Trenutni status i perspektiva primjene daljinskih istraživanja u upravljanju poljoprivrednim usjevima

Knowledge of the spatial distribution of agricultural crops and crop rotation is necessary for the understanding of the farming practices concerning the long-term sustainability of agricultural production. Agricultural crops are increasingly subject to drought due to the effects of global climate change, and the same is true for Croatia due to the constant rise in mean annual temperatures and uneven rainfall distribution. Remote sensing methods have proven to be superior in the detection and monitoring of drought compared to conventional methods of observation from meteorological stations. Information on the condition of crops in the early stages of development indicates potential irregularities in the development of agricultural crops. The objective of this paper is to provide a perspective for the application of remote sensing in crop management using state-of-the-art methods. Analysis of the possible implementation of these methods in Croatia was performed on a macro- and micro-level. Spatial classification, cropland suitability multicriteria analysis, drought assessment, weed detection and crop density calculation were evaluated according to the necessary equipment and data processing segments. Remote sensing application in crop management offers a potential basis for better crop management both at the macro-level for land use planning and at the micro-level for family farms.Poznavanje prostorne raspodjele poljoprivrednih usjeva i plodoreda neophodno je za razumijevanje poljoprivredne prakse za dugoročnu održivost poljoprivredne proizvodnje. Poljoprivredni usjevi su sve više izloženi suši zbog učinaka globalnih klimatskih promjena, a isto vrijedi i za Hrvatsku zbog stalnog porasta srednjih godišnjih temperatura i neujednačenoj količini padalina. Metode daljinskog istraživanja pokazale su se superiornima u otkrivanju i praćenju suše u usporedbi s konvencionalnim metodama promatranja s meteoroloških postaja. Podaci o stanju usjeva u ranim fazama razvoja ukazuju na potencijalne nepravilnosti u razvoju poljoprivrednih usjeva. Cilj ovog rada je pružiti perspektivu primjene daljinskog istraživanja u upravljanju usjevima korištenjem najsuvremenijih metoda. Analiza moguće primjene ovih metoda u Hrvatskoj provedena je na makro i mikro razini. Prostorna klasifikacija, multikriterijska analiza pogodnost usjeva, procjena suše, otkrivanje korova i izračun gustoće usjeva analizirani su u odnosu na potrebnu opremu i segmente obrade podataka. Primjena daljinskog istraživanja u upravljanju usjevima nudi potencijalnu osnovu za bolje upravljanje usjevima kako na makro razini za planiranje uporabe zemljišta, tako i na mikro razini za obiteljska poljoprivredna gospodarstva

Global Open Data Remote Sensing Satellite Missions for Land Monitoring and Conservation: A Review

The application of global open data remote sensing satellite missions in land monitoring and conservation studies is in the state of rapid growth, ensuring an observation with high spatial and spectral resolution over large areas. The purpose of this study was to provide a review of the most important global open data remote sensing satellite missions, current state-of-the-art processing methods and applications in land monitoring and conservation studies. Multispectral (Landsat, Sentinel-2, and MODIS), radar (Sentinel-1), and digital elevation model missions (SRTM, ASTER) were analyzed, as the most often used global open data satellite missions, according to the number of scientific research articles published in Web of Science database. Processing methods of these missions’ data consisting of image preprocessing, spectral indices, image classification methods, and modelling of terrain topographic parameters were analyzed and demonstrated. Possibilities of their application in land cover, land suitability, vegetation monitoring, and natural disaster management were evaluated, having high potential in broad use worldwide. Availability of free and complementary satellite missions, as well as the open-source software, ensures the basis of effective and sustainable land use management, with the prerequisite of the more extensive knowledge and expertise gathering at a global scale

Određivanje pogodnosti poljoprivrednog zemljišta za uzgoj kukuruza (Zea mays L.) primjenom višerazinske GIS multikriterijske analize u kontinentalnoj Hrvatskoj

Previous research in continental Croatia indicated that cropland suitability levels are highly variable and existing natural resources could be better utilized. To overcome this issue, a method of multilevel Geographic information system (GIS)-based multicriteria analysis based on Analytic hierarchy process (AHP) for maize cropland suitability determination was proposed and evaluated. Free spatial data and free open-source software were implemented in the research in 250 m spatial resolution. The effects of air temperature, precipitation, soil and topography were modelled with the multilevel approach to avoid inconsistency in pairwise comparison due to criteria count. The maize suitability index (MSI) was calculated using the weighted linear combination, with higher values indicating proportionally higher suitability. According to the results of the AHP method, the weight consistency ratio was lower than the borderline value (0.042 < 0.100). Soil type and mean air temperature in June had the most impact on the suitability result, with 14.3% and 13.6% influence, respectively. The highest MSI values were achieved along the Sava River, especially in the proximity of Sisak and Nova Gradiška, resulting up to 3.9 of the possible 5.0. The sensitivity analysis of criteria indicated precipitation data in May, July and August particularly impactful in continental Croatia, while mean air temperature in September had very low variability of the suitability and was the least impactful on calculated MSI values.Prethodna istraživanja u kontinentalnoj Hrvatskoj pokazala su da su razine pogodnosti poljoprivrednog zemljišta vrlo promjenjive i da bi se postojeći prirodni resursi mogli iskoristiti na bolji način. Da bi se prevladalo ovo pitanje, predložena je i evaluirana metoda višerazinske GIS multikriterijske analize temeljena na analitičkom hijerarhijskom procesu (AHP) za utvrđivanje pogodnosti poljoprivrednog zemljišta za uzgoj kukuruza. Istraživanje je temeljeno na besplatnim prostornim podacima i besplatnom GIS softveru otvorenog koda s prostornom razlučivost od 250 m. Kriteriji temperature zraka, oborina, tla i topografije modelirani su višerazinskim pristupom kako bi se izbjegla nedosljednost u usporedbi parova kriterija. Indeks pogodnosti područja za uzgoj kukuruza (MSI) izračunat je primjenom težinske linearne kombinacije, pri čemu veće vrijednosti proporcionalno ukazuju na veću pogodnost. Prema rezultatima AHP metode, omjer konzistencije težina bio je niži od granične vrijednosti (0,042 <0,100). Vrsta tla i srednja temperatura zraka u lipnju najviše su utjecali na rezultat pogodnosti, s 14,3%, odnosno 13,6% utjecaja. Najveće MSI vrijednosti postignute su uz rijeku Savu, posebno u okolici Siska i Nove Gradiške, rezultirajući s 3,9 of mogućih 5,0. Analiza osjetljivosti kriterija ukazala je da su podaci o oborinama u svibnju, srpnju i kolovozu bili posebno utjecajni u kontinentalnoj Hrvatskoj, dok je srednja temperatura zraka u rujnu imala vrlo nisku varijabilnost pogodnosti i najmanje je utjecala na izračunate MSI vrijednosti

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Agricultural Land Evaluation Using GIS-Based Matching Method in Highland Areas for Oil Palm Cultivation

Oil palm (Elaeis guineensis) is one of the commodity crops and mostly found in tropical lands. This study aimed to analyze the current and potential land suitability for oil palm using the geographic information system (GIS) technique. The study was conducted in the Ranau area, State of Sabah, Malaysia. Field activity was carried out to collect soil samples and land information in the study area. Land suitability was then assessed using the matching method and GIS software was employed to produce a land suitability map for oil palm. The results indicated that the current land suitability classes in the study area were highly suitable (S1) with a total area of 99,118 ha (27.4%), moderately suitable (S2) with 110,108 ha (30.4%), marginally suitable (S3) with 109,533 ha (30.2%), currently not suitable (N1) with 2,728 ha (0.7%), and permanently not suitable (N2) with 40,693 ha (11.3%). While the potential land suitability classes showed highly suitable (S1) was 198,206 ha (54.7%), moderately suitable class (S2) was 123,281 ha (34%) and permanently not suitable (N2) was 40,693 ha (11.3%). Suitable areas that could be planted with oil palm included the gently sloping flank and the low gradient slope margin. Availability of nutrients and work capability were the dominant limiting factors in the study area. The output of this study recommends that the Ranau area had the potential for oil palm although it still needs land improvements for sustainable oil palm cultivation

Agricultural Land Evaluation Using GIS-Based Matching Method in Highland Areas for Oil Palm Cultivation

Oil palm (Elaeis guineensis) is one of the commodity crops and is mostly found in tropical lands. This study aimed to analyze the current and potential land suitability for oil palm using the geographic information system (GIS) technique. The study was conducted in the Ranau District, Sabah State, Malaysia. Field activity was carried out to collect soil samples and land information in the study area. Land suitability was then assessed using the matching method and GIS software was employed to produce a land suitability map for oil palm. The results indicated that the current land suitability classes in the study area were highly suitable (S1) with a total area of 99,118 ha (27.4%); moderately suitable (S2) with 110,108 ha (30.4%); marginally suitable (S3) with 109,533 ha (30.2%); currently not suitable (N1) with 2,728 ha (0.7%) and permanently not suitable (N2) with 40,693 ha (11.3%). Meanwhile, the potential land suitability classes showed 198,206 ha (54.7%) for S1; 123,281 ha (34%) for S2 and 40,693 ha (11.3%) for N2. Suitable areas that could be planted with oil palm included the gently sloping flank and the low gradient slope margin. Availability of nutrients and work capability were the dominant limiting factors in the study area. The outputs of this study recommend that the Ranau District has the potential for oil palm although it still needs land improvements for sustainable oil palm cultivation

Prostorna predikcija udjela teških metala u tlima kontinentalne Hrvatske usporedbom metoda strojnog učenja i prostorne interpolacije

Soil contamination caused by heavy metals presents a potential long-term issue to human health and biodiversity due to the bioaccumulation effect. Previous research at the micro level in Croatia detected soil contamination caused by heavy metals above maximum permitted values, which also implied the necessity of their current spatial representation at the macro level in Croatia. The aim of this study was to provide a spatial prediction of six heavy metals considered as contaminants of soils in continental Croatia using two approaches: a conventional approach based on interpolation and a machine learning approach. The prediction was performed on the most recent available data on cadmium (Cd), chromium (Cr), copper (Cu), nickel (Ni), lead (Pb) and zinc (Zn) concentrations in soils, from the Ministry of environment and energy. The conventional prediction approach consisted of the interpolation using the ordinary kriging (OK) in case of input data normality and stationarity, alongside the inverse distance weighting (IDW) method. For the machine learning approach, random forest (RF) and support vector machine (SVM) methods were used. IDW outperformed RF and SVM prediction results for all soil heavy metals contents, primarily due to sparse soil sampling. Soil Cr contents were predicted above the maximum allowed limit, while elevated soil contamination levels in some parts of the study area were detected for Ni and Zn. The highest soil contamination levels were observed in the urban areas of generalized land cover classes, indicating a necessity for its monitoring and the adjustment of land-use management plans.Onečišćenje tla uzrokovano teškim metalima uzrokuje potencijalno dugoročnu opasnost za zdravlje ljudi i biološku raznolikost zbog učinka bioakumulacije. Prethodna istraživanja na mikro razini u Hrvatskoj otkrila su onečišćenje tla teškim metalima iznad maksimalno dopuštenih vrijednosti, što je ujedno impliciralo potrebu poznavanja njihove trenutne prostorne zastupljenosti na makro razini u Hrvatskoj. Cilj ovog istraživanja bio je provesti prostorno predviđanje šest teških metala u tlu koji se smatraju onečišćujućima u kontinentalnoj Hrvatskoj koristeći dva pristupa: konvencionalni pristup zasnovan na interpolaciji i pristup strojnog učenja. Predviđanje je provedeno na najnovijim dostupnim uzorcima tla kadmija (Cd), kroma (Cr), bakra (Cu), nikla (Ni), olova (Pb) i cinka (Zn), prikupljenim od strane Ministarstva zaštite okoliša i energetike. Konvencionalni pristup predviđanja sastojao se od interpolacije korištenjem uobičajenog kriginga (OK) u slučaju normalnosti i stacionarnosti ulaznih podataka, zajedno s metodom inverzne udaljenosti (IDW). Za pristup strojnog učenja korištene su metoda slučajnih šuma (RF) i metoda vektora podrške (SVM). IDW je nadmašio rezultate predviđanja RF i SVM za sve sadržaje teških metala u tlu, prvenstveno zbog nedovoljno gustog uzorkovanja tla. Sadržaj Cr u tlu predviđen je iznad najveće dopuštene granice, dok su za Ni i Zn utvrđene opasne razine onečišćenja tla na dijelovima istraživanog područja. Najveće razine onečišćenja tla zabilježene su u urbanim područjima generaliziranih klasa zemljišnog pokrova, što ukazuje na potrebu za njegovim praćenjem i prilagođavanjem planova upravljanja korištenjem zemljišta

Prostorna predikcija udjela teških metala u tlima kontinentalne Hrvatske usporedbom metoda strojnog učenja i prostorne interpolacije

Soil contamination caused by heavy metals presents a potential long-term issue to human health and biodiversity due to the bioaccumulation effect. Previous research at the micro level in Croatia detected soil contamination caused by heavy metals above maximum permitted values, which also implied the necessity of their current spatial representation at the macro level in Croatia. The aim of this study was to provide a spatial prediction of six heavy metals considered as contaminants of soils in continental Croatia using two approaches: a conventional approach based on interpolation and a machine learning approach. The prediction was performed on the most recent available data on cadmium (Cd), chromium (Cr), copper (Cu), nickel (Ni), lead (Pb) and zinc (Zn) concentrations in soils, from the Ministry of environment and energy. The conventional prediction approach consisted of the interpolation using the ordinary kriging (OK) in case of input data normality and stationarity, alongside the inverse distance weighting (IDW) method. For the machine learning approach, random forest (RF) and support vector machine (SVM) methods were used. IDW outperformed RF and SVM prediction results for all soil heavy metals contents, primarily due to sparse soil sampling. Soil Cr contents were predicted above the maximum allowed limit, while elevated soil contamination levels in some parts of the study area were detected for Ni and Zn. The highest soil contamination levels were observed in the urban areas of generalized land cover classes, indicating a necessity for its monitoring and the adjustment of land-use management plans.Onečišćenje tla uzrokovano teškim metalima uzrokuje potencijalno dugoročnu opasnost za zdravlje ljudi i biološku raznolikost zbog učinka bioakumulacije. Prethodna istraživanja na mikro razini u Hrvatskoj otkrila su onečišćenje tla teškim metalima iznad maksimalno dopuštenih vrijednosti, što je ujedno impliciralo potrebu poznavanja njihove trenutne prostorne zastupljenosti na makro razini u Hrvatskoj. Cilj ovog istraživanja bio je provesti prostorno predviđanje šest teških metala u tlu koji se smatraju onečišćujućima u kontinentalnoj Hrvatskoj koristeći dva pristupa: konvencionalni pristup zasnovan na interpolaciji i pristup strojnog učenja. Predviđanje je provedeno na najnovijim dostupnim uzorcima tla kadmija (Cd), kroma (Cr), bakra (Cu), nikla (Ni), olova (Pb) i cinka (Zn), prikupljenim od strane Ministarstva zaštite okoliša i energetike. Konvencionalni pristup predviđanja sastojao se od interpolacije korištenjem uobičajenog kriginga (OK) u slučaju normalnosti i stacionarnosti ulaznih podataka, zajedno s metodom inverzne udaljenosti (IDW). Za pristup strojnog učenja korištene su metoda slučajnih šuma (RF) i metoda vektora podrške (SVM). IDW je nadmašio rezultate predviđanja RF i SVM za sve sadržaje teških metala u tlu, prvenstveno zbog nedovoljno gustog uzorkovanja tla. Sadržaj Cr u tlu predviđen je iznad najveće dopuštene granice, dok su za Ni i Zn utvrđene opasne razine onečišćenja tla na dijelovima istraživanog područja. Najveće razine onečišćenja tla zabilježene su u urbanim područjima generaliziranih klasa zemljišnog pokrova, što ukazuje na potrebu za njegovim praćenjem i prilagođavanjem planova upravljanja korištenjem zemljišta

SUITABILITY MODELING OF AGRICULTURAL LAND FOR BARLEY CULTIVATION USING THE MULTICRITERIA GIS ANALYSIS

Važnost primjene višekriterijske analize primjenom Geografskoga informacijskog sustava (GIS-a) u planiranju poljoprivredne proizvodnje u porastu je zbog sve veće potrebe za boljim i održivim iskorištavanjem zemljišnih resursa. U ovome je istraživanju izrađen model izračuna pogodnosti poljoprivrednoga zemljišta Osječko-baranjske županije za uzgoj ječma primjenom višekriterijske GIS analize. Postupak istraživanja proveden je u skladu s temeljnim koracima višekriterijske GIS analize: postavljanjem cilja analize, određivanjem kriterija, standardizacijom vrijednosti, određivanjem težina kriterija, objedinjavanjem standardiziranih vrijednosti i težina kriterija te validacijom rezultata. Pri izračunu pogodnosti korišteno je pet skupina kriterija: geomorfometrijski, klimatski, pedološki, hidrološki i ograničavajući kriteriji. Težinski koeficijenti kriterija određeni su metodom analitičkoga hijerarhijskog procesa (AHP). Vrijednosti pogodnosti reklasificirane su prema standardu pogodnosti poljoprivrednoga zemljišta Organizacije za prehranu i poljoprivredu (FAO) u pet klasa. Rezultati istraživanja pokazali su da je prostor Osječko-baranjske županije dominantno umjereno pogodan (S2) za uzgoj ječma s 53,00% poljoprivrednoga zemljišta. Najviša točnost izračunane površine u odnosu na površine testnih ARKOD čestica postignuta je za vrlo pogodnu (S1) kategoriju pogodnosti u iznosu 99,82%. Izrađeni model predstavlja temelj za izračun pogodnosti ostalih kultura na području Osječko-baranjske županije, na mezo- i mikrorazini istraživanja.The importance of a multicriteria Geographic Information System (GIS) analysis in an agricultural production planning is being expanded because of an increasing necessity for a better and sustainable exploitation of land resources. In this research, a calculation model of agricultural land’s suitability for barley cultivation in Osijek-Baranja County was developed, based on a multicriteria GIS analysis. The research procedure was based on the fundamental steps of a multicriteria GIS analysis: setting the analytical goal, determining the criteria, standardizing the values, determining the criterial weights, combining the standardized values and the criteria weights, and validating the results. Five criteria groups were used for a suitability calculation: the geomorphometric, climatic, pedological, hydrological and constraint criteria. The criteria weighting coefficients were determined by the Analytical Hierarchical Process (AHP) method. The suitability values were reclassified in five classed pursuant to the agricultural land’s suitability standards stipulated by the Food and Agriculture Organization (FAO). The research results have proven that a moderately suitable (S2) class for barley cultivation is predominantly present in Osijek-Baranja County, occupying 53.00% of agricultural land. According to the test ARKOD particles, the highest suitability calculation accuracy was achieved for the very suitable (S1) suitability class, with a 99.82-percent accuracy. The developed model represents a basis for suitability calculation of other crops in the Osijek-Baranja County area at the research’s meso- and microlevels