indirect) environmental implications, both positive and negative. The present study contributes to clarifying the intrinsic nexus between long-term urban expansion and forest dynamics in a representative Mediterranean city based on diachronic land-use maps. We discuss some counterintuitive results of urbanization as far as forest expansion, wildfire risk, and biodiversity conservation are concerned. Forest dynamics were investigated at two time intervals (1936–1974 and 1974–2018) representing distinctive socioeconomic contexts in the Rome metropolitan area in Central Italy. Additionally, the spatial relationship between forest cover and urban growth was evaluated using settlement density as a target variable. All over the study area, forest cover grew moderately over time (from 18.3% to 19.9% in the total landscape), and decreased along the urban gradient (i.e., with settlement density) more rapidly in 2018 than in 1936. The diversification of forest types (Shannon H index) was higher in areas with medium-density settlements, indicating a tendency towards more heterogeneous and mixed structures in rural and peri-urban woods that undergo rising human pressure. The dominance of a given forest type (Simpson’s D index) was higher at high settlement density areas. Evenness (Pielou’s J index) was the highest at low settlement density areas. The long-term assessment of land-use dynamics in metropolitan fringes enriched with a spatially explicit analysis of forest types may inform regional planning and environmental conservation, which could delineate appropriate strategies for sustainable land management in Southern European cities.

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